The Singularity

Every modern business — regardless of industry, size, or sector — runs on expertise. Analyzing, deciding, drafting, advising, designing, inventing. For the entirety of human history, this kind of work could only be done by people. Every company, every institution, every market we operate in today was built around that fact. The cost of expertise shaped the price of nearly everything that depended on it: legal advice, medical care, financial analysis, strategic counsel, marketing, design, education, support. It is the assumption underneath every modern business.

That is no longer the case. AI systems are now performing expert-level work — at quality that meets or exceeds trained professionals, at a marginal cost approaching zero, across an expanding range of fields. This is not a forecast. It is the current state of affairs in 2026, and it is accelerating. Within the next five to ten years, AI will perform the majority of the work that today requires educated humans. Within twenty, it will perform nearly all of it, alongside humans and increasingly without them.

I, along with a small number of others, have been making this argument for several years. We are now in the period where the argument no longer needs to be made — it only needs to be acted on. Most of the world has not yet absorbed what this means. Most companies are responding to it tactically when the situation demands something architectural. Most leaders are evaluating tools when they should be reconsidering the foundations of their businesses.

Too much of the AI conversation is happening at the wrong altitude. It concerns which models to license, which platforms to integrate, which workflows to automate. These questions matter. But they obscure the more fundamental one: what is your business actually worth, and what is it actually for, in a world where the expertise that defined it can be generated on demand?

My view is that this is the most significant change in the structure of work in modern economic history — comparable in magnitude to the industrialization of physical labor, and arriving on a far shorter timeline. The companies that understand this clearly, and reorganize themselves around it deliberately, will define the next era. The ones that do not will spend the next decade being quietly outmaneuvered by competitors who did.

Why is this happening now?

Seven decades into the computing revolution, three decades into the consumer internet, and roughly a decade into the era of practical machine learning, the technology required to put expert-level capability into the hands of every business — at a price that makes it economically meaningful — finally works. Models that produce coherent, nuanced, professional-quality output across a wide range of fields are now widely available. The cost of running them is collapsing. The integration tooling is mature. And the rate of capability improvement, year over year, shows no sign of slowing — if anything, it is compounding.

The result is that AI is now eating expertise in every industry where expertise is the product. Which is, eventually, all of them.

A walk through the industries

Law. Contract review, due diligence, regulatory analysis, brief drafting, deposition summaries — work that once required armies of associates billing hundreds of hours is now being produced in minutes at meaningful quality. The pyramid of leverage that has defined large law firms for a century is beginning to invert. The remaining moat is judgment, relationships, and courtroom presence — not capacity.

Financial services. Equity research, deal memos, financial models, screening, due diligence — increasingly generated by AI systems with human oversight rather than human analysts with software assistance. Investment banks, asset managers, and research desks are being quietly rebuilt around a smaller number of more senior people supervising orders of magnitude more output.

Consulting and professional services. The entire industry was built on the scarcity of high-quality analytical thinking. That scarcity is now dissolving. The remaining defensible work is judgment, taste, executive trust, and access — not deck production or analysis. Firms that fail to recognize the difference are pricing themselves out of relevance.

Medicine. Diagnostic support, image analysis, clinical documentation, triage, and increasingly direct patient-facing consultation are being absorbed by AI systems already deployed at scale across major health systems. The economics of clinical practice are being rewritten under conditions most regulators have not yet caught up to.

Software engineering. The defining skill of the digital era is being remade by the same technology it built. AI now writes, reviews, refactors, and debugs code at expert levels across most common languages and frameworks. The role of the human engineer is shifting from author to architect — and the productivity differential between teams that have made that shift and teams that have not is no longer a margin. It is an order of magnitude.

Marketing, content, and design. Copy, imagery, video, ad variants, brand assets, campaign strategy — the entire creative production stack is being reorganized. The agencies that survive will be the ones that traded headcount for taste, and pricing models built on hours for pricing models built on outcomes.

Customer support and sales. The first form of expert work to fully industrialize. Voice agents, chat agents, and increasingly autonomous outbound systems now handle a meaningful percentage of inbound and outbound interactions across enterprise. Human agents are becoming a premium tier, not the default.

Education and training. Tutoring, curriculum design, assessment, and personalized instruction at a quality once available only to wealthy families with private tutors are now available, in early form, to anyone with an internet connection. The implications for everything from K–12 to corporate L&D to professional certification are profound and largely unmetabolized.

Research. Literature review, hypothesis generation, experimental design, and analysis — the scaffolding of every research-driven organization, public and private — is being rebuilt around AI tools that collapse weeks of work into hours. The science itself is moving faster as a consequence.

I could continue. Recruiting. Real estate. Architecture. Government. Journalism. Insurance underwriting. Compliance. Translation. Accounting. The list is not exceptional. It is the rule.

If your business produces decisions, analysis, advice, or design — and nearly every modern business does — your business is in the process of being restructured by this technology, whether or not you have noticed yet.

What this means for the businesses being built today

Viewed the right way, this is the largest opportunity of a generation. The companies that move now — that absorb AI deliberately into the core of how they operate, decide, build, and serve their customers — will compound advantages over the next decade that slower competitors will not close. Lower costs. Faster execution. New offerings that were previously not economically possible. Customer experiences no traditional company can match. Pricing power that flows directly from being five times more productive than the competition. The leaders who recognize this early, and act on it with discipline, will not merely outperform their peers. They will define what the next generation of category leaders looks like.

The companies that come out of this era stronger will not be the ones that adopted AI fastest, or spent the most on it, or wrote the loudest press releases about it. They will be the ones that asked the harder questions early. Where does AI create genuine, durable leverage in our business — and where does it merely create the illusion of progress? Which capabilities do we want to deepen as irreplaceable human strengths, and which are we comfortable treating as commodity infrastructure? What is the version of our company that is more valuable, not less, when expertise itself is available on demand?

These are not technology questions. They are strategy questions of the deepest kind. They will be answered, one way or another, by every leader making decisions today — consciously or by default. The cost of answering them poorly is not immediate. It is compounding. The companies that get this wrong will not collapse in a quarter. They will be slowly outpaced over five and ten years by competitors who understood the shift earlier and built around it.

There is a real difference between a company that uses AI and a company that has thought clearly about what AI means for what it is building. The first is now common. The second is rare. And the gap between the two is the gap that will define the next decade of business.

Why Foresight Labs exists

Foresight Labs was founded to close that gap. We are not an implementation shop, and we are not another consultancy attaching AI to a familiar playbook. We work with leaders who recognize that this moment requires more than a technology strategy — it requires a clearer account of what their business is becoming, and the discipline to build toward that future deliberately rather than react to it after the fact.

Our work sits at the intersection of strategy and execution. We help organizations identify where AI creates real, durable advantage; where it introduces hidden fragility; and how to develop the internal judgment required to keep adapting as the landscape continues to shift — because it will continue to shift, faster than most plans assume. We are as comfortable in a board conversation as we are in a working session with an engineering team. The two are no longer separable, and treating them as if they were is one of the more expensive mistakes a company can make in this environment.

The name reflects how we think about the work. Foresight is not prediction — no one can predict where any of this lands. It is the discipline of seeing the present clearly enough to act on it well. To understand which changes are durable and which are noise. To know the difference between a feature and a strategy. To build a company today whose decisions will still make sense five and ten years from now. That clarity is, in our view, the most valuable asset a leader can develop right now. It is also the rarest. And it is the work we have built our practice around.

We are at the start of one of the most consequential transitions in modern history. The companies that meet it with seriousness will own what comes next. I know where I’m betting.