Best of Philipp Lackner — April 2026

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   Video

   Philipp Lackner·6w

   3 Theoretical Limits of AI - These Things Can't Be Fixed

   A critical look at three fundamental, unfixable limitations of current LLM-based AI: (1) the learning ceiling problem — LLMs can't exceed the collective intelligence of their training data, especially as AI-generated content pollutes future training sets; (2) hallucination as an architectural inevitability — the same mechanism that enables creativity also produces confident incorrect outputs, and these can't be separated; (3) the frame problem — LLMs operate strictly within the context given to them and lack the ability to reframe a problem the way an experienced developer would. The author argues the truth lies between AI replacing developers and AI being useless, and that developers who understand these limits and use AI skillfully will gain a real productivity edge.

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2. 2
   Video

   Philipp Lackner·7w

   Is the cost of AI a dead end?

   AI companies like OpenAI, Anthropic, and big tech giants are burning massive amounts of capital in a race to build ever-larger models, with no clear path to profitability. However, the cost to achieve equivalent AI performance has dropped dramatically — GPT-3.5-level performance fell from $20 to $0.07 per million tokens in just two years, a 285x reduction. The argument is that cost alone won't burst the AI bubble; instead, growth will likely slow as training costs hit a ceiling, consolidating the market to two or three dominant players. The analogy to the dot-com bubble is explored: like the internet, AI's underlying business value is real and unlikely to disappear, but the hype cycle may cool into slower, steadier growth.

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