AI's Spotify Moment

When intelligence becomes a commodity, the model is not the product.

The air in late spring in Split is this perfect temperature where you don’t notice it. Sun high. Breeze off the Adriatic cutting the heat. Boats drifting in the harbour through the coworking window.

And I’m sitting here thinking about how the entire AI industry is about to have the rug pulled out from under it.

It won’t be regulation. It won’t be a breakthrough. The thing coming for AI is much more boring and much more inevitable. Economics.

Here is what I mean by “AI’s Spotify Moment.”

When Spotify launched, it didn’t invent streaming. It stepped into a market where the underlying product had been stripped of its value. A CD cost fifteen dollars. A stream cost fractions of a cent. The music itself became a commodity. So Spotify had to win on something else. Playlists. Discovery. Network effects. The wrapper became the product.

The AI industry is hitting that same curve. Faster than most people realise.

Every week there is a new model release. Llama 4. Mistral Large. Qwen 3. DeepSeek V4. Gemini 2.5 Pro. Claude Opus 4. They all claim to be the best at something. They are all within spitting distance of each other. And they are all getting cheaper at a rate consumer hardware has never seen.

Run the numbers. In 2023, getting GPT-4 class performance cost around $30 per million output tokens. As of mid-2026, that number has fallen below $2 for several providers. Claude Opus 4.7 charges $5 per million input tokens. GPT-5 Nano runs at $0.10. Some open models run on consumer GPUs at fractions of that. The cost curve is not linear. It is falling off a cliff.

When inference becomes that cheap, the barrier to entry becomes zero. Everyone has access to near-frontier intelligence. The differentiation vanishes.

This is where the grey market becomes instructive.

There is a thriving economy of API proxy services, aggregators, and arbitrageurs who sell AI access at a fraction of the official price. In a piece on the hidden cost of cheap AI tokens, Souk documented how these “transfer stations” route traffic through unofficial accounts, swap models without telling you, and harvest your data as the real profit centre. The price difference looks like a hack. The reality is your data being monetised in ways you cannot see. (That Cheap Claude API You Bought)

But here is the part that matters for this argument.

That grey market exists at all. It exists because AI inference is already being treated as a raw commodity. The same way grey market GPU dealers treat compute as fungible units. The proxy economy proves that the market has already decided AI output is interchangeable. It is a widget. You buy it by the token, by the prompt, by the request.

And when the market treats something as a widget, the only question is who can sell it cheapest.

That is the Spotify Moment arriving early. The official market hasn’t caught up yet. The shadow market already lives there.

The implications are uncomfortable for anyone building in this space.

The model companies themselves are in the toughest spot. They are burning enormous capital on training runs, infrastructure, and talent. They are differentiating on benchmarks the market is increasingly ignoring. Every quarter, their product becomes cheaper to replicate. Open models are closing the gap. Hosted providers are racing to the bottom on price. The investor narrative is shifting from “who has the best model” to “who has the best distribution.” That is a brutal place to be if your entire company is built on the first thing.

The winners in this environment are the bundlers. The platform companies that wrap multiple models behind a unified interface, add security, compliance, data governance, workflow tooling. The ones who sell peace of mind, not intelligence. Companies like Vercel, Databricks, Snowflake, the AWS/GCP/Azure trio. They don’t care if the underlying model is Claude or Gemini or Llama. They care that you stay on their platform. The model is the loss leader. The infrastructure is the margin.

The losers are the point solution AI companies. The ones who built a wrapper around GPT-4 and called it a product. When the underlying model is a commodity, a wrapper is a thin margin business. And thin margin businesses in a race-to-the-bottom market do not survive.

This is also the moment where the open model ecosystem becomes genuinely dangerous to the incumbents. The danger is not open models being better. It’s that they’re free. When a Llama 4 class model runs on a laptop, and Mistral Large runs on a mid-range server, the question shifts from “can I afford the API” to “why would I pay for the API at all.” That calculation is already happening in enterprises. The cost of self-hosting is dropping faster than the cost of API calls.

The smartest people I know in AI are not building better models. They are building better products on top of the models that already exist. They are betting the intelligence floor rises high enough that the differentiation moves to interface, memory, personalisation, reliability, tool integration. The thousand small things that make a product feel like it was built for you.

That is the real Spotify play. Streaming was the technology. Curation was the experience.

The models are going to zero. The value is going to everything around them.

So if you are building in AI right now, the question is not whether you can access the best model. You can. Everyone can. The question is what you build that makes people stay. What you build that makes the model invisible, and the experience undeniable.

The breeze coming through the window at Smartspace is still the same temperature. The boats still drift. Outside this window, an industry is changing faster than anyone is ready for.

The question is who will be ready anyway.