piotrbukanski

VC @ Acurio Ventures | private views | lived in 🇵🇱🇬🇧🇸🇪🇩🇪🇱🇻

[piotrbukanski @piotrbukanski1]

@pitdesi Buen camino!

[Prompter @PromptLLM]

Crazy take from Claude

[piotrbukanski @piotrbukanski1]

@Nicolas_Keller @pawell Booster draft would be great!

[piotrbukanski @piotrbukanski1]

@pawell Yes for MTG! 👋🏻

[OpenAI @OpenAI]

GPT-5.4 helped drive a medicinal chemistry project from literature review to a validated experimental result.

Paired with https://t.co/gcDaph8b2B’s Maria AI and specialized lab, the model proposed an unexpected way to improve a widely used reaction in drug discovery.

[Midjourney @midjourney]

Announcing a new division of Midjourney called "Midjourney Medical"

[Midjourney @midjourney]

A technical dive inside our new "Midjourney Scanner"

[piotrbukanski @piotrbukanski1]

@SebJohnsonUK 🤭🤭

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[Nav Toor @heynavtoor]

You have noticed it. ChatGPT feels dumber than it used to. Your prompts that worked six months ago produce worse results now. The writing sounds flatter. The ideas sound safer. The internet itself feels like it is shrinking. Every article reads the same. Every email sounds the same. Every answer sounds like it was written by the same voice.

You thought it was you. It is not you.

Researchers at Oxford and Cambridge published a paper in Nature proving what is happening. They call it Model Collapse.

Here is the mechanism in one sentence. AI trained on AI-generated data gets dumber every generation until it forgets what real human data looked like.

The internet is filling with AI-generated content. Blog posts. Articles. Reviews. Comments. Social media. AI companies scrape the internet to train the next generation of models. Which means the next generation of AI is being trained on the output of the current generation.

Each cycle loses information. Not randomly. It loses the rarest, most unusual, most creative parts first. The researchers call these the "tails of the distribution." The weird ideas. The unexpected perspectives. The things that made the internet feel human. Those disappear first.

What remains is the average. The safe. The expected. The bland.

Then the next generation trains on that. And loses more. And the next generation trains on that. And loses more. The researchers proved this is not a slow decline. Major degradation happens within just a few iterations. Even when some of the original human data is preserved.

They tested it on large language models. On image generators. On statistical models. The pattern was the same every time. The output converges toward a narrow, flattened version of reality that looks nothing like the original data.

The lead researcher put it plainly. "Large language models are like fire. A useful tool. But one that pollutes the environment."

The pollution is invisible. You cannot see which sentence on the internet was written by a human and which was written by AI. Neither can the AI that is about to train on it. And once the tails are gone, they do not come back. The damage is irreversible.

This is not a prediction anymore. It is a diagnosis.

The internet you grew up on was built by humans writing things no algorithm would have written. Strange, personal, imperfect, alive. That internet is being diluted. One generation of AI at a time. And the models trained on what remains are learning a smaller and smaller version of the world.

Model Collapse is not a technical problem. It is a cultural one. The thing that made the internet worth reading is the thing that disappears first.

[Tommy @Shaughnessy119]

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening

- Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs

- For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval

- On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect

- This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API

I shared more on this here: https://t.co/iZrqrCAIRW

- And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed)

- Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1.

More on open source inference provider raises here: https://t.co/7kf56P44yQ

- And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th

- Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free

- This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models

- Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months

- So go forward, what happens?

- I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter)

- It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122%

- With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it)

- The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses

- Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more

- This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

[The Tennis Letter @TheTennisLetter]

Maja Chwalinska has earned more prize money at this year’s Roland Garros than she’s earned in her entire career.

Her total career earnings before Roland Garros:

$864,030.

What she’s earned at this tournament:

$870,000.

She will be the new world No. 30 when the new rankings come out.

She started Roland Garros as the world No. 114.

A life-changing journey that started with a dream from qualifying.

She took a 4 month break after a 2 year battle with depression due to the pressure & stress of playing professional tennis.

She didn’t know if she would ever return to this sport.

Everything she’s earned this tournament and throughout her entire career has been earned the hard way.

The fact that she’s where she is today is nothing short extraordinary. 🥹

🇵🇱❤️

[piotrbukanski @piotrbukanski1]

Honestly, it's so encouraging to hear and see! 🇵🇱 🤍 ♥️ Let's go Poland! Let's go Europe! 💙

[piotrbukanski @piotrbukanski1]

Spent today at the @ElevenLabs Warsaw Summit at Teatr Wielki. #Poland has a genuine breakout company. Not a regional success story, but a world-class AI lab reshaping voice technology. Like @villigm said, the same way Skype put Estonia on the map…

[piotrbukanski @piotrbukanski1]

… for a generation of builders, ElevenLabs is doing that for Poland now.

As a Pole living abroad, I see a real shift happening in how people talk about Poland. It's finally being associated with quality, ambition, growth, with global leaders, with clean and safe towns.

[piotrbukanski @piotrbukanski1]

🥹🇵🇱

[Mati Staniszewski @mati]

Homecoming. Surreal!

[piotrbukanski @piotrbukanski1]

@kirbyman01 Let’s go 🇵🇱❤️

[piotrbukanski @piotrbukanski1]

'The best companies have always known that people are not an input to the company, but rather are the company. But in AI, that truth becomes sharper because everything else is moving so fast.'

[Jaya Gupta @JayaGup10]

https://t.co/5vDrndcmFn

[piotrbukanski @piotrbukanski1]

“In a world where it’s cheap to build almost anything, the real edge is choosing what is actually worth building and staying with it long enough to learn something the market doesn’t know yet.”

[Aditya Agarwal @adityaag]

https://t.co/AoyPtuI9jx

[Peter Gostev @petergostev]

How do you know London is winning in AI?

Because we have AI data centres right in the middle of the city #londonmaxxing