I’m not a natural “doomsayer.” But unfortunately, part of my job as an AI security researcher is to consider the more unpleasant circumstances.

I’m like a mechanic scrambling last-minute checks before Apollo 13 takes off. If you request my take on the situation, I won’t talk about the quality of the in-flight entertainment, or explain how gorgeous the stars will appear from space.

I will tell you what could fail. That is what I mean to do in this story.

Now I should clarify what this is precisely. It’s not a prediction. I do not expect AI development to be this quick or as untamable as I represent. It’s not pure dream either.

It is my worst nightmare.

It’s a sampling from the futures that are amongst the most disastrous, and I think, disturbingly plausible [1] - the ones that the majority of keep me up during the night.

I’m informing this tale because the future is not set yet. I hope, with a bit of foresight, we can keep this story a fictional one.

Thanks to Daniel Kokotajlo, Thomas Larsen, and Ryan Greenblatt and others for conversations that influenced these stories. This post is composed in a personal capability.

Ripples before waves

The year is 2025 and the month is February. OpenEye just recently published a brand-new AI model they call U2. The product and the name are alike. Both are increments of the past. Both are not completely unexpected.

However, unlike OpenEye’s prior AI items, which lived inside packages of their chat windows, U2 can utilize a computer.

Some users discover it spooky to watch their web browser flash at irregular intervals and their mouse flick at inhuman speeds, as if there is a ghost at the keyboard. A portion of employees with form-filler tasks raise the eyebrows of their employers as they fly through work almost two times as quickly.

But by and large, U2 is still a specialized tool. To most who are taking note, it is a creature viewed through the glass boxes of X (or, if you do not like Elon, “Twitter”). Sometimes U2’s wacky habits trigger a chuckle. Sometimes, they cause an anxious scratch of the chin.

Meanwhile, researchers are drawing lines on plots, as researchers like to do. The researchers attempt to comprehend where AI development is going. They are like Svante Arrhenius, the Swedish Physicist who observed the levels of CO2 in the atmosphere were increasing in 1896. Like the clinical neighborhood in the time of Arrhenius, few experts comprehend the implications of these lines yet.

A pattern that is getting specific attention is self-governing ability. Drawing these standards out predicts that, by the end of 2026, AI representatives will accomplish in a few days what the very best software application engineering contractors might do in two weeks. In a year or 2, some state, AI agents might be able to automate 10% of remote workers.

Many are hesitant. If this were true, tech stocks would be soaring. It’s too huge of a splash, too rapidly.

But others see what doubters are calling ‘too huge a splash’ a simple ripple, and see a tidal bore on the horizon.

Cloudy with an opportunity of hyperbolic growth

Meanwhile, OpenEye is busy training U3. They utilize the very same simple dish that baked U2: Generate thousands of shows and mathematics problems. Let models “think” until they arrive at a response. Then enhance the traces of “believing” that cause A-grades.

This process is duplicated over and over, and when the flywheel gets begun, it begins to spin practically on its own. As U2 trains, it sculpts more tough and reasonable tasks from github repositories on the web. Models are finding out to train themselves. Long before AI agents might automate research, a steady sort of “self-improvement” had actually started.

Some engineers could still hardly believe this worked. It’s like a perpetual movement device. Certainly intelligence can’t emerge from a swimming pool of silicon and lightning, isolated from the real world other than through the exhaust of society spewed onto the internet.

And yet the benchmark numbers continue to climb day after day.

During many of 2024, these RL training runs cost around $1 million, sometimes $10 million. These runs were little more than exploratory. But by 2025, the researchers at OpenEye (and across the world) knew they had actually discovered the secret sauce. It was time to scale up.

Over the first half of 2025, $10 million RL training runs develop into $50 million runs, and then to $100 million runs. While U2 might do a bit of data munging and run small experiments, this brand-new model - the design scientists are calling U3 - is changing the daily lives of the technical staff.

U3 is like a blazing-fast intern, and engineers are learning how to wrangle its sleepless energy. Researchers flick through terminals, giving terse commands, like a CEO managing personnel over Slack channels.

By October 2025, U3 is composing nearly all of the code at OpenEye. Researchers are nearly never ever bottlenecked by application. More than ever, calculate is the lifeline of AI advancement, and the ‘bottleneck’ is deciding how to use it.

If instructed to, U3 can run experiments, however U3 does not have actually taste as fine-tuned as human researchers at OpenEye. It struggles to prioritize in between research study concepts, so human beings still choose where to bore into the huge fields of algorithms to mine efficiency enhancements.

But these researchers are working long hours to put themselves out of a task. They require AI agents that can believe ahead, so engineers train representatives to anticipate. They hold out training data before 2024, instructing models to contemplate for hours to predict occasions in 2025. Then, they use the same technique as in the past, distilling considering into an instinct. Forecasting capability is a broad foundation. The scientists build specialized ML research study abilities on top of it, training U3 to forecast the outcomes of every ML paper and ML experiment ever taped.

The technical staff at OpenEye are now surprised at how often U3’s guidance seems like their most skilled peers, or when it is opaque and alien (“train on random noise before programming”), and is nonetheless correct.

The incompetencies of U3 that congested the pipelines of research development are beginning to dissolve, and a fire-hose of optimizations is gushing out. Most experiments U3 runs are not asked for by a human now. They are totally autonomous, and OpenEye’s workers skim 1% of them, maybe less.

As the winter season months of December 2025 approach, clouds roll over San Francisco in the afternoons. Once-competitive-programmers gaze out their windows, with excitement, with worry, but usually, with confusion. Their world is spinning too quickly. It’s tough to know what to do, what to state, what to look at on the computer screen.

Storms are brewing in Washington too. Top personnel from the NSA and US cyber command collaborate with OpenEye to retrofit a semblance of security for U3’s weights before senior leaders in China, Russia, Israel, North Korea, or Iran recognize just how valuable OpenEye’s software has become.

And there’s a fact still unidentified to many of the world - aside from in the offices of OpenEye and corridors of the White House and the Pentagon. It’s a fact about those ‘straight lines’ people were talking about in early 2025. The lines are not straight any longer.

They are flexing upward.

Flip FLOP thinkers

In late 2025, U2.5 is launched. Commercial models are starting to level up in bigger increments again. Partly, this is due to the fact that progress is accelerating. Partly, it is due to the fact that the designs have actually become a liability to OpenEye.

If U1 explains how to cook meth or writes erotica, the audiences of X would be entertained or pretend to be worried. But U2.5 is another story. Releasing this design without safeguards would be like putting Ted Kaczynski through a PhD in how to make chemical weapons. It would resemble providing anyone with >$30K their own 200-person scam center.

So while U2.5 had long been baked, it needed a long time to cool. But in late 2025, OpenEye is prepared for a public release.

The CEO of OpenEye states, “We have attained AGI,” and while lots of people think he shifted the goalpost, the world is still pleased. U2.5 really is a drop-in replacement for some (20%) of understanding employees and a game-changing assistant for the majority of others.

A mantra has actually ended up being popular in Silicon Valley: “Adopt or die.” Tech startups that efficiently use U2.5 for their work are moving 2x faster, and their competitors know it.

The remainder of the world is beginning to capture on too. Increasingly more individuals raise the eyebrows of their managers with their stand-out efficiency. People know U2.5 is a huge offer. It is at least as big of a deal as the personal computer system transformation. But a lot of still do not see the tidal bore.

As individuals view their browsers flick in that spooky way, so inhumanly rapidly, they begin to have an anxious sensation. A sensation humankind had not had given that they had actually lived amongst the Homo Neanderthalensis. It is the deeply ingrained, primitive instinct that they are threatened by another species.

For lots of, this feeling rapidly fades as they start to utilize U2.5 more often. U2.5 is the most pleasant personality most know (even more likable than Claudius, Arthropodic’s lovable chatbot). You might alter its traits, ask it to crack jokes or tell you stories. Many fall for U2.5, as a friend or assistant, and some even as more than a pal.

But there is still this eerie sensation that the world is spinning so quickly, which maybe the descendants of this new creature would not be so docile.

Researchers inside OpenEye are considering the issue of offering AI systems safe motivations too, which they call “positioning. “

In truth, these scientists have seen how terribly misaligned U3 can be. Models sometimes tried to “hack” their reward signal. They would pretend to make progress on a research study question with an impressive-looking plot, but the plot would be fake. Then, when researchers provided opportunities to compromise the makers that calculated their score, they would seize these opportunities, doing whatever it took to make the number increase.

After a number of months, researchers at OpenEye straighten out this “reward hacking” kink, but some still worry they only had swept this problem under the carpet. Like a child in front of their moms and dads, U3 might be playing in addition to the OpenEye engineers, saying the best words and doing the best things. But when the back of the moms and dads are turned, possibly U3 would sneak candy from the candy container.

Unfortunately, OpenEye researchers have no concept if U3 has such intentions. While early versions of U2 “believed aloud” - they would stack words on top of each other to reason - “chain of idea” did not scale.

Chain of thought architectures subject AI models to a condition similar to the protagonist of the film Memento. Approximately every 15 minutes, the protagonist forgets his experience. He is forced to write notes for himself and tattoo his body in order to make progress toward his objectives.

AI representatives compose notes to themselves, however the notes start to stack up, [strikez.awardspace.info](http://strikez.awardspace.info/index.php?PHPSESSID=96aebea8dea36a19c963b97981909aad&action=profile