AI Opinion & Analysis
Every few months the internet holds another referendum on artificial general intelligence. Are we two years away, ten years away, or is the whole idea a mirage? People with real credentials line up on both sides and yell past each other. It is a fun argument. It is also, for almost everyone actually using these tools, a waste of a good afternoon.
Nobody agrees what the word means
Start with the obvious problem: AGI has no agreed definition. Ask ten researchers and you get ten thresholds. Human-level performance on most economically valuable work. The ability to learn any task a person can. Something that can do original science. A system that beats experts across every benchmark we can invent. These are not variations on one idea. They are different ideas wearing the same three letters.
An argument where the central term means something different to each participant is not a disagreement about the world. It is a disagreement about vocabulary, and those never resolve, because there is no fact that could settle them. You can watch a debate run for an hour and realize at the end that one person was talking about job displacement and the other was talking about consciousness, and they both thought they were talking about the same thing.
When a target keeps moving, hitting it stops meaning anything. Systems now do things that would have counted as AGI to a researcher in 2015: hold a coherent conversation, pass the bar exam, write working code from a description. We did not throw a party. We shrugged and moved the line. That reflex tells you the concept is doing rhetorical work, not descriptive work.
The questions that actually pay rent
Here is what the AGI argument crowds out. Is this tool useful for the thing I need done? Is it reliable enough that I do not have to check every output by hand? Can I afford to run it at the scale I need? Those three questions decide whether AI helps you this quarter, and none of them require anyone to agree on when or whether the machines wake up.
They are also answerable. You can measure whether a coding assistant saves your team time. You can count how often a support bot hands back a wrong answer. You can read a pricing page and do arithmetic. These are boring, concrete, checkable things, which is exactly why they get less airtime than the cosmic stuff. Nobody writes a viral thread about "we ran it for three weeks and it was fine, mostly."
The gap between capability and reliability is where all the real action sits, and AGI talk skips right over it. A model that can ace a graduate exam can also state a made-up statute with total confidence. Both facts are true at once. The exciting frontier and the boring failure live in the same box, and if you are deploying anything, the boring failure is the part that ends up in your incident report.
Skeptical is not the same as cynical
I want to be careful here, because dismissing AGI talk can slide into dismissing AI, and that is not the point. These tools are genuinely useful. I use them every day and would be annoyed to lose them. The point is narrower: whether they eventually become general in some philosophical sense has almost no bearing on whether they are worth your money and attention right now.
Treat the far-future question as a hobby, not a strategy. It is fine to find it fascinating. Read the essays, argue at dinner, place your bets. Just do not let it stand in for the work of evaluating what is actually in front of you, because the two have almost nothing to do with each other. A team that spends its meetings debating timelines is a team not testing the tool on its own data.
The honest position is unsatisfying and I will say it anyway: I do not know when or whether AGI arrives, and neither does anyone selling you a strong opinion about it. What I do know is whether the model in front of me got the invoice numbers right, and that is the question that pays my bills. When someone wants to argue about superintelligence, I am happy to listen. Then I go back to checking whether the thing works, because that is the only argument with an answer I can use.
Every few weeks someone asks me if AI is a bubble, usually hoping I will pick a team. Yes or no, hype or real, tulips or telephones. I find the question a little boring, because the honest answer is that it is obviously both, and the useful conversation starts only after you accept that.
Open almost any app you used happily a year ago and there is a new little sparkle icon in the corner, promising an AI assistant you did not ask for and probably will not use. The bolt-on chatbot is the defining product decision of the year, and I think it is quietly making a lot of software worse. I say this as someone who loves the underlying technology.
Read more: What we lose when every product bolts on a chatbot
Read enough launch posts and you notice something strange: every new model is state of the art. All of them. Simultaneously. That cannot be true, and yet each chart is technically honest. Welcome to the benchmark wars, where nobody is exactly lying and almost everyone is being misled, gently, on purpose.
There is a huge industry teaching people how to use AI: prompt courses, tool roundups, productivity threads without end. Almost nobody teaches the skill that is quietly becoming more valuable than any of them: knowing when not to use it. In 2026, the people doing the best work are not the ones using AI the most. They are the ones who know when to close the tab.
Read more: The most useful AI skill in 2026 is knowing when to turn it off