The Torch Has Passed
Anthropic filed for an IPO at a $965 billion valuation. OpenAI is preparing its own. Venture money that used to flow toward incremental improvement is now being placed on foundational bets. If you’ve been watching the AI industry long enough to remember when these were scrappy research labs with no clear revenue path, the scale of what’s happening now is genuinely difficult to process.
Most of the coverage treats this as a market story. It isn’t, or at least not primarily. What the valuation numbers signal is something more fundamental: a generational shift in which companies get to define the technological infrastructure everyone else builds on. That kind of shift has happened before — most recently when Google, Amazon, Facebook, and Apple consolidated the internet layer — and the companies that emerged from it didn’t just win markets. They set the terms for every business that came after them. We’re at the same kind of inflection with AI, and it’s worth being clear about what that means and what it doesn’t.
A New Class of Foundational Companies
The torch has passed. Anthropic and OpenAI represent the first genuinely new class of foundational technology companies in roughly two decades. The models they’re building sit beneath everything else in ways that parallel what the database, the operating system, and the browser did in their eras. That’s real. But the old guard — Microsoft, Google, Apple, Amazon — still controls something the new companies are only beginning to build: existing software relationships with virtually every enterprise on the planet. Google has bundled Gemini into every Workspace plan at no extra charge. Microsoft is folding Copilot permanently into Microsoft 365 bundles. Incumbent AI doesn’t have to win a procurement battle — it arrives as a line item in a contract enterprises already signed.
What makes this complicated is that the incumbents know they can’t build frontier capability on their own, and their responses reveal how differently each one has positioned itself. Microsoft bet early and heavily on OpenAI — Copilot runs on OpenAI’s models, which means one of the most widely deployed enterprise AI products in the world depends on a company Microsoft no longer has exclusive rights to. Google built Gemini in-house and is genuinely competitive at the capability level. Apple is the starkest case: a company controlling a billion devices spent years promising Apple Intelligence features that didn’t work, then signed a $1 billion-a-year deal with Google to fill the gap. Controlling the device layer didn’t solve the capability problem. Distribution gets you into every enterprise’s workflow. It doesn’t close the gap. The innovator’s dilemma isn’t theoretical anymore — it’s playing out in product announcements and earnings calls in real time.
The Moat Isn’t the Model
For senior leaders, the practical implication of all this is not “which foundation model should we use.” That question is less important than it sounds, and I want to be direct about why.
Today’s frontier models can handle tasks that would have required substantial teams of specialists just three years ago. But past a certain threshold, raw capability yields diminishing marginal utility for actual enterprise work. A well-designed agent system using mid-tier models handles the vast majority of knowledge work at high quality. I run Compound on a combination of models, and the routing decisions — when to reach for frontier capability versus when a smaller, faster, cheaper model is sufficient — matter far more than the top-line quality of any single model. The race to build the most capable model is a different business from the race to build enterprise value. The real moat is orchestration, institutional knowledge, integrations, and the surrounding system — not the frontier model itself. Open source will keep eroding the raw capability gap for everything below the frontier, which actually accelerates the urgency for frontier labs to compete on surrounding value rather than raw benchmarks. Take that argument to its logical conclusion and you arrive at a question most senior leaders are actively avoiding.
What Nobody Says Plainly
Everyone understands that AI will affect knowledge work. Very few people will say plainly what the economics have already made clear. So I will.
Except for on rare occasions, it no longer makes sense for me to hire someone to do knowledge work. My team of agents works to my exact specifications — faster and often better than a human, always available, and improving with every task. Research that used to require a junior analyst now runs as a background task. Strategic documents that would have required a senior hire get produced by an orchestrated sequence of specialists I’ve refined over months. Software development, data science, competitive intelligence, financial analysis — these run as assigned work, not as prompts to a chat window. I am one person doing the work that used to require a team. That is not a prediction about the future. It is a description of how I work today.
I want to be precise about what I’m not claiming. I’m not saying my situation generalizes without modification to every business — the complexity of industries, regulatory environments, and the nature of knowledge work varies in ways that matter. And I’m genuinely uncertain about what the new categories of human work look like. Most of the specific jobs that will emerge in that world don’t exist yet. I can’t name them with confidence, and I won’t pretend to. What I can say is that the transition is already underway, its pace is faster than most institutional decision-making can respond to, and society is not prepared for what’s coming.
The managers who are avoiding this reckoning are not making the conservative choice. They’re making the decision to stay exposed while the situation changes around them. The distinction between building toward this and waiting matters enormously — not because of abstract competitive positioning, but because the decisions that compound over the next two years will determine whether your organization’s AI capability is something you built or something that happened to you.
The question for any leader today is not whether AI will affect how knowledge work gets done — that’s settled. The question is whether you’re making the decisions now that will determine how your organization navigates that transition. Most leaders are waiting for enough certainty to act. But what they don’t realize is that by the time it arrives, the options will have narrowed significantly.
