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Literally every time a major AI model fails a basic logic test, the response from the hype community is to say, “Just wait for the next trillion parameters.” This has become so tiresome. Large language models (LLMs) are essentially sophisticated autocomplete tools; they don’t actually understand anything—they just predict the most probable next word based on their training data.
It’s simply not feasible or ethical to solve complex reasoning problems by stacking more GPUs and hoping it stops them from hallucinating. Recent formal verification leaderboards show promising alternatives like Evidence-Based Models (EBMs) that can prove their logic, which is crucial for systems like aviation where failure could be catastrophic.
- The “just add more compute” argument has been a staple of AI hype but lacks substance when it comes to addressing fundamental limitations in how models operate.
- There’s an urgent need for AI systems that can mathematically verify their correctness before executing critical tasks, rather than relying on opaque and unreliable autoregressive models.
- The failure of major models to pass basic logic tests underscores the necessity for alternative architectures that provide verifiable reasoning capabilities.
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This HTML snippet encapsulates a brief editorial stance against the “just add more compute” argument, highlighting its shortcomings in addressing core AI limitations and advocating for more robust, verification-capable systems.
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