Meta's Muse AI agent is very powerful — but not enough to overcome my kids' annoying school apps
THE SO WHAT
Even a powerful agent with access to email, payments, and health data can’t paper over fragmented, badly designed workflows—AI is running into legacy UX and vendor silos. For operators, the near-term win is not ‘one agent to rule them all’ but targeted integrations where you control both the system and the surface.
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Applied AIMathematicians Hate AI. They Can’t Quit It
When mathematicians both fear and depend on AI, you’re seeing the same pattern that will hit every expert-heavy domain—epistemic authority shifts from pure human derivation to human–machine co-discovery. If your value prop is ‘trusted expertise,’ start defining how AI fits into your proof stack, not outside it.
Microsoft’s AI CEO says ‘controlling’ AI ‘is going to be a really, really big challenge’
When a major AI leader says control is a ‘really big challenge,’ governance risk just moved from academic debate into executive accountability. If you’re deploying frontier models, you now need a board-level narrative for how you’ll bound behavior, not just a roadmap of use cases.
Applied AIPay $39.99 once to put ChatGPT, Claude, Gemini, and more in a single workspace for life
A $39.99 lifetime ‘meta-client’ for 50+ models is a reminder that the UX and orchestration layer around LLMs is commoditizing fast. If your product is just a prettier front-end on public models, assume your pricing power is near zero and differentiate on workflow depth or proprietary data instead.
Applied AIOpenAI told investors in February its compute bill would be around $600bn. A July presentation puts it at $856bn.
An internal reset from ~$600bn to $856bn in projected compute and infra spend, even as expected free cash flow burn improves, shows how violently the capital stack is tilting toward hardware and power. If you’re planning to ride frontier models, assume your real constraint is access to subsidized compute, not just API availability.