Wet lab experiments are slow and expensive, forcing scientists to pursue safer, incremental hypotheses. AI models can computationally test riskier, 'home run' ideas before committing lab resources. This de-risking makes scientists less hesitant to explore breakthrough concepts that could accelerate the field.

Synthetic customer feedback is fast for minor tweaks, but businesses demand real human insights for multi-million dollar decisions and novel concepts. This creates a clear market segmentation where accuracy and trust outweigh the speed of pure AI, especially when launching expensive campaigns.

Scientists constrained by limited grant funding often avoid risky but groundbreaking hypotheses. AI can change this by computationally generating and testing high-risk ideas, de-risking them enough for scientists to confidently pursue ambitious "home runs" that could transform their fields.

Unlike traditional desk research which finds existing data, generative AI can infer responses for novel scenarios not present in training data. It builds an internal "model of human nature," allowing it to generate plausible answers to new questions, effectively creating research that was never done.

In high-stakes fields like pharma, AI's ability to generate more ideas (e.g., drug targets) is less valuable than its ability to aid in decision-making. Physical constraints on experimentation mean you can't test everything. The real need is for tools that help humans evaluate, prioritize, and gain conviction on a few key bets.

It's impossible to generate human data at the scale of in silico experiments. The key is to create highly accurate simulations of human physiology (digital twins) and then validate their predictions with limited, strategic human data. If the model proves reliable, it could drastically accelerate R&D.

Contrary to the idea that AI will make physical experiments obsolete, its real power is predictive. AI can virtually iterate through many potential experiments to identify which ones are most likely to succeed, thus optimizing resource allocation and drastically reducing failure rates in the lab.

A key application for synthetic research is exploring questions that arise after a traditional, human-powered study is complete. Instead of launching a new project, researchers can quickly run a few follow-up questions with a synthetic audience. This provides directional answers to stakeholder queries without the cost and delay of re-fielding a survey.

Many users know about AI's research capabilities but don't actually rely on them for significant decisions. A dedicated project forces you to stress-test these features by pushing back and demanding disconfirming evidence until the output is trustworthy enough to inform real-world choices.