Two AI Stories You Might Have Missed — Including One Genuine Breakthrough
The AI industry is spending more compute on money-making products like browsers and short-form video than on pushing frontier intelligence. That dynamic has created a perception of slowdown. But beneath that surface, genuinely novel discoveries are happening. Here's two stories worth your attention.
A Small LLM Pushing Science Forward
While much of the industry chases the next flagship model, one relatively small language model is actually advancing biological science. It's called C2S Scale — based on Google's Gemma 2 architecture from over a year ago. This model generated a novel hypothesis for a cancer drug that wasn't in any literature.
The researchers trained it with reinforcement learning rewards for accurately predicting how cells would react to drugs, particularly regarding interferon. The goal: make cold tumors hot — detectable by the immune system. C2S Scale converts each cell's gene activity into sentences, essentially reading biology the way it reads text.
The model identified a drug candidate called Sil Mittertib that wasn't linked anywhere in literature for this capacity. Its in-vitro predictions were confirmed multiple times in laboratory settings. Human testing will take years — that's how medicine works — but the implications are significant: language models can generate genuinely new, testable scientific hypotheses.
This result provides a blueprint for a new kind of biological discovery. When people claim LLMs won't accelerate science, remember this story.
The Quest for an AGI Definition
A paper by several prominent AI researchers proposes the first conclusive definition of AGI using what they call the Cattle Horn Carroll theory — an empirically validated model of human cognition applied to artificial systems.
The resulting scores show GPT-4 at 27% and GPT-5 at 58%. But that doesn't mean GPT-6 or 7 would reach full AGI. The theory breaks cognition into ten discrete categories, each weighted equally at 10%, including general knowledge, reading ability, math competence, spot reasoning, working memory, long-term memory storage, visual processing, listening ability, and reaction time.
One category stands out as the most significant limitation: memory. Language models can't remember things beyond their conversation context. They don't continually learn on the job.
The authors write that without continual learning, AI systems suffer from amnesia, limiting their utility. Every bit of context adds cost to API calls, so providers deliberately limit how much context these models take in. Without more context, they make huge blunders because they simply don't understand the situation — and they won't remember it next time.
Without the ability to continually learn, AI systems suffer from amnesia, which limits their utility, forcing the AI to relearn context in every interaction.
This is fundamentally different from just adding more context. It's a question of whether we'll solve continual learning itself.
The OpenAI Quote That Reveals Why
Jerry Tuar, OpenAI's VP of Research, recently addressed this limitation directly. His interview revealed something crucial: current reinforcement learning happens during training runs, not in real-time with users in the loop. Some companies like Cursor are trying to train models online with users in the loop — theoretically possible with GPT and other products — but it's a dangerous path.
Tuar argued that without robust safeguards, this approach could enable all kinds of harmful training. Until we have strong safeguards, with something as complex as GPT would be reckless.
The real issue isn't benchmark performance — Gemini 2.5 Deep Think just broke records on Frontier Math, the hardest mathematics benchmark. It's not about who wins a benchmark today. The fundamental limitation is that AI systems can't learn continuously. They forget everything between conversations. That may soon change.
Bottom Line
The drug discovery story represents something genuinely new: an LLM generating novel, testable scientific hypotheses in biology. That's different from incremental benchmark improvements. But the AGI definition paper and OpenAI's quote reveal the real constraint holding back artificial general intelligence — not capability benchmarks but fundamental memory and continual learning problems that remain unsolved. Watch for solutions to those problems; that's where the next breakthrough will happen.