This misses the fundamental point about how AI actually creates value.
What they're "discovering" 🧐
- LLMs struggle with highly complex reasoning tasks - There's a point where additional reasoning tokens don't help - Models can fail at exact computation and algorithmic thinking - Performance varies across different complexity levels
Why this isn't groundbreaking: (Duh!) 🤔
- We've known about LLM limitations since GPT-2 - The "reasoning collapse" at high complexity is well-documented - Any founder building practical AI solutions already accounts for these limitations - The paper focuses on edge cases rather than the 90% of use cases where AI excels
The real issue:
Academic researchers often miss the forest for the trees. Whilst they're nitpicking about perfect reasoning on abstract puzzles, millions of people are using AI daily to write better emails, analyse data, generate code, and solve real business problems.
Key flaws in the research approach:
- Obsession with abstract puzzle-solving ignores real-world applications - Assumes AI needs human-like reasoning to be valuable - Tests edge cases rather than the 90% of problems AI solves effectively
The speed reality:
As Neil Lawrence notes: humans share information at 2,000 bits per minute, machines at 600 billion bits per minute. That's 300 million times faster—like comparing walking pace to light speed.
We're not building AI to think like humans. We're building it to operate in its own computational realm.
☑️What's actually working: 📉Financial analysts: processing earnings in minutes vs hours 🎉Legal teams: reviewing contracts with unprecedented thoroughness 👩🏼🚒Engineers: debugging complex systems faster than ever 📈Sales teams: personalising outreach at impossible scale
At Synaptyx AI, our clients measure concrete outcomes: processing time, decision quality, time-to-market. None require flawless logical reasoning. All deliver transformational results. The academic fixation on AI's theoretical limitations ignores the commercial reality: imperfect AI solving real problems beats perfect AI that doesn't exist.
The commercial truth: 💸💰 Perfect reasoning isn't the goal. Augmented human capability is. The hybrid workforce isn't waiting for perfect reasoning. It's already delivering measurable value.