The layer that can route to the best AI model for the particular job is going to increase in value substantially. There are at least 3 big reasons: * Cost optimization: there are plenty of use cases where you need frontier intelligence for some tasks and something far cheaper for others. Even in the same task you may use frontier intelligence for planning and review of the work, but an OSS or cheaper model for the bulk of the workload. This is going to be standard across large buckets of work going forward. * Capability maximization: despite the bitter lesson and models generally getting better in the same direction, there are still lots of differences between models. Some are better at tool use, others better at coding, and others again better at certain domains of knowledge work. The ability to route between these at different times is a huge advantage. * Risk mitigation: while the Fable situation is somewhat of a black swan, it’s possible we’re heading toward a regulatory environment where governments may restrict models at different times based on their approval mechanisms or new things they discover. This means you’re going to want flexibility in being able to deploy workloads across different providers as a form of risk mitigation. Ultimately, it’s going to increasingly be a a strategic advantage for the applied AI layer that they can effectively route between models. Will be very interesting to see how this evolves.

And @romanchernin applaudes the @nebiusai customers #NebiusInflection26

Mastercard finally got those pesky humans out of the loop with $NBIS Impressive customer set across: Superlabs ($META and $MSFT) AI Labs AI Product Companies Digital Natives & Enterprises Learnings from Superlabs improving services for the long tail of customers

Jensen to $NBIS at inaugural Inflection event “You started with deep cloud engineering DNA, then rebuilt your platform for the AI era, scaling from one data center to gigawatt scale AI factories in just two years.” “The infrastructure must live where the demand lives.” Nebius’ message: shift from tokenmaxxing to valuemaxxing

Everyone's quoting Jevons now. A year ago you had to explain why cheaper tokens meant a bigger bill but now it's consensus. So let me push past the part everyone stops at, because the part Jevons doesn't cover is the one that matters: where the margin goes once the pie grows. Start with what got cheap: the model did -> Chinese open weights commoditised the single most expensive thing an inference provider owns. DeepSeek v4 reportedly codes within a hair of the frontier on SWE-bench at roughly 1/30th of the price, and a quite a few companies just moved all their traffic onto it and mentionned performance going up. When the scarce thing stops being scarce, its margin goes with it. At @nebiustf we work with customers who are already moving their production workloads onto these open models, exactly where the margin has now migrated (I see this every day) That's how you get the two facts everyone treats as a contradiction: - frontier-lab revenue is reportedly off the charts while their margins are reportedly deeply negative. Both at once. Revenue was never the problem, the model stopped being a moat, and you can't charge moat prices for a commodity when a cheap, frontier-grade open weight is one API call away. Here's the step past Jevons: - the extra tokens don't disappear, and neither does the money, but it stops pooling at the model. Usage compounds, the per token price keeps falling, and the value migrates to whatever part of the stack is still scarce. The model is the part that got abundant, the compute it runs on, the memory, the power, did not. That's the reframe, and it's the part almost nobody prices. Stop valuing AI labs on the strength of the next model. The model is the commodity now (altough not every single frontier model is identical yet, the hardest long horizon agentic tasks still favor some closed models, and we'll see what 5.6 and Mythos hold), but the gap has collapsed so far that open weights are now the practical default for the vast majority of production workloads. This is exactly the shift I've been writing about in my recent posts on inference. Cheaper tokens, more tokens, and a model that's no longer where the money is.

Token costs are becoming one of the hottest topics for any enterprise I talk with right now. It’s very bullish for AI in general because it means these systems are being used at a scale that wasn’t contemplated before. It also gives way to another form of differentiation that will emerge for the applied AI layer, which is model routing. As tokens take on a significant amount of the cost of any given workflow, then companies will inevitably want to ensure that their dollars go into the most efficient use of tokens for the particular job at hand. Frontier intelligence will always be relevant at the high end of tasks, like coding, legal and financial analysis, healthcare, and more. And dollars spent here will only go up over time. But, equally, you can peel off individual tasks to lower cost models (whether they’re from open weights vendors or the major labs) and deliver a more efficient end outcome. To do this effectively, the applied AI layer needs to understand the workflows in their domain better than anyone else, and be able to mix and match models to different jobs. If you’re doing document extraction, you need to know which models perform better or worse for any given document type. If you’re legal analysis, you want to know which models perform various types of tasks best. And so on. This will become one of the bigger differentiation points over time. The companies with the best evals, the best ability to route the workloads, and those that have business models directly aligned to customers financial goals, will be in a great position.

The massive bull case for $NBIS (Nebius) which most will not see coming : Enterprise ROI 🤯 Right now, enterprises using closed source behemoths (Claude Opus, GPT-5.5) for day to day automations / operations are the equivalent of dropping a F1 turbo engine into a Toyota Corolla to drive to work. It's a massive, expensive overkill. (1/4)

Being told from multiple desks this is a win win for $CRWV and $NBIS . Great American Artificial Intelligence Act of 2026 . Expected to formal by tomorrow politico.com/2026/06/04/ober…