The Final Frontier of Intelligence

AI is not a bubble, LLMs are. Language is a lossy compression of reality. You can predict the next word better and better, but eventually you have extracted every bit of structure that exists in text, and there is no more text to read. The internet was a one-time windfall, and it is nearly spent. The scaling laws make it concrete: every 10x more compute buys less than the last 10x did.

Synthetic data does not save you. Distilling a model from its own distribution only recombines what is already there. LLMs and agents are enormously useful, which is why they are worth hundreds of billions, but they cannot generate genuinely new knowledge about the physical world. They are a finite resource dressed up as an infinite one.

The frontier is moving from language to reality.

Recursive Self-Improvement

The interesting question is what AI does once LLMs and agents become commodity infrastructure. It stops consuming human knowledge and starts producing it. Google DeepMind called this a new golden age of discovery, and it is already underway: in late 2025 the U.S. Department of Energy launched the Genesis Mission to put AI to work across all 17 of its National Laboratories.

The mechanism is a loop:

1. The AI proposes something new: a molecule, a material, a design, a configuration.
2. A simulator models how it would behave in the real world.
3. The result tells the AI how to improve its next proposal.
4. The best candidates are checked against reality, in the lab or at higher fidelity.
5. Those real results make the simulator itself more accurate.
6. Repeat.

Early versions of this already exist. The crux is the fifth step. The system is not just searching, it is improving the thing that does the searching. Each turn produces better data, better data produces a better model of reality, and a better model produces better proposals on the next turn.

This is recursive self-improvement. It applies to the system itself, not just its model of the world. By mid-2026 Claude was writing most of the code at Anthropic, and its engineers had shifted from writing software to choosing which experiments to run. The same loop that discovers a new material improves the system doing the discovering. That is intelligence.

LLMs are bounded by how much humanity has written down. This loop is bounded only by how fast it can check its ideas against reality. That is still a limit, but a vastly larger one, and it grows every time the loop runs.

The Value of Computing

An LLM is consumption: a user asks a question, gets an answer, and the value ends there. An agent is automation: it does the work, and as that work gets cheap, things become abundant. Recursive self-improvement is production: AGI, ASI, whatever the label, it produces knowledge that did not exist before. Each cycle can yield a new drug, a new material, a new process, with enormous and lasting downstream value.

The world already spends nearly $3 trillion a year on research and development. As that abundance spreads, the figure climbs, because the only scarcity left is new knowledge, and new knowledge is what this compute produces. Every serious discovery problem becomes a market for it, and those markets compound as each discovery opens the next.

The constraint is mostly hardware. The chips we run AI on were each optimized for one kind of work, and this is not it.

These chips sit on a spectrum from flexible to fixed. The FPGA is reconfigurable down to the wire, able to become almost any circuit, but slow and inefficient at heavy math. The GPU runs anything and wins for that reason, though its generality leaves most of the chip idle on any workload that is not a steady stream of large, regular operations, and nearly a third of GPU users run below 15% utilization. The TPU is built from big systolic arrays optimized for big matmuls, which is what AI currently is, at high throughput and low precision (bf16, fp8, int8), fast but rigid. The ASIC burns a single model paradigm into silicon, fastest of all and useless for anything else.

This is not about what intelligence must be. It is about what the frontier needs from its hardware today: three things, at the same time.

1. (Dense) linear algebra. GEMMs are the workhorse of neural networks, and linear algebra is not going anywhere.
2. Precision as a choice. Most AI training runs low, bf16 and fp8, but spiky loss landscapes need fp32 and scientific work needs fp64.
3. Irregularity. Intelligence is not one big monolithic model, it is sparse, conditional, and full of irregular memory access, closer to a brain than a dense matrix. DeepSeek V4-Pro has 1.6 trillion parameters and fires 49 billion of them per token.

No single point on the spectrum offers all three. The TPU and the ASIC are too constrained, fast at dense low-precision math and unable to do the rest. The GPU is too general, able to do all of it but wasting most of the silicon to get there. FPGAs are out of the question.

The bottleneck is not a missing idea or a lack of data. It is the hardware. The work needs all three, and the freedom to choose between them per problem: dense throughput, the full precision range, and irregular execution. That flexibility is the "lagom" range between a GPU and a TPU. Until the hardware catches up, the ceiling on what AI can discover, AGI included, is set by silicon, not ideas.

The Final Frontier of Intelligence

Get past the hardware and there is no ceiling on this. It is not bounded by how many researchers exist or how big the simulation market is today, but by how much is left to discover, which is effectively unlimited. Every problem solved opens new ones.

This is a self-improving loop. AI produces new knowledge, new knowledge produces better models, and better models produce deeper discoveries.

The question is not whether this becomes a trillion-dollar market. It is whether AI learns to make real discoveries. If it does, everything else follows. If it does not, none of this matters anyway.

We are building the machines that get us there. If that excites you, come build them with us: