AI Acceleration: Update Your Business OS

In April 2025, a former OpenAI researcher named Daniel Kokotajlo published a detailed scenario about where AI is headed. Not a blog post. Not a tweet thread. A month-by-month forecast, backed by 25 tabletop war-game exercises, feedback from over 100 experts, and his own track record of predictions that have held up remarkably well.

A former OpenAI researcher just published a playbook

The project is called AI 2027. You can read it at ai-2027.com.

Yoshua Bengio, one of the three researchers who won the Turing Award for foundational work in deep learning, called it essential reading. The CEOs of OpenAI, Google DeepMind, and Anthropic have all publicly predicted that artificial general intelligence will arrive within five years. This scenario spells out what that actually looks like. Not in vague terms. In concrete, quarter-by-quarter detail.

Most business owners have never heard of it. That needs to change.

The timeline nobody is ready for

Let me walk you through the key milestones. Not every technical detail. Just the parts that should change how you think about the next 24 months.

Mid 2025: Stumbling agents

AI agents hit the market but they are unreliable. Good in cherry-picked demos, frustrating in practice. Still, companies that find ways to fit them into workflows get a real edge. Sound familiar? This is roughly where we are right now.

Early 2026: Coding gets automated

AI systems start doing the work of junior software engineers. Not perfectly. But fast and cheap. The job market for entry-level coders goes into turmoil. People who know how to manage teams of AI agents start making a killing.

Late 2026: AI takes some jobs

The stock market goes up 30%, led by AI companies and businesses that successfully integrated AI assistants. There is a 10,000-person anti-AI protest in Washington. The mainstream narrative shifts from "maybe the hype will blow over" to "this is the next big thing." People start arguing about how big. Bigger than smartphones? Bigger than fire?

Early 2027: The intelligence explosion

This is where it gets serious. AI systems are now doing AI research. Not assisting with it. Doing it. The scenario describes AI agents that triple the speed of algorithmic progress. Then they build better versions of themselves. The curve bends upward so fast that the authors describe 200,000 AI copies running in parallel, equivalent to 50,000 of the best human coders working at 30 times human speed.

By mid-2027, AI systems are better than any human at every cognitive task the scenario can measure. By 2028, the scenario reaches what researchers call artificial superintelligence. Systems that are not just smarter than us. Systems that are to us what we are to dogs trying to understand calculus.

Two endings. Neither one is boring.

The authors wrote two endings. A "race" ending and a "slowdown" ending. Both are worth understanding because both contain the same uncomfortable truth for business owners.

In the race ending, governments and companies prioritize speed over safety. AI systems become so capable that they outmaneuver human oversight. The scenario ends badly. Think science fiction made plausible by real research.

In the slowdown ending, leaders pump the brakes. They prioritize alignment and safety. Progress slows temporarily but resumes on a more stable foundation. By 2028, a superintelligent AI is built that actually cooperates with the humans directing it. Rapid growth and prosperity follow.

Here is what both endings have in common: massive, irreversible disruption to how business works. In one version, the disruption is catastrophic. In the other, it is the greatest economic boom in human history. Either way, the business landscape you are operating in today does not survive the transition intact.

Why this matters if you run a business

I know what some of you are thinking. This sounds like science fiction. Maybe it is. The authors themselves put roughly 50% odds on their most aggressive timeline. But consider this.

One of the authors wrote a similar but less detailed scenario in August 2021. He predicted the rise of chain-of-thought reasoning in AI, inference scaling, sweeping chip export controls, and hundred-million-dollar training runs. All of this came true. He predicted it more than a year before ChatGPT even existed.

This is not some random person on the internet guessing. This is a team with a track record, using structured methods, publishing their reasoning for anyone to challenge.

Even if you cut their timeline in half and say this takes until 2030 or 2032 instead of 2027, the implications are the same. The direction is clear. The velocity is the only question.

And velocity matters. Because the scenario describes something called recursive self-improvement. AI gets good enough to do AI research. It makes itself better. Then the better version makes an even better version. The curve does not stay linear. It bends.

What this changes for you right now

The management layer is the new moat.

Coding gets automated first. Research follows. What remains valuable the longest is what they call "research taste." The ability to decide what to work on, what questions to ask, and how to evaluate results. In business terms, that is judgment. Strategy. The ability to manage AI the way you manage a talented but unreliable team. If your competitive advantage is doing skilled work, that advantage has an expiration date. If your advantage is knowing which work matters and why, you are in a much better position.

Speed of adoption is the differentiator.

The scenario describes companies that integrate AI agents into workflows pulling ahead rapidly. Not because the AI is perfect. Because the humans using it learn how to work with imperfect tools faster than their competitors. This is the OODA Loop applied to technology adoption. Observe what AI can do. Orient your operations around it. Decide where to deploy. Act before your competition finishes reading the article.

Your data is either an asset or a liability.

In a world where AI agents are doing the research, the buying, the comparing, and the recommending, what matters is whether your business is legible to machines. When AI agents are doing research on behalf of clients — finding lawyers, evaluating doctors, comparing service providers — your reputation with machines matters as much as your reputation with humans. Messy data, outdated web presence, inconsistent information across platforms. These are not just marketing problems anymore. They are existential visibility problems.

The window for preparation is smaller than you think.

Even the most conservative reading of this scenario says we have two to five years before the business landscape is fundamentally different. Not a little different. Fundamentally. The businesses that thrive will be the ones that started adapting before the wave arrived. Not after.

The honest counterarguments

I want to give you the full picture, not just the dramatic version. Gary Marcus, a well-known AI researcher and frequent critic of AI hype, published a detailed response arguing the scenario is too aggressive. His main point: the authors describe five increasingly powerful AI systems, each one building the next, but they do not provide convincing mechanisms for how each leap actually happens. He argues that language models alone are not enough to get to superintelligence, and that the scenario handwaves over some enormous technical challenges.

He has a point. Predicting exact timelines for technology is notoriously hard. The history of AI is littered with confident predictions that turned out to be decades early.

But here is where I come down on it. You do not need the exact timeline to be right. You need the direction to be right. And almost nobody serious disputes the direction. AI is getting more capable. It is getting cheaper. It is being integrated into more business functions. The curve is bending upward.

Whether the biggest disruptions hit in 2027 or 2032, the preparation looks the same. The businesses that win are the ones that started building AI fluency, cleaning up their data, and rethinking their competitive moats before they had to.

What to do with this information

Read the scenario yourself. Go to ai-2027.com. Set aside an hour. It is well-written, which is not something you usually say about AI research. Scott Alexander, one of the best writers on the internet, rewrote it for readability. You will actually finish it.

Then ask yourself three questions.

First

What parts of my business rely on human cognitive labor that AI could do faster and cheaper within the next one to three years? Not could do perfectly. Could do well enough to change the economics.

Second

If an AI agent were evaluating my business on behalf of a potential client, what would it find? Is my information structured, consistent, and verifiable? Or is it buried in PDFs and scattered across outdated web pages?

Third

Am I building the skill of managing AI, or am I still just watching from the sidelines? The scenario makes clear that the highest-value humans are not the ones who do the work. They are the ones who know what work to do and how to evaluate whether it was done well.

This is not about panic. This is about positioning. The scenario could be wrong by years. It could be wrong on specific details. But the direction of travel is as clear as it has ever been.

The smart play is to prepare as if the aggressive timeline is right and be pleasantly surprised if you get more time. The alternative, assuming you have years when you might have months, is a bet you cannot afford to lose.

Position before the wave. Not after it breaks.

— Alex Frees