Today's Overview
We're past the theoretical stage of AI compute constraints. Nvidia's B200 GPU rental prices grew 114% in six weeks-not because supply is shrinking, but because demand has accelerated beyond what the industry expected. Lightning AI reports forty of its customers are seeking 400,000 GPUs when they currently have 40,000. Microsoft now requires Blackwell customers to commit to at least 1,000 chips for a year and is cutting off smaller customers whose servers sit idle. The real risk, according to Azeem Azhar, isn't that we've invested too much in AI infrastructure. It's that we haven't invested nearly enough.
Where Physical AI Stops Being Sci-Fi
While the industry obsesses over humanoid demos, the actual traction in robotics is happening in less glamorous places: collaborative assembly cells, automated inspection systems, and mobile manipulation tasks that handle real variability. Physical AI isn't just about better models-it's about robots that learn from their environment instead of following scripted paths. But here's the problem: when your robot prototype works in the lab, scaling it to 10,000 units requires solving manufacturing, supply chain, and serviceability challenges that most AI-first companies underestimate. The robotics companies that will dominate the next decade are those treating hardware innovation with the same rigor as software.
One specific technical shift matters enormously for collaborative robots: latency. Cloud-based vision systems work fine for analytics, but when a cobot is working alongside a human, even 100-200 milliseconds of round-trip delay creates a 200-400mm blind spot at typical operating speeds. At that distance, safety zones get wider, speeds get slower, and throughput drops. The solution is moving inference to the edge-running AI directly at the workcell, not in the cloud-and connecting it straight to the robot controller, bypassing the legacy PLC entirely. This enables real deterministic latency below 30ms, which means cobots can adapt dynamically to human movement without sacrificing safety or productivity.
The Context Problem in Agentic Coding
A different kind of scaling challenge is emerging in AI-assisted development. Evolution Strategies (ES)-an old idea from the early deep learning era-is getting a serious second life for LLM fine-tuning. Instead of adjusting parameters through gradient descent, ES treats the model as a black box, creates perturbed copies in parallel, scores each one, and keeps only the changes that improve performance. The latest work shows ES can compete with standard reinforcement learning methods on billion-parameter models, and when you add low-rank perturbations (EGGROLL), it becomes efficient enough on GPUs to use at scale. This matters because ES doesn't need gradient access-useful when you're optimizing a model for complex, long-horizon tasks where credit assignment is messy.
But the deeper challenge in agentic coding is context, not compute. AI agents can now generate code quickly, but generating code that fits your team's patterns, your codebase conventions, and your past architectural decisions is a different problem. Generic RAG, bigger context windows, and MCP servers don't solve this-they just add more noise. The companies getting real value from coding agents are the ones building context engines: reasoning layers that curate organizational knowledge and deliver only what the agent actually needs for the task. Without this, agents generate code that works in isolation but requires long review cycles to align with reality.
The pattern across all three threads-compute shortages, physical AI scaling, edge robotics architecture, and agentic systems-is the same: the easy part is the algorithm. The hard part is making it work at the scale and context where it actually matters.