Autonomous Experts and the Next S-Curve: Rethinking Where Value Will Accrue
Last evening, I had the opportunity to dine with a small group of corporate venture and innovation leaders (thanks to Shahid Azim and C10 Labs) to discuss a question that is becoming increasingly urgent:
Where should corporate venture invest as AI moves from copilots to autonomous operators?
I've outlined my key thoughts and takeaways in the attached writeup and welcome perspectives from others thinking about this transition; particularly how you're seeing autonomy reshape investment strategy and enterprise innovation.
Where should corporate venture invest as AI moves from copilots to autonomous operators?
Last evening, I had the opportunity to join a small group of corporate venture and innovation leaders to discuss a question that is becoming increasingly urgent:
Where will value accrue as AI systems move from copilots to autonomous operators?
Having spent the better part of my career across corporate venture (Microsoft), public-private investment (MassCEC), university ecosystems (KAUST), and large-scale innovation platforms (NEOM), I've seen multiple technology cycles unfold. What feels different now is not just the pace of AI innovation-but the nature of the shift itself.
From Tools to Autonomous Operators
For decades, enterprise software has been built on a simple premise: humans use tools to make decisions. We are now entering a phase where software increasingly makes and executes decisions. This transition-from tool-centric to autonomous or agentic systems is not incremental. It fundamentally changes how value is created, captured, and sustained.
Where Value Will (and Won't) Accrue
Much of the current conversation is centered on foundation models. While clearly important, history suggests that infrastructure layers tend to commoditize over time.
The more durable control points are likely to emerge higher up the stack:
Orchestration & autonomy frameworks: Systems that manage reasoning, coordination, and execution across tasks
Domain-specific expert systems: AI deeply embedded in vertical workflows, powered by proprietary data
In other words: Models will be essential, but WORKFLOWS will be the MOAT.
The Corporate Venture Advantage
Corporate venture groups are uniquely positioned in this transition, but only if they play to their strengths. Unlike traditional VCs, corporates sit on three critical assets:
Proprietary data
Distribution and customer access
Real-world deployment environments
The implication is clear: The edge in corporate venture is not just identifying innovation it is deploying it at scale within real systems.
This requires a shift from passive investing to active integration and co-development.
A New Model for Enterprise Innovation
AI-native companies are emerging with fundamentally different economics:
Smaller teams
Faster iteration cycles
Tighter integration between product, data, and operations
For incumbents, incremental AI adoption within existing workflows will not be sufficient.
We are moving toward a model where:
Innovation is distributed, not centralized
Product, operations, and AI are deeply intertwined
Corporates must decide when to build, partner, or acquire autonomy capabilities
The winners will not necessarily be the best builders-but the best integrators of autonomous systems.
The Underestimated Constraint: Trust
One theme that deserves more attention is not technological, but institutional.
As systems become more autonomous, questions around:
Accountability
Liability
Explainability
become central to adoption.
In many industries, trust-not capability-will be the gating factor.
This creates an entirely new layer of opportunity around governance, monitoring, and control systems.
A Broader Perspective
If I synthesize the discussion, this transition can be understood across three dimensions:
Technological: From tools to autonomous operators
Economic: Value shifting from infrastructure to orchestration and vertical systems
Organizational: Enterprises evolving from process-centric to autonomy-enabled architectures
Final Thought
The question is no longer simply: "Where should we invest in AI?"
But rather: "Where does autonomy intersect with real-world systems in a way that creates durable control and defensibility?"
That is where the next generation of venture-scale outcomes-and strategic advantage-will emerge.
I'd welcome perspectives from others thinking about this transition, particularly how you're seeing autonomy reshape investment strategy and enterprise innovation.
This thesis was originally published on LinkedIn. Join the discussion, add your thoughts, and follow for regular updates.
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