"Will AI take my job?" is probably the most-asked question about AI — and one of the worst-answered. Most headlines pick a lane: either your career is doomed, or nothing will change at all. Neither is true, and neither helps you decide what to do on Monday morning.

So we built something more useful.

Introducing: AI & Your Job — Exposure Check

It's a free interactive tool that asks you six quick questions and gives you a personalised AI exposure score from 1 to 10 — along with what that score actually means and, more importantly, what to do about it. It takes about two minutes.

Take the Exposure Check →

How it works

Your job title alone doesn't determine your exposure — what you actually do all day does. Two people with the same title can face very different futures.

The tool starts with a baseline score for your field, drawn from the ILO's 2026 global index of occupational exposure and SHRM's 2026 displacement-risk research. Then it adjusts that baseline based on your answers: how repeatable your work is, how much of your output is digital, how central human relationships are to your role, who carries accountability when things go wrong, and how much you already use AI.

No black box, no marketing quiz. Every score traces back to published research, and the sources are listed right on your results screen.

A high score is not a doom score

Here's the part most coverage misses: exposure is not the same as replacement. According to the Anthropic Economic Index (June 2026), 57% of workplace AI use augments human work rather than automating it away. High exposure often means your job is changing fastest — which also means the biggest opportunities are opening up for the people who adapt first. PwC's 2026 Global AI Jobs Barometer found that workers with AI skills now earn a 56% wage premium.

That's why your results don't stop at a number. Based on your field, the tool recommends specific upskilling paths — from agent building to AI governance — each with a concrete first step you can take this week, not vague advice to "learn AI."

Updated every week

AI research moves fast, so the tool does too. We review new studies and labour-market data weekly and update the scores, recommendations and sources — the "what's new" note at the top of the tool tells you what changed.

Try it

The Exposure Check is free for Cyla Research members — and membership is free too. Signing up also gets you our weekly newsletter on where AI is creating opportunities, not just risks.

Sign up free and take the check →

Cyla Research — A retired AI investor on the long buildup nobody noticed, the breakthrough that convinced him, and where the opportunity actually lives.

Michael spent his career in tech hardware, much of it in semiconductors. He rode the industry through the internet, consumer electronics, phones, and the mobile internet, and when he retired he used that background to catch the HBM memory boom — the chips that feed AI data centres. Now he invests in AI full time. I sat down with him for half an hour to ask the question most of us are quietly asking: is it too late to get in?

The setup nobody watched

His first move was to push back on the premise that AI arrived suddenly at all.

"People say 'now it's the AI era,' but this process started long ago," he told me. Twenty years back, computers could already read handwritten Chinese characters — badly, but they could. IBM built speech recognition that dazzled in the lab and flopped in the real world, defeated by accents and dialects. Then machines conquered chess, and later Go, the game he considers the highest form of human strategic play. "It's like a movie — there's always a setup. Things don't explode out of nowhere."

What changed with large language models, in his view, wasn't the intelligence. It was the door. The system that beat Go was a narrow tool most people could never touch — "most people can't play Go." Language is different. "If you solve a problem through something everyone can use, then everyone's in."

The moment he became a believer

He paid for his first AI subscription in early 2023 and treated it like a lab. The moment that converted him from curious user to investor came about a year later, when he sat with an Oxford PhD student and fed the model questions about the student's own cutting-edge research. The answers were startlingly current — the machine was operating near the frontier of a specialist field. But the deeper shift, he says, was long context: the model's ability to hold an entire conversation, twenty exchanges deep, and stay coherent. "It's as if there's an intelligence within language itself. We created language, but language seems to carry intelligence." Early models hallucinated and lost the thread. Then, gradually, they didn't. "That's when I felt AI had produced something like real intelligence."

One breakthrough a year

From there he sketched a timeline that doubles as an investing map. 2024 was the year of long context — suddenly everyone was summarising, drafting, reporting. 2025 was reasoning — models that could pull a hundred websites and think over them for a long stretch (his family used one to pick a hotel everyone actually liked). 2026 is agents — AI that carries out long, complex tasks through code. "When I first retired I tried to write AI programs and simply couldn't. Now it's no problem at all."

He's watched the research showing that roughly every seven months, the length of task AI can handle doubles. Will it continue? Here he reached for an analogy any parent recognises: a teenager growing ten centimetres a year. "This year ten, next year ten. But we all know a person can't keep growing forever. At some point it stops."

So is it too late?

Which brings us to the money question. Is AI priced in?

"A lot of AI stocks really are already priced in — no doubt about it," he said. His sharper claim: markets have largely absorbed the growth story through the end of 2027, because that's where most forecasts see the curve flattening. "How to invest for 2028 and beyond is genuinely challenging. There's no clear conclusion right now."

But he bristled at the phrase "chasing the top." "For a stock that's going to rise ten times, going up two or three times first is completely normal. A good one doesn't ten-x in a day — it zigzags." Waiting for a dip that never comes, he suggested, is its own way of losing.

His actual advice for ordinary people was strikingly un-flashy. Invest where you have an edge: if you spent years building generators, or working in semiconductors, or running any specific industry, you know things about how AI will hit that industry that outside investors don't. And never invest money you might need. "If you're investing money you'll need next month, that's the wrong money to invest." He flatly refused to name stocks or amounts.

Tractors, factories, and what comes next

On jobs, he was calmer than most commentary I read. Every tool has forced this migration — tractors emptied the fields and filled the factories; computers did it again. "You hand the machine what it can do, and humans go do what the machine can't." The catch is the condition attached: people need the ability to keep learning, to migrate with the tool. Those who can will be empowered. Some who can't may be replaced. "That's the challenge." He pointed to the new work already appearing — data labelling, data-centre construction, AI education — unglamorous, but real.

Seven hundred papers

And if he were thirty today, with no connections? "You absolutely have to get your hands on it. Buy a subscription, use the models, use the new features — start with the things related to you." Take whatever you already do — design, code, running a travel agency — and add AI's intelligence on top of it. Then he added the line I haven't stopped thinking about. In 2024, using AI as his reading partner, he worked through roughly 700 research papers. Seven hundred, in retirement. "You have to have volume first. Anything has to have volume before it yields anything."

The market may have priced in the next eighteen months. It hasn't priced in what you do with them.

Notes from Binance Blockchain Week, and why the most interesting story wasn't on the main stage.

I spent the first few days of December in Dubai, at Binance Blockchain Week. Picture the Coca-Cola Arena packed with something like five thousand people, a wall of LED screens, and the low roar of a few thousand conversations happening at once. I went in expecting the usual: price talk, token launches, people in branded hoodies handing out stickers. Some of that was there. But the thing that gripped me wasn't a coin or a chart. It was a story about a shipment of cars.

It came from a talk by YZi Labs, the investment firm that used to be called Binance Labs. The speaker's point was simple. We keep treating crypto as something that might be useful one day, when it's already quietly doing real work. They used the example of a company in Mexico that paid for a shipment of electric cars using crypto, simply because waiting on banks to wire dollars across the border was slower and more expensive.

I wrote that one down. This wasn't someone trading coins or chasing a pump. It was a real business, buying real goods from another country, reaching for crypto because the old way was a pain.

And once you see it, the logic is obvious. Sending money between countries the old-fashioned way is slow. A wire can take days, passes through a handful of middlemen who each take a cut, and if the two sides use different currencies, somebody pays again to swap them. On a big order, that adds up fast.

Crypto skips most of that, especially "stablecoins," which are just digital coins pegged to a normal currency like the dollar, so the value doesn't swing around. Money moves in seconds, costs almost nothing, and doesn't care what time the banks close. And this isn't theoretical: YZi Labs recently put $50 million into a company building exactly this kind of plumbing, with digital versions of the Mexican peso, the Brazilian real, and others. The car story isn't a fluke. It's the early shape of a pattern.

Here's where it got interesting for me, though. It happened on the floor, not the stage.

I spent a couple of hours just walking the booths and talking to people. After enough of those conversations, a theme jumped out: almost everyone is trying to glue AI and crypto together somehow. One team I talked to, builders in the "decentralized compute" corner who filled the AI-and-DePIN sessions that week, rents out spare computer power to run AI and gets paid for it in crypto, automatically. No invoices, no waiting thirty days. The machine does the work, the payment lands. They could barely sit still talking about it.

That's the part most people haven't clocked yet. If a company can pay another company in seconds, the next question writes itself: what about software paying software?

AI tools are starting to do things on their own, like booking, fetching data, or renting computing power. When they do, they have to pay. And it turns out they're terrible credit-card customers. Most of what an AI buys is tiny, a fraction of a cent, thousands of times an hour. A card charges roughly 30 cents a swipe plus a percentage, so paying a one-cent bill with a card costs more than the bill itself. Cards also have fraud checks and waiting periods that make sense for a human at a till but jam up a machine that needs to pay right now.

Crypto fixes both. The payment is instant, costs next to nothing, and there's no card company in the middle. There are already tools, one's called x402, that let an AI pay for something online by itself: ask for the data, send a few cents in stablecoins, get it, no human clicking "confirm." Coinbase built it and handed it to a neutral foundation; Google added support. Between mid-2025 and early 2026, AI software quietly settled an estimated $73 million in these little payments.

I'll be honest, because we're a research outfit and not a hype machine: the numbers here are messy. Some early figures were puffed up by fake activity, and the real volume, once you strip that out, is still small. It's early days. But the direction is hard to miss, and the biggest names in payments are already building for it.

So what does any of this mean if you're thinking about your career?

This is the part I want to stress on. The crypto job market as a whole has cooled off. But the work tied to payments has held up better than almost anything else. The companies building stablecoin systems, cross-border rails, and this new AI-payment plumbing are still hiring while flashier corners shrink. Banks now want people who understand both finance and blockchain. Compliance people, the ones who keep all this inside the rules, are genuinely in demand, because regulators are watching closely. And the pay shows it: experienced engineers in this space regularly clear well into six figures.

The bigger point is that you don't have to be a crypto trader to ride this. The growth is in the unglamorous, useful jobs like payments, security, compliance, and plain engineering, the kind that keep money moving safely. As more trade and more AI run on these rails, those skills only get more valuable.

Which is a funny place to end up. I flew to Dubai expecting noise about the next big coin. I came home thinking about a shipment of cars, a booth full of people who couldn't stop talking about AI, and a quiet, steady demand for the folks who'll build the payment systems underneath all of it. Crypto's real moment might not look like the headlines promised. It looks like infrastructure, and the people who keep it running.

Most of what you hear about AI lands in one of two camps: it's going to save us, or it's going to ruin everything. We started Cyla Research because the truth is messier and more interesting than either story, and because people deserve a clearer view than the headlines give them.

We're a non-profit news agency. Our job is to report on AI honestly: what it actually does, where it's already making life better, and the opportunities that get buried under the fear. We think a lot about the people feeling the most uncertain right now: workers watching their industries shift, and graduates trying to figure out where they fit. For them especially, we dig into the jobs AI is creating and the skills genuinely worth learning.

What makes our reporting different is where it comes from. We go to AI conferences around the world and talk directly to the people building this stuff, the kind of insight you won't find sitting at the top of a search result.

Everything we make is free: the website, the weekly newsletter, all of it. We just want you to face this moment informed, not afraid.