📨 Weekly digest: 26 2025 | The pervasive misconception of what AI is
We are conflating "less wrong" with "reliably right"
👋🏻 Hello, legends, and welcome to the weekly digest for week 26 of 2025.
I want to challenge a pervasive misconception that's quietly undermining your strategic thinking around Artificial Intelligence. For too long, we've celebrated every incremental improvement in Large Language Models as "the LLM getting better." This comfortable narrative, while true on the surface, obscures a critical, and often unacknowledged, distinction that will define the winners and losers in the AI-driven future.
We are conflating "less wrong" with "reliably right." And for the decisions you make, the companies you build, and the capital you deploy, this distinction is not just nuanced; it is existential.
Consider this: there are tasks where an LLM becoming "less wrong" is genuinely valuable. Generating creative content, drafting initial summaries, brainstorming ideas – these are areas where a model moving from 50% accurate to 90% accurate is a measurable and welcome improvement. It's a spectrum, and we appreciate the climb.
However, there are tasks – often the most critical ones – where "less wrong" offers precisely zero value. For these binary, high-stakes decisions, there is no spectrum of "right." There is only right or wrong. A legal contract clause, a financial projection, a medical diagnosis, a critical line of code – these are not tasks where 99% accuracy is "better." One percent wrong in these scenarios isn't a slight imperfection; it's a catastrophic failure.
This is why, for your "Deep Research" – your market analysis, your competitive intelligence, your strategic due diligence – if you cannot absolutely rely on every single output, then its perceived "betterness" is a dangerous mirage. If you still need a human to painstakingly verify every fact, every inference, and every conclusion, you have gained speed at the cost of ultimate trust and, frankly, efficiency. You are not automating a core process; you are merely shifting the point of human intervention.
Stop chasing "better" in a generalized sense for all applications. Instead, rigorously identify your "reliably right" tasks.
For these mission-critical functions:
Demand absolute reliability: Don't be swayed by impressive benchmarks on aggregate data. Focus on provable, verifiable correctness for individual outputs. If an LLM cannot meet an exceptionally high bar for binary truth, then it is not a tool for automation in that domain; it is, at best, a glorified brainstorming partner.
Invest in validation, not just generation: Your investment should shift from solely perfecting generative capabilities to building robust, independent validation layers. How can you programmatically verify outputs or establish trust boundaries that quarantine unreliable information? This is where the real value lies for critical applications.
Recognize the actual cost of error: A model that is "wrong less often" still imposes a significant hidden cost: the human effort required to mitigate, correct, and recover from its errors. This cost often far outweighs the apparent efficiency gains.
For startup founders, this means focusing your innovation on solving the reliability problem for specific, high-value binary tasks, rather than broadly chasing generalized intelligence.
For decision-makers and board members, it means scrutinizing AI proposals through the lens of absolute trust, not just statistical improvement.
The future of AI for the enterprise is not just about intelligence; it's about trust. And trust, for the most critical decisions, is not a spectrum. It's binary. Ignore this at your peril.
Yael.
Yael on AI, my personal views
Chips, ontology, and the enterprise AI's future
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📨 Weekly digest: 25 2025 | AI: is it still playtime?
👋🏻 Hello, legends, and welcome to the weekly digest for week 25 of 2025.
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