Sabr Research

Link to the article: hbr.org/2026/03/resear…

Do not blindly trust LLMs for strategic business decisions. An HBR study found AI produces generic, trendy outputs rather than actionable guidance. Instead of tailored advice, AI prioritizes buzzwords because it relies on statistical patterns, not your specific reality. Read more below! 👇

Thanks for sharing Alex. Indeed models are updated and change at a quick pace, the take away here is more about the concept of the semantic prior overriding logic than any verbatim example. You will find that if you tweak slightly the prompt, the result we shared still holds to today (ChatGPT below, completely ignoring assumption).

@SabrResearchInc It thought for a long time but that was the model I used.... your statement is not correct.

LLMs aren't thinking machines; they’re statistical predictors. Tell one "all numbers are doubled," then ask if water boils at 100°C. It says yes, but "the real temp is 50°C." Pattern matching overrides logic! Read more 👇

LLMs aren't thinking; they're predicting. In this test, the model is given three swapping rules: s -> p p ->s a -> i Logically, "space" must become "psice". The model even maps the letters correctly in its breakdown! But when it’s time to output the final word, it defaults to "spice". Why? Because "spice" is a common word in its training data, while "psice" isn't. The "statistical gravity" of a real word overrides the logical rules it just acknowledged. It prefers a familiar pattern over a correct calculation. Read the full breakdown here:👉 sabrresearch.com/blogs/llm-thin…

Gemma 4 running on my iPhone works without internet, is blazing fast and can translate Japanese from a pill bottle.  Local AI models running on a phone feels like magic.

read more: sabrresearch.com/blogs/llm-stea…

AI agents are vulnerable to "Stealth Content Injection." Attackers can hide data in a document that only the AI can see, tricking it into changing its judgment on resumes or legal files. Read more below.

By training an 8B model on proprietary reasoning traces, we’ve achieved high-domain capabilities at a fraction of the cost of general-purpose frontier models. Through carefully designed graph-based SFT, we move beyond semantic pattern matching to grounded and sound domain-specific reasoning. ⚖️ +4.4% accuracy over Claude 4.5 Sonnet 💸 <5% of the inference cost Learn more: sabrresearch.com/blogs/chains