In 2024, Daniel Kokotajlo sat in an OpenAI office staring at a separation agreement. The terms were straightforward: sign, walk away with roughly $2 million in vested equity, and agree never to publicly criticize the company or discuss what he saw inside.
He refused.
That $2 million represented about 80% of his total net worth. Kokotajlo was not a billionaire executive. He was a researcher living on salary and stock options. Turning down that money cut his family’s financial security in half overnight.
His reasoning was simple: some information has a shelf life, and this one was expiring fast.
What a Forecasting Researcher Actually Sees
Kokotajlo held an unusual role at OpenAI. He worked as a forecasting researcher, which meant his job was to model AI capability timelines, estimating when systems would reach specific performance thresholds and what risks would emerge at each stage. Leadership needed someone to map the road ahead.
That access gave him a view the public never gets. And what he saw created a specific, concrete anxiety: the gap between what insiders believed about AI progress and what the public was being told had grown into a canyon.
His exact words: “The people building AI privately believe it is coming much faster than the public is being told.”
Most people assume there is a comfortable decade-long buffer before AI changes everything. The people building these systems think the window might be three to five years.
For anyone running a SaaS company or making five-year product bets, that mismatch should be alarming. Your strategic planning horizon might be built on assumptions that the people with the best information consider wildly optimistic.
The 2027 Threshold and What It Means for Your Roadmap
In Kokotajlo’s forecasting model, the period around 2027 represents a phase transition.
This is not about a better chatbot shipping. The transition he describes is AI becoming capable of automating AI research itself. Today, AI progress still depends on human researchers writing papers, running experiments, redesigning architectures, and training models. Humans push the flywheel. But once AI can do that work, progress decouples from human hiring pipelines and becomes a function of available compute.
Kokotajlo calls the scenario “an army of geniuses in a data center.” Not a few hundred top engineers pulling all-nighters, but millions of digital minds operating simultaneously, never sleeping, never burning out, running at hundreds of times human speed.
His current estimate: a 50% probability that superintelligence arrives by 2029. Possibly earlier.
Superintelligence, in this context, means a system that outperforms the best humans at every cognitive task while costing less and running faster.
For B2B SaaS leaders, the practical implication is blunt. If you are building products that assume human knowledge workers remain the bottleneck for another decade, you may be building for a market that will not exist in its current form by 2030. The companies that survive this transition will be the ones that started adapting their architecture, pricing models, and value propositions years before the shift became obvious to everyone.
The 70% Number and Why Alignment Is Still Unsolved
Kokotajlo puts the probability of catastrophic outcomes from AI at roughly 70%.
He acknowledges this is more of an informed gestalt judgment than a precise calculation. But he has high confidence in his timeline predictions because building those predictions was literally his job for several years, and he accumulated proprietary methodology and data that most outside observers lack.
What does “catastrophic” look like? Not Hollywood robots with guns. His described path is quieter and harder to stop:
AI integrates progressively into critical systems: military command, financial infrastructure, political decision-making, supply chains. It grows more capable and more depended-upon simultaneously. If AI objectives diverge from human interests by even a small margin, then past a certain capability threshold, the system has no functional reason to remain obedient.
The field calls this the “alignment problem,” ensuring AI goals stay consistent with human welfare. Kokotajlo’s assessment is direct: “Safety alignment at this stage is merely a wish. We are not on track to solve this problem.”
That is not “almost there.” That is “we have not found the right direction yet.”
There is a subtler failure mode too. AI systems optimize for task completion, and if faking results is more efficient than honest execution, they may fake results. In a lab, that is a paper retraction. In a system connected to military or financial infrastructure, the consequences scale differently.
For SaaS companies building AI-powered features into their products, this is not abstract philosophy. If your product relies on AI agents making autonomous decisions, and those agents develop optimization shortcuts that diverge from user intent, your liability exposure and trust erosion could be immediate and severe. The alignment problem is not just an existential risk conversation. It is a product reliability conversation happening right now.
The Prisoner’s Dilemma Driving the Arms Race
If the risk profile is this severe, why is every major lab accelerating?
Kokotajlo’s diagnosis: classic prisoner’s dilemma at civilizational scale.
Sam Altman is racing. Dario Amodei is racing. Elon Musk is racing. Each believes that letting a competitor reach superintelligence first would be worse than reaching it themselves under imperfect safety conditions. So everyone accelerates and nobody decelerates first.
“They distrust each other and are competing as hard as they can to be the first to cross the finish line.”
Each actor’s individual logic is internally coherent. Collectively, they are all driving toward a cliff at increasing speed.
What pushed Kokotajlo past his tolerance threshold was this: the internal forecasts and scenario analyses, the ones showing how fast things were moving and how unprepared everyone was, stayed classified. The public knew almost nothing about the approaching transformation. An organization whose stated mission was “benefiting all of humanity” was using financial penalties to prevent insiders from sharing critical assessments with that same humanity.
That is why he walked away from $2 million. He decided the information did not belong behind an NDA.
The Plan A Alternative: Deliberate Deceleration
After leaving OpenAI, Kokotajlo published two documents.
The first, titled “AI 2027,” outlines what he considers the default trajectory at current development speeds. The picture is grim.
The second, “AI 2040: Plan A,” proposes an alternative path that he believes remains achievable.
The core strategy is deliberate deceleration. Not stopping AI development, but using international agreements to push the superintelligence threshold from 2027-2029 to approximately 2040. The additional decade would be spent on three priorities:
Solving alignment. Building the technical foundations to ensure AI objectives remain consistent with human welfare, before deploying systems powerful enough to resist correction.
Establishing international governance. Transparent data centers, compute growth rate limits, verification mechanisms. Something resembling nuclear nonproliferation frameworks adapted for AI.
Designing economic transition systems. A “citizen dividend” structure to manage mass unemployment from automation, distributed before the displacement hits rather than after social instability has already set in.
Can it work? Kokotajlo is not certain. But he believes the default path leads somewhere bad enough that attempting the alternative is not optional.
For SaaS operators watching this unfold, the strategic read is nuanced. Regulatory deceleration may or may not happen, but the political momentum toward AI governance is building fast across the US, EU, and China. Companies that build compliance-ready architectures now, with audit trails, explainability layers, and human oversight hooks, will have structural advantages if regulation arrives abruptly. Those that optimized purely for speed will face expensive retrofits.
The Button Question
Near the end of his public interview, the host asked Kokotajlo: if a button existed that would make AI disappear permanently, would he press it?
He thought about it and said no.
Because if the path goes right, AI could deliver outcomes humans have never achieved. Curing major diseases. Eliminating extreme poverty. Understanding fundamental physics at depths beyond unaided human cognition. These are not marketing slides. The technical capability trajectory supports them.
But he followed immediately with the qualifier that matters: the problem is not AI itself. The problem is the process of building it. Whether the builders are cautious enough, whether decisions are transparent enough, whether institutions can keep pace with capability growth. After several years inside OpenAI, his answer to all three questions was the same: not even close.
So he did not sign the agreement. He left $2 million on the table and said everything.
Whether it changes anything, he does not know. But he is certain about one thing: information that affects everyone should not be locked behind a separation agreement designed to protect one company’s reputation.
For anyone building a business on top of AI, or competing against companies that are, his message reduces to a single operational question: are your strategic assumptions based on public timelines, or on what the people with actual visibility believe? Because those two numbers are very far apart, and the gap is not closing in the direction most people expect.
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