AI is growing up — and so are the bills

Greg Brockman, OpenAI's co-founder and chief scientist, told Axios the company is preparing for its first public offering. He didn't name a date or a price, but the fact that the person who actually runs the engineering side is talking about it means the wheels are moving. Brockman has been the quieter force behind OpenAI's growth — less media, more building — and his appearance on this story signals the company is shifting from private startup to public company.

The broader picture is what makes this one worth watching. OpenAI has been one of the biggest beneficiaries of the AI gold rush, raking in revenue from its chatbot and API while staying private. Now it's heading to the market, which means retail investors get a shot at buying a piece of the company. That's unusual for a tech IPO — most of the big winners have stayed private longer, or gone public through SPACs that nobody trusted. OpenAI's path to the stock market is being done the traditional way: a real IPO with real underwriters.

What's less clear is what the market will do with it. OpenAI is valuable, but it's also a company that has spent years arguing about whether it should be for-profit at all. The tension between its founding mission and its business model hasn't gone away. When you buy the stock, you're betting on the business, not the mission.

Why this matters for us: if OpenAI goes public, it becomes another big tech company traded on the open market — the kind of thing you might see in a 401(k) or a brokerage app — and the people who built it get a chance to cash out while the rest of us buy shares at whatever price the market sets.

OpenAI wired Cerebras $100 million for a 4.2% stake, just before the company drops its fastest AI chips — the CB1 — in the next few days. The deal was filed with the SEC on September 18, and the timing is the story. Cerebras has been building custom silicon for the past few years, betting on one massive wafer-scale chip instead of stacking dozens of smaller ones. It runs big models at a fraction of the power a GPU farm would eat.

The money was raised by Cerebras itself, so OpenAI bought into the company, not just its products. The SEC filing is a 13D, the kind that forces disclosure when an investor crosses the 5% threshold, and OpenAI sat just shy of that line. The deal is part of a broader trend: big model makers are quietly backing the hardware companies that keep them running. Cerebras has already shipped chips to dozens of customers, including a few large AI labs, and the CB1 is supposed to push the performance envelope even further.

Why this matters for us: when OpenAI is quietly funding the chip makers, the hardware is locked in before the rest of the market even knows what's coming — and that means the price of running AI is about to shift in ways only the insiders see coming.

Zhipu AI dropped GLM-5.3, an open-weight model with 800 billion parameters. The weights are public — anyone can download and run them. The model scores well on coding and reasoning benchmarks, and the company is positioning it as a competitor to the big closed models.

What makes this one worth knowing: open weights mean a shop with a beefy GPU can run it themselves, no API key, no per-token charges, no vendor lock-in. For a small team or a dev with a 4090, that's a different calculus than paying OpenAI or Anthropic per request. It also means the community can fine-tune it on local data — Spanish-language docs, regional legal stuff, internal codebases — without shipping it off to a cloud API.

Zhipu is a Beijing-based company, so the geopolitical angle is there. But the weights are out. The model will show up in Hugging Face, in tools like Ollama, in whatever you're already using. That's the real story — the gate between open and closed just got thinner.

Why this matters for us: open weights mean the tools aren't controlled by one company — our community can run these models on our own hardware, in our own languages, at our own pace.

A new paper from researchers at Baidu, Oxford, and others introduces Little Learner, a tiny LLM that weighs in at roughly 900MB — small enough to run on a low-end tablet or even a Raspberry Pi without an internet connection. The model is built on a 1.6 billion parameter architecture, pruned and quantized to fit in flash storage, and trained from scratch rather than distilled from a bigger model. That matters: distilled models carry the biases and blind spots of their parent, while this one learns its own patterns.

The team reports competitive results on MMLU and GSM8K — standard benchmarks for reasoning and knowledge — despite the size. The real test is deployment: running inference locally means no API bills, no latency spikes from a server farm, and no privacy leak when a kid types a question into a device they already own. For families and schools operating on thin budgets, this is the kind of thing that lets you put a capable model on a tablet and walk away.

Why this matters for us: los primos que estudian en casa con un iPad viejo no necesitan la nube — este modelo se lleva en la mochila.

Sean Goedecke lays out why the whole benchmarking circus is broken. Models are being trained to game the tests — memorizing the questions, not learning the skills. The result is a world where a model scores 90% on a benchmark but can't actually do what you need it to do. Meanwhile, the models you actually want — the ones that reason, plan, and handle edge cases — are getting buried because they don't optimize for the metrics everyone's chasing.

It's the same problem that hit every industry. You tell people to optimize for a number and they find the fastest path to that number. The number stops meaning anything. Benchmark scores have become a signal of how well a model was tuned for the test, not how useful it is. The people building real systems know this. The people selling hype don't care.

Why this matters for us: the models we depend on for work, for side businesses, for keeping the operation running — we need to pick them by what they actually do, not by whatever leaderboard the press is writing about.

Snowflake is retiring the bytes scanned metric as its pricing unit, moving to a compute-and-storage model instead. The change was announced this week and is rolling out in the coming months. It affects every Snowflake user — from solo data folks to Fortune 500 teams — and it changes how you think about query costs in a way the old model never did.

The bytes-scanned metric was always a blunt instrument. Queries that touched the same data in different ways could cost wildly different amounts, and it was hard to tell whether a slow query was expensive because it was doing real work or because it was reading too much cold data. The new model ties cost to what the warehouse actually spends — compute cycles plus the storage you hold. That's a more honest ledger. You still pay for the data you keep, but you're not punished for running a query that happens to read a lot of bytes in one shot.

There's a companion project worth a look: EmbrasureAI's spark-observability-skills, which brings similar observability to Spark workloads. If you're running Spark on Databricks or a self-hosted cluster, it gives you a way to see how much compute each job is burning. Snowflake's move makes the same point — you need visibility into what's actually costing you, not just how much data you touched.

Why this matters for us: The shift to compute-based pricing hits the small teams and side businesses hardest, since they're the ones who can't afford to leave warehouses idling or run queries that scan terabytes for no reason — visibility is the difference between a $40 bill and a $400 one.

OpenAI just filed for an IPO — and the numbers are staggering. It's raising $20 billion at a $150 billion valuation, making it one of the most valuable privately-held companies ever. The cap table reads like a tech who's who: Microsoft holds 49%, SoftBank 10%, and the founders collectively control about 10%.

That Microsoft stake is the real story. The cloud giant has poured billions into OpenAI's infrastructure and training runs, and now owns nearly half the company. It's the kind of vertical integration that could quietly lock in the AI race for years.

Why this matters for us: when one company controls both the model and the compute, the folks running the side hustle or the neighborhood shop get priced out of the tools they need to compete.