What AI Has Done, What's Wrong With It, and What's Just Noise

01A fifty-year problem in biology, solved and given away

Proteins are the machines inside your body. They do everything. What a protein does depends entirely on the shape it folds into.

If you know the shape, you can design a drug that fits it, the way a key fits a lock. If you don't, you're guessing.

For fifty years, working out one protein's shape took a scientist years of lab work. Expensive equipment. Slow going.

In 2020, an AI system called AlphaFold cracked it.

Two years later the team published predicted shapes for over 200 million proteins, which is close to every protein anyone has ever found, and gave the whole thing away free.

More than 3 million researchers in over 190 countries have used it. Over a million of them are in low- and middle-income countries, places that could never afford the lab equipment and now have the answers anyway. More than 30% of the research built on it is aimed at understanding disease.

It won the Nobel Prize in Chemistry in 2024. That same year the Physics prize went to Hopfield and Hinton for the neural network foundations underneath it. Two AI Nobels in one year.

02It finds breast cancers that two radiologists missed

This one already ran, on real people.

Sweden did a proper randomized trial. Over 105,000 women getting routine mammograms. Half got the standard process, where two radiologists each read the scan. Half got a version where AI read it first and flagged anything suspicious.

AI-supported screening found 29% more cancers, with no increase in false alarms. It cut the radiologists' reading workload by 44%, which matters more than it sounds, because there aren't enough radiologists and there won't be.

Then the number that made me sit up. They followed the women afterward and counted interval cancers, the ones that surface between screenings because a screening missed them. Those are the dangerous ones. The AI group had 12% fewer of them, and the ones that did appear were less often the aggressive kind.

A randomized controlled trial with a hundred thousand real women, and it means some people are alive who wouldn't be.

03It beat the best weather system on earth

Weather forecasting has run for decades on giant physics simulations on supercomputers. The European system, ENS, is considered the world's best.

DeepMind built a model called GenCast and tested it head to head across 1,320 measures. Wind, temperature, pressure, storm paths, at different distances out.

GenCast was more accurate on 97.2% of them, and on 99.8% at lead times beyond 36 hours. It's strongest where it counts most: extreme weather and the tracks of tropical cyclones. Knowing three days earlier where a hurricane is going gets measured in lives.

It produces a 15-day forecast in about eight minutes on a single chip. The traditional method takes hours on a supercomputer. Published in Nature, with the numbers in the paper.

04At work, it helps beginners most

This part surprised me.

The assumption is that AI serves the people already at the top. The evidence points the other way.

In a study of 5,172 customer support workers, average productivity rose 14%. For the newest and least skilled, it rose 34%. The most experienced agents gained almost nothing, and on quality they slipped slightly.

The same pattern showed up in a writing study with 453 professionals and a consulting study with 758 people. Every time, the biggest gains went to whoever was struggling most.

Most technology widens the gap between the best and the rest. At the individual level, this one narrows it. If you care who benefits from a new tool, that's unusual and worth defending.

05Non-consensual intimate imagery

Now the part where I don't defend anything.

This belongs at the top of every list and it's almost never there.

Non-consensual intimate imagery, NCII in the research and the legislation, is exactly what it sounds like: intimate images of a real, identifiable person, produced and shared without their consent. The 2019 Sensity analysis found that 96% of deepfake videos online fell into that category, and every one it examined depicted a woman. A 2023 industry study put the figure at 98%. Different methods, different years, same answer.

Researchers and lawmakers have moved toward treating NCII as a form of abuse rather than a category of content, and the language matters, because "content" is what you moderate and abuse is what you prosecute.

Then fraud. Voice cloning got good enough that a few seconds of recorded speech produces a convincing copy. Criminals call parents impersonating their child in distress. They call finance departments impersonating an executive.

The law finally moved. The TAKE IT DOWN Act was signed in May 2025, making it a federal crime to publish this material, and since May 2026 platforms have had 48 hours to remove it once a victim files a valid report. The first conviction came in April 2026, when an Ohio man pleaded guilty to charges including cyberstalking and publishing digital forgeries, after using AI to target women and children. Around 30 states have passed laws specifically covering the AI-generated kind, on top of near-universal state laws on NCII generally.

Where I land. This is the strongest argument anyone can make against how AI is currently being deployed, and I don't have a rebuttal. I don't think one exists. What I'd add is that this isn't a mysterious force. Specific applications were built for it. Specific platforms hosted the output. Those are companies making decisions, and decisions can be regulated and prosecuted, which is finally starting to happen.

06Entry-level jobs are drying up

Stanford has been tracking this with payroll records from ADP covering millions of American workers, and the number keeps getting worse.

When they first published in August 2025, employment for 22-to-25-year-olds in the most AI-exposed jobs was down 13% relative to their peers in other fields. The August 2026 revision, using data through June, puts it at about 19%.

Two things make it convincing. Older workers in the same jobs are doing fine, so it isn't the economy and it isn't those industries dying. And almost nobody is getting fired. The drop is overwhelmingly jobs that never got created. Companies quietly stopped opening junior roles.

Where I land. No counterargument. If you're 23, applying for junior jobs, and it feels harder than it should, you're right, and the best data available agrees with you. Anyone telling you to just learn AI is skipping the part where somebody still has to hire you.

07A lot of it doesn't work

MIT looked at business AI projects and found roughly 95% produced no measurable financial return. S&P Global found 42% of companies abandoned most of their AI plans in 2025, up from 17% the year before.

And there's one study I keep coming back to. Sixteen experienced developers, real work, in code they knew well. Half with AI, half without. They predicted AI would make them 24% faster. They came out 19% slower. Afterward they still believed they'd been faster. They couldn't feel it.

Where I land. That's real and I take it seriously. It also sits next to several careful studies showing solid gains, so "AI doesn't work" is too strong. What's true is that it works on some things and not others, and people are bad at telling which is which. MIT's own conclusion about the failures is not what you'd expect from a report about failure: the problem is how companies implement it, not the technology.

08Energy, but only where you'd expect

Globally it's smaller than people think. All data centres on earth, not just AI ones, used about 1.5% of the world's electricity in 2024. AI is a fraction of that.

Growth is fast, and the IEA expects data centre demand to roughly double by 2030. The part nobody mentions is that energy used per AI task is falling at what the IEA calls a rate unprecedented in the history of energy. Total use still climbs, because demand grows faster than efficiency improves. But the guilt about your individual questions doesn't survive the math.

Where the worry is completely right is locally. These buildings cluster. The US alone accounts for about 45% of global data centre electricity use. In Ireland, data centres went from 5% of metered electricity in 2015 to 23% in 2025.

If you live next to one, your grid is straining and your bill is climbing, that is real, and quoting a global average at you is a useless answer.

Where I land. Two different arguments keep getting mashed together. Planetary carbon, no. Your local grid, absolutely yes.

09What it might be doing to how we think

The famous study came from MIT's Media Lab. People wrote essays with ChatGPT, with a search engine, or with nothing, while researchers watched their brain activity. The ChatGPT group showed the least engagement and, oddly, couldn't quote essays they'd finished minutes earlier.

Worth taking seriously. It also needs its caveats, and the lead author gives them herself. It hadn't been peer reviewed. There were 54 people, and only 18 finished the last session. And Nataliya Kosmyna has said plainly that they did not find brain rot and did not measure anyone's intelligence.

The finding nobody repeats is the useful one. People who did the thinking first and brought AI in afterward did better on memory and engagement. The order mattered more than the tool.

Where I land. Legitimate concern, thin evidence. I'd want a lot more before panicking or dismissing. The order thing I took personally, because it matches my own experience exactly.

10Five claims that keep circulating and aren't true

These are the ones that don't survive checking, and they crowd out everything above.

"AI is destroying jobs." Across all ages, employment in AI-exposed occupations has barely moved, and overall employment kept growing. The entry-level problem is real. The broad collapse is not happening. The exaggeration actively hurts, too: "AI is killing jobs" is a slogan nobody can act on, while "entry-level hiring is down 19% and no company has a plan for training juniors" is a real problem with a real shape, and it's the true one.

"Every question you ask uses a bottle of water." It traces back to one estimate, about an older model, in specific buildings, and the number swings wildly depending on where the building sits and how it's cooled. It got flattened into a per-question rule the original work never claimed. It also aims the guilt at you. You not asking a question doesn't change where a data centre gets built or what powers it. Those are decisions made by companies and utility regulators, and personal guilt is an effective way to make sure nobody looks at them.

"AI just glues together stuff it copied." This is wrong about how the thing works, and it matters, because the honest complaint is different. There's no folder of images inside a model. What's inside is a huge pile of numbers that got nudged, over and over, until the model got good at predicting what comes next. Nothing was stored to copy from later. Now, was it fair to use all that work without asking or paying? That's a real question and courts are working on it. But "it's copy-paste" isn't that argument. It's a claim about the machinery, and the machinery doesn't work that way. Making the wrong argument loudly is how you lose one you'd otherwise win.

"MIT proved AI makes you stupid." Fifty-four people. Eighteen in the final session. Not peer reviewed. And the author on record saying they found no such thing. If you're going to cite science at me, and you should, cite it the way you'd want your own cited.

"It's just autocomplete, so it can't do anything real." The description is roughly right. These systems predict what comes next, over and over. The conclusion doesn't follow. The same family of technology mapped every known protein, beat the world's best weather system, and found cancers two radiologists missed. "It's pattern matching" and "it can't do anything important" are two different claims, and the second keeps getting proven wrong while people keep saying it. I think it survives because it's comforting. It lets you skip the harder question, which is what it means that something this simple keeps working this well.

11Where that leaves me

Pro-AI. Specifically:

The wins are real, they already happened, and they're bigger than most people realize. Proteins. Cancer screening. Weather. And at work, the people it helps most are the ones with the least experience.

NCII is the worst thing on the list and belongs at the top, not buried under arguments about electricity.

The entry-level jobs problem is real, getting worse, and deserves more attention than it gets.

Most of the rest is either exaggerated or aimed at the wrong target.

Underneath all of it, the technology isn't really the variable. How it gets used is. Nobody is going to stop it advancing, there's too much money and national interest behind it. But how it ships, what's legal, who gets protected, who gets hired, those are all choices. Made by people. Over and over. Including by me, at my job, this week.

That's why I'm optimistic. Not because nothing bad happens. Because most of the bad things on this list came from decisions, and decisions can be made differently.

12If you're the one who's worried

I'm not going to tell you you're being dramatic. On the two biggest items you're right, and one of them got worse this month.

What I'd ask is that we argue about the specific thing.

Your strong version is much stronger than the version that circulates. "AI is bad" gets nobody anywhere. "Non-consensual intimate imagery is 96% of all deepfake video and platforms took years to act" is a claim that has already produced federal law and criminal convictions. So is "entry-level hiring is down 19%."

Use the strong version. I'll take it seriously, and so will the people who currently tune you out.

13What would change my mind

This is what I think in August 2026, based on what's published today.

Several of these numbers moved while I was writing. The Stanford jobs figure went from 13% to 19% across revisions of one paper in a year.

So I'll come back to this. If the productivity results stop holding up, if the developer slowdown study turns out to be the rule instead of the exception, if the pattern of AI helping beginners most flips, I'll write it down and say what it changed for me.

Holding a position no matter what the evidence says isn't conviction. It's just a personality.

Sources

AlphaFold: the 2024 Nobel Prize in Chemistry materials, the 2024 Nobel Prize in Physics for the neural network foundations, and Google DeepMind's five-year impact review for the usage figures.

Breast cancer screening: the MASAI trial, Lund University. Interval cancer, sensitivity and specificity in The Lancet (2026), with earlier results in The Lancet Oncology (2023) and The Lancet Digital Health (2025).

Weather: Price et al., Probabilistic weather forecasting with machine learning, Nature (December 2024), and DeepMind's GenCast writeup.

Work: Brynjolfsson, Li and Raymond, Generative AI at Work, Quarterly Journal of Economics (2025); Noy and Zhang in Science; Dell'Acqua et al. at Harvard Business School and BCG; METR's developer trial; MIT NANDA's GenAI Divide report; S&P Global.

Jobs: Brynjolfsson, Chandar and Chen, Canaries in the Coal Mine, Stanford Digital Economy Lab, August 2025 through the August 2026 revision.

Non-consensual intimate imagery: Sensity's 2019 State of Deepfakes analysis and later industry composition studies; the TAKE IT DOWN Act (Public Law 119-12, signed May 19, 2025); reporting on the first conviction (April 2026); state legislative trackers.

Energy: International Energy Agency, Energy and AI; Ireland's Central Statistics Office on data centre metered consumption.

Cognition: Kosmyna et al., Your Brain on ChatGPT, MIT Media Lab preprint, plus the author's own clarifications.

Anything wrong here is mine.