AI In Scientific Research

AI adoption is one thing, transformation is something else entirely.    Few companies have been bold enough to fundamentally change how they are organized and how decisions are made. At Pfizer, we are determined to break through that inertia and be the most AI-forward company in our industry because of what this technology will do for patients.    In R&D, as I said on our earnings call this week, our ambition is to build an AI-native organization where every insight, from target discovery through medical evidence, continuously informs the next decision.    We are making great progress, so I'd like to share more about how we think about it overall: why our 177 years of data is our alpha, why I chose a federated model over a central team, and why we are certifying every eligible colleague, including me, in AI fluency.

Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. My latest free deep dive: https://lnkd.in/gV_b9AsR ✍ A loop can be thought of a recursive goal where you define a purpose and the AI iterates until complete. I believe this may be the future of how we work with coding agents. However, its still early and you absolutely have to be careful about token costs. For two years, the way you got something out of a coding agent was simple: write a good prompt, read what came back, write the next prompt. You held the tool the whole time, one turn after another. That part is changing. Peter Steinberger of OpenClaw put it like this: "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agents." Boris Cherny, head of Claude Code, said the same thing differently: "I don't prompt Claude anymore. I have loops running that prompt Claude." A loop is five things: → Automations - a heartbeat that runs discovery on a schedule so you're not the one going around checking → Worktrees - isolated branches so parallel agents don't collide on the same files → Skills - project knowledge written down once so the agent doesn't re-derive your conventions from scratch every run → Plugins/connectors - MCP connections to your real tools, so the loop opens PRs and updates tickets instead of just telling you what it would do if it could → Sub-agents - one agent writes, a different agent checks. The one who wrote the code is too nice grading its own homework. Plus one more thing that sounds too simple to matter: a memory file. Markdown, a Linear board, anything that lives outside the conversation. The agent forgets between runs. The repo doesn't. What surprised me is this isn't a bespoke scripting problem anymore. A year ago building a loop meant a pile of bash only you could maintain. Now the pieces ship inside the products. Claude Code and Codex both have all five. But three problems get sharper as loops get better, not easier: The loop changes the work, it does not delete you from it. And three problems actually get sharper as the loop gets better, not easier. Verification is still on you. A loop running unattended is also a loop making mistakes unattended. Your understanding still rots if you allow it. The faster the loop ships code you did not write, the bigger the gap between what exists and what you actually get. Thats comprehension debt and a smooth loop just makes it grow faster unless you read what the loop made. And the comfortable posture is the dangerous one. When the loop runs itself its very tempting to stop having an opinion and just take whatever it gives back. I called that cognitive surrender. Designing the loop is the cure when you do it with judgement. Build the loop. But build it like someone who intends to stay the engineer, not just the person who presses go. #ai #softwareengineering #programming