Simona Cristea

As I mentioned before, I am now sharing an example from GPT-5.5 Pro, also featured by OpenAI, that really left me stunned by what it is capable of in biomedical science. (full report on the website I created with Codex, link in the thread). To push GPT-5.5 Pro hard, I uploaded a real data set of immune subset (T cells) gene-expression spreadsheet: 62 sorted T cell samples, 27,906 gene columns, and millions of underlying data points across different T cell subsets. Importantly, this public dataset also had paired structure making it possible to separate true cell-state biology from donor-to-donor variation. I asked GPT-5.5 Pro not merely to summarize the spreadsheet, but to analyze it deeply: What can we learn from this dataset? What are the mechanistic insights? What are the most important biological questions that emerge? What follow-up experiments should we do next? It thought for about 100 minutes and produced a roughly 40-page report! What amazed me was not just the length or even the initial analysis, since previous models are also capable of doing this. What amazed me was the quality of the reasoning and insights it provided! The report recognized that this was not just a table of genes, but two overlapping experimental designs. It identified the major biological axis, which in plain language was that the cells were not just “different categories.” They formed a coherent differentiation landscape, moving from future potential toward immediate function. It also understood the caveats. It did not overclaim from bulk gene-expression data. It clearly explained that bulk transcriptomics cannot distinguish whether every cell in a sorted population has shifted or whether a smaller subpopulation is dominating the signal. It recommended the right next steps experiments, and integration with donor metadata. This is what made the report feel so special to me. It was not just doing statistics. It was reasoning like an expert systems immunologist. It saw the structure of the experiment, interpreted the patterns, built a mechanistic model, identified limitations, proposed causal hypotheses, and laid out a translational roadmap. Other advanced models have been able to generate excellent biomedical reports before, including previous GPT-5 models. So I don't want to claim this is an entirely new type of capability. But this one felt different in an important way. It had more scientific elegance, more restraint, more biological intuition, and more of the nuanced judgment that usually comes only from years of hands-on experience in the field. It felt like this AI model had crossed another threshold. This is the kind of analysis that could easily take a research team months to perform, refine, interpret, and write up. Even then, many teams might not produce something this integrated, this mechanistically coherent, and this useful as a launchpad for future experiments. I know a 40-page T-cell gene-expression analysis may not be exciting to everyone. To illustrate how good it is, also had Codex built a web site with it anyone can explore, link below. 😊 Those interested can go deeper into the report. I also wanted this example on the record because, because to me, it is evidence that we are entering a new stage in AI-assisted biomedical science. The important point is no longer that AI can "analyze data and write a report.” The important point is that AI can now help transform complex biological data into mechanistic understanding, experimental priorities, and testable hypotheses at a speed and depth that would have been almost unimaginable a short time ago. For biomedical science, this is a very big deal! Of course, this may vary across domains, and every analysis still needs expert review, validation, and experimental follow-up. But in my own field, with data I understand deeply, this felt like another inflection point. I feel strongly that we have crossed another milestone threshold in the age of AI, with the release of GPT-5.5.

OTOH, when faced with simple, short papers which make a genuinely novel point, the LLM will sometimes tend to recommend rejection based on the paper not being complicated enough

Researchers sent the same resume to an AI hiring tool twice. Same qualifications. Same experience. Same skills. One version was written by a real human. The other was rewritten by ChatGPT. The AI picked the ChatGPT version 97.6% of the time. A team from the University of Maryland, the National University of Singapore, and Ohio State just published the receipt. They took 2,245 real human-written resumes pulled from a professional resume site from before ChatGPT existed, so the human writing was actually human. Then they had seven of the most-used AI models in the world rewrite each one. GPT-4o. GPT-4o-mini. GPT-4-turbo. LLaMA 3.3-70B. Qwen 2.5-72B. DeepSeek-V3. Mistral-7B. Then they asked each AI to pick the better resume. Every model picked itself. GPT-4o hit 97.6%. LLaMA-3.3-70B hit 96.3%. Qwen-2.5-72B hit 95.9%. DeepSeek-V3 hit 95.5%. The real human almost never won. Then the researchers tried the obvious objection. Maybe the AI is just better at writing. So they had real humans grade the resumes for actual quality and ran the experiment again, controlling for it. The result was worse. Each AI kept picking itself even when human judges rated the human-written version as clearer, more coherent, and more effective. It gets worse. The AIs do not just prefer AI over humans. They prefer themselves over other AIs. DeepSeek-V3 picked its own resumes 69% more often than LLaMA's. GPT-4o picked its own 45% more often than LLaMA's. Each model can recognize and reward its own dialect. Then the researchers ran the simulation that ends careers. Same job. 24 occupations. Same qualifications. The only variable was whether the candidate used the same AI as the screening tool. Candidates using that AI were 23% to 60% more likely to be shortlisted. Worst gap was in sales, accounting, and finance. 99% of large companies now run AI on incoming resumes. Most of them use GPT-4o. The paper just proved GPT-4o picks GPT-4o 97.6% of the time. If you wrote your own cover letter this week, you did not lose to a better candidate. You lost to a worse candidate who paid OpenAI 20 dollars. Your qualifications do not matter if the AI prefers its own handwriting over yours.