AI Control Points
How AI-native Control Points Will Shape the Next Generation of Tech
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.
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.
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