The market correctly sees biology's potential but often misunderstands its timeline. Even with AI, biology is fundamentally harder and slower than software. Daniel Fero warns this mismatch in "tempo" expectations leads to over-funding hype cycles while under-funding foundational companies that are simply moving at the pace required for rigorous biological R&D.

Tech-focused venture firms are finding their AI investment thesis fails in biotech. Despite massive paper profits in tech AI, their biotech AI portfolios show negative returns. This is because AI has yet to solve the complex biological bottlenecks of drug development, particularly in clinical trials, which remain slow and costly.

Antonov argues that publishing papers is insufficient. He calls for an integrated framework where scientists can contribute computational models, which are then experimentally grounded and combined. Contributors would share in the upside if their model helps develop a new asset, incentivizing collaboration.

Unlike ventures in established biological pathways, startups tackling novel biology must first prove a specific drug product can work. The primary question isn't about the platform's potential applications but whether a single, tangible therapeutic is viable. Focusing on a broad platform too early is a mistake.

The dominant biotech VC model incentivizes startups to act like real estate developers: build an asset to a certain stage (e.g., early clinical data) and then sell it to a large pharmaceutical company. This focus on short-term exits discourages the long-term, ambitious company-building required for revolutionary platforms.

The prevailing biotech model is shifting from an asset-centric approach to one focused on creating a "learning system." The most successful future companies will be those with a repeatable engine for discovery and validation that can consistently generate new insights and a diversified pipeline of assets.

The venture creation strategy for platform biotechs isn't about finding one blockbuster drug. It's a binary bet: either the underlying scientific platform is sound and can repeatedly generate many medicines, or the entire concept fails. There is no middle ground of succeeding with just one product from the platform.

A profound capital shift has occurred where both venture investors and large pharma partners focus on clinically validated assets. This moves investment away from riskier, early-stage science, creating a significant funding gap for foundational research and pre-clinical startups.

The next wave of longevity investment favors 'subtractive' therapies over traditional 'additive' drugs. Startups like Nanotics, which use nanorobots to remove specific harmful molecules, are gaining traction because they avoid the inherent side-effect risks associated with introducing new compounds.