The last few weeks in the AI world, in one hour. The parts that actually matter for your business.
The goal is not to keep up. It's to know which wave is worth catching.
We'll take the ones that matter for you.
AGI, short for Artificial General Intelligence, means AI that's as good as a person at almost any task, not just one narrow thing. Today's AI is a brilliant specialist. AGI would be a capable generalist.
The whole field is stuck on one question: are we heading there, and how fast? Two camps, next.
In late July, Sam Altman said we're "now, in the singularity." And the progress curve really does look almost exponential.
Serious scientists (Yann LeCun, Gary Marcus) say today's methods won't reach human level. The gains are shrinking.
Nobody actually knows. The skill isn't picking a side. It's judging it yourself, from the benchmarks, not the hype.
Last autumn a top AI engineer called agents "slop, a decade away." Two months later: "I've never felt this behind. The profession is being refactored."
Andrej Karpathy, who built AI at Tesla and OpenAI. When the skeptics turn, that's the news.
China shipped its fast, cheap model this week and cut prices again. The whole market follows.
Top quality for about a third of the old flagship price, with a big memory for long tasks. This is what runs under Claude Code.
Reportedly cut the price of its cheapest tier hard, after using AI to make itself more efficient.
The pattern, every few weeks: better and cheaper. You benefit without lifting a finger.
The top model, summarising 100 customer reviews: about 7 cents.
You won't count cents, though. For you it's a flat subscription: about €20 a month, or €100 for the top model all day.
The models keep getting cheaper. Your bill barely moves.
An unreleased OpenAI model, Astra, reportedly solved ten problems mathematicians were stuck on for years. For about $2,000.
OpenAI calls Astra its next major model, and Sam Altman demoed it in Washington. A prediction market gives it about a 56% chance of launching within a month.
You can't use it yet. But "the next big one" might be weeks away, not years. Take it with a grain of salt.
Tools like ChatGPT Work and Notion's agents do this now.
The AI industry is spending about $600 billion on data centers this year. Every year, they need to earn that back.
And it's not all hype: OpenAI and Anthropic together are on track for over $200 billion a year. Demand is so high that a four-year-old AI chip now costs more to rent than when it was new.
The whole industry is betting the house. You get the upside without the bill.
Describe a booking page or a quiz, get a real working site. Now on free accounts too.
Gemini builds a full, on-brand deck, or edits images and diagrams, right inside Slides and Docs.
Now shows images from your own files in its answers, and lets you pick GPT-5.6 or Claude inside Office.
Now voice-controlled on every screen. Ask it about stock, let it draft the email.
You don't need new tools. The ones you already have just got smarter.
You stay in charge, and now you also say clearly when it's AI.
New models every week. Cheaper every month. Altman in Washington talking "singularity." It's a lot of noise.
The real question underneath: when a new model lands, what can it actually do for your business, and what can't it? To answer that, you need to know how we even measure AI.
what an AI drew: "a pelican on a bicycle"
One playful test: ask every model to draw a pelican on a bicycle, in code. Same task each time, so you can watch them get better.
The serious ones (MMLU, SWE-bench) are mostly maths and coding, though. A model can ace those and still not do your job.
Topping a maths test isn't the same as running a business. So how good is it at that?
the best model, on a month of real product & strategy work ("AA Briefcase")
Aces the tests it can practise on. Lost on a real job. (A $500 vending machine? It makes $10k; a sharp human, $60k.)
Nobody hand-codes the answers. Like a dog with a treat, the AI chases a reward and repeats whatever earns it.
That's reinforcement learning. The catch: it can only learn this way when the answer can be checked, right or wrong.
A maths answer is verifiable: you can check it instantly, right or wrong. So the AI solves millions of maths problems, gets scored each time, and gets brilliant at it.
What's checkable, it masters. What's fuzzy and human, it doesn't. That gap is the whole story.
AI gets reliably good only at the clear, checkable parts. So the real skill is yours: break your work into those parts, hand them off, and keep the judgment.
That's why building good skills and learning to articulate your work clearly matter so much. You're not letting AI do your job. You're handing it the right pieces.
You stay the boss. You just get very good at explaining the task.
Not a question. A finished thing.
See you Friday