Yesterday's signals, distilled.

Yesterday's signals, distilled, A look back at March 24.

OpenAI killed a consumer video app to save compute for ChatGPT. Intel and AMD CPU supply tightened just as memory stayed constrained. Google Research pushed extreme compression to run frontier-scale behavior on cheaper hardware. Databricks turned SIEM into “just another lakehouse workload” and wired agents directly into security operations. Kleiner Perkins raised $3.5B to underwrite the next wave of AI-native companies.

The connective tissue isn’t “AI progress.” It’s a repricing of infrastructure constraints and a reshaping of who owns the margin stack.

Compute is no longer a background assumption, it’s the primary product manager. Capital is moving accordingly, from generalist venture into infra-heavy, workflow-heavy bets. And the software layer is quietly re-bundling around data gravity and agentic surfaces, not around traditional app categories.

If your 2026 plan assumes “more features, more models, more usage” without a view on where your compute, data, and security surfaces actually sit in this new stack, you’re not just exposed, you’re building on someone else’s bottleneck.

BLUF

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INFRASTRUCTURE / COMPUTE

Compute ceilings are now the real product roadmap

OpenAI killed its standalone Sora video app to conserve compute for ChatGPT growth, per Business Insider. The decision routes GPU budget toward the assistant and core APIs instead of a new consumer video surface.

The Bet: The assistant and API ecosystem will generate more durable revenue and data than a viral video app, and are worth hard tradeoffs in user-facing innovation.

So What? This is a public admission that the constraint on frontier AI isn’t demand or ideas, it’s GPU allocation. Product surface area is now gated by infra, not imagination. If OpenAI is triaging features, every downstream builder is implicitly exposed to the same ceiling: your vendor’s capacity planning, not your own roadmap.

The Risk: If you anchor your product on a single frontier provider, their internal prioritization can strand your features, rate limits, latency, or outright deprecation. And if you’re betting on video or other heavy modalities, you’re competing directly with your vendor’s flagship products for the same compute pool.

Action: • Map your dependency on any single model vendor, by feature, by SKU, by revenue, and quantify what happens if your effective capacity is c