Question 1

Are large language models the path to AGI, or an off-ramp?

Accepted Answer

_LLMs are powerful and useful, but scaling them may be a detour rather than the road to general intelligence._

> I don't think LLMs are AGI, nor that fully scaling up LLMs is going to get you to AGI. I sort of agree with Yann LeCun that on the path to AGI, LLMs are an off-ramp. I still think we can get there by 2029 or even sooner, because the pace of progress is just insane and there are a lot of other techniques now being tried at smaller scale.

Source: [Rethinking the AGI Race, with Benjamin Goertzel (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052826/)

Question 2

How are AI agents rewriting the internet and changing shopping?

Accepted Answer

_OpenAI and other LLM providers are becoming the dominant interface for online discovery._

> OpenAI's ambition to become the universal interface to what we knew as the Internet isn't surprising. If anything, they might be late. Remember Perplexity's 'Shop Like a Pro'? Their goal was to handle all of shopping — from research to payment.

Source: [AI Agents - The Internet Is Being Rewritten - Survey Users Vs Brands?](https://www.forbes.com/sites/lutzfinger/2025/07/24/ai-agentsthe-internet-is-being-rewrittensurvey-users-vs-brands/)

Question 3

Why are fine-tuned models on proprietary data the real moat, not another foundation model?

Accepted Answer

_The same transformer everyone has becomes a defensible asset only when trained on data nobody else holds._

> Cyriac uses the same transformer that is the basis for AI generally, but he trains it on data from his own wet lab that nobody else has, to get a fine-tuned model that can answer questions nobody else can. The future right now is really fine-tuned models trained on specific data nobody else has. There is a race on data and a race on fine-tuned models, and the same underlying idea is happening in every industry across the globe.

Source: [Tomorrow's Medicine, with Cyriac Roeding (eCornell Keynote)](https://ecornell.cornell.edu/keynotes/view/K052126/)

Question 4

Why does traffic from ChatGPT convert 9x better than traditional search?

Accepted Answer

_LLMs behave like trusted advisors, not search engines, changing how brands must optimize._

> Traffic from ChatGPT-style experiences converts up to nine times better than traditional search. Why? Because large language models behave more like trusted advisors than search engines.

Source: [Answer Engine Optimization — What Brands Need To Know](https://www.forbes.com/sites/lutzfinger/2025/06/19/answer-engine-optimization-aeo--what-brands-need-to-know/)

Question 5

Why is AI autocomplete not the same as understanding in healthcare?

Accepted Answer

_LLMs excel at sequence modeling but cannot replace human judgment in medicine._

> AI makes clinicians and scientists much more effective: better priors, better search, better filters, faster loops. Humans stay in the driver's seat because biology is noisy, evidence is incomplete, incentives are asymmetric, and accountability is real.

Source: [Healthcare's AI Lesson: Autocomplete Isn't Understanding](https://www.forbes.com/sites/lutzfinger/2026/01/16/healthcares-ai-lesson-autocomplete-isnt-understanding/)

Q&A, LLMs

Are large language models the path to AGI, or an off-ramp?

How are AI agents rewriting the internet and changing shopping?

Why are fine-tuned models on proprietary data the real moat, not another foundation model?

Why does traffic from ChatGPT convert 9x better than traditional search?

Why is AI autocomplete not the same as understanding in healthcare?