That logic held for 20 years until AI broke it, and as we argued in The ARR Challenge, software now has real physics. Every interaction carries an inference cost, and the “all-you-can-eat” subscription model is giving way to consumption-based pricing that better reflects this reality.

The implications go beyond pricing. They change what retention itself means. A customer that renews in an AI business is not automatically profitable because AI workloads carry real infrastructure weight. NRR can be 120% while the economics underneath quietly deteriorate.

There is a more fundamental problem with aggregate NRR: it compresses your entire customer base into a single number. Early-stage accounts still figuring out the product get averaged with mature accounts that have rebuilt workflows around it. A small group of power users expanding aggressively can offset a larger group quietly reducing usage or churning altogether. The headline number can look healthy even when the underlying distribution is falling apart.

Retention in AI requires answering two questions. Who actually stayed, and can you afford to keep them?

The Behavioral Reality

a16z published two great pieces on this problem. In “Retention Is All You Need“ and “The Cinderella Glass Slipper Effect“, they analyzed hundreds of AI companies and identified a pattern that traditional SaaS metrics weren't built to capture: the first months of an AI product’s life are structurally different from what comes after.

When an AI product launches or a new model drops, a wave of users signs up. Some are genuinely searching for a tool that solves a deep workflow problem, but many others are what a16z calls “AI tourists,” curious experimenters who try the product for a few weeks, don’t find an immediate fit, and move on. The initial revenue picture looks encouraging because both groups are active, generating usage and spend. Then the tourists leave, accounts downgrade, and revenue dips. This is the adoption valley, and it terrifies founders who mistake a structural phase for a product failure.

In an empirical study with OpenRouter1 analyzing over 100T tokens of real-world LLM usage, the earliest users of Claude Sonnet and Gemini Pro retained roughly 40% by month 5. Cohorts that arrived just a few months later churned almost entirely. The difference comes down to timing, rather than product quality. The survivors, or “foundational users,” are the ones who found a genuine fit between the product and a real workload. When they lock in and start rebuilding processes around the tool, usage compounds and revenue recovers, often exceeding the initial tourist-inflated peak.

This is why the best practice is now rebasing retention calculations from Month 0 to Month 3. The first three months are noise that tells you very little about long-term health. The real signal begins when the tourists have left and you can measure the foundational cohort on its own. When an AI product truly nails a workflow, these cohorts can retain and expand at rates that rival the best traditional SaaS.

Understanding who stayed is necessary work that still falls short of the whole picture.

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The Financial J-Curve

In traditional SaaS, there was really only one J-curve. You burned cash to acquire a customer and then served them at a marginal cost close to zero, so the curve bent upward on its own as long as you held onto the account.

AI inverts this. The cost to serve a retained customer scales with their usage because every query triggers inference and agent runs consume tokens. As AI products improve and costs per token drop, customers use dramatically more of them (Jevons Paradox) as workloads get deeper and agents get chained together in ways that multiply resource consumption, so the efficiency gains flow into heavier usage rather than margin expansion.

Under consumption pricing, more usage does mean more revenue, yet a company running entirely on third-party model APIs ties its margin trajectory to someone else’s pricing roadmap. Token costs have been falling and will likely continue to, but every company on the same API captures the same deflation - so, there is no structural advantage!

The financial J-curve bends upward only when you reduce the cost per unit of value faster than customers increase their consumption.

This means your most valuable customers are also your most expensive to serve. We have a phrase for this at SaaSholic: retention without margin is charity.

Bessemer Venture Partners offers a useful framework for this trajectory2, by splitting AI companies into “Supernovas,” early-stage companies running at ~25% gross margins with unoptimized infrastructure, and “Shooting Stars,” mature companies that have reached ~60% after deliberate work on cost structure and pricing. The distance between those two stages is the financial J-curve.

That framework reflects the US growth-stage reality. In Latin America, the starting point is likely steeper. Most seed-stage AI companies in the region are fully dependent on third-party APIs with no scale leverage on pricing, operating with smaller revenue bases where fixed infrastructure costs hit harder per unit. The path from Supernova to Shooting Star is longer here, which makes cost-to-serve discipline even more urgent.

You cannot wait for scale to solve the margin problem, you have to engineer it early.

The chart below shows two paths from the same starting point. Which one a company lands on is not a matter of luck or market timing. It is a matter of whether the team treats margin as an engineering priority from day one, or assumes that growth will eventually solve the problem - which is unlikely to happen.

The Takeaway

We truly believe that the AI winners will be the ones that can open their cohort data and show two things: a customer base that survived the behavioral valley and moved from experimentation to core workload, and unit economics that are improving rather than compressing as that base scales. The first without the second is a growth story with an expiration date.

This is what we look for and it shapes how we advise our founders to build their metrics infrastructure from day one. The aggregate dashboard is not enough. What matters is isolated monthly cohort data that tracks both retention and contribution margin trajectory over time. If your Month 6 cohort is retaining well but costs the same per user to serve as your Month 1 cohort, the financial curve is flat and growth will not fix it.

There are some cost-improvement levers available today. Founders should be routing queries by complexity so that expensive frontier models only handle what simpler models cannot, caching repeated inference to avoid paying twice for the same answer, and investing early in fine-tuned models for the highest-frequency tasks.

On the pricing side, the goal is aligning revenue capture with the value delivered so that heavier usage generates proportionally higher margin, rather than offering flat access that subsidizes your most active accounts.

The bar is higher than it was in traditional SaaS. But for the founders who clear it, the prize is larger because consumption-based models uncap the revenue ceiling in a way that seat-based pricing never could.

If you are building a business that fits this vision, we’d love to learn more!

Founders, Apply for Funding!

And then, in 2026, something changed: on January 3rd, the US removed Nicolás Maduro from power, and the political landscape of the country started to shift, opening a door that had been closed for years. Our dealflow followed. By May, we had already received more than 30 Venezuelan opportunities.

The specific trigger for our deeper engagement was a founder introduction: Rodrigo Tognini, the founder of Conta Simples, one of our portfolio companies, introduced us to Enrique Rivas, CEO & Co-Founder of Fina, an SMB ERP growing unusually fast in Venezuela, and that conversation revealed something deeper than expected. Venezuela lacks basic financial infrastructure that was solved more than a decade ago in Brazil and other more developed economies. The problem goes well beyond the absence of venture capital. Payment rails, credit infrastructure, merchant software, recurring billing, and digital fiscal invoicing, the things that make a modern digital economy work, haven’t been built yet.

And yet, that gap hasn’t stopped companies from growing. Venezuelan startups are using creativity and extreme capital efficiency to find profitable growth paths in conditions that would have killed most companies elsewhere. That curiosity got us to commit time trying to better understand if a “Venezuelan Opportunity” exists. We met many founders, mapped the ecosystem and started a fascinating deep dive in order to size such opportunity.

Ultimately, this got us on a plane, and we spent a week in Caracas for Venezuela Tech Week, the country’s tech entrepreneurship conference. While we were there, Venezuela formally began renegotiating one of the largest unresolved sovereign debt loads in the world, estimated at $150-170B, with the country and PDVSA (Venezuela’s state oil company) in default since 20171. This has very practical implications: sovereign default blocks startups from international bank partnerships, access to credit, international settlement, digitally compliant payment rails, and prevents access to venture capital and exit opportunities. Debt restructuring is the precondition for those operational channels to reopen.

We are here to share what we learned and the opportunities we found.

TL;DR: Post-January 3rd, Venezuela is being reconstructed unevenly, politically, and with no clear rulebook, but the optimism and momentum on the ground are real. Great minds are returning to the country, companies are growing at rates that are hard to find anywhere else in the region, and if the regulatory and institutional framework catches up with what founders are already building, many significant opportunities will arise.

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A $100B economy sitting on $372B of potential

Official government data shows 5 consecutive years of real GDP growth. These figures come off a catastrophic base, since the economy collapsed from roughly $372B in 2012 to around $100B today, with oil exports reducing by 95% from its peak.

Venezuela holds the world’s largest proven oil reserves at over 300B barrels, and production has collapsed from over 3M barrels per day in the late 1990s to ~1M today.

The installed infrastructure is enough to ramp up a good share of past production, and this ramp-up is already underway: oil exports rose to 1.25M barrels per day in May-26, a third consecutive monthly increase and 61% above the same month a year earlier, as Washington eased sanctions and foreign companies returned to the country. The oil ministry has forecast output of 1.37M bpd by year-end, a level not seen since US energy sanctions were first imposed in 20192. Reaching 2M barrels per day would require $8-9B per year in investments and could take until the early 2030s. A full return to historical production levels would demand even more, with estimates ranging from $53B-110B over 10-15 years3.

Beyond resources, the country has meaningful internet penetration (around 60%, the same level Brazil was a decade ago), and most surprisingly, a high banking penetration: in a session at the event, Calixto Ortega (the former head of Venezuela’s central bank and current economy VP) cited a banking penetration above 95% for Venezuelans over 18, a level that’s in line with Brazil and US penetration.

Venezuela is a financially distorted country, and the population moves constantly between bolivars (official currency), dollars, cash, Pago Movil (Venezuela’s instant payment system), informal FX, remittances, and crypto/stablecoin-based workarounds. A normal consumer or merchant in Caracas may understand liquidity management more viscerally than a much wealthier consumer in a cleaner economy, because the cost of getting it wrong is immediate, simply because, by holding Bolivars, they are losing 1% of their value daily, thus, any capital preservation effort requires converting your money to dollars fast, and accepting high spreads (3-7%). FX friction actually intensified financial behavior, forcing people to become more sophisticated about managing money across multiple currencies and instruments.

That distortion has also made the population crypto-native by necessity. Stablecoins, Binance Pay, and other crypto workarounds are embedded in daily commerce, for instance, Venezuela’s 2025 crypto transaction volume reached $44.6B4. People adopted crypto because the traditional financial system couldn’t serve their needs, and that adoption now represents a genuine advantage for fintech companies building in the country.

On the regulatory side, the environment remains a serious obstacle, but Venezuela appears to be entering a phase of selective, state-managed modernization. We spoke with several fintech startups in the region, all working towards obtaining their licenses to operate in the fintech space, many reported significant progress since Jan 3rd. One clear example, although still early, is that the Telco regulator just announced the approval of 15 new telecom operators in 20265, a signal that Venezuela’s regulatory gates may be starting to reopen for business. That single decision enabled an investment commitment of $500M by Telefonica6 to invest in the region’s digital infrastructure.

For fintech, licensing frameworks are still opaque, legal certainty is still unclear, and companies operating in good faith still find themselves in gray areas because the rules themselves are unclear or incomplete. SUDEBAN (banking regulator), SUNACRIP (digital assets regulator), FX policy, bank connectivity regulations, and compliance requirements all need significant improvement before institutional capital can deploy at scale. The government is starting to listen and engage with private-sector input, but the gap between openness and clarity is still wide.

Our call here is that as this modernization process continues to move forward, it will create asymmetric opportunities for companies that provide the infrastructure the country needs. It’s unclear how long it will take until Venezuela becomes a “normal” open market, but right now the country appears willing to approve new operators, renew licenses, and modify regulations in pursuit of rebuilding itself.

Credible currency: A necessity for the rebuild

The currency sits underneath many of the investment risks and opportunities in the country. Full dollarization is a problem for any economy: it means surrendering monetary policy, losing the ability to manage credit cycles and depending entirely on external dollar inflows. Venezuela needs a functioning domestic currency, which means the bolivar needs a credible revamp, something structurally similar to what Brazil did with the Plano Real in 1994.

Hyperinflation creates a self-reinforcing cycle in which people expect the bolivar to lose value, spend it as fast as possible, and hoard dollars, and that velocity of spending reinforces the very inflation they’re trying to escape. Breaking that cycle requires institutional credibility, fiscal discipline and a monetary anchor that people trust. Venezuela is still far from having those conditions in place.

One useful mental model we absorbed is that tracking the gap between the bolivar/USDC rates on crypto exchanges and the official exchange rate is a good proxy of the country’s stability (or journey towards it). According to several people we spoke to, a year ago, that gap was around 80-90%, and it has since narrowed to roughly 30-40%7. This gap effectively measures how much the market trusts the official rate, and by extension, how much it trusts the institutions behind it. Closing that gap further is a condition for the kind of macro normalization that would unlock the next wave of investment.

Capital scarcity breeds a specific kind of company

The absence of institutional capital for the last decade fundamentally shaped the Venezuelan startup ecosystem, and the companies that survived developed a very specific profile.

Most global platforms and US incumbents stayed out of the country due to sanctions, compliance risk, and FX complexity, and local companies filled the vacuum with limited competition but also without access to capital. They hired strong local talent at relatively low costs, built lean teams deeply familiar with the operating conditions and grew profitably by design, but also fast. Multiple startups are in the $1-10M revenue range, doubling or tripling year over year, a few with little (oftentimes less than $100K) outside capital and most with zero investments. This lack of venture capital forced a discipline that is unusual in any developed market. They had to sell, collect, retain, operate, and survive without the luxury of subsidized growth, which created an extremely resilient cohort of entrepreneurs.

Capital scarcity is brutal and kills most companies. But the ones that survive often look unusually efficient when capital finally returns.

In any other market, these numbers8 would already have attracted significant institutional capital, but in Venezuela, most of these companies built this scale nearly alone.

This looks familiar

If you’ve followed Latin American venture capital for the last 15 years, you’ve seen this pattern before. What is happening in Venezuela today resembles what happened in Brazil during the first wave of venture capital between 2010-15, when a set of local companies emerged to fill category gaps that global platforms either couldn’t or wouldn’t address.

Brazil’s first tech unicorn was 99, a ride-hailing company acquired by Didi for $1B in 2018, and Ridery is a clear Venezuelan parallel. iFood became Brazil’s dominant food delivery platform, currently valued at $5.4B with near-monopoly market share. Yummy is building a multi-vertical super-app in Venezuela spanning ride-hailing, delivery, and marketplace, and has already moved into fintech with driver and merchant financing. Nubank redefined consumer financial services in Brazil and is now a $60B+ public company with over 110M customers, and Cashea is the closest parallel as a consumer credit platform that has reached massive scale. Omie and ContaAzul pioneered cloud-based SMB software in Brazil. In Venezuela, Fina is embarking on a similar trajectory, and although early, it seems to be taking the right steps towards building the operating system for small merchants in a market where most businesses still run on pen and paper.

As we mentioned in our “Man From the Future“ letter, sometimes, when you’ve watched a market structure mature in one geography, going to another geography where the same transformation hasn’t happened yet is like traveling to the past with insider information. Each country has its unique dynamics, but the pattern recognition shows us that there is a positive prognosis for the region, similar to what started for startups in Brazil around 2010.

Achieving fast growth with no funding is the strongest possible signal of product-market fit. When a company keeps doubling revenues without a dollar of venture, the demand is real and the unit economics have to work. That is happening for many startups in Venezuela right now.

The timing also helps. AI has fundamentally changed how much a small team can build. A five-person engineering team in a garage in Caracas today is also using Claude Code to ship at the speed that would have required 50 people a decade ago. Venezuela lost a massive share of its skilled workforce in the diaspora, and that would be a hard constraint on how fast an ecosystem can grow, but AI changes the math. The talent that stays becomes dramatically more productive, founders are finding creative solutions and leapfrogging infrastructure gaps that would have taken years to close manually. With AI, the ecosystem can reach critical mass without waiting for the talent to physically return and, for instance, this is a country with a population 5 years younger than Brazil’s9.

Brazil’s first venture wave happened at a time when building software was expensive and labor-intensive, Venezuela’s is happening at a time when it has never been cheaper or faster to build.

Another historical pattern is that the first wave of a nascent tech ecosystem is almost always consumer-facing. B2C companies reach scale first because they address the most immediate and visible needs. If the pattern holds, a B2B wave will follow, and the infrastructure layer of payments, compliance, merchant tools, credit scoring, and treasury management is where the next set of opportunities will emerge.

Every startup is a credit business

Understanding why every Venezuelan startup eventually gravitates toward financial services requires understanding what the financial system actually looks like on the ground.

Venezuelans already live inside digital and semi-digital money movements every day. The fintech opportunity is entirely about infrastructure: the tools, rails, and platforms to make it work properly.

Pago Movil gives Venezuela something that looks, from a distance, like real-time payments, but the architecture is far from what Brazil built with Pix. The user and merchant experiences are clunky and primitive by modern standards10. Fee friction on both the sender and merchant side also remains a structural weakness, and since credit cards account for less than 1% of transactions in the country, the entire consumer and merchant economy runs on a patchwork of cash, bank transfers, Pago Movil and crypto.

Think about what a Venezuelan merchant needs to know every day: what she sold, in which currency, whether the payment actually cleared, whether she can restock tomorrow, whether the bolivars she holds will lose value before she pays the supplier, whether her invoice is compliant, and if anyone will lend to her based on her track record. All of those questions map to infrastructure that exists in other countries and is absent or broken in Venezuela.

The consequence is that almost every valuable startup in Venezuela eventually gets pulled toward credit and financial services. In more developed markets, categories stay cleaner for longer, with B2B software remaining B2B software, mobility remaining mobility, and delivery remaining delivery. In Venezuela, the categories collapse into each other. Ridery can expand into driver financing, vehicle financing, insurance, fleet credit, and logistics liquidity. Yummy already offers driver and merchant financing through cash advances, and can expand further into courier financing, wallets, ads, and working capital. Fina’s real strategic value lies in the operational data it collects, including sales frequency, inventory turnover, and payment patterns, which become the raw material for credit scoring and working capital distribution.

January 3rd changed everything. Clarity still hasn’t followed.

January 3rd changed everything about how the world looks at Venezuela, and everyone we spoke with during the trip acknowledged it. In conversations with venture investors, founders, Ridery and Yummy drivers, hotel staff and random people on the street, we heard a consistent view: virtually everyone supported what happened, and there is a genuine sense that the country has turned a corner. People believe elections will happen eventually (MCM is winning our street polls by a very far margin), but our overall take is that elections are not what people are focused on right now.

The priorities we heard were: political stabilization, people want to know that the situation will hold and that the opening is durable; and fixing the basic things that have been inaccessible or broken for years, from infrastructure to public services to economic normalcy. The sense is that once those foundations are in place, a more open political process can follow. What brings them some comfort is that the international community appears engaged and attentive to how things unfold.

One telling anecdote: we were on the ground when Trump posted about Venezuela becoming the 51st U.S. state, and we asked Adolfo, our Ridery driver, whether he had seen the news. Surprisingly, he just waved it off. “Mi pana, aquí hay vainas más importantes que resolver [there are more important things to deal with]”. “People need their money to be worth something again. We need to know what the bolívar is worth today, and what it will be worth tomorrow. What matters now is whether the U.S. is buying Venezuelan oil and they just bought something like $600 million of it. The 51st state thing? Eso es ruido [that’s just noise]. I do think there will be elections by the end of the year, and I think María Corina is coming back. But for most people, esa no es la prioridad ahorita.”

We had the opportunity to meet senior economic officials, regulators, and government-linked entities to make the government’s posture visible and the engagement felt somewhat genuine. In a public session with foreign investors, Calixto Ortega laid out the current regulatory framework: Venezuela is reviewing its approach across banking, financing, real estate, hydrocarbons, mining, and other areas, and the fintech framework is open to input and potential amendments. When asked about SUDEBAN, SUNACRIP, and the fintech roadmap, the answer was closer to “we are revising everything” than “the rules are solved.” That’s constructive, and it represents a shift from a recent past in which everything that touched fintech/crypto regulation and licenses happened over closed doors (and thus was immersed in corruption11), but it is still far from the kind of clarity we’d like to see.

The government’s priority is to protect and relaunch the bolivar for domestic use while remaining open to tools that help Venezuelan entities interact with the international financial system. The founders who understand that framing and build compliant infrastructure that helps reconnect Venezuela to international finance will likely find more regulatory tailwind than those who position around replacing the bolivar.

Institutional clarity needs to improve significantly before the country can absorb serious capital at scale.

Company Spotlight: Fina Partner

Fina makes the thesis concrete because it embodies the full arc from operating pain to financial infrastructure. Three of the founders previously built Caracas Ron, a 24/7 liquor and food delivery business during COVID, and when lockdowns ended and demand declined, they struggled with inventory visibility, cash-flow management and working-capital constraints. Those are the same problems that every small merchant in Venezuela faces daily, and that experience directly surfaced the need for simple, affordable, localized operating software.

Fina’s typical customer is a micro or small business, usually owner-operated, with 2-10 employees, monthly revenue of roughly $1-5K, heavy reliance on Excel or notebooks before adopting software, and little or no access to formal credit. The product today covers daily operations including sales, inventory, basic financial control, and workflow visibility. The company has roughly 4,200 active customers, crossed $1.5M in ARR in a year after launch, and has a CAC payback below 2 months, growing at a very fast pace.

Their roadmap follows the financial-gravity pattern: fiscal invoicing, automated Pago Movil verification, real-time cash visibility, integrations with the four largest banks, POS integration, and credit. In its initial credit pilot with a Venezuelan bank, Fina reported zero delinquency, with customers paying their first installment an average of two days early, 10% prepaying the second, and not a single phone call required to collect. In a credit-starved market, the operating system of the merchant can become an underwriting system.

The risks are clear too. The customer base has limited pricing power, churn is front-loaded in the first three months due to incomplete activation and payment friction as there is no local Stripe yet, or an easy alternative recurring payments infrastructure. It’s all being built right now. The same infrastructure gaps that create the opportunity also make the company harder to scale. The company recently raised a seed round with local and foreign investors and is now in a strong position to accelerate growth even further.

At Fina’s offices we saw a high energy, great culture environment. We saw founders with the resilience required to tackle the challenges that their country presents. We’re confident that they will be able to devise clever solutions around the country’s challenges, and by being the first mover, that resilience puts them in a very interesting spot.

Sanctions accidentally built a moat

One assumption worth questioning is that if Venezuela opens, global platforms return and win. That may eventually happen in some categories, but it deserves skepticism. Venezuela never received the default global tech stack, and local companies were forced to build their own alternatives over years, oftentimes inventing completely new categories.

• Will Visa/MasterCard eventually take over Cashea’s business? It’s hard to predict, but it doesn’t seem likely to us. Cashea became embedded in the company’s consumer culture and habits.

• Will Uber eventually be a threat to Ridery/Yummy? More than just a mobility app, the company rents cars, gives credit to drivers, sells local ads and has a sophisticated business model that goes beyond what the surface shows.

These companies learned bank integrations, credit discipline, informal FX, local compliance and customer education in the field, over years of operating while everyone else was absent. Also, the average Venezuelan consumer is working on an older-generation Android device, connectivity is often limited to 3G speeds and apps need to be built from the ground up to perform under those constraints. A product designed for the latest iPhone on a fast LTE connection will struggle in that environment. The companies that grew in Venezuela learned to build lightweight applications that work on the hardware that people actually have, which is difficult to retrofit back.

Global platforms may eventually come back, but they will not come back to an empty field, and even when they come, it might make more sense to acquire and partner up than to try to tackle a complex market by themselves.

TAM can be a concern, but this is also a winner-take-most market

TAM is the obvious investor objection in Venezuela: the market is smaller, poorer, and more macro-fragile than Brazil, Mexico or Colombia. But that framing can miss the more important point: in markets where core infrastructure is broken or underbuilt, category leaders can capture an unusually large share of spend very quickly. Right now, there are at least 4 Venezuelan startups that have reportedly scaled to more than $100M in GMV / annual revenue within 3-5 years, implying that the country can still produce outcomes with venture-style return potential when a company owns a high-frequency, trust-critical use case.

Cashea is the clearest example: according to our sources, the company crossed $350M in ARR on just $2M of early funding, only recently raising a round close to $80M that puts it on a path to unicorn status. That matters because in Venezuela, a startup doesn’t seem to need a perfectly clean macro backdrop, since they have never seen one before. What they need is to become the trusted operating layer in a market where customers are desperate for reliability, and the first company to earn that trust dominates. And the market ceiling is higher than the headlines suggest, since in the good scenario where the country’s GDP returns to historical levels, it could grow 3x over the next 5 years.

Optimism informed by difficulty

The Venezuelan founder diaspora is starting to reconnect, and that reconnection works as a graph of capital, mentorship and distribution. We heard about founders and operators who spent years at Rappi, Nubank, or Mercado Libre, grew a Series B in Bogotá or Miami, or studied at top-tier universities, and are now starting to reconnect and build in a greenfield market. Companies like Slash, a Venezuelan-founded fintech building global payment infrastructure, prove that Venezuelan talent can build at global scale, and that same talent, credibility, and capital is starting to flow back into Caracas. These founders can skip a learning curve that Brazil’s first generation had to live through.

The infrastructure gaps are enormous and the addressable market is large, however the risk is also huge, since regulation is unclear, political transition is uncertain, the institutional credibility is still being rebuilt, the bolivar needs a solution and international access remains constrained.

But what stands alongside those risks is something hard to find elsewhere: companies growing at rates and capital efficiency levels that are almost unmatched in any other market we cover. The companies described in this piece have proven product-market fit under conditions far harder than what most startups face, and that kind of validation carries weight.

Easy markets don’t leave this much infrastructure unbuilt. Venezuela is being rebuilt. For investors willing to engage with the complexity, the asymmetry exists.

We are committed to continue spending time understanding the country and the opportunities it might bring, and willing to help local founders, regulators, and other investors figure out how to develop this ecosystem in a way that is good for the country, good for its population and ultimately delivers strong returns for investors. We came back more optimistic than when we arrived.

If you’re a Venezuelan founder solving one of these problems, we’d love to hear from you.

Venezuela is moving, and so are we.

Thanks to all our Venezuelan friends that reviewed early copies of this article!

Victor Charles Yitzhak, Freddy Genatios, Enrique Rivas, Daniel Bolivar, Nicolas Passaro, Luis Pernia, Carlos Ramirez & Adolfo Pecchio

TLDR: If you’re building your second or third company, we want to be your first institutional check. We invest at pre-seed, before the deck is polished and before traction makes things easy. Being the first check means helping shape the cap table, governance, and narrative from day one. Interested? Apply Now!

There is a reason the book Once You’re Lucky, Twice You’re Good resonates with us here at SaaSholic. The second time around, success is rarely luck. It is pattern recognition. Second-time founders move differently.

• They understand dilution emotionally, not just mathematically.

• They have felt hiring mistakes in their burn rate.

• They have navigated board dynamics under pressure.

• They recognize false traction before it becomes expensive.

We are comfortable engaging early and helping shape the foundation properly, and we're committed to work alongside you to help in the next step of your journey.

This is not about claiming repeat founders are better than first-time founders. Many extraordinary companies were built by first-time entrepreneurs.

But this initiative is a focused commitment to builders who have already gone through a full cycle and want their first institutional partner aligned on speed, clarity, and governance from day one.

What “First Check” Means

Being the first check is not just about wiring capital early.

It means showing up before the story is polished, before traction exists, and before consensus reduces risk. We focus on pre-seed and seed. In many cases, we are the first institutional investor.

That means helping you:

• Design your cap table intentionally from day one.

• Attract talent, angels and advisors of the highest caliber.

• Think through investor sequencing before it becomes reactive.

• Structure governance early instead of patching it later.

• Leveraging our network to help you land that big enterprise client logo.

More importantly, it means working hands-on when the company is still fragile, helping make it robust enough to cross the earliest uncertainty and become ready to scale up the VC ladder. To share a few examples:

• Fundraising: At SaaSholic, 81% of the companies we invested at raised an additional round of funding and 75% of those were lead by Global VC funds. And if you ask our founders, you'll see that we worked hard alongside them to help them achieve this.

• Talent: We made 62 senior introductions to our portfolio companies in 2025 alone and that resulted in 40 placements.

• Client Relationships: Over the last year, we made 52 enterprise client connections to our portfolio, resulting in 21 deals closed and 8 still in the pipeline to close.

That's the kind of support we offer.

The Clad Story

When Danilo Costa reached out before launching Clad, he was exploring his second venture after building Educbank.

There was no refined deck yet, no round in motion and no polished narrative. But there was an amazing founder, and amazing thesis that we wanted to explore together. We didn't just invest and disappear. We worked hard alongside him:

• We flew to Arizona to validate the thesis in-market and speak directly with competitors.

• We ran customer interviews alongside him.

• We worked together to shape the narrative and bring additional investors and strategic partners into the round.

That is what “first check” means to us. Early conviction. Early co-work. Real partnership at idea stage. We've backed many other second timers before, such as Alberto & Alejandro Rosas (Didit), Bruno Sayão (Sinky), Yaak (Liquid) and a many others… We want to double down on it!

Ready to Build Again?

If you are building company number two or three and want your first institutional partner engaged at idea stage, we would love to meet you and we encourage you to apply. We respond to every application and we want to hear about your vision!

Although this seems like pure fiction, we believe there is a parallel in investing. We call it the “Man From the Future” thesis.

When you’ve watched a market mature in one geography, going to another geography where the same (or similar) transformation hasn’t happened yet feels like traveling to the past with the answer key. The change feels inevitable, it’s a matter of timing. The gap between “inevitable” and “priced” is where alpha lives.

We haven’t articulated this thesis in a structured way before, but we realized we have been practicing it since our first investments back in 2019 and our conviction that it works has grown with every new case.

To be frank, an emerging Brazilian manager claiming it can invest in the US by exporting Latin American knowledge sounds ambitious at best and delusional at worst. We know that. But what we’ve found is that the pattern recognition built in this region is exactly what gives us an edge in opportunities that global players haven’t thought about yet.

In this letter, we walk through the thesis, the investments where we’ve applied it, and where we think it goes next. We are also excited to announce the first investment of SaaSholic Fund III, the newest and perhaps clearest expression of this idea.

Becoming the Man From the Future

We invest across geographies by identifying what one market has already built and deploying it where the transformation hasn’t happened yet.

However, the instinctive read is that this only works one way: see what succeeded in a more developed ecosystem like the US and replicate it in Latin America (aka copycats). That was the dominant playbook of Latam tech for over a decade.

MercadoLibre was founded in 1999 as the “eBay of Latin America” and eventually surpassed eBay itself in scale1. 99, the first Brazilian unicorn, replicated the ride-hailing model and was acquired by Didi for $1B2. Resultados Digitais brought HubSpot’s marketing automation playbook to Brazil and was acquired by TOTVS for R$2B3.

The model worked, produced some of the region’s most valuable companies and exits, and may continue working in the future. But sticking only to it is first-level thinking, too obvious and pursued by dozens of like-minded people.

We believe great opportunities also exist in the opposite direction: Latin America exporting what it has already built to the world.

Ignoring that is a massive underestimation of what the region is. The entrepreneurs behind Brex and dLocal built and operated in Latin America first, then applied that experience to build global companies from scratch. Wellhub, Tractian, Wildlife, VTEX and Pipefy were born and scaled in Brazil before expanding across dozens of countries. Nubank started in Brazil and now serves over 100M customers across Latam and is starting its expansion into the US4.

These founders were forged in a tougher business environment: high taxes, high interest rates, scarce funding and complex regulation, but when they move to markets with easier conditions, they carry a structural advantage. The founder who built profitably at 15%+ interest rates doesn’t panic when rates go from 0% to 5%.

Why Latin America Built It First

There are spaces where Latin America has built technology and infrastructure that are genuinely ahead of the rest of the world. All of it was built by necessity.

When interest rates run at 15%+ for decades, holding cash becomes expensive and the entire financial system is forced to move money faster, settle in real time, and create instruments that generate daily returns. When fraud is endemic, the system builds identity verification and real-time monitoring before anyone else has to. When the tax environment is notoriously complex, commercial transactions get digitized earlier because paper cannot keep up with compliance. These pressures accelerated innovation, and the infrastructure that emerged is ahead of what most developed economies have built.

PIX is the most visible example. Brazil created CDI in the 1980s, an overnight interbank rate that trained the entire population to expect daily yield on their money, something with no equivalent in the US or Europe.

That culture of daily yield and intolerance for idle cash is what produced PIX, launched by the Central Bank in 2020. PIX allows anyone to send money in real time, 24/7, for free, and went from zero to 32% of all transactions in four years5. It is ahead of India’s UPI in volume, per capita usage, and growth despite India having 7x the population6. PIX has already generated over $14.5B in value by eliminating cash-handling and card processing costs7. The US is only beginning to attempt something similar with FedNow, at a fraction of the adoption.

Another, less visible but perhaps more important example for us, is receivables.

The same cost of capital that made PIX urgent also made waiting 30, 60, or 90 days to receive a payment unsustainable. For a Brazilian merchant, getting paid two weeks earlier can be the difference between restocking and shutting down. That pressure created massive demand for receivables anticipation, which required infrastructure to verify who owns a receivable, whether it has already been pledged, and whether it is available to be financed.

Before 2019, none of that existed, and merchants’ receivables were locked to their acquirers. Brazil built that infrastructure. Electronic invoicing turned commercial transactions into legally valid digital records. A Central Bank regulation required receivables to be registered in authorized registries operated by CERC, Núclea, and B3, and made them accessible to any credit grantor.

Since the system went live in 2021, monthly volumes at CERC grew 8x to R$244B while merchant discount rates dropped from 18% to 9%, turning every invoice into a traceable, financeable digital asset8.

The same pressure to build trust and verification infrastructure extended across the entire financial system. Open Finance lets consumers share their full financial history across institutions and has already reduced fraud cases by 27%9. Regulation enabled fully digital banks in 2016 and fintechs to lend in 2018, and today 43% of Brazilians hold accounts at digital banks10. Anyone who has used a Brazilian fintech and then tried to make a transfer at a US bank can feel the gap firsthand.

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Where We’ve Applied the Thesis

The advantages described above are exportable, and we have been exporting them. What began as recognizing a familiar business model in a neighboring country evolved into incubating new companies in the US and, most recently, identifying infrastructure gaps that Latin America solved years ago.

Here are three cases that shaped our thinking.

1. ContaSimples & Cuenta Mono - Fund I

When we invested in Banky in 2019, the thesis was rooted in a gap that felt obvious. Nubank had already revolutionized B2C banking in Brazil with simplicity, transparency and zero fees. But B2B banking was lagging years behind on customer experience. At the same time, mature BaaS solutions like Dock were emerging and would enable a new generation of financial players to arise without needing a banking license.

The conditions were in place for someone to do for SMBs what Nubank had done for consumers. Rodrigo Tognini saw that opportunity and founded Banky as an account for the “PJotinha,” a banking solution for the smallest businesses in Brazil.

What the team quickly discovered was that “PJotinhas” were not the customer segment with the biggest pain points or best unit economics. The real opportunity was in B2B online spenders, companies buying media on Meta, Google, and AWS, that were terribly served by incumbents. Banks were slow to issue new cards, user management was poor, credit limits were laughable and anti-fraud systems were constantly blocking legitimate transactions because they weren’t tuned for B2B online spending.

The company pivoted and rebranded to ContaSimples to solve exactly this. Virtual cards issued instantly, user management built as a core feature, “Crébito” generating strong interchange-based revenue, and fine-tuned anti-fraud that meant media campaigns would no longer shut down for no apparent reason. Customers could rely on it, and acquisition grew organically through the community.

ContaSimples was soon accepted into Y Combinator, marking international recognition and opening access to global investors. Valor, Base10 Partners, and Jam Fund invested after us. Today, the company keeps reinforcing this value proposition while developing credit offerings and is, hands down, the best investment of Fund I.

By August 2020, with ContaSimples showing strong traction, we started asking ourselves: where else does this problem exist and no one is solving it?

We mapped the B2B banking landscape across several Latin American markets. SMBs across Spanish-speaking Latin America faced the same pain. They were unserved by large financial institutions, and when served, the experience was far from good. Colombia stood out for the sheer depth and attractiveness of the opportunity. After validating the market and mapping the teams building in the space, we became convinced by Juan Camilo’s vision and team. In April 2021, we invested in Cuenta Mono, whose proposition was ContaSimples for Colombia.

One year after our investment, the company also joined Y Combinator and later raised a large round with Tiger Global. The company is one of the leading B2B neobanks in Colombia and is now at the forefront of processing transactions on BRE-B, the “Colombian PIX”, and is seizing this unique opportunity.

BRE-B moved from certification into production this year, and Mono was among the first participants to go live on the rail. If BRE-B follows a similar trajectory to PIX, it has the potential to transform the Colombian financial ecosystem the same way PIX did in Brazil. Mono is perfectly positioned to ride that wave.

ContaSimples taught us the model. Mono was the first time we consciously exported it. We saw the business work in Brazil, deeply understood why it worked, recognized the same gap in Colombia, found the right team and invested before the market. That experience planted a seed. If the thesis worked across Latin American borders, could it work in a completely different direction?

2. Clad - Fund II

Clad is counterintuitive at first glance.

William, one of SaaSholic’s partners, was an angel investor in EducBank, founded by Danilo Costa. Danilo is an educator and M&A lawyer turned entrepreneur who, in 2019, created School Income Guarantee in Brazil, a category that didn’t exist before him.

Private schools in Brazil wasted too much time chasing delayed payments from parents. EducBank guaranteed their tuition income by advancing 100% of invoiced revenue on the due date, absorbing collection and default risk, in exchange for a take rate similar to the delinquency the school already had.

EducBank grew fast, scaled across hundreds of Brazilian schools, raised a R$200M round from Vasta, and reached a strategic partnership with the largest education group in Brazil in 2022. A fast follower called Isaac saw the model, replicated it, and was acquired by Arco. Two strategic exits in two years validated a category that one founder had created from scratch.

After exiting, Danilo spent time at Stanford studying the US education market. He came to William with a question that was far from obvious at the time. Would the model he built in Brazil work in the US?

In Brazil, basic education is largely private and paid. In the US, it is predominantly public and free, so a similar business had no obvious place to exist. But after a site trip to Arizona, we discovered something we weren’t expecting.

The most acute pain wasn’t in traditional K-12. It was in childcare. In 45 states, two children in center-based care cost more than annual mortgage payments. More than 92,000 licensed childcare centers run this market, with no underwriting, no collections team, and no balance sheet to absorb delinquency11. The pain was real, but invisible unless you had already built the solution somewhere else.

In 2023, Danilo came to us with an idea and a track record, without a deck or a co-founder. We brought him into our Entrepreneur in Residence program and worked alongside him for six months on the translation of the model to the US.

Clad reached significant traction less than nine months after product launch and continues to grow fast, with zero churn since the first customer was onboarded. Every single customer came through a school-to-school referral. Its take rate runs at three times the closest US payments substitute.

Clad is the same model exported to a market five times larger, with better unit economics at every layer.

With Mono, we exported a proven model within Latin America. With Clad, we exported a Brazilian financial model to the US and proved that the thesis could work across the continent.

The next question was whether the same logic could apply to something deeper than a single business model: could it apply to infrastructure?

3. Enter Heft - Fund III

After diving deep into the proprietary data market and actually investing in the space with Liquid, Didit and others in Fund II, we found a huge gap in the US.

The trust layer underneath the US receivables market still runs on borrower-submitted PDFs, screenshots, and shared portal access. Equities have the DTCC. Payments have card networks. Consumer fintech had Plaid. Receivables still lack a neutral verification primitive.

The consequences of this gap are showing up in billions of dollars in fraud. At the same time, the US private credit market has grown 5x since 2009, reaching $1.3T12. The infrastructure to verify and monitor the collateral underneath it hasn’t kept pace. The market is scaling on trust that no system is checking.

As we were studying this gap, Henrique Tormena from CERC introduced us to the founders behind Heft, and our conviction only grew from there. We are happy to announce that Heft is the first investment of SaaSholic Fund III as a direct expression of the thesis.

Heft is led by two founders who embody the Man From the Future idea.

Mauricio Vergara, the CEO, spent nine years at Google and Unity Technologies building the demand side of platforms where developers, partners, and capital allocators all needed to be coordinated. He lived the SMB cash-flow problem firsthand, speaks the language of both invoice owners and funders, and is already recognized inside the receivables finance industry.

Pete Thomas, the CTO, co-founded C2FO, the world’s largest on-demand working capital marketplace, with $200B+ deployed across 160+ countries and $570M raised at a unicorn valuation. He spent 16 years as its CTO turning invoice liquidity into software at scale.

Together, they combine the technology platform experience and the deep receivables market knowledge needed to build this.

Verified receivables should be cheaper to fund, safer to monitor, and easier to trade. Heft is building the infrastructure to make that happen.

The company has already verified over $500M in receivables across with its paying design partners. For a pre-seed company, these are unusually strong data-scale signals that gave us the conviction to lead the round.

Heft is applying Latin American market-structure memory to a market 10x larger. We couldn’t be more excited to kick off Fund III with an investment that so clearly embodies the conviction we’ve been building.

Where This Goes Next

The thesis is a permanent lens we use to source deals and build conviction. We see it working across multiple directions: US to Brazil and Latin America, Brazil to the US, Brazil to Latam and now Latam to the world.

ContaSimples, Cuenta Mono, Clad, and Heft each represent a different vector and we believe there are many more to explore.

This mindset is also pushing us to study markets across the Americas that have historically been outside our scope. In Fund III, we are diving deep into other geographies where similar transformations can happen. More to come on this soon.

For decades, the venture industry took what worked in the US and replicated it in Latin America. We believe the reverse has not been articulated or practiced with the same intentionality. As we scale Fund III, we expect this to become a defining part of how we invest.

Founders, Apply for Funding!

If you've recognized an opportunity in one market that hasn't been built in another, we want to hear from you. We're deploying Fund III and looking for founders who carry the kind of cross-border pattern recognition!

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This happens because venture is a power law business. A few true outliers built by exceptional entrepreneurs end up moving the needle for several funds. Those founders are rare, so when some investors think they’ve found one, they lean in early and try to make the outcome feel inevitable.

“Kingmaking” is the name of this move: a deliberate attempt to define the category winner, then reinforce that conviction with extremely large checks and loud signaling.

This is one of those self-fulfilling prophecies, built on the idea that overfunding raises your chances of success disproportionately. Because the winners are often the most-funded companies, we get the chicken-and-egg problem. Did they win because they received the most funding or did they receive the most funding because they were already winning?

For entrepreneurs, this seems like pure upside. Since dilution is limited within rounds, larger rounds mean higher valuations, translating into cheaper capital. In fact, most times, you should take the “kingmaker” offer if you receive one. Still, in this article we want to present the overlooked side effects that can come with it.

At SaaSholic we don’t believe in kingmaking. We have seen multiple cases where the underdog beats the overhyped player and multiple companies bootstrap their way to success1. We also think it hurts venture returns, which is bad for the asset class and its LPs (we personally like investing in contrarian underdogs too, but that’s another story!). Here are the reasons we’re skeptical.

Mercenaries over Missionaries

Extra funding can help you hire the most pedigreed talent, however, early in the journey it’s well known that you need “missionaries” over “mercenaries”2. You want people with an owner’s mindset and the grit to make your company win, even when it feels irrational from the outside.

Having extra money will help to hire the Dev from big tech, or the polite leader with the right vocabulary, but those aren’t the ones that move the needle. More often than not, this profile will bring politics into the organization earlier than the company can afford.

In startups, even at later stages when the risk of ruin has apparently dropped, you still need to run with the “only the paranoids survive”3 mindset. The moment you start believing your crown is real, you stop doing the things that would earn it in the first place.

Startups are living experiments. You must iterate, iterate and iterate until you can (hopefully) find your way to success. You can’t buy your way into it.

We heard the clearest rebuttal to kingmaking from someone who would benefit from it. In a recent 20VC episode4, Winston Weinberg, founder & CEO of Harvey, the $6B legaltech that raised $1B from OpenAI, Sequoia, Kleiner Perkins, a16z and Coatue, explained why the idea breaks down in practice.

Harry Stebbings: Why do you disagree with kingmaking as a theory?

Winston Weinberg: One, they think of kingmaking as it provides you with more capital. More capital does not mean you run a better business. You could have as much capital in the world as you want. If you make the wrong product decisions, you’re just going to invest in all the wrong places and it doesn’t matter. It’s the same as VC. You have a hundred billion and if you put it all into the wrong things, that still goes to zero, right?

[…] How many times a day do you think something goes wrong at Harvey? Constantly. Like 24/7. How many times a day do you think we feel like there’s an existential threat? […] All the time. Startups are very difficult places to work.

And so you think from the outside, “Oh wow, they’re growing revenue so much. They’re the category leader. They have all these investors. But internally at all of these companies, it’s chaos and morale goes up and down. You face really difficult things and then you have to figure out how to get through them.

And so being a missionary really does matter because the reality is once you’re on the inside, the brand of the company and the success of the company matters less than when you’re on the outside. […] People just don’t realize that because they aren’t inside of these companies, they’re on the outside.”

Overfunding raises the bar

Most VCs run 30-50 company portfolios, often with multiple funds operating simultaneously while having limited headcount. They also need to raise new funds, source new companies and manage their own firms. As brutal as this can be, many end up following Peter Thiel’s advice5:

“This implies two very strange rules for VCs. First, only invest in companies that have the potential to return the value of the entire fund. [...] But because of the power law, the apparent ‘losers’ are mostly just irrelevant. The biggest secret in venture capital is that the best investment in a successful fund equals or outperforms the entire rest of the fund combined.

[However], most venture capitalists spend their time on the most problematic companies... The power law means that investors should focus on the few companies that are successfully going from 0 to 1, but most investors spend their time trying to fix the companies that aren’t working.”

If we connect the dots, the conclusion is unavoidable:

1. Most VCs will focus attention on their winners.

2. The “winners” are the ones expected to be fund returners.

3. When kingmaking occurs, entry valuations are higher.

4. When valuations are higher, the hurdle to become a fund returner gets much higher.

5. The chance that VCs stop caring if you miss these more ambitious hurdles increases. VC attention may disappear earlier than most founders expect.

This is why you should not only optimize for the highest valuation you can get, but also make sure that the partner you’re choosing will be with you even after they realize you may not be the decacorn needed to return their fund.

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The Brex vs. Ramp case

It’s easier to illustrate with an example. Last month, the Brex exit was announced in a $5.1B deal to Capital One. This is a remarkable outcome by any standard. As Brazilians, we feel really proud of seeing founders winning globally and shaping the global software landscape. Pedro and Henrique belong in the pantheon of Brazilian founders.

Much of the post-exit debate has been focused on the competition between Brex and Ramp, and why it’s a clean counterexample to the kingmaking narrative. When Ramp started and raised its $4.5M seed, Brex had already raised $282M at $2.6B, backed by several tier 1 investors.

By February 2020, Brex was valued at $2.75B while Ramp was closing a round at $75M, a 37x gap. Four years later, the picture looks different. Ramp has continued to compound, recently raising at $32B, with $1B in revenue run-rate6.

There is still a long road to see how this journey will end, but the lesson is that a competitor’s funding and investor list are only the start of the story, not the finish line. Don’t get discouraged by it.

Customers don’t buy VC credentials

The job of a VC is to distill signal from noise while working with limited information. VCs tend to anchor on credentials or status signaling when underwriting a founder because it feels like a shortcut to quality when you don’t have much more to analyze. The big mistake is projecting that same logic onto customers.

Most customers do not care whether a product was funded by Andreessen or Sequoia or how much it has raised; they care about outcomes, reliability, and something that works inside their workflow.

It’s easy to counterproof it

Looking at the largest venture outcomes in Brazil and globally, they in fact were the ones that raised the most capital7. Many people stop there and treat it as evidence that capital is the ultimate competitive advantage. The pattern is real, but the conclusion is wrong.

Not every large raise leads to a large outcome, we all know plenty of overfunded disappointments. As markets become more competitive, and more firms are searching for the few potential decacorns in a category, capital concentrates around the companies that are already showing real momentum. In other words, success often brings money, money doesn’t necessarily create success.

SoftBank’s $7.6B Latin America Fund is a useful real-world stress test of this idea. A single vehicle, roughly equal in size to the total venture deployment in Latam across 2024/25, had enough scale, brand and access to back perceived category winners early and repeatedly. Yet, according to public data8, the 2019 fund will only generate modest returns to its investors, even with meaningful wins in its portfolio.

It is the cleanest reminder that capital is only one of the successful inputs needed for a great outcome.

The SaaSholic approach

We think the easiest, but most long-term harmful path in venture is to raise very large funds, writing very large checks into consensus “hot deals.” In the short term, this would mean more management fees, more logos and external validation. In the longer run, however, it would probably mean mediocre returns.

We decided to follow a different path. Smaller funds, fewer companies, investing early and staying very close to founders. We focus less on trying to buy speed with burn, and more on helping teams get the fundamentals right. It may take longer to look like a winner, but it reduces the number of avoidable deaths in the portfolio. Fewer deaths help drive higher hit rates and better returns.

We prefer to play the positive-sum game of helping create opportunities than the zero-sum game of fighting for the allocation on hot opportunities.

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For Founders, some practical advice

Money is a weaker advantage than it looks. Don’t get discouraged because a competitor raised big, and don’t get deluded because you did. The journey is far from over!

When deciding whether to take a term sheet, we agree with Paul Graham’s advice to YC founders9:

“We advise founders who go on to seek VC money to take the first reasonable deal they get. If you get an offer from a reputable firm at a reasonable valuation with no unusually onerous terms, just take it and get on with building the company. Who cares if you could get a 30% better deal elsewhere? Economically, startups are an all-or-nothing game. Bargain-hunting among investors is a waste of time.”

The nuance is that taking a “reasonable deal” requires a lot of work. Spend your time on the part that matters most, who you are adding to your cap table. Make sure you choose investors that will help even if the story gets messy.

Ho Nam from Altos Ventures has a practical way to reference-check that most founders skip10. VC reputations are shaped by a tiny handful of winners, and if you only talk to the home runs you will get a skewed picture. Talk to the long tail. Ask founders in the struggling or average outcomes how the firm showed up and what support looked like during bad times.

If you end up receiving a kingmaker offer and conclude that the partner is truly worth having on your cap table, the best advice may be to take the money but pretend you didn’t. Don’t increase burn just because the bank account is bigger, even if that creates pressure to “move faster.”

Founders, Apply for Funding!

If this perspective matches how you want to build, we’re deploying Fund III. If you want an early, hands-on partner who stays close even when the work gets hard, apply here!

Apply for funding!

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SaaS has, for two decades, been the holy grail of tech investing from Silicon Valley to Wall Street. The business model combines economics that let it compound over time: high growth, high margins, an asset-light structure with high switching costs and recurring revenues, leading to great retention that enabled tech companies to grow faster than other publicly traded industries.

Cloud software has consistently grown 30% annually for 20 years, giving comfort and the benefit of the doubt for investors to “pay up” for a growth that would always arrive. High growth was rewarded in a world where growth in the real economy was harder to find and interest rates were 0. This led SaaS to trade at high revenue multiples (without needing to worry about profitability, yet), with the median SaaS trading from 5–10x ARR and top-quartile growth names at 20–30x.

However, it seems like AI is challenging every single one of the assumptions that made SaaS the hottest business model in town.

As seed investors not directly exposed to the public downturn, we believe the AI-driven reshuffle is good news. The largest names in software a decade from now will probably look very different from today, opening the doors for newcomers to build the next wave of winners.

Let’s break down what is driving the market pessimism.

Public market growth is not what it used to be

Markets want high growth. However, growth rates for public SaaS have been declining for years. At the same time, AI is the new highest-growth opportunity available, and we have seen many AI companies beating record revenues in short periods of time, whether in the LLM space (Anthropic growing from $0 to $10B ARR in 3 years), the application layer (Cursor from $0 to $1B in 2 years), or infra (with NVIDIA’s revenue quadrupling to $130B since 2022).

Markets are searching for growth and public SaaS isn’t fully delivering it (which also helps explain why more dollars are flowing to private markets).

Many factors can explain this slowdown. SaaS has eaten the lion’s share of the on-premise software market, but it has now reached a stage where most large customers that need a CRM or an ERP at scale already have one (in developed markets, Latam still relies heavily on pen and paper). The growth fight is no longer SaaS versus on-prem, it is SaaS versus SaaS, be it horizontal or vertical, all competing for the same budgets and workflows.

This was already true before the AI era, but now, as the marginal cost to produce software is approaching zero, competition is becoming even harsher. There is now a new source of pressure from foundational model companies pushing into the application layer and vibe coding tools that make it easier for teams to build software internally.

SaaS has also historically been sold by seats, and as AI replaces parts of human work, seat growth naturally slows, removing one of the simplest engines of expansion for most businesses.

Markets are pricing in that these forces reduce marginal demand for traditional software, hurting perpetuity growth rates and justifying lower multiples.

Real Profitability is Almost Nonexistent

One of SaaS’s strengths was the near-zero marginal cost to serve, but gross margin masked a growing set of costs elsewhere. As markets matured, sustaining growth required meaningfully higher Sales & Marketing spend, more ads, larger go-to-market teams, which raised CAC and reduced sales efficiency. Attracting and retaining top talent also started requiring richer compensation packages, often delivered via Stock Based Compensation (SBC).

Financially, the incentives pointed in that direction. Even if sales efficiency deteriorated and a company had to spend, say, $2 in S&M to generate $1 of revenue, if that revenue was valued at 10x then it was still turning $2 of CAC into $10 of market cap, a 5x return. It was rational for companies to seek top line growth over profitability, as long as the market kept rewarding it.

After the ZIRP (Zero Interest Rate Policy) era, growth has slowed down and investors started to require a “get profitable” approach, but most legacy SaaS companies have still been unable to accelerate growth or profitability.

In the AI age, the growth outlook for these players is more challenging and serving customers is no longer free, since AI inference introduces a real cost to deliver value. If a company struggled to produce strong margins during the blue-ocean years, investors assume those margins will be even harder to reach in this new environment.

Switching Costs are Down

High retention was table stakes for great SaaS. Once a company installed a CRM or an ERP, switching was painful and risky. Even if a cheaper alternative existed, teams were already trained on the product, workflows were built around it and the software had become the single source of truth for the business.

Migration was not only expensive, it was a career risk. If you pushed a switch and it failed, you would get fired. This dynamic lets the best SaaS companies upsell their installed base with limited churn, with top players reaching roughly 120% net revenue retention.

Investors are challenging this moat in the AI age.

First, a surge of AI tools that execute workflows on top of existing systems of record is appearing, capturing value at the agent layer while using the underlying system primarily as storage. OpenAI’s Frontier2 release explicitly points in this direction, agents acting on top of users’ existing systems, rather than forcing a full rip-and-replace.

Second, AI agents may lower the practical switching costs by making migration less painful. If agents can move data from one system of record to another in a faster and cheaper way than in the pre-AI age, another part of the traditional retention moat weakens.

However, both dynamics only become real if incumbents keep APIs open enough for external agents to act on top of their platforms and if they fail to move fast enough to embed agents natively. Switching costs may fall, but only to the extent that systems of record allow themselves to be disintermediated.

Salesforce has been the prime example on both fronts. Agentforce, its AI agent platform, has already reached $540M in ARR, growing 330% Y/Y3. In May 2025, Salesforce limited Slack’s API, allowing only one call per minute and 15 messages per request, explicitly to limit third-party activity on top of their data4.

Incumbents will do what they can to build walls around the valuable data and distribution they already control.

So, is Software Dead?

What we have done so far is compile the most common bear arguments about the death of traditional SaaS. We don’t believe the market is getting everything right.

We understand that the combination of slower growth, lower profitability, and rising competition on public names naturally leads to a wider dispersion of outcomes, so the discount rate should go up and some level of correction is justifiable.

However, it’s crucial not to throw the baby out with the bathwater. As in every tech wave, AI will create winners and losers. The winners (and the market hasn’t decided who is who yet) will be the ones that can reaccelerate growth by embedding AI into their business or becoming truly profitable, earning their way back to top multiples. The losers will be disrupted and ultimately trade on FCF / earnings multiples, far below what an ARR multiple looks like.

George Soros’ reflexivity theory applies here. When SaaS stocks were up, the category benefited from a presumption of success, which reinforced more and more capital to be deployed. Now that many of these stocks have fallen, the presumption flips to failure. SaaS is not only down because it will perform badly, but investors start believing it will perform badly because it’s down.

Since the market punishes full categories faster than fundamentals change and the best companies often get repriced alongside the rest, this creates opportunities.

AI is also Software!

What the broader narrative gets wrong is mixing the negative impact on some mature public SaaS names with the death of software as a category.

Software is becoming more powerful than ever in the AI age, and many of the so-called AI winners that are “killing software” are, in reality, software companies using AI to deliver more value. That is true for all newly minted AI-native unicorns, such as Harvey, OpenEvidence, Sierra, Lovable, and Perplexity, and for the hottest Latam rounds too.

We believe this is one of the best moments in history to build and invest in software, and in the next sections we’ll pressure-test the loudest arguments claiming the opposite:

Companies won’t vibe-code their own SaaS

Applications won’t die because companies will rebuild them from scratch on Replit or Lovable. And it’s important to remember that Claude Code and Codex will also help enterprise vendors to ship faster!

SaaS includes the “software”, but also the “as-a-service” component. That service component is what keeps the product bug-free, secure and compliant 24/7, which is where vibe-coded software tends to lose to off-the-shelf products.

Additionally, IT spend is usually 5-10% of companies’ total budget. Why would a company focus scarce engineering resources to rebuild something that is currently working instead of trying to find new AI revenue lines that can drive real product improvement and growth? The math doesn’t add up.

Companies will still buy off-the-shelf software applications and the apps that can generate the most value, be it startups or incumbents, will keep winning.

Foundational Models won’t Replace (All) Apps

Another argument is that foundational models will have capabilities that are enough to replace entire applications. There have already been software applications totally disrupted by foundational models5, and LLM companies are increasingly going after verticals (earlier than we expected). OpenAI has launched Health, and Anthropic is going after Legal and Finance. For us, this is a more serious threat.

However, it seems unlikely that a generalist company will consistently ship the opinionated UI, workflow depth, and edge-case handling required across every vertical and geography, which is why applications still thrive.

Different foundational models are also better at different tasks. Gemini tends to excel at some tasks, while Claude and ChatGPT at others, which is why the best applications will often orchestrate multiple models instead of relying on just one.

We believe there are also ways to defend against this. Proprietary data in a vertical that general models cannot access can let you build a more capable, cheaper system for that specific task, and create differentiation even as the base models improve.

Seats are being reduced, Agentic work is growing, and fast…

We agree that the seat-based pricing model is under pressure, and we have been calling this out for a while, like in our Superintelligent Software and The ARR Challenge essays. We are already seeing it in practice. Just last Thursday, Jack Dorsey’s fintech Block announced6 it is cutting 40% of its headcount as AI tools absorb more work. The stock jumped more than 20% in the next session. We expect other public SaaS companies to follow, driven by AI automation and the unwind of COVID-era overhiring, limiting net-new seat growth.

We do not think this means software stops growing. We think growth migrates to pricing models that are better aligned with value creation, be it usage-based, consumption-based, and in the best cases outcome-based. The chart below illustrates what this shift in the growth profile may look like: “traditional SaaS” trends toward residual growth, while the agentic layer drives most of the net new revenue.

This shift has already started. 2026 is expected to be a year of not only growing, but accelerating software demand. As more budgets move from labor to software, the best players will capture outsized value and expand margins, while companies that rely only on seat-based expansion will feel the squeeze.

Startups have the right to win!

“The battle between every startup and incumbent comes down to whether the startup gets distribution before the incumbent gets innovation”, Alex Rampell7

This market reshuffle is the window for startups to rewrite categories. Startups can ship faster, take riskier product bets, and iterate continuously, while incumbents often have to navigate politics before doing anything bold.

We are happy to be on the innovators’ side of the table. This is the moment to rebuild legacy systems for the AI age and we are excited to back the teams redefining what software looks like next.

How to Win in the AI Era

We have been communicating since last year what we find valuable and continue to focus on the key moats software must have in the AI era, moats we believe will matter just as much for Latam seed as they will for the blue-chip public software globally.

In our view, enduring AI winners will share a common set of characteristics:

• War-ready leadership team. Leaders who go into “founder mode” with the paranoia that what used to work won’t work anymore, rethinking how you generate value for clients and moving before the market forces you to.

• Network effects, the ultimate moat. When usage improves the product and that improvement attracts more usage, the network advantage becomes hard to catch. In AI, where models and features commoditize, network effects can be the difference between a feature and a platform.

• Product velocity and momentum. Shipping is faster and cheaper than ever. There is no reason to wait six months to release meaningful value. Momentum itself becomes a moat when the market is shifting weekly.

• Touching money. Software that processes transactions, originates credit, or generates demand sits closer to value capture. It becomes harder to replace because it plugs directly into cash flow, not just workflow.

• Proprietary data and vertical depth. Winners that leverage domain-specific data that other companies (even foundational models) do not have. They turn that data into outputs clients cannot get elsewhere and into products that help with the messy edge cases of a real industry.

• Outcome-based pricing through a system of action. Becoming a system of record is not enough. The long-term prize is evolving to become the system that executes work, closing the loop by having the context and execution. Aligning pricing with outcomes is what enables labor replacement and captures a fair share of the value created.

• Memory moats, products that get better with usage. The best products learn from usage and context. When the software remembers how work gets done, switching becomes an operational nightmare.

• Local moats. Regulatory compliance and local market structure can bring defensibility. In over-regulated or highly local industries, an off-the-shelf global product often cannot compete with a solution built for local realities.

• API integration and agent readiness. The platform should integrate cleanly into the tools clients already use and be usable by agents, not just humans. Easy integration makes adoption faster, and it turns the product into infrastructure rather than a destination.

• Low-friction, low-CAC distribution. More companies than ever are competing for attention and paid acquisition keeps getting more expensive. The winners build distribution that scales without linear spend, through virality, partnerships, or proprietary channels.

• Multimodal, where the work happens. Work is moving across WhatsApp, voice, email, and existing systems, not into new dashboards. Products that meet users where they already operate will be adopted faster and stick longer.

We’re certain that what brought the winners of the cloud wave here will not bring them future success, which is why we are more excited than ever to be investing today and backing the future winners. The prize for the winners is greater than it has ever been!

This standardization was built on a specific reality of the near-zero marginal cost of software. Once code was written, serving the next customer cost pennies. This allowed vendors to offer “unlimited seats” or flat fees, knowing that heavy usage wouldn’t hurt their bottom line.

That era seems to be coming to an end.

In the AI era, software has real physics. Every prompt incurs an inference cost. Relying on traditional ARR to measure an AI business is like driving a Tesla using a road map from 1999.

We don’t necessarily believe in reinventing the concept of recurring revenue, we are evolving it. When every interaction carries a cost, the “all-you-can-eat” subscription naturally breaks. If you stick to flat pricing today, your most engaged customers - the ones running the most prompts - will destroy your gross margins.

To survive this “AI Tax”, revenue must align with cost! But this shift is not only about protecting downside, it is also about unlocking massive upside.

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The Floor vs. The Ceiling

Once you uncap your pricing to cover your costs, you inadvertently uncap your potential revenue. You move from a game of Cost Protection to a game of Value Capture.

• Subscription: High Floor / Capped Ceiling.

• Consumption1: Low Floor / Uncapped Ceiling.

If a customer derives $10M of value from your software but you charge $50/seat, you capture pennies, leaving the vast majority of the value on the table. Consumption models remove the ceiling. Whether you charge for Usage (Inputs, Tokens, Comput) or Outcomes (Outputs, Resolutions, Transactions), the physics are the same: you are trading linear certainty for exponential scale.

This isn’t just theory! When we look at the defining chart of the AI Era (the fastest companies to reach $100M ARR) a clear pattern emerges. The breakout leaders didn’t get there by selling flat-rate subscriptions. Every single AI player on that list achieved exponential-growth through a consumption-based model.

Consider Riskified2 (NYSE: RSKD). While traditional security software charges for “access,” Riskified charges for Outcomes, as they approve e-commerce transactions. If they approve a fraudulent order, they cover the cost. At the end of the day, they charge for the absence of fraud instead of software, which is the ultimate alignment: they only win when the customer wins.

The New Reporting Standard: Behavior > Formulas

In the subscription era, ARR was a simple formula, while in the Consumption Economy, there is no universal template. Large companies are actively engineering new ways to smooth out the volatility of usage, and it all depends on how their customers behave.

Snowflake3, for example, separates RPO (Bookings/The Promise) from Product Revenue (Consumption/The Reality), creating a critical leading indicator. If RPO grows faster than Revenue, you have “Shelfware Risk” (customers buying but not deploying). If Revenue grows faster than RPO, you have “Burn Risk” (customers consuming backlog too fast - which might be a great opportunity for you CS team!).

Confluent4 breaks out Confluent Cloud (Usage) separately from their legacy Confluent Platform (Subscription). This allows investors to isolate the hyper-growth engine (~30%+ YoY) from the steady-state legacy business. By decoupling them, they prevent the “Capped” business from dragging down the valuation of the “Uncapped” one.

MongoDB5 calculates ARR for their consumption product (Atlas) using a 90-day lookback. This window wasn’t chosen randomly, it was derived from analyzing their specific customer behavior. They recognized that usage fluctuates monthly, so they use a 90-day rolling average to separate signal from noise.

The Metrics Evolution

The traditional SaaS formulas rely on variables that are assumed to be constant (fixed pricing, linear retention). In an AI model, these variables are volatile, rendering the standard equations useless.

So, how do we measure health now?

1. NRR is now The Behavioral Truth In SaaS. Net Revenue Retention was often a Sales Metric, driven by a rep negotiating an upsell or cross-sell.

In AI, NRR is a Behavioral Metric. It acts as the aggregate score for all value delivered, summing up Usage (Inputs) and Outcomes (Outputs), measuring the daily reality of the customer’s life:

• Usage: Are they integrating the tool deeper?

• Outcome: Are they actually solving the problem?

If the behavior indicates value, the spend expands organically across both vectors. If the behavior stops, the revenue stops.

It’s important to remember that this depends on how your customer uses your product. They might use it on a quarterly basis, which means that you should never look at NRR on a monthly basis!

2. GRR became the anti-whale mechanism. In consumption models, one exponentially growing customer can hide the churn of dozens of others in your NRR.

GRR forces you to face the truth because it strips out this expansion. By capping retention at 100%, it reveals “Shrinkage” - customers reducing usage - without the buffer of your biggest spenders. If you have high NRR but low GRR, you don’t have a broad product-market fit, you have a leaky bucket subsidized by a few big contracts.

3. For Churn, context is king. In the new world, it is a behavioral metric.

If your product is used quarterly (e.g., tax software), a customer with zero usage in July hasn’t churned. You must define churn based on the natural frequency of your user.

4. Realized LTV has replaced Predictive LTV. The standard formula (ARPA*Gross Margin)/Churn fails because it assumes usage is flat.

In AI, usage follows a volatile “J-Curve,” rendering static variables useless. You must replace prediction with history. Instead of guessing future value based on last month’s data, analyze your historical cohorts to answer the only question that matters: “How much cumulative gross profit has this specific cohort actually generated to date?”

Our Take

At SaaSholic, we have always been avid defenders of a standardized SaaS Metrics approach. Unfortunately, in the AI Era, that spreadsheet no longer exists.

We aren’t throwing away the old playbook, but we are rewriting the chapters on what matters. When we evaluate AI businesses today, we look through a new lens:

• Contracts are the Floor, Habits are the Ceiling. We still value contracts for downside protection, but “Workflow Stickiness” is the ultimate retention mechanism. A dollar of revenue from a daily habit is worth infinitely more than a dollar from a “shelfware” contract that is destined to churn.

• Revenue is Vanity, Contribution is Sanity. While we believe inference costs will continue to drop, we don’t rely on it6. We prefer businesses that have viable unit economics today and will see them supercharged as costs fall, rather than those dependent on future hardware deflation just to become profitable.

This requires a shift in mindset, as you can no longer manage your business by looking at a dashboard of generic SaaS metrics. You must understand the specific physics of your customer’s consumption. We now strongly incentivize our founders to build their metrics stack from first principles, which requires a level of maturity and self-awareness that goes beyond filling in cells in Excel.

We truly believe that the new winners won’t be the companies with the smoothest charts. They will be the ones who accept the volatility of consumption in exchange for the uncapped upside of the value they generate.

We are learning alongside our founders just how challenging this shift can be, and we are seeing a brand new cohort of companies emerge with entirely unique business models, metrics, and pricing. If you are building a business that fits this vision, we’d love to learn more!

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In 2026, the one “soft skill” I want to get much better at spotting before we commit to a new investment is agency. I didn’t always have that word in my vocabulary, but looking back, we were selecting for it the whole time. When we compare our best-performing founders against public benchmarks, there’s a pattern that’s hard to unsee: the ones doing unusually well almost always signaled agency in the first iteration.

Agency is the difference between being pushed by life and steering it. As Albert Bandura wrote, “to be an agent is to influence intentionally one’s functioning and life circumstances” (Bandura, 2006, p. 164).

Agency matters more now because the cost of execution collapsed. With AI, a single founder can do in a weekend what used to take a squad a couple of months, and once that becomes true, the bottleneck moves. In B2B software, the two hard parts used to be building and selling. Now AI compresses both. So when a team isn’t moving, it’s rarely because they lack tools. It’s because they lack agency.

One heuristic we’ve always liked at SaaSholic is simple: we want to back companies that are already moving. Not necessarily huge, but already winning early battles and growing fast in a way that suggests capital is gasoline, not oxygen. With our capital, they don’t suddenly become good. They become even better and even faster. The best founders don’t treat fundraising as a prerequisite, or as a bottleneck. They build anyway, sell anyway, learn anyway, and if they’re right, reality starts bending before anyone wires money.

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A proxy I still love is that the best founders land their first ~20 non-affiliated customers before institutional money. Not friends, not family, not a pilot that exists because someone likes them. Real customers who complain, demand features, and still pay, because whatever they’re getting is meaningfully better than what they used to have. These founders also tend to have receipts, almost by default. They don’t tell you they’re moving, they show you. Screenshots, Looms, dashboards, a pipeline list, customer quotes in Notion, some kind of weekly cadence. Not to impress investors, but because that’s how they operate.

But motion alone can be circular. High agency without direction turns into noise. So right after agency, we look for two stabilizers that keep that motion pointed toward an outcome: taste and curiosity.

Taste gets misunderstood because people think it’s aesthetics or vibes. In B2B software, taste usually looks like product empathy and strategic minimalism. It’s knowing which 10% of features solve 90% of the problem, and having the discipline to ignore the rest. AI makes it easier to ship more. Taste is what stops founders from shipping the wrong more.

Curiosity is what keeps founders close to truth. Markets move too fast for rigid people. Curious founders keep asking why, keep running experiments, and keep changing their mind when reality disagrees. They don’t defend assumptions. They stress-test them. The most dangerous founders aren’t the ones who don’t know. They’re the ones who stop being curious.

So the trio that seems to matter most in 2026 is agency, taste, and curiosity, in that order. Agency creates motion. Taste prevents wasted motion. Curiosity keeps the motion aligned with what’s real.

You can see all three in the first conversations, especially if you don’t let the conversation drift into pedigree. I genuinely don’t care where you studied, if you even studied, if you’re from the countryside, if your English is broken, who your advisors are, who invested before. If you have agency, you’ll learn what you need. If you have taste, you’ll avoid obvious traps. If you have curiosity, you’ll keep adapting. So we start with what matters: what have you built so far, what did you ship in the last 14 days, and show us the last 6 to 12 months of metrics. If it’s too early for metrics, show evidence instead. Pipeline, retention, usage, customer conversations, whatever is real.

Over time, this pattern has held up better than we expected: founders who show agency early almost always keep compounding. It doesn’t make the path linear, but it makes progress inevitable.

And I’ll end with a dumb but brutally accurate proxy we’ve noticed. Every company we’ve backed that sends their investor update on time, consistently, for 12+ months, is tracking top-decile expectations. We can’t say the same for the ones that miss it. That’s not organization. That’s agency.

Our take is that the risk appetite across the market has clearly risen and there are visible pockets of excess emerging. We also acknowledge that we, like most of the market, lack the data to state anything definitive since only a fraction of information is public and the rest lives in rumor territory.

Despite the noise, we continue to believe AI is the next platform paradigm. It will allow software to capture larger shares of corporate budgets, generate more value per dollar invested, and replace labor meaningfully. We stay as bullish as ever on the applications emerging as the technology evolves, giving rise to Super Intelligent Software.

What reassures us as investors is that, while the world debates the AI mania, we are playing a different game in Latin America. Venture activity in the region is at decade lows and scarce capital and competition makes the “AI bubble” narrative far less relevant to our daily work. This gives us enough distance to look at the global picture without taking biased conclusions.

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Is AI a Bubble? The Case for “Yes”

If you open any major media outlet or listen to investor conversations, you’ll find that most people already believe there is a bubble. Headlines say it, and sentiment surveys back it up.

Let’s break down the core arguments supporting that view:

Staggering Big-Tech Capex: Hyperscalers (Microsoft, Google, Amazon and Meta) are committing unprecedented sums to AI infrastructure, sometimes exceeding their entire current cash balances.

Capex as a share of GDP has already surpassed the dot-com bubble levels and trails only the railroad boom of the late 1800s, when rails reshaped how the world moved products and absorbed extraordinary amounts of capital. The concern is that today, these companies are overbuilding ahead of a demand that may never fully materialize.

A recent JP Morgan analysis1 estimated that, for these investments to yield a 10% return hurdle, the hyperscalers would need ~$650B in additional annual revenue through 2030, roughly 0.6% of the global GDP or the current combined revenues of Microsoft and Alphabet. At this scale, even small miscalculations can erase a lot of value.

Headline Reflex: Public markets became monothematic. If you’re perceived as an AI winner you go up; otherwise you shrug. Even blue-chip firms like Oracle have seen 40% single-day stock jumps after announcing new AI partnerships2, long before any execution shows up. When anything labeled “AI” surges in value overnight, we may be seeing speculative dynamics playing. Narratives are starting to matter more than fundamentals.

Circular Financing: Behind the stock charts, there are creative structures. Deals like Nvidia’s investment in OpenAI, tied to OpenAI’s commitment to purchase Nvidia’s GPUs3 raise questions about how much of the money reflects genuine value creation versus capital simply cycling between partners.

Grid-Sized Assumptions: OpenAI became the emblem of the scale of expectations. Projections suggest its compute demand alone could require power comparable to the entire US grid by the end of the decade. At the same time, the company has committed to invest $1.4T in the coming years, despite having around $20B in annualized revenues4 and no intention to become profitable anytime soon.

Taken at face value, these assumptions stretch today’s economics. By 2029, OpenAI would need to reach close to $600B in revenue for the math to make sense. As a comparison, Google currently has $350B in revenue and would reach a similar figure if it grows 12% annually until then5.

If nothing else, the planned spending and commitments present an enormous level of potential and ambition.

Mega-Rounds Math: The same exuberance spills into private markets. Record funding rounds and mega–funds are adding fuel to the fire and pushing expectations to extremes. Companies are hitting multi-billion-dollar valuations at incredibly early stages and platform-VCs are raising funds that require enormous exits to work.

Mira Murati’s Thinking Machines may be the prime example. The former OpenAI CTO has raised a $2B seed round at a $12B post-money valuation(!) with a16z, and is supposedly in talks to raise again at $50B, roughly 4x within months6.

The mega-fund math is also stretched. a16z recently raised $10B across different vehicles7. Assuming they own 10% of winners at exit they would need to return $120B to deliver 5X net returns to their LPs, implying $1.2T in total exit value8. For reference, this is almost the entire market cap of all US IPOs over the last five years.

Taken together, these points describe a market that looks like a bubble. Excesses are real and the risk tolerance has risen significantly.

Stopping the analysis here, however, is a short-sighted read of what is happening and misses the changes already underway.

The Case Against the Bubble

“The growth of the Internet will slow drastically (...) By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.”

- Paul Krugman, winner of the Nobel Prize in Economics, in 1998

History is full of confident calls that did not age well. It’s important to not throw the baby out with the bathwater and confuse the visible excess with a verdict on the technology itself. Recognizing that some behavior looks bubbly is different from dismissing structural shifts AI is already setting in motion. With that in mind, it is worth examining the arguments that support a more constructive view.

Trillions Optionality: the most bullish investors view the current capex through a macro lens. The “Gambling Man” of tech, Masa from Softbank, argues that AI could lift global GDP by 5-10%9. With 2025 global GDP around $180T, a 5% uplift would represent $9T of additional value per year. In that framing, the trillion-dollar infrastructure spend is rational, since today’s investments would be a relatively small price to pay for the potential upside.

This is far from our base case and sits on the most optimistic end of the range. Still, it explains why some investors are comfortable funding such aggressive buildouts.

Real Value is Being Created: behind the extreme rounds sits a product people love. ChatGPT launched less than three years ago and its relevance shows up in usage, retention, and revenue across the board. AI is being adopted faster and with broader reach than any prior technology.

ChatGPT’s retention curves make this even more explicit. Newer cohorts are performing better than earlier ones, which shows the product is compounding in quality and usefulness over time. As it improves, users come back more often and find new ways to use it. Even the weakest early cohorts settle above 50% retention more than two years after first use, something almost unheard of for a “single-player” product.

Hyperscalers Capex: Sources of Funding Matter. Earlier we framed hyperscalers’ capex as a potential euphoria sign. The other side of this story is the funding source. Today’s investments are largely financed by the vast free cash flow these companies generate, rather than by external sources. This is a sharp contrast with the dot-com era, when most spending relied on capital markets and cheap credit.

This does not guarantee that every dollar invested will earn attractive returns. It does mean that large, durable businesses are reinvesting their own profits into AI to protect and expand their core franchises. Some of this behavior is driven by game theory rather than clean spreadsheet logic.

In practice, the capex looks less like a financial exercise and more like an arms race. None of the hyperscalers can risk falling behind their closest peers, which helps explain why spending keeps accelerating even as the numbers look stretched10.

These companies also know how expensive it is to miss a platform shift. Microsoft and Google have already watched Apple dominate mobile, despite their scale and resources. AWS is now starting from behind in the AI race, while Azure and GCP gain share as the “AI clouds”. Meta can feel the risk as OpenAI moves into B2C with products like Sora competing directly for user attention and screen time.

With that backdrop, the cost of sitting out the next tidal wave looks far higher than a few years of over-investing their own cash flows, even if that means accepting lower returns on some of this capex.

There is an additional caveat to the “overbuilding” story. Capex is rising fast, but contracted demand is rising just as quickly. Cloud backlogs (or RPO), which represent revenue that is already signed but not yet recognized are growing at roughly the same pace as spend:

• Azure: capex rising from about $44.5B in 2024 to $65B this year, up 45%, while backlog is up 51% to $392B.

• AWS: capex increasing from $78B to $125B, up 61%, with backlog around $200B in signed contracts.

• Google Cloud: capex moving from $52B to $92B, up 75%, and backlog jumping 82% to $158B11.

These numbers show that a large share of the current buildout is already backed by contracted demand. The constraint today is compute supply, not demand, which makes the overbuilding argument less convincing than it first appears.

Larger Outcomes: There is also a history of outcomes exceeding prior standards. In August 2018, Apple became the first company to reach a $1T valuation12. Fast-forward to today, there are a dozen companies above that threshold, with one of them above $5T.

The size of the winners keep getting larger as adoption compounds, but investors frequently underestimate how big the category winners can become.

Multiples are High, but Nowhere Near Dot-Com: valuations have, in fact, moved up in recent years. Nasdaq’s next-twelve-months P/E is ~30% above its 10-year average, however, it’s still roughly 3x below dot-com levels. The same gap appears when we compare today’s winners with the leaders of that era.

Nvidia, often treated as the poster child for the AI bubble, has seen most of its value appreciation driven by earnings growth rather than multiple expansion. Price has moved, but so has the underlying business, which keeps beating earnings expectations quarter after quarter.

AI is the only game in town: Public markets look monothematic because AI has become the primary driver of growth. Large companies cannot afford to ignore AI or sit on the sidelines while real revenue is already coming in and growing.

AI infrastructure has overtaken SaaS as the main source of net new revenue. The concentration of attention on AI may be a feature of where growth truly resides, not only a symptom of speculation.

Operating Leverage Proof Points: tangible gains are already visible. One of the clearest signals is in entry-level roles. In functions highly exposed to automation, such as software engineering and customer success, the traditional “first job” is frequently not being opened because teams are being redesigned around smaller, more senior groups supported by AI tools. Companies are shipping more with fewer people, payroll in those functions is shrinking and efficiency is improving.

Today’s AI is the Worst AI We Will Have: AI scaling laws13 make each generation of model more capable and autonomous, handling a broader set of tasks without intervention. We must keep in mind that the impact we are seeing comes from the least capable versions of these systems. As they improve, new categories, products, and workflows will become viable to automate. The most exciting part is that this intelligence is accessible anywhere in the world, just an API call away.

The Bay Area is not the Whole Word: media coverage tends to focus on outliers. If you only read the headlines, it is easy to believe that every AI deal is a unicorn at seed. Aggregate data tells a more nuanced story.

Carta’s VC funding distribution data shows that median and even top quartile valuations sit in line with historical ranges. The top decile and percentile run far ahead prior norms, and those are the rounds that generate headlines and social media buzz. A large share of the excess appears concentrated in this slice at the top of the distribution.

There are still questions we cannot fully answer, such as whether circular financing structures or the most exuberant rounds will ultimately pay off. Despite these unresolved concerns, AI’s underlying results are hard to ignore. Products are being used at scale and only improving, revenue is compounding while costs trend down, and entire workflows are being reshaped. Taken together, these signs make us believe the outcomes of this cycle can be incredibly compelling.

What About LatAm?

Until now, we wanted to provide our overview on the current market. It is equally important to note that much of this has limited impact on our day-to-day decisions as early-stage investors in LatAm.

Latam sits outside the global hype loop. Funding is at ten-year lows and there is simply not enough fuel to inflate a real bubble here. Ultra-competitive rounds that close over a weekend do not exist. Valuations in most cases are grounded, competition for deals is manageable and we can still back top-tier AI founders at fair terms.

Additionally, most of the bubble concerns we discussed relate to foundational models and infrastructure buildout. These are arenas where Latam lacks the capital to compete at a global scale (and that’s fine). Our edge here is to leverage this ever-improving technology to create value at the application layer. This is where we combine global building blocks with local data, workflows and distribution to create products that solve real problems for end-users.

Every major technology wave has produced both global and Latam winners and we expect AI to follow the same pattern. The common thread is local winners have historically emerged at the application layer because regulation, customer behavior, language, financial rails and compliance here are unique and require deep in-market understanding.

All prior waves, from on-prem and e-commerce to fintech and SaaS, created regional champions built around these specifics. AI will be no different.

We remain focused on the fundamentals. AI is a transformative wave that will reshape software economics, and we are investing for that future. Latin America is still vastly underpenetrated by technology, often because it has been easier to “throw people at problems” than to automate work. AI changes that equation by moving software from a simple productivity tool to agents that actually execute the work faster and cheaper. Together with every management team we meet exploring how to implement AI, the setup for leapfrogging is strong.

AI, after all, will become similar to electricity and the internet: taken-for-granted technology that all businesses will use and won’t remember how it was like operating without it.

Seen from this angle, there is even a case that a “bubble” could end up being positive for our region. As global players race to deploy AI infra, they compete to drive down compute costs through scale, better hardware and more energy-efficient solutions. If demand ultimately falls short of expectations, that extra capacity and efficiency will translate into lower prices for consumers of compute. Another powerful tailwind for the Latam application layer.

Where we are Navigating towards

We are laser-focused on verticals where software can replace labor, embedding AI into complex workflows. As discussed in Doing It Wrong, Again, we are looking for:

• Vertical, AI-native platforms in labor-intensive workflows. Replacing pen-and-paper and spreadsheets in complex industries to deliver huge improvements in speed, accuracy and economics.

• Scalable professional services automation. Law, accounting, finance, consulting and other language / process-heavy fields, where high labor costs and repetitive work make them natural candidates for AI agents.

• Proprietary Small Language Models (SLMs) tuned on domain data. Companies that can accumulate the largest pools of proprietary, vertical-specific data and use reinforcement learning to turn that data into products that are 10x better than what any horizontal player or generic model provider can deliver, at a fraction of the cost.

• Pricing models aligned with client outcomes. Ensuring clear ROI and capturing a fair share of the value created. Software that can bridge from traditional IT budgets and eat into labor budgets.

• Low-friction, low-CAC distribution. Powered by teams with sharp storytelling, deep field expertise and founder-market fit, often paired with multimodal interfaces (text, voice, video) that meet users where work already happens.

• Geographic and regulatory barriers. Since capital in Latam is scarce, global competitors will often be more funded. We favor business models where local context, regulation, compliance and market structure create natural moats and make it harder for foreign players to compete on equal terms.

For us, this is where the real edge lies, and the bubble debate matters far less than it may seem at first glance. We acknowledge that some parts of the market look excessive, and our response is simple: we stay out of the rounds where exuberance dominates. What matters is staying disciplined, meeting our target ownership and working closely to our founders to help them win.

Some of those exuberant bets may still work; whether they do or not, we are comfortable watching from the sidelines.

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A decade ago we stepped into Latin American venture convinced the region could create global software champions. However, the standard playbook used here felt mismatched. The region demanded a unique approach adapted to its nuances, so we decided to write our own, with concentrated investments, disciplined economics, and deep operational support.

After two successful funds, we decided it’s time to take this approach to the next level. We’re now entering a new phase with the same conviction, and we’re proud to keep “doing it wrong.”

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We Dare to be Different

We’re not like other VCs. The conventional Latam venture wisdom was mirrored on the Silicon Valley playbook:

• Cheap money fuels blitz‑scale. Capital is abundant, raise early, spend faster to secure distribution and brand before rivals can react.

• Price doesn’t matter. Most successful exits occur through Nasdaq or US strategic buyers, so the seed valuation rarely influences ultimate returns.

• Metrics can wait until Series A. Seed is about testing and iterating; enforcing strict KPIs too early can choke the experimentation needed to find product‑market fit.

• Take bets, a lot of bets, in a very large portfolio. Venture returns concentrate on a few outliers. Diversifying across several dozen tickets raise the odds of owning a breakout company, capturing the power‑law while limiting exposure to any single failure.

• Great founders will figure it out. Early traction is less important than the caliber of the founding team; great entrepreneurs pivot, iterate, and eventually land on the winning product without needing close investor guidance.

But we flipped that script and created a venture approach where:

• Capital efficiency beats hyper‑growth. Because in Latam, every dollar matters and competition is thinner. Lower CACs let distribution be won with limited burn; ROI beats growth at all costs.

• Investment discipline outranks speed. Liquidity is scarce; valuations paid must enable you to return the fund even if a Nasdaq exit never arrives. A global exit is the upside case, not the underwriting assumption.

• Metrics come first, narrative second. Software economics reveal themselves early. We believe great retention, paybacks and growth can spot winners far better than résumés or hype.

• Focus over fashion. We stay within our circle of competence, making fewer, high-conviction investments where our expertise compounds, ignoring the “flavor-of-the-month” to win through depth. No spray and pray.

• Founders need help as much as capital. Money is the ticket; our operator support is the edge. We stand beside founders until the finish line.

In a world where venture capital is still largely shaped by Bay Area orthodoxy, our approach stands out, and not always in flattering terms. We’ve heard it for years:

• “You won’t win seed deals with that bar.”

• “You’re too conservative.”

• “You need to be faster and more aggressive.”

In the eyes of many, SaaSholic is doing venture “wrong.” And yet, our results, both financial and operational, tell a different story.

Been There, Done That

SaaSholic was built around a simple premise: Great companies in Latin America need more than capital. They need the right capital and our model is built for that.

It’s not popular. We won’t be on every headline deal, but it is consistently bringing great outcomes.

Fund I proved our access and thesis, even with small, angel-like checks.

Fund II evolved the model: leading rounds, securing board seats, supporting our portfolio hands-on and helping 70% of our portfolio to successfully raise up-rounds in a down market.

Across both funds, we’ve seen low portfolio mortality and multiple companies grow into clear category leaders.

It works, not in spite of the fact that it looks different, but because it is different.

Some examples from our portfolio show how this edge plays out:

Jusfy. Under the Radar Legaltech

Back in 2022, Jusfy was a non‑obvious investment: a vertical SaaS founded by a practicing lawyer outside Brazil’s usual tech hubs, in a category scarred by prior failures focused on solos and small firms many view as churn-prone. Despite these surface-level flags that initially drove most investors away, we dared to deep dive into the thesis, guided by promising traction ($0.5M ARR by then) and a product that users loved.

Before investing, we spent four months fixing governance, cleaning the cap table and setting an option pool. We then worked on pricing, product, and team, helped recruit a seasoned CTO and later a CFO, and sharpened an AI roadmap that shipped a legal Copilot, adding thousands of users and boosting retention.

Two years after our initial check, we led (again) a Pre-Series A to reinforce the cash position, brought in strategic local and US investors, and placed an experienced board member to strengthen governance and accelerate growth.

The result: a company so capital-efficient it grew 10x in 24 months with minimal burn and now serves ~50,000 lawyers, tracking to $10M ARR by the end of the year.

Overlooked by most, we saw the early signal, provided close support, and helped turn Jusfy into a consensus, Series A-ready company in the applied-AI space.

Clicksign. Contrarian E-signature Leader

At first glance, Clicksign looked hard to underwrite. A significantly larger company than seed, in a “boring” category that needed 4x our maximum check within weeks, seemingly a unique and timely opportunity. However, a deep dive into fundamentals showed a different picture: sustainable unit economics, working customer acquisition channels, and category leadership in a segment growing 50% annually, valued at compelling terms.

We asked the founders for 48 hours to secure the necessary funds (thanks to our LPs who quickly understood the magnitude of the opportunity and committed to the co-investment) and went all-in. After committing, we helped right-size the team, and reinforced the culture needed to lift productivity, returning the business to profitability within months.

Later, we increased our ownership significantly by buying an early investor’s stake at the same valuation we paid 18 months earlier, when the company was 40% smaller and faced some uncertainty. Since then, we’ve helped hire a CFO, Head of Sales, and Head of Marketing, supported the revamping of the GTM strategy and further advanced the product toward an AI focus.

The company has operated profitably since 2023, has exceeded the Rule of 40 since our entry, and its current ARR already surpassed the price we paid.

Clicksign was a contrarian investment, proving that correctly pricing risk, fixing the machine, and doubling down when others won’t unlock durable growth.

Clad. Turning a Brazilian Insight into an American Opportunity

Clad presents another side of our thesis, the SaaSholic Studio, where we incubate high-potential ventures with second-time founders.

When Danilo Costa, fresh from his successful exit at Educbank, came to us with a simple question, “Could this model work in the US?”, the answer wasn’t obvious. Most K-12 schools there are public, regulation is complex, and incumbents are entrenched. But we saw something different: a $100B private education market with 6 million students and schools struggling with the same liquidity gaps Danilo had already solved in Brazil.

Our team flew to the US alongside the founder for customer interviews and business development to validate the thesis, which built the conviction to sponsor an Entrepreneur-in-Residence (EIR) period, recruit a co-founder, and develop a business plan. After the incubation period, we raised a Pre-Seed round with global investors from our network: RallyCap, FJ Labs, BackFuture and K50 Ventures, a round of almost 10x the size of our initial commitment to fund execution.

The Clad thesis shows how our high-conviction, hands-on support makes us the first call for ambitious founders and gives our investors unique access.

The Best Vintages Are Forged in Doubt, Not Euphoria

Being a venture investor in Latin America is contrarian by default. Double‑digit interest rates, political noise, currency swings and structural inefficiencies nudge investors toward low-risk bonds. On the surface, this feels prudent.

Yet, history argues the opposite. Latam’s breakout stories were born when capital was tight and macro gloom prevailed1. Top-quartile VC funds from those vintages outperformed every local public and private asset benchmark. Scarcity, not abundance, minted the outsized returns2.

The same setup is back. Global capital has retreated3, valuations reset, and a new wave of AI-native founders are building with battle-tested playbooks. For investors willing to lean in, the window is wide open once again.

Latam VC fundamentals are solid:

• Large, underpenetrated market. Latin America is already online, with over 600M people connected, yet its business processes still remain largely manual. Cloud adoption, B2B software penetration, and enterprise AI deployment still trail developed markets, leaving spreadsheets, paper, and WhatsApp to run critical workflows.

• Repeat founders on the rise. Regional ecosystems are maturing fast. Second-time founders and veteran operators of Latam’s first unicorn wave are launching second acts armed with scar tissue and global networks. Their share of new startups nearly doubled from 2019 to 20244, and they are twice as likely to reach unicorn outcomes5. This didn’t exist in previous batches.

• Capital efficiency as a feature. Latin American startups return more exit value per invested dollar than other emerging markets6 because less capital means fewer bidding wars, lower CACs, and founders with the runway to build products before competition crowds in.

• Local idiosyncrasies create moats. Heavy tax complexity, labor informality, multiple currencies and a proprietary financial infrastructure open doors for homegrown solutions where global players can’t compete.

• A widening funding gap. Deal volume has fallen back to 2019 levels7, while every enterprise is being pushed to have an AI strategy. Ambitious founders are racing to seize the gap, growing faster than any previous cohort with minimal outside capital. As early‑stage funding remains scarce, the opportunity to back these inevitable companies at great terms is still wide open for seed investors.

Where We’re Going: The Rise of Super Intelligent Software

Latin America is overlooked while AI mania grips the rest of the world. Global investors are going all‑in on how AI is reinventing software:

• Every boom rides a super‑trend. Internet, cloud, and mobile each forged outsized winners; AI is next. Every wave compounded on the last, amplifying the outcomes. SaaS drove most $2–20B exits in the past decade, and enhanced by AI it is now giving birth to Super Intelligent Software (SIS). The end game will be larger, faster-growing companies with superior economics.

• Agents do the work. SIS platforms deploy AI agents to run end‑to‑end workflows, cutting headcount and turning weeks of effort into minutes. With continuous live-data feedback, these systems will outperform human experts in speed, accuracy, and cost. This shift is already clear in customer service, coding, and sales, and soon will become true for other industries.

• Markets expanding ten‑fold. Clients now pay for results, not seats. Outcome-based pricing lets software capture a larger share of the value it creates, expanding contract sizes and aligning incentives. Budgets shift from payroll to software, raising the global addressable market from a $650B license pool to an $8T labor wallet8.

• Record-breaking unit economics. Distribution physics have changed: social media turns virality into instant adoption. AI-driven superior UX/UI cuts time-to-value to minutes, reducing onboarding and shortening sales cycles. Growth curves are steepening, and companies are reaching ARR thresholds in record time.

• Bigger exits than the last cycle. Traditional SaaS produced 300+ unicorns9; add agents, data moats and outcome pricing, and Super Intelligent Software is set to unleash an even larger wave of multi‑billion‑dollar winners.

Latam is already climbing this ladder. Founders here are already leapfrogging by skipping legacy stacks and jumping straight from spreadsheets to intelligence. Our AI Benchmark10 shows 60% of startups born since 2023 are AI-native and growing 1.6x faster than peers. Yet their valuations have a 50-70% discount to US comparables11.

Funding has not caught up to reality. The talent is in place, the economics are superior, and the valuations compelling. Owning this generation before it becomes consensus is the logical extension of everything we’ve done so far.

Our Unfair Advantage: How SaaSholic Will Win

We are here, on the ground, as the tide turns. Over our two funds we’ve refined our model, and a decade of execution has sharpened our edge.

More than ten years of sector focus give us superior pattern recognition. We’ve published the largest SaaS + AI benchmark datasets12 of the region, enabling us to ignore the noise and focus on metrics and fundamentals. We’ve built a system to spot contrarian founders early and back them before the market does.

Every dollar we invest arrives with operating leverage. Founders reward that partnership with first calls and fair terms, and LPs get the upside. That flywheel is our edge.

Over time we’ve refined our processes into a system that drives returns, from sourcing to exit.

Sourcing: Proactive, Thesis-Led Dealflow

We don’t wait for decks to land in our inboxes. We proactively research high-potential markets and founders. Research reports, media, and a network of founders and LPs surface promising teams before they start thinking about raising a round. By engaging and adding value early, with market insight, product feedback and a company-building vision, we become our founders’ first true believers.

Investment Criteria: Fast, Disciplined, Data-driven

Price matters. Every opportunity we pursue runs through our investment napkin, distilled from the patterns of past winners. We invest only when exceptional founders, great metrics and fair pricing intersect. The scorecard delivers a quick, transparent yes / no, earning respect even when we pass deals. Conviction lets us lead; discipline keeps us selective.

Portfolio Success: Hands-On Partnerships to Help our Companies Win

Day 0 starts with a founder-signed 100-day plan. We front‑load impact by providing them with a recommended tech stack of cloud, AI, payments, and partners, along with a founding team design. This makes our investment pay out fast to founders, making us often win deals even against larger checks and higher valuations.

Every month, we hold strategic reviews for each investment, analyzing the evolution of core metrics: ARR growth, cARR13, NRR, burn, and revenue/gross margins per FTE. If a metric slips, a partner sprints to help solve it as soon as possible.

Our hiring guild has placed several C-Level leaders and co-founders, and helped to structure proper governance. We help install KPI tracking, board-grade reporting and governance standards that raise the bar and prepare companies for future rounds in record time.

Fundraising & Exits: Engineering Liquidity Early

We stay hands-on from fundraising preparation to close, sharpening the narrative, polishing decks, and opening warm intros. Because their best partners will often be abroad, we coach founders to global governance and reporting standards to satisfy top-tier funds. Most Fund II companies raised their next rounds with global funds from our network.

On exits, we bring potential buyers into the conversation early, sometimes years before a deal is feasible, so they can track performance and build conviction. IPOs can deliver the largest outcomes, but we recognize their low probability. Therefore, we underwrite every deal to meet our hurdles without relying on public markets.

How We’ll Capture the Latam Opportunity

We’re now taking our most ambitious step. In our new phase, we’ll continue to execute our investment strategy at a larger scale, building on the lessons learned to date.

What Remains Unchanged

• Betting on misfits. Backing overlooked founders with urgency, obsession and grit, tackling non-obvious markets.

• Remaining lean, agile and close. A small, highly-incentivized team that ensures speed and founder proximity. No corporate bureaucracy and politics.

• Leading early, doubling down. Continue being the first institutional check leading at Pre-Seed and Seed and preserving ownership through pro-ratas in every winner.

• Champion capital efficiency. Every dollar matters. Fast payback, low burn, and consistent work towards traction and retention ensure a high ROI per dollar invested.

• Bridging the gap between Latam and Silicon Valley. Bring global partners to our companies soon after investing, pushing world-class benchmarks.

What is Leveling Up

• Ability to truly lead rounds. Invest larger checks that will let us solo-lead rounds, secure the full runway founders need, and anchor larger companies while holding 15%+ stakes. Founders can spend less time fundraising and we can stay focused on investing.

• More governance and influence. Board seats on every lead turn oversight into operating leverage as our champions mature.

What we’re looking for

We’re searching for founders who will define the Super Intelligent Software wave. The things that excite us most are:

• Vertical, AI-native platforms embedded in labor-intensive or friction-heavy workflows, replacing pen-and-paper and spreadsheets to deliver 10x outcomes.

• Scalable professional services automation for law, accounting, finance, consulting and other language and process-dense professions, perfect for AI. These sectors face high labor costs, repetitive tasks, and growing data complexity.

• Proprietary Small Language Models (SLMs) tuned on domain data to outperform foundational models at a fraction of the cost.

• Pricing models aligned with client outcomes, ensuring ROI and capturing a fair share of the value created. Software that can bridge from traditional IT budgets and eat into labor budgets.

• Low friction, low-CAC distribution powered by teams with sharp storytelling, field expertise and founder-market fit.

• Multimodal interfaces, using text, voice and video, meeting users where they already work.

• Platforms with immediate business impact, while laying the groundwork for superintelligence.

The founders we back are already thinking several steps ahead, building with a clear view of what becomes possible in a world shaped by abundant intelligence.

Arriving Early for the Super-Cycle

We’re at the dawn of an AI-driven super-cycle. Super Intelligent Software will create hyper-efficient, high-growth companies and the outsized returns that follow.

Latin America is the blind spot. Talent is ready, adoption curves are leapfrogging, yet capital remains scarce. The rules of the game are shifting and no one holds the pole position. This gap will close quickly.

If you believe the region’s founders are ready to build the next generation of world-changing companies and deserve real partners, our approach is built for you.

506(b) Non‑Solicitation Disclaimer

This post is for informational purposes only and does not constitute an offer to sell or a solicitation of an offer to buy any securities. Any offer or solicitation will be made only to prospective investors with whom we have a pre‑existing, substantive relationship and only by means of definitive offering documents. Interests in any investment vehicle will not be offered or sold through this post. Past performance is not indicative of future results. Any references to portfolio companies are illustrative and are not intended as investment recommendations.

Venture Capital is a decade‑long game that pays out only at the finish line. Unicorn press releases, triple‑digit paper markups and unrealized IRRs feel great at the moment, but they are only signals that a fund might be heading in the right direction, never the ultimate goal. Chasing the “next hot thing” can dress up a portfolio with quick markups, yet if the gains are not followed by profitability and durable growth, they divert attention from the work that truly matters: bringing DPI to LPs.

Valuation marks swing with macro cycle, liquidity tides and FOMO, relying solely on them is risky. When managers anchor decisions to those signals, judgment drifts away from fundamentals.

Whenever a markup excites us, we remind ourselves the game is still on. We return to first principles and focus on the levers we control. Realizing we lacked a metric to track those levers, we decided to build one.

At SaaSholic, we operate with a simple belief: our job is to help our companies grow.

Whether that means refining pricing, hiring a CRO, reducing burn, making a hard call, like parting ways with a co‑founder or writing off a segment of revenue, we show up for the work.

Bill Walsh, the legendary coach who turned the San Francisco 49ers from 2‑14 into a dynasty, captured this mindset in The Score Takes Care of Itself1. He insisted on “Standard of Performance”: precise practice scripts, clean locker rooms, and relentless attention to detail. His lesson is simple: concentrate on the process, and the scoreboard will follow.

For the same reason our monthly portfolio reviews ignore fund optics and paper money. They focus on one thing: how are our companies evolving? What did we help improve? What do we need to fix next?

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Introducing cARR: A Metric That Keeps Us Honest

Traditional metrics such as MOIC and IRR are backward‑looking. They update only after a financing event and can arrive months or even years later. We needed a real‑time pulse that shows, month by month, whether each company is compounding value the way we expect and tells us what to do next.

That’s why we created cARR, the Cost of Annualized Run Rate.

cARR = Total Capital Invested / (Current ARR x Current Ownership %)

The formula answers a single question: How many dollars have we invested for every dollar of Run Rate Revenue we still own today?

cARR gives us three actionable insights:

1. Growth support - Are we helping our companies scale the right way?

2. Pricing discipline - Did we value this investment properly at entry?

3. Ownership retention - Have we been too diluted?

We don’t aim to minimize cARR on day one; we expect it to fall over time. After a few years, a low and declining cARR is a strong signal that we are on the right path and returns will follow.

• A low cARR reflects disciplined entry pricing, limited dilution and effective growth.

• A high cARR flags one of three gaps: overpayment, sluggish revenue or excessive dilution, and shows exactly where we should focus next.

Because cARR improves as companies grow, we don’t need new rounds or external validation to provide us with feedback. It rewards real traction rather than market swings.

Rolled up across the fund, it offers an honest snapshot of portfolio maturity and capital efficiency. That's our real north star metric.

The VC “Margin of Safety”

We’re building a playbook poised to deliver strong returns in any markets, without hoping for exit multiples to expand. Latin America is capital-constrained, and most exits occur in the 3-6x ARR range, therefore counting on peak valuations is unsustainable. A successful VC model requires the margin of safety to deliver returns even during harsh market conditions.

cARR keeps us grounded in what we can control: the price we pay and the speed at which our capital turns into durable revenue. SaaS multiples swing dramatically2, but our discipline cannot.

Although we back future category leaders that we expect to command premiums above median valuations, our model must still work even if markets trade at the lows when exit time comes. cARR shows whether growth and capital efficiency are converging fast enough to make that true.

What “Great” Looks Like

We have defined what winning looks like in cARR standards. Our targets are:

• Deal-by-deal: cARR < 1 within 3 years

• Fund-level (aggregate): cARR < 1 by year 7. Non negotiable.

A simple example

We invest in a company with $1M ARR at a $6M valuation. That means our entry cARR is $6. Our expectation is that ARR should grow to at least $6M within no more than 3 years, pushing cARR to 1.0, our baseline success mark.

If it gets there faster? Awesome, we might be doing something well. If it takes longer or falls short? We dig in to understand what went wrong and how to correct our course.

It’s important to note that a cARR <$1 is good, but does not mean our job is done. Companies can still disappoint by flattening the growth at sub-scale size (low liquidity) or because they got there in an unsustainable way. No metric is perfect, but we believe this can be a good one.

This model reflects discipline and realism. We don’t need every company to be a unicorn. We just need them to grow into the price we paid, then keep executing.

Our goal is not to minimize cARR at the time of the investment. Chasing artificially low entry prices can make cARR look great on day one, yet mask adverse selection, companies that are cheap for a reason. We seek to bring it down as fast as possible after our investment is done.

To guide that work, we mapped an “optimal cARR curve” that sets “Ok”, “Good” and “Great” bands for each year of the fund life, at both company and portfolio levels.

Achieving these audacious thresholds means we backed great companies at reasonable terms, and helped them grow the right way. Ultimately, we’ve followed Bill Walsh’s advice: "Concentrate on what will produce results rather than on the results, so results become a consequence." The analysis in Figure 4 projects the potential fund-level returns if we stay on that curve.

Returns Follow the Work

Our goal is a model that performs in bull and bear markets, without depending on perfect conditions. By helping founders compound revenue and drive cARR lower each quarter, we let fundamentals guide us instead of hype.

Sharing this framework invites the ecosystem to track progress through an execution-driven lens and gives founders, VCs, and LPs a tool that highlights operational fundamentals while cutting through the noise.

Ever since Satya Nadella suggested that 'SaaS is dead' in the era of AI, our community has been wrestling with what that means. At SaaSholic, this question hits close to home.

Over the past quarter, we went deep into this question. We published the LatAm AI Benchmarks, met with founders and investors across San Francisco, New York, and São Paulo, and spent time with teams building in the field. This letter provides our structured perspective on the topic.

Our view is simple: SaaS is not dead. SaaS is evolving and compounding. And most importantly, it's accelerating as AI infrastructure evolves. The Software as a Service model was the largest creator of $2–20B exits in the last decade, and we expect even more and larger exits in the decade ahead.

We’re extremely bullish on how AI is propelling the SaaS business model, and see this moment as a generational opportunity in software investing: Super Intelligent Software.

To ground our vision, we’ll briefly revisit the path that brought us here and how SaaS has been declared dead and reborn multiple times over the past decades. We’re not here to retell history, but to distill the lessons that help us see what will change, what won’t, and why that matters.

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The Compounding Evolution of Software

If we can learn one thing from its evolution, it's that software doesn’t leap forward, it layers. Each phase in software’s history builds on the last. From cloud infrastructure to AI-driven agents, we’ve seen a steady march toward intelligence and autonomy. This journey mirrors AI’s own trajectory, going from task-specific towards the promise of superintelligence. Along the way, each layer of progress has laid the groundwork for the next in the “Intelligence Stack".

Software as a Service, The Cloud Awakens

In 1999, Mark Benioff announced Salesforce CRM and started campaigning for the “end of Software"1. The old was dead, and SaaS became the new king, dethroning the incumbents like Oracle and Siebel. Cloud-based SaaS enabled scalable, up-to-date, always-on access to software. Businesses standardized their workflows and connected their operations via horizontal and vertical tools. These weren’t intelligent systems, but they created structured data and digital workflows that would become the bedrock for everything to come. We think of it as the pre-intelligence world.

BI Era: From Infrastructure to Business Insights

Once Cloud made data accessible and standardized, the next wave was business intelligence (BI). Tools like Tableau (acquired by Salesforce) gave companies dashboards, charts and alerts. Software could now reflect business performance, although it could not act on it. The BI era made data-driven culture mainstream. It also began teaching users to think in metrics, models, and patterns. Data could now be consumed by humans, to enable them to make better decisions.

SaaS applications suddenly had to rush to incorporate BI and reporting features, as these became table stakes. Those who didn't adapt, died. Most successfully adapted and thrived. BI made SaaS apps more powerful, but still passive. It was a step towards making people smarter, not smarter systems.

AI-Enhanced Software: The Narrow Intelligence Era

With the rise of foundational models, such as GPT-3, software became intelligent within constraints. AI copilots that could generate text, respond, or classify data emerged.

This was AI embedded into everyday tools. We got AI copilots: great at specific tasks, limited in scope, such as: summarizing documents, flagging anomalies, improving search.

And once again, SaaS evolved and became able to provide AI's signature feature: conversational interfaces, enabling the combination of structured and unstructured data to augment these apps. Very few SaaS players died simply due to this new layer. But still, AI enhanced systems are only assistive, and there's always a next step.

Intelligent Software Era: From Tools to Agents

We believe we're living in this phase right now. Software is starting to connect the dots and it's becoming smart. By leveraging the intelligence and knowledge of foundational LLMs, agents can now act on the users’ behalf, providing applications with the ability to react and reason across workflows.

Most AI Agents today illustrate a clear example of Broad AI2 in action: early-stage task orchestration with memory, context and the ability to use different tools, including software apps.

With this technology broadly available, software applications started incorporating agents everywhere, and it's clear that they are on the way to becoming pervasive.

Agents are Taking SaaS to New Levels

There is a growing belief in the market that SaaS and AI agents are mutually exclusive and that the rise of one marks the decline of the other. We believe this is a false dichotomy.

Agents by themselves are far beyond copilots, but when they are not fed with tons of proprietary data to keep evolving, they remain inherently fragile. They behave more like platform features than true solutions.

Some early agents might deliver impressive short-term value, but by lacking the infrastructure to sustain their evolution, several of these products are becoming the so-called "vibe revenues3" businesses, tools that deliver short-term results, but fail to retain customers over the long term. We're not particularly interested in this subset.

On the other hand, we also believe that many players of this “intelligent” or “agentic software” era will eventually graduate to superintelligence, and that's the opportunity that excites us the most today: Software that collects massive volumes of proprietary data, has deep network effects and clear control points, being augmented by AI agents. We believe that these applications have a shot at developing agents that will have deep domain mastery, being at least an order of magnitude smarter (and in the future, cheaper) than foundational models. That's how we bridge to Super Intelligent Software.

Super Intelligent Software: The Endgame of Work

“We’re at the beginning of a new Industrial Revolution. But instead of generating electricity, we’re generating intelligence… [Open source] activated every single company. Made it possible for every company to be an AI company” — Jensen Huang, NVIDIA CEO

Each software era unlocked new value. Now, for the first time, we are combining every moat, pricing model, and delivery mechanic into one compounding system.

Super Intelligent Software is no longer a layer around the work. It does the work.

Super Intelligent Software combines deep vertical expertise, proprietary data moats, and AI to autonomously perform complex business processes with precision and context no general-purpose model can match. It’s specialized, self-improving, and outcome driven.

Unlocking Super Intelligence

Today, client expectations have risen, and software is expected to have a baseline “IQ” equivalent to ChatGPT. Companies seeking true differentiation have already started training LLMs with proprietary data, connecting generic intelligence with industry-specific capabilities. This evolution particularly excites us. It sets the stage for a new kind of software: systems that do the work themselves, with expert level intelligence and are able to keep learning and improving, autonomously.

The road towards superintelligence is paved by leveraging foundational LLMs as commodity infrastructure and augmenting them with proprietary insights, becoming orders of magnitude superior to foundational LLMs in their specific tasks. As they evolve, these systems can adapt to real-world scenarios and deliver outcomes exceeding even the top 1% of professionals in their fields.

The Budget is Shifting: Labor and Services Spending Moving to IT Pockets

In past cycles, software was purchased by IT and measured through license counts and uptime. In Super Intelligent Software, budgets are expanding to cover entire workflows and job functions. Instead of marginally boosting productivity, these systems are beginning to take over meaningful parts of the workforce, reducing the client's labor and professional services costs.

Morgan Stanley surveyed CIOs to understand how AI budgets are being funded4. 41% reported net new spending in AI initiatives, and 35% pointed to existing software budgets. This indicates that most of the money fueling this shift is not cannibalizing current software spend, but likely coming from other sources, such as labor and services. These are the early signs of a broader reallocation of corporate spend at scale.

This shift expands software’s TAMs by an order of magnitude, reaching domains that were historically unreachable. Quantifying the opportunity, the current enterprise software market is estimated at $650B, while Foundation Capital places the white-collar labor market at $8.5T (13x larger)5. Sequoia is even more optimistic, estimating the combined Services + Software opportunity at $10T (15x larger!)6. While the exact number is uncertain, the size and direction of the change are undeniable, and the budget shift is already starting to happen.

If a software can replace a human being who costs $100K/year, wouldn't it be expected to capture a piece of these productivity gains?

New Systems of Record

Traditional SaaS brought us structured databases and systems of record. CRMs, ERPs, ATS platforms, and other software applications became the organization's databases for each functional area.

Super Intelligent Software creates a whole new system of record. They go beyond recording and reading transactions by also tapping into unstructured data. Traditional data silos lose importance as connectivity between systems becomes fluent in this new era. Structured and unstructured information can now be combined into a cohesive, evolving memory.

As memory and proprietary data compound, these applications capture the inputs needed to continuously evolve and train Super Intelligent, industry-specific LLMs.

Structured data tells you what happened. Unstructured data explains how and why. That difference is critical. In this new era, memory is becoming the new moat.

The Impact on Incumbents

The journey towards Super Intelligent Software is forcing incumbents to rethink their core business logic. Legacy, seat-based products designed for human-in-the-loop workflows are being disrupted by AI-native systems delivering outcomes instead of interfaces. Products such as Agentforce, by Salesforce, and Zendesk's AI agents already charge per AI support ticket resolution. Leaders aren’t asleep at the wheel. In his now-viral memo7, Shopify CEO Tobi Lütke called for a radical focus on AI. Salesforce continues to acquire AI startups and roll out GPT-powered features, while HubSpot is embedding AI copilots into its CRM.

The war for talent is just as fierce. Acquihire multiples have exploded: from ~$2M per AI engineer a decade ago to $10M+ in today’s market. In 2008, Microsoft acquired Powerset for $100M; today, we believe that a comparable team would likely command $1B+. Recent AI-native targets like Windsurf and io8 show how incumbents are buying capabilities they can’t build fast enough.

Vertical Focus is a Shortcut to Superintelligence

We think most winners from this next wave will come from the application layer, particularly in vertical, labor-intensive markets where context and outcomes matter most.

By vertical, we mean companies focused on either a specific industry (i.e., restaurants, law firms, hospitals) or on highly specialized use cases that span multiple sectors (i.e., credit collections, payroll, vendor onboarding). These are the environments where general-purpose tools can’t compete because success requires capturing role-specific data and creating systems that outperform humans.

The opportunity here is clear: Developing a vertical specific superintelligence. Vertical players have natural data gravity, positioning themselves perfectly to develop AIs that are 10x superior to foundational models.

We also believe that most horizontal, generalist AI agents are likely to be absorbed by Big Tech or foundational model companies. They have both the incentive and infrastructure to embed broader use cases into their platforms, but won’t address the long tail of industry-specific or role-specific needs.

If the last cycle produced over 300 SaaS unicorns9, we expect the Super Intelligent Software wave to exceed that by a wide margin.

Latin America's Strategic Position

We believe Latin America is one of the most compelling geographies for building and investing in this new category. The region remains underpenetrated by traditional SaaS, faces a bottleneck of knowledge workers, relies heavily on manual labor, and is full of vertical workflows that are regulation-heavy, repetitive, and ripe for automation. Latin America is simply too big to be ignored. We are a region of over 600M people that must embrace the digital transformation.

These conditions are perfect for leapfrogging directly from spreadsheets to superintelligent platforms. In our recent LatAm AI Benchmarks study, we found that more than 60% of new startups founded since 2023 were born AI-native and these companies are growing 1.6x faster than their non-AI peers. Besides that, what is even more exciting to us is the fact that the valuation premiums for AI companies remain modest at 10-20%, unlike in the US market, where AI commands valuation premiums of at least 40%10 due to deeper capital availability.

These dynamics make Latin America a rare environment to invest in fast-growing AI startups solving real problems at rational prices.

Our Approach to the AI Shift

“You will get rich by giving society what it wants but does not yet know how to get. At scale.” —Naval Ravikant

Super Intelligent Software is a new chapter in how software is built. It is reshaping foundational mechanics of the industry, and to stay ahead, we’ve begun evolving our frameworks. Although most of the implications of this shift are still unknown, here's some that we're already seeing and adapting for:

• The traditional concept of ARR has to be reconsidered. In a world where pricing is increasingly tied to consumption and business outcomes rather than seat counts, revenue becomes less strictly recurring. We now assess recurrence based on consistent value delivery, rather than contract terms alone. The industry's definitions of ARR/MRR need to evolve to seize the opportunity.

• Margins do not matter as much today as they did in the past. AI-SaaS companies often appear compressed due to high computing costs. But with a 100x drop in processing costs last year and more ahead11, today’s constrained margins may become tomorrow’s opportunity. These products will soon resemble SaaS again, but with faster feedback loops and greater upside.

• Go-to-market is more challenging than ever. CACs are rising fast, and while building has become easier, distribution has grown significantly tougher. Yet, we’re seeing a new wave of products reach $10M+ in ARR in under three years by delivering 10x better solutions12. That level of traction used to be rare and now it’s increasingly within reach for those who build undeniable value.

What We’re Looking For

We’re raising the bar. With this new lens, we’re only committing when we see the potential for a company to define a vertical through Super Intelligent Software. That means:

• Vertical AI-native platforms, deeply embedded in high-friction or labor-heavy workflows and designed to deliver measurable outcomes to their clients.

• Scalable Professional Services Automation. Industries such as Legal, accounting, and consulting are language and process-heavy fields, perfect for AI. These sectors face high labor costs, repetitive tasks, and growing data complexity. Software that automates workflows can unlock massive value, with strong demand and willingness to pay.

• Proprietary Small Language Models (SLMs) providing efficient and superior alternatives to massive foundational models. In healthcare, for example, agents built on SLMs can be trained exclusively on medical literature, patient records, and compliance protocols. By leveraging foundation models and evolving through fine-tuning, SLMs bring a clear path to specialized superintelligence at a fraction of the cost.

• Pricing models aligned with client outcomes, ensuring consistent ROI and capturing a fair share of the value created. We're looking for software that can bridge from traditional IT budgets and eat into labor budgets.

• Low friction, low-CAC distribution in times of inflationary CACs also deserves a premium. This is often delivered by the combination of a) Teams with sharp storytelling, deep expertise and strong founder-market fit and b) Ability to replace a legacy workflow with a 10x superior solution, that takes CACs close to 0.

• Multimodal Interaction: Software now is expected to operate through text, audio, video, beyond traditional interfaces.

We are looking for platforms that are already delivering real value today, while laying the groundwork for superintelligence. The founders we’re drawn to are thinking several steps ahead, building with a clear view of what becomes possible in a world shaped by super abundant intelligence. This is the mindset we believe will define the next generation of outliers.

Why This Is Our Moment

There are no incumbents with a 20-year head start in this category. There is no Gartner Magic Quadrant. No playbooks. This creates a rare kind of opportunity, one where context, speed, and conviction matter more than pattern recognition.

We are underwriting a new software architecture. One that is intelligent, embedded, and capable of replacing work itself.

The next generation of multi-billion-dollar companies is being built right now. Getting in early, as we do, is the most asymmetric way to capture disproportional returns.

Thank you for your continued partnership.

Sincerely,

—The SaaSholic Team

This is a redacted version of our Letter from the GP from 1Q25, and does not include sensitive information due to confidentiality reasons

Built on anonymized data from 400+ startups across the region, the report brings much-needed visibility into key metrics like growth, funding, valuations, and real-world adoption across sectors and stages. This is the data the ecosystem had been missing — and it’s finally here.

Packed with insights. Backed by founders, operators, and top investors.

To everyone who contributed data, time, and feedback — thank you. This is a collective effort, and we’re proud of what it represents.

A big thanks to our Platinum Sponsor: AWS; our Silver Sponsors: ClickSign, Salvy, and NVIDIA; and our Research Partners: Cubo, Latitud, Pingback, Scenius Latam, Magma Partners, MicroSaaS, BossaBox, and Maestro ABM — your support helped bring this project to life.

📥 Download the full report here

Let’s keep putting Latam on the global map!

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In the early days of software-as-a-service (SaaS), the idea of "control points" wasn't openly discussed. Yet, quietly, this concept defined the winners and losers of the SaaS revolution.

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Control points are strategic nodes within workflows or systems that become deeply embedded, making it tough for users to leave. Salesforce controlled your customer data; SAP managed your finances; Slack became central to your team's communication; Rippling owned your HR processes. Each created subtle yet powerful loyalty.

Today, I see a similar quiet shift in AI. Large Language Models (LLMs), like GPT-4 or Google's Gemini, are rapidly converging. The amazing differences we once admired (slightly smarter completions, more human-like interactions) are quickly becoming standard. Soon, the intelligence (or "synapses") powering these models might become a commodity.

So, what's next?

As synapses commoditize, I believe AI-native businesses must rely on new strategic control points. Specifically, I see five key AI-native control points emerging:

1. Memory & Personalization (The AI-native System of Record)

Why it's a control point?

This becomes the AI-native equivalent of a CRM or ERP: your personal and organizational data embedded into every interaction. But unlike static databases, this memory evolves. Users invest time fine-tuning, guiding, and shaping it, creating an invisible moat. Over time, switching isn’t just operationally hard, it feels like erasing a relationship.

2. Proprietary & Actionable Data (The New Data Gravity)

Why it's a control point?

Access to unique, high-value, and real-time data builds an uncopyable edge. When AI learns from proprietary interactions, calls, workflows, outcomes, it drives compounding value. The more usage, the better the output. The better the output, the more usage.

Whoever controls specialized data sets, real-time streams, or high-value domain-specific data, creates gravity.

3. Distribution & Embedding (AI-native Workflow Lock-in)

Why it's a control point?

Distribution isn't just about reach, it's about entrenchment. When AI is embedded in critical workflows, it becomes part of the daily muscle memory. Like Microsoft Copilot in Office, removing it means disrupting behavior, not just software.

Even if a rival model emerges, disrupting established workflows becomes prohibitively costly. AI startups must quickly identify and embed deeply within essential user workflows.

4. Tooling and Ecosystem (AI-native Integrations)

Why it's a control point?

Similar to how Salesforce’s AppExchange and Slack’s integrations created robust ecosystems, AI companies developing integrations, plugins, specialized extensions, and agents build strong retention mechanisms.

The more integrated the AI into workflows and extensions, the stronger the ecosystem’s gravitational pull.

5. Trust & Governance (The Compliance Moat)

Why it's a control point?

In regulated industries, trust becomes crucial. AI companies offering compliant, transparent, and safe outcomes create deep defensibility. Users naturally gravitate to trusted providers, recognizing that critical errors aren't just costly but potentially catastrophic.

SaaS vs. AI-native Control Points

What does this practically mean?

As foundational AI intelligence inevitably commoditizes, startups must quickly establish these new strategic control points. Companies failing to do so risk becoming like chip manufacturers competing only on processing power. Useful, but no longer strategically critical.

Tomorrow's AI-native winners won't just provide intelligence; they'll create ecosystems users enter willingly yet find nearly impossible to leave. Identifying and building these control points will define the next generation of enduring AI-native businesses.

The competition for synapses might soon end, but the battle for strategic control points is just beginning. If you're building something AI-native around these ideas, I'd love to hear from you.

Be Part of Latam’s First AI Startup Benchmark Survey!

In partnership with Amazon Web Services (AWS), we're launching the most comprehensive study on AI adoption in LatAm. Tech startups at all stages are encouraged to participate!

Why participate?
- Get early access to exclusive benchmarks
- Be eligible for up to $100K in partner benefits
- Takes less than 10 minutes

Take the survey here: bit.ly/LatamBenchmarks2025!

The more responses we get, the stronger the benchmarks will be. If you support this, share it with your network!

Update: Our 2025 Latam AI Benchmarks Report has just launched! Check it out here!

The AI landscape in Latin America is evolving at an unprecedented pace. Yet, key questions remain for founders:

🔹 How does my AI startup compare to others in the region?
🔹 What strategies are driving success in funding, growth, and market positioning?

To answer these and more, we’re launching the Latam AI Startup Benchmark Survey 2025 in partnership with AWS. This is the first initiative dedicated to providing data-driven benchmarks on the region’s AI ecosystem.

Building on the success of our State of SaaS research, which set key industry standards, we’re now bringing the same level of actionable insights to AI founders across Latin America.

Why Participate?

✅ Benchmark your growth against AI startups in the region.
✅ Early access to key findings before anyone else.
✅ Partner benefits – qualify for up to $100,000 in credits to scale your AI solutions.
✅ It takes less than 10 minutes – but the insights will shape your startup’s future!

📩 Take the survey now and help shape the future of AI in Latin America!

For Investors & Ecosystem Builders

The success of this initiative depends on engaging as many AI founders as possible. If you support the growth of AI startups in LatAm, we’d love your help in spreading the word!

🔁 Share this survey with founders in your network.
📢 Post about it on LinkedIn and Twitter – let’s drive more participation!
💬 Tag startups that could benefit from these insights.

By amplifying this initiative, you’re helping strengthen the AI ecosystem in Latin America and ensuring founders have the data they need to make smarter decisions.

🚀 Let’s make this a movement! #LatamAI #Startups #VentureCapital #AIInnovation

Update: Our 2025 Latam AI Benchmarks Report has just launched! Check it out here!

Over the past decade, the Latam VC ecosystem has experienced remarkable growth, with more than 27 unicorns emerging1. These startups have created critical solutions, delivering hundreds of billions of dollars of value for society and its investors. During this period, top-quartile VC funds have outperformed every other private and public asset class in the region2, rewarding investors beyond the risks taken.

As in every market cycle, this growth has attracted a wave of new investors, many of whom often attempt to apply the Silicon Valley VC playbook to Latam’s early-stage ecosystem. We believe this is a fundamental misstep, one that is contributing to inflated seed round valuations and overfunded startups. In our view, the nuances of the region demand a more tailored investment strategy.

In this environment, we chose to stay disciplined to our fundamentals, resisting broader market excesses by making fewer investments while doubling down on our strongest portfolio companies. This approach has been allowing us to generate superior returns, while building the foundation for long-term success.

In this letter, we break down why the traditional Silicon Valley VC model doesn’t translate to Latam, the unintended consequences of this approach, and how we are doing things differently to capture the region’s unique opportunities.

Not all Seed Rounds are created equal

For better or worse, seed investing in Latam is a different game than in the US.

At the seed stage, Latam startups are nearly twice as large as their US counterparts, in terms of revenue. In many cases, investors are acquiring larger companies, with less product risk (slightly less, maybe).

A strong example from our portfolio is Jusfy, a legal tech startup that recently announced a pre-Series A round, co-led by us and Maya Capital.

Compared to Clio, a US peer, it takes Jusfy 2-3x more customers to reach the same ARR threshold, primarily due to currency differences. While this might seem like a disadvantage, it actually results in a more battle-tested product, stronger operational expertise, and a broader base for cross-selling and upselling for the same level of ARR.

Another key difference lies in customer acquisition costs (CAC). Despite both companies deploying similar amounts of capital to drive traffic in their respective markets, Jusfy’s cost per click (CPC) is 8x cheaper than Clio’s. More importantly, its CPC as a percentage of revenue is 3x lower. This superior capital efficiency is possible because some Latam markets remain largely unexplored, with lower competition and higher ROIs, allowing startups to scale more efficiently.

Latam founders operate in capital-constrained environments, demanding efficiency from day one. Data from the State of SaaS Latam 2024 report shows that local SaaS companies consistently outperform US peers in capital efficiency, with superior LTV/CAC ratios and faster payback periods3. Many founders bypass earlier-stage funding rounds (angel, pre-seed), entering seed at a later, more mature stage with less dilution.

Certain Latam segments exhibit winner-takes-all dynamics, allowing dominant players to scale faster with less competition. As highlighted in Atlantico’s 2024 Digital Transformation Report, tech remains vastly underpenetrated in the region, and main the question isn’t if there is value to be created in Latam, but who will capture it.

These factors reinforce our conviction in the region’s potential, and we remain excited to back founders poised to capture this opportunity.

US Seed dynamics: beware, they tend not to be the same here

"The bottom line is that no asset class or investment has a lock on a high return. There's no investment that’s so good that it can’t be overpriced. And there are few that are so bad that they can’t be underpriced.", Howard Marks.

Entry price is king. Despite Latam’s strong fundamentals, many investors continue replicating US underwriting practices here.

Today, a typical Silicon Valley seed round raises $4M at a $16M pre-money valuation (20% dilution), with companies taking around two years to graduate to Series A4. However, Latam operates under different dynamics, with less capital in the asset class and structural factors that should lead to different fundraising needs.

Yet, many investors continue to apply a one-size-fits-all investment model, leading to inflated valuations, unnecessarily large rounds, and unrealistic expectations, ultimately distorting the market and putting long-term returns at risk.

A direct consequence of this approach is overfunding, as investors mirror US capital requirements without questioning whether they truly apply to Latam. In the US, a startup may need $3-5M to graduate from Seed to Series A, but in Latam - where costs are lower and competition less intense - companies often require significantly less. Talent costs alone suggest why. Salary benchmarking data shows that a mid-level software engineer (3–5 years of experience) in the US earns $120k–$150k per year, whereas in Latam, an equivalent professional earns $40k–$60k, a 50–70% reduction5.

Customer acquisition costs (CAC) follow the same pattern. As seen in the Jusfy/Clio analysis, Latam startups operate in markets with significantly lower competition, leading to more cost-effective customer acquisition, and ultimately cheaper growth. These dynamics, among others, create a structural advantage in capital efficiency, yet many Latam seed rounds are still based on inflated Silicon Valley standards.

While exceptions exist - particularly in highly competitive, consumer tech sectors - in SaaS, we rarely see deals that justify such excessive capital to reach Series A.

At the same time, we see a concerning trend of abnormally high valuations in very early-stage deals. It’s becoming increasingly common to see pre-revenue startups raising multi-million dollar rounds. We believe many of these rounds exist not to meet the company’s actual capital needs, but to fit in the portfolio construction of large fund size VCs. Some big funds are leading $2M+ in pre-seed rounds simply because it’s the smallest check they can write. Given that dilution remains relatively constant, the only way larger funds can deploy their check sizes is by driving valuations up.

While this provides founders with cheap capital in the short term, it often leads to problems down the road. Overvaluation at seed can make Series As difficult to achieve, leading to down rounds or failure to secure funding altogether—a pattern we’re already seeing play out.

In some cases, raising a large round provides cushion and de-risks the path to Series A, especially when paired with disciplined capital deployment. However, more often than not, excess capital leads to inefficient allocation, with startups prematurely building management layers that slow execution. Instead of focusing on building a superior product, founders scale too early and hire expensive talent over creative, adaptable team members. Overpaying also compresses upside for future investors, making future fundraisings more challenging. If a company falters and needs a bridge round, those funds often come on harsher terms at the worst possible time.

Another key misalignment is unrealistic exit expectations. Public markets clearly reflect a regional discount for Latam players. VTEX, a Brazilian company listed on NASDAQ, trades at 4.4x Implied ARR, while its US benchmark trades at 7.3x, despite similar growth rates and unit economics. The same applies to other Brazilian SaaS companies like TOTVS and LWSA. While public markets are not valuing local companies with US multiples, somehow some early-stage private investors are6.

As 2021-22 vintage funds burn through their remaining dry-powder7, some valuation decompression may occur. However, we can’t develop a venture investment model purely based on this assumption.

Liquidity constraints further complicate the equation. The region faces high capital costs, tax complexity and currency instability - factors that limit exit windows and create venture vintages that are highly correlated with macroeconomic cycles. Unlike in the US, where venture cycles tend to be more predictable, Latam requires a different, more flexible approach.

Why are we worried?

In the US, we’re already seeing new VC vintages significantly underperforming, due to irrational exuberant seed valuations over the last few years.

We fear that the same overvaluation and overfunding has also become a real trend in Latam, that will likely impact VC returns negatively over the next decade, harming founders, GPs, and LPs.

Defying the Silicon Valley approach

“Differentiation is Survival and the Universe Wants You to be Typical”, Jeff Bezos

To succeed in Latam seed investing, we believe that our investment model has to be built within the local constraints. Thus, here at SaaSholic we strive to do things our way. We don’t pretend to have all the answers - our goal is to share our perspective and foster discussions that push our ecosystem forward.

As SaaSholic, we focus on:

• Real growth trajectory, not just potential market share capture.

• Capital efficiency, tracking how much startups raised before seed.

• Solid unit economics, avoiding capital-intensive thesis that requires speculative leaps of faith.

• Solution stickiness, client retention and the ability to develop deeper product-market fit by expanding into the client’s wallet with mission-critical products.

Our Investment Strategy is somewhat simple:

• Invest in right-sized rounds based on what the company needs, not mirroring US seed rounds. Some companies may need a few million, but most can graduate to Series A with $2M or less raised at the seed stage. Many can even bootstrap their way there. Scarcity fosters discipline, while abundance can kill culture and execution.

  Clicksign, a company we've invested in, illustrates this principle well. By the time we invested in their seed round, the company had around $10M in revenues, despite having raised less than $1M at that point. Since then, the company has almost doubled, reinforcing the power of investing with a capital efficiency mindset.

• Be highly selective. Smaller rounds don’t mean we are looking for bargains nor small TAM, niche business. In fact, we invest with a significantly higher bar than most VCs we observe and closely monitor our portfolio’s success.

  It’s difficult to find companies that beat our hurdles, but we prefer to invest only when these conditions align, rather than following market hype. Our “investment napkin” outlines key metrics we look for at each stage, providing a clear benchmark for the companies we back.

• Due to this approach, we made only two new investments in 2024, while doubling down on high-conviction companies within our portfolio. Sometimes, the cost of being contrarian is staying out of the market.

• Provide disproportionate value beyond capital. Most VCs say this, but we try hard to do it and we truly believe in this as a firm principle. Many early-stage companies need strategic and tactical guidance as much as they need money (though there are some exceptions that we also are humbled to have the opportunity to partner with).

  Upon taking our funding, we take a very active, hands-on role within each company, helping with hiring key talent, improving reporting, accelerating their go-to-market and developing fundraising strategies. Don’t take our word on it, ask our founders!

• Optimize for different liquidity avenues. While we invest early and take larger stakes than most traditional seed funds in Latam (at least 10% ownership in each company we become partners), we remain flexible in our exit strategy. IPOs aren’t the only viable exit path and we actively consider selling stakes to Series B/C investors once our target returns are met.

  In Fund I, we partially exited our position in Conta Simples during its Series A round, generating a 10.1x MOIC at the investment level and contributing to a 33% DPI at the fund level. We have already seen some early exits in Fund II, and while not home runs, they returned capital while maintaining a 0% mortality rate to date. Paper returns are important for tracking a fund’s health, but unless they translate into real exits and LP distributions, they remain just that - paper.

Looking Ahead

We believe that when investing in seed-stage startups in Latam, VCs should develop their unique approaches rather than trying to replicate Silicon Valley in the region, which, we’d dare to say, does not seem to be working.

We are seeing a lot of hype in deals, too often, which risks diminishing returns and damaging the credibility the venture capital asset class has built in recent years.

Despite the frothy market, we remain impressed by the new generation of founders building high-growth, capital-efficient businesses and seizing unique opportunities in our region. At the right price, we remain extremely bullish on early-stage investing in Latam and are committed to supporting startups where we can add real value.

We look forward to partnering with Latam's next software leaders.

Thank you for your trust.

- SaaSholic Team

This is a redacted version of our Letter from the GP from 4Q24, and does not include sensitive information due to confidentiality reasons

I was thinking this morning about how essential the "memory feature" is becoming in AI. It’s not just about bigger models or more tokens, it’s about truly remembering you. Once you’ve invested in training an AI with your spreadsheets, presentations, and personal preferences, it stops being a generic chatbot and becomes more like a second brain.

What’s fascinating is how this can strengthen network effects for companies building GPT-like features. The more each user adds their own data and context, the more these "fragmented islands" of hyperpersonalized knowledge grow. Eventually, you end up with a second source of truth unique to each individual, connected to the company’s primary systems. It’s pure lock-in: who wants to abandon something that’s so intimately curated?

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Of course, it’s not all smooth sailing. Privacy is a concern, and AI memory can "hallucinate" or mix up past information. But if companies nail the privacy safeguards and refine how memory is stored, the payoff is huge. Every interaction continuously upgrades the AI’s understanding of you and your team. In markets where products look increasingly similar, like some B2B SaaS categories, an AI with robust memory becomes the difference between a passing novelty and a mission-critical tool.

And here’s the kicker: most of the data we’re working with is unstructured. PowerPoints, PDFs, Slack chats, random Google Docs… figuring out how to harness it is the next big thing on everyone’s roadmap. Whoever masters this combination of memory and unstructured data first won’t just have a feature, they’ll have an unbeatable moat.

Are you building something like this? Please let us know.

If you’re a fit, we encourage you to apply by sending us your CV at team@saasholic.com. And if you’re not, but you’re excited to see us taking this important next step, please encourage someone great to join us by sharing this post with awesome, talented people!

Would you like to skip the CV evaluation? Feel free to go straight to our online test. It takes about 40 minutes to answers, but your CV will go straight to the top of the pile if you complete it!

Job Description:

VENTURE CAPITAL RESEARCH AND CONTENT ANALYST

THE ROLE

SaaSholic is seeking a dynamic Venture Capital Research & Content Analyst to drive our thought leadership and strengthen relationships with founders and Limited Partners. You will develop strategic content, manage key events, and foster engagement within LatAm's SaaS ecosystem.

This role blends strategic communications, research, content creation, and community management with operational rigor. You’ll work on impactful projects like research reports, LP engagement, SaaSholic Week, and Investor Day, while actively contributing to the firm’s growth and reputation as a trusted partner for SaaS founders.

RESPONSIBILITIES

• Develop and execute a world-class content, communications, and events program.

• Manage an annual calendar, including research reports, LP updates, and community-building events.

• Create engaging reports, presentations, and materials that showcase SaaSholic’s thought leadership.

• Lead SaaSholic’s digital presence by managing the website, blog, and newsletters.

• Design initiatives like workshops and campaigns to engage founders and LPs.

• Develop resources such as playbooks, case studies, and ecosystem-focused content.

• Streamline editorial operations and expand content formats like blogs, podcasts, and events.

REQUIRED EXPERIENCE

• At least 3 years of professional experience, including 1-2 years in IB, M&A, VC, PE, equity research, management consulting, corporate strategy or equivalent. Pretend you didn't see the 3-year requirement if you believe you are capable and willing to develop quickly.

• Strong writing skills and fluency in English.

• Strong financial analysis skills. Excel/Google Sheets ninja.

• A self-starter with excellent communication, organizational, and strategic thinking skills.

• Passion for innovation and the startup ecosystem, particularly SaaS and AI.

DESIRED EXPERIENCE

• CRM and digital marketing tools experience or willingness to learn.

• ChatGPT prompt engineering/hacking skills.

• Experience with lead generation or demand strategies in B2B or SaaS.

CAREER OPPORTUNITIES / THE JOURNEY:

• Direct involvement in shaping the firm's strategies, from content to portfolio success.

• A platform to expand your presence in the venture ecosystem, becoming a recognized leader in SaaS-focused VC.

• Partner early with bold entrepreneurs during the most exciting times for the software industry.

• Be a key player in building an iconic VC firm in LatAm.

WHAT YOU CAN EXPECT FROM US?

• Competitive total compensation in line with the market.

• Participation in weekly planning and strategy meetings.

• Weekly 1-on-1s with the partners & a clear 100-day plan for success.

• As much “teaching time” as needed.

• Guidance and clear goals for career progress.

ABOUT SAASHOLIC:

SaaSholic is a Venture Capital firm managed by LatAm tech operators Gustavo Souza, William Cordeiro, and Diego Gomes. Our name speaks for itself: we're addicted to SaaS!

At SaaSholic, we aim to find Latam's software winners early and grow them fast. Our investments in Clicksign, Jusfy, Curseduca, and Salvy represent the forefront of SaaS/B2B innovation.

We are building a boutique, high-performing venture capital firm with:

• A “small is beautiful” approach, combining selective LPs and small, high-talent teams.

• A reputation as the go-to partner for software founders in SaaS and beyond.

• A focus on consistent outcomes by fostering successful investments.

• Concentrated portfolio with deep founder support.

If you identify with what we're looking for, dare to send us an email at team@saasholic.com with your CV no later than the Jan 30th. Hurry up!

Would you like to skip the CV evaluation? Feel free to go straight to our online test. It takes about 40 minutes to answers, but your CV will go straight to the top of the pile if you complete it!

Saasholic Vc Research & Content Job Posting

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Back in the 80s, Bill Gates predicted that every home would eventually have a computer. Prompted by this, Laercio then wondered: how many computers would a company own?

Laercio, alongside Ernesto are some of the most successful Software entrepreneurs of our time, having built TOTVS (BVMF: TOTS3), the leading ERP vendor in Latin America. It may be flying under the radar outside of Brazil, but TOTVS is the indisputable unsung giant in the region. Their story and their journey are among the titans of the tech industry. 

It is impossible not to put TOTVS in the race to compete with its ERP counterparts. With 34% of the market share in 2023, the company is a notable local challenger to giant global competitors SAP and Oracle. In Q3 2023 alone, TOTVS reached R$ 1.2 billion (~ US$250M) in Net Revenue, with 19% growth YoY. 

Having three main clusters of revenue streams, the “Management SaaS” alone - which comprises their core SaaS and Cloud services - generated R$ 1,038 billion in  revenue (~ US$216M). Meanwhile, their “Biz Performance” unit generated R$ 115,4 million (~ US$24M), and their newly inaugurated joint venture with Itaú (the biggest bank in Latam) called “Techfin” generated R$ 97,8 million (~ US$20M). 

TOTVS is also a top-performing company when it comes to generating profits. In Q3 2023 they were able to increase their Cash Earnings margins by 28,9% YoY and reached an impressive amount of R$ 214,8 million, representing a net margin of 17,9%.

It's not by chance that the world is looking at TOTVS. In March 2023, Fitch affirmed 'AA+(bra)' rating towards the company, praising its capacity for generating new businesses while diversifying its solutions portfolio and generating positive cash flow in uncertain macro scenarios.

TOTVS started as a small software house in São Paulo, Brazil, to become one of the most relevant software companies in Latam and the world. And they did it on the hard mode even before VC was a reality in Brazil. 

The company is the ultimate example of an untold story of how a handful of Brazilian entrepreneurs, in the first days of hardware and software, were able to build a world-class tech behemoth.

Not only were they pioneers in developing new technologies for businesses, but they also created investment attention that would be important for entrepreneurs in the tech industry for decades to come.

It all started with naive and ambitious desires, and it became the dream of many entrepreneurs nowadays. Here's their story from their perspective:

The software market in the 1980s

Back in the 1980s, there were zero Venture Capital firms actively deploying capital in Brazil. It means that while Larry Elisson was pitching Oracle to Don Valentine at Sequoia, Laercio Cosentino, and Ernesto Harberkorn, the founders of TOTVS, were fighting for every customer dollar. It was especially difficult with the macro climate in Brazil at the time. The currency in Brazil was the Cruzado and its accumulated inflation reached 364% in 1987. 

Nowadays, if compared to the 1980s and 1990s it's easy to get stats to plan for an audacious goal. Access to information is abundant and only a couple of keystrokes away. However, it was an immense challenge to launch a company with limited information and no external capital. The barriers to entry were much higher than they are today and internet access was almost nonexistent.

The internet had not yet reached significant penetration - having arrived only a few years before in 1981 - and it was difficult to sell investors a vision of the future we are today. In the year 2000, while internet penetration in the U.S. was at 43%, in Brazil it was reaching only 3%.

Even with the challenges back then, Ernesto and Laércio didn't back down. Instead, they saw the challenges as an opportunity to march ahead and build what would become one of the most impressive tech companies in the world.

Ernesto's Odyssey: Pioneering Software in Brazil 

When reading and watching everything about Ernesto Haberkorn, you'll notice how inebriating it is to be around him and his positive attitude. From the best of his 80 years, he seems unstoppable, running multiple projects at the same time while making sure he's being of value at every waking moment. 

Ernesto started his career at Deca, a plumbing parts manufacturer company. The opportunity to work there turned up as he got his business administration degree in 1964 at Fundação Getulio Vargas (FGV-EAESP), one of the top business schools in Latam. 

Business schools were so incipient that he felt he was being conned by the school. It was a waste of time to spend so much time studying something that appeared to be, as he describes, ridiculously common sense.

As a business bachelor, he left business school hungry to apply his knowledge. His first job was to calculate the costs of every single part that goes into plumbing mechanisms. His boss, seeing how much time that took, presented him with a solution that would open his eyes to a whole new world. He took young Ernesto to IBM, where all of the taxes from the plumbing company were done.

When seeing an IBM 1401 and the punched cards for the first time, processing hundreds - maybe thousands - of documents in a few minutes, Ernesto felt the joy of discovering something so impressive that would change his life.

The next day Ernesto signed up for his first coding course. The programming language was Assembly, "a way better language than the terrible Javascript I'm learning now", as he later describes.

After leaving Deca, Ernesto started developing financial planning systems in every place he worked. One day, while working at Siemens, he found out that the company was going to shut down operations in Brazil. He then asked his boss if he could implement the software he invented in a friend's company, to which his boss skeptically replied: "Well if the system works one day… you can, sure".

There's nothing like scratching your own itch to start something of lasting value. This was only the beginning of a love story between Ernesto and software development that led him to build his own company: SIGA.

Enterprise Resource Planning (ERP) was already a reality outside of Brazil. But until then, in 1974, Ernesto's ambition was to build his own company and become a successful entrepreneur to live off of his work comfortably.

And that's exactly what he did by growing a one-man business to a 50-employee software house that had a shy but increasing presence among small businesses in Brazil.

Ernesto's Apprentice: Laércio Cosentino's Climb from Intern to Industry Titan

Somewhere else in São Paulo, a few years before the birth of SIGA, Laércio Cosentino was a young man who was getting interested in business early in life. But he had no idea it was called business at the time. It was almost impossible for him to have such a grasp as a 9-year-old. However, doing debt collections for his family's tiny grocery shop taught him a few lessons.

That aspect of him flourished early and lasted for all his youth. One of the first deals he made as a young man was with his own father. He asked his father to pay him half of his tuition as a salary if he could get admitted to a university where tuition wasn't necessary. Needless to say, that's exactly what happened. Laércio got into Universidade de São Paulo (USP), one of the most competitive universities for engineering in Latam.

Laércio was still in college when he joined SIGA, the 50-employee software house in São Paulo founded by Ernesto, as a software development intern. He didn't get a salary when he joined SIGA, but that didn't make him less motivated. 

When Laércio joined, his fast-learning abilities quickly caught up to realize the management system of the company was inefficiently vertical. All employees reported directly to Ernesto Haberkorn, his (and everyone else's) boss. Little by little, Laércio became essential to the business, slowly accumulating responsibilities (and power) and convincing Ernesto to let him take care of core business areas.

In a 2 year stint, he went from an unpaid intern to running a meaningful part of the company’s operations, including sales, which he considered to be the heart of the business.

Ernesto, his boss at the time, was an entrepreneur managing his business long before the ambitious young intern came into the picture. Even though Ernesto knew his company was inefficient, he didn't think there was an urgent need to change anything. After all, if it wasn't broken, why fix it?

His boss's fixed mindset didn't stop Laércio from going from an intern at 18 to director at 23. After winning many battles to restructure the organizational management of SIGA, Laércio knew there was still a long way to go.

Laercio had figured out a clear opportunity. It all started with a simple, but ambitious perception: "When Bill Gates said that every home would have a computer at home, imagine how many computers would a company have?"

Laércio had the ambition to drive that venture's future growth. The company’s entire business was shifting from mainframe computers to microcomputers, and unless they became an entirely new organization, they would go out of business.

He had a bold vision. He wanted to be part of something he could have real ownership of. To make the vision a reality, he invited his boss to an apparently casual lunch.

The meeting started with Laercio selling the vision he had for a transition plan. He wanted to agree with Ernesto on a new programming language and business plan. Laércio even went ahead and made a new logo, under a new name, Microsiga.

With a lot of push and pull, Laércio started the conversation by proposing to own a fraction of the company but amidst the conversation, he insisted that it would only work if he and Ernesto became equal partners in the new venture. 

Laércio knew where he wanted this conversation to end up. So much so that he already had a typed contract in his folder for him to sign at that very dinner table, written: 50% ownership for each of the partners.

The plan proposed by Laércio was to take over Latin America with their new software: Protheus. They defined the new programming language (what it would do, how it would do it, and why they should use it) and how it would go to market. But despite the bold vision, little did they know that the partnership Laércio was proposing was going to change both of their lives forever.

To make the plan work, back in 1989 Laércio and Ernesto realized that they needed to expand their geographic presence. If it hadn't happened at the time, one of their competitors would've done it in their place. So, in that year, they started something completely daring for the time -- a novel plan for expansion through franchising.

Despite being told by seasoned professionals in the tech industry that the franchise model would not work, they committed to expanding software sales through franchising throughout Brazil. That plan multiplied revenues by 10x, conquered the country, and proved that the franchise model was beyond ambitious at that moment. 

However, Laércio describes this time as one of the most challenging parts of his career. Working without much help, spending 50% of his time programming and the other 50% doing everything else, he had to prioritize.

"In the beginning, I had to do everything. I was a hands-on guy, doing a little bit of everything — programming, traveling, doing customer service […] So in 1994 I stopped programming software. I needed to focus on making the company expand."

The franchise model was the foundational stone of the business. But something was missing. To grow faster and challenge competitors, Laércio realized he needed to increase Microsiga's awareness internationally. Only one thing would do it: partnering with a well-known global Private Equity firm to take the company to another level.

"I wanted the company to grow faster and gain even more confidence in the market. The negotiation was difficult and there was even a moment when I thought about giving up [...] I talked to 12 funds until I reached Advent International, an alternatives powerhouse that up to these days holds over 90 billion in assets under management. It certainly would add important credibility and help Microsiga grow”.

Having Advent onboard in 1999 meant they were ready to fight global giants of the software industry. It was a stepping stone to go even further. By that time, they had already conquered a lot of territory in Brazil, fighting with international behemoths such as SAP and Oracle.

Fast forward to today, this is TOTVS' market share in Brazil, against its counterparts in the ERP vertical:

Partnering with Advent International paved the way for a master plan that included Microsiga’s preparation for a public offering in the former BMF&Bovespa, São Paulo’s stock exchange now called B3.

Laércio and the team did everything to make sure governance was on point to take the company public. They spent 2 years preparing for this move until 2001 when a plane hit the World Trade Center and turned Microsiga's IPO roadshow in the U.S.  into a plan B. 

It was time to regroup.

The Road to the IPO

Zé Rogerio, the rally navigator

When José Rogério Luiz, or Zé Rogerio, joined the company, Microsiga was beyond walking with its own two legs. It was running fast, selling close to R$100M (~US$34.4M adjusted for inflation from 2001 to 2023) yearly. However, it was difficult to improve margins at the same pace as the company grew.

At the beginning of the 2000s, Microsiga had a 10% EBITDA margin and was plateauing on a 5-10% yearly revenue growth. Domestic competitors such as Logocenter and RM Sistemas were challenging Microsiga's deals and growth was getting more and more challenging. Laércio was a man at a crossroads. It was time for a strategic change.

Already this far in the game, Laercio was proud of having built his business from nothing into a company with a respected name. The ERP market seemed to have achieved meaningful maturity and he was struggling to return to accelerated growth. He knew that he couldn't do it alone. He needed someone to help him lead it into the future—and luckily for him, he found Zé Rogério.

When Zé Rogério met Laércio for the first time, he could sense how much passion Laércio had for his business and how much potential it held. Although he had never worked with anyone like him before, he knew that together they could take Microsiga to new heights.

At the same time, Laércio knew that Zé Rogério wasn't just some lofty executive with a head full of big ideas — he seemed like someone Laércio could work with, grow alongside, and learn from. 

Before joining Microsiga, Zé spent a decade working as a banker at Citi and later made his name as the CFO of Grupo Antarctica during its merger with Brahma, two behemoths in the brewery industry. This transaction led to the creation of Ambev, which was the largest brewery in the world at the time and later morphed into AB InBev.

When Zé Rogério entered the company, he identified two core issues holding Microsiga back: the company's franchise system wasn't working as well as it could have been, and Laércio himself was too focused on the operation, with little time to take a step back and think of new growth levers for the company.

It was difficult for Laércio to take that step back and see the bigger picture. He was already running the company for 15+ years. He had to let a new driver take the wheel, while he was still stepping on the throttle. 

Laércio was proving some of his own medicine. It was the same medicine he had given Ernesto in the past, and it was an arduous task for him to take on. But it was time to step aside from strategy and open space to the rally navigator. 

Zé Rogério presented a transition plan to Laércio that would take Microsiga from R$100M in revenue (~$34.4M adjusted for inflation) and a 12% EBITDA margin to R$250M (~$86M adjusted for inflation) in revenue in 4 years, a 2.5x revenue growth and 2x margin increase to 24%. When compared to its domestic counterparts, RM and Logocenter, Microsiga was already one of the main players in the ERP market in the region.

It was just the beginning of the master plan to put Microsiga on the R$ 1 Billion (~$344M adjusted for inflation) revenue path. Their competitors could not fathom how strong the power of Microsiga's momentum was.

Besides the competitors mentioned before, there was one that Microsiga was particularly interested in overcoming, Datasul. Its principal was Paulo Caputo, a self-made developer who learned to code when he was only 15. Paulo graduated as a lawyer at the Faculdade de Direito de São Paulo, then built a career as a Corporate lawyer at Machado Meyer.

When Paulo joined Datasul as the CFO in 1999, his mission was to prepare for receiving funding. He never knew Microsiga would become the competitor it did a few years later. In one of his first meetings at Datasul, he asked a team member what "Microsiga" was, to which he got: "Don't worry, they're not a competitor."

Datasul was one of Laércio's rivals. He saw in this competitor not only his way to winning the Brazilian ERP market but a clear opportunity to go toe-to-toe with international software vendors such as the giants of the industry, SAP and Oracle. To one day achieve enough relevance to be compared with them, there was only one milestone that would do: reaching R$ 1 Billion (~$344M adjusted for inflation) in revenue.

Getting The Ducks in a Row

Zé Rogério and Laércio understood they had to consolidate the local ERP market. There were too many players competing for customers and this was terrible for everyone. The first idea from Zé Rogério was to make sure that these players were talking among themselves. To do so, they created the "Software Alliance". An association formed by the biggest four ERP players in Brazil – Microsiga, Datasul, Logocenter, and RM.

The group's ritual was simple: the heads of the 4 Alliance members were going to meet regularly at a hotel, coming one at a time, at different times not to raise any eyebrows, to talk about the development of the sector in Brazil. The last one to leave the meeting would pay the bill. That was always Laércio.

This was an incredible exercise for all involved. It was great to finally put everyone in a room to make sure they were all rowing to the same side and develop a fruitful ecosystem to offer technology for small and medium businesses. These regular meetings started creating a closer relationship between these brands and they were on a path to consolidate a fragmented market.

Laércio kept thinking after those meetings that he had to make things whole. The status quo wasn’t working anymore. He finally understood, together with Zé Rogério, that to reach their audacious revenue goal, they had to reorganize the whole industry. 

The rally navigator and the ambitious founder then had an Eureka moment. They had to start lining up the ducks. They concluded that there could only be one winner in the ERP market at that time. The Highlander's mission was to acquire the main competitors, but first, they needed to do some cap table cleaning. 

In February of 2005, TOTVS had its D-Day. What they did was a master play only fit for a select few. The kind of deal-making done is worthy of an award, if they gave one.

In 24 hours the company bought back the shares of Advent Internacional, acquired one of its main competitors, Logocenter, and welcomed a new investor. 

The first transaction that made Microsiga's revenue grow by 25%, was the purchase of Logocenter.

This set the ground for a new transaction that very day, which was getting funding from the Brazilian Development Bank (BNDES). They received R$ 40 million for 17,6% that day, making BNDES a shareholder of the company.

To buy back Advent International, Laércio had wisely set terms for future buyback since the day Advent joined their cap table. And so he did, buying their shares back, growing the company by 25% with a financial play only fit for a selected few while neutralizing an important competitor. 

Microsiga and Datasul would then fight for the 1st place in software sales in Brazil. To be there alone, one of these companies would have to acquire their competitor, RM. It was a similar challenge for both Datasul and Microsiga. Both of them had to raise capital through an IPO to finally acquire RM, but Microsiga had the jumpstart and started negotiating the acquisition pre-IPO. 

Laércio and Rodrigo Mascarenhas, founder of RM had many back and forths about the acquisition price but finally agreed on the number: R$ 164,8 million with another R$ 41,2 million depending on the accomplishment of some goals. It seemed a bit bitter to swallow for Rodrigo, but he was willing to do so. 

Microsiga had it in the bag. They could IPO and go after Datasul later with much more resources. They had the plan and the deal ready. 

On March 9, 2006, by the BM&F BOVESPA opening bell, Microsiga became TOTVS, which translates as "totality" in Latin, went public, and announced RM's acquisition, consolidating itself as the main software developer in Brazil. 

Amazed by the master play, 2 years later Datasul decided to sell to TOTVS, the company that in just 3 years went from R$173M in annual revenue to an astonishing R$734M  in 2004, growing 4x in this period and showing to everyone that TOTVS was the meaning of "everything".

PART II coming soon...

This is the second edition of a series called Giants of SaaS Latam, a series portraying the untold stories behind the most valuable SaaS companies in the region. When it comes to solving complex problems with astonishing elegance, Latam founders are able to build long-lasting, solid businesses.

TOTVS' story was made by Latam founders and executives who challenged an incipient market and helped inaugurate the internet in Brazil. Its story is now told by William Cordeiro and Leo Torres, authors of this essay.

William is the Managing Partner of SaaSholic, a venture capital firm that specializes in investing in early-stage Latin American SaaS companies, having raised $10M+ to invest in ambitious founders building category-defining businesses from idea to IPO and beyond. To date, SaaSholic is at their second fund, with a portfolio of 30 startups such as Conta Simples, Clicksign, Yooga, and Acquire.com

Leo is the author of I’m No Economist, a newsletter covering global investment trends. Despite not being financial advice (Take no advice from me. I’m No Economist) the newsletter is read by investment professionals across VC, PE, M&A, family offices, and tech entrepreneurs.

A special thanks to those who participated in telling untold stories for this essay – including investment analysts from renowned firms that preferred not to be mentioned.

In the startup world, "success" has long been measured by the ability to attract venture capital, secure large rounds of funding, and achieve high valuations.

I'm not here to say fundraising is meaningless for startups nor that it's a piece of cake to get done. The problem is that fundraising sucks all the oxygen from other more crucial aspects of business development.

Worshiping false goals

Securing a significant investment is typically a cause for celebration in the startup ecosystem. It's seen as a stamp of approval, a sign that the business is on the right track. This perception is amplified by media coverage and societal reverberation, placing fundraising on a pedestal.

And I'm not here to solely point fingers at founders and at the media. VCs are also a part of the problem and perversely incentivized. New funding rounds means that their portfolio gets marked up and they can show these numbers to their investors.

Myopia

When fundraising becomes the primary goal, there is a risk of misaligning incentives. Entrepreneurs might prioritize decisions that favor short-term gains in valuation over long-term business health. This misalignment can lead to unsustainable business models, a lack of focus on customer needs, and ultimately, a failure to create real value.

This glorification overlooks the fundamental objectives of entrepreneurship: solving problems for customers, creating value, and building sustainable businesses.

How we're taking action

Understanding the pitfalls of overemphasizing fundraising, we at SaaSholic have taken a significant step in our last Annual Investor Day. We've instituted a pivotal segment in our yearly event to acknowledge and congratulate the founders who are excelling in a crucial metric for any SaaS company: Annual Recurring Revenue (ARR) Growth.

Annually, we will be bringing these exemplary founders who've reached specific ARR milestones ($1M, $5M, $10M, $20M, $50M, and beyond) to the stage to share their growth stories in front of their portfolio peers and our LPs. We intend to realign everyone's focus on what truly matters: sustainable revenue growth and the long-term health of their businesses.

We believe this new tradition is a step in the right direction and hope it resonates beyond the SaaSholic walls. By celebrating real, sustainable business achievements, we set the best standard for what success means during the entrepreneurial journey.