very interesting an potentially influential view on AI and future
"14:39 LLM psychology: LLMs = "people spirits", stochastic simulations of people,where the simulator is an autoregressive Transformer.
Since they are trained on human data, they have a kind of emergent psychology, a
nd are simultaneously superhuman in some ways, but also fallible in many others.
Given this, how do we productively work with them hand in hand?"
Andrej Karpathy: Software Is Changing (Again) - YouTube
Karpathy vs. McKinsey: The Truth About AI Agents (Software 3.0) - YouTube
1. Software 3.0 Paradigm Shift: Andrej Karpathy argues the next “language” of coding is English, forcing teams to rethink every layer of software design—from data pipelines to deployment.2. LLMs as “People Spirits”: Large language models are stochastic simulations with jagged intelligence; they feel human but still need tight human supervision and constrained output.
3. Human-in-the-Loop by Design: Success hinges on making validation frictionless and deliberately limiting AI generation so reviewers can keep up.
4. Builder Honesty vs. Boardroom Hype: Karpathy names CI/CD gaps and edge-model limits, while McKinsey’s “agentic mesh” offers a seductive yet unbuildable fairy tale.
5. Enterprise AI Reality Check: Plug-and-play agents and tiny edge models don’t exist; incremental crawl-walk-run adoption with clear culture change is mandatory.
6. Risk of Consultant Oversimplification: CEO faith in word-salad frameworks stalls projects and wastes budgets—tech leaders must push for empirically grounded plans.
7. Edge Computing Debate: Despite bets from Apple and others, large centralized models still outperform small edge deployments in 2025; prudence beats hype.
"enterprise consulting speak" (useless)
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