After running a survey, feed the raw results file and your original list of hypotheses into an AI model. It can perform an initial pass to validate or disprove each hypothesis, providing a confidence score and flagging the most interesting findings, which massively accelerates the analysis phase.

Large Language Models struggle with obvious, real-world facts because their training data (text) over-represents uncertain topics open to debate—the 'maybe sphere.' Bedrock, common-sense knowledge is rarely written down, leaving a significant gap in the AI's world model and creating a need for human oversight on obvious matters.

AI expert Andrej Karpathy suggests treating LLMs as simulators, not entities. Instead of asking, "What do you think?", ask, "What would a group of [relevant experts] say?". This elicits a wider range of simulated perspectives and avoids the biases inherent in forcing the LLM to adopt a single, artificial persona.

A UK startup has found that LLMs can generate accurate, simulated focus group discussions. By creating diverse digital personas, the AI reproduces the nuanced and often surprising feedback that typically requires expensive and slow in-person research, especially in politics.

Richard Sutton, author of "The Bitter Lesson," argues that today's LLMs are not truly "bitter lesson-pilled." Their reliance on finite, human-generated data introduces inherent biases and limitations, contrasting with systems that learn from scratch purely through computational scaling and environmental interaction.

Unlike deterministic search algorithms, LLMs have a "temperature" feature that introduces randomness. Instead of picking the most likely next word, it randomly chooses from a pool of likely options. This makes AI-generated search results inherently unpredictable and variable over time.

M&A Science's "intelligence hub" differentiates from generalist AI like ChatGPT by grounding answers in a closed ecosystem of 400+ expert interviews. It provides sourced, experiential intelligence rather than generic internet-scraped guesses, making it a reliable tool for high-stakes professional work.

While AI labs tout performance on standardized tests like math olympiads, these metrics often don't correlate with real-world usefulness or qualitative user experience. Users may prefer a model like Anthropic's Claude for its conversational style, a factor not measured by benchmarks.

General-purpose LLMs generate responses based on the average of vast datasets. When used for leadership advice, they risk promoting a 'median' or average leadership style. This not only stifles authenticity but can also reinforce historical biases present in the training data.