Max Levchin claims any single data point that seems to dramatically improve underwriting accuracy is a red herring. He argues these 'magic bullets' are brittle and fail when market conditions shift. A robust risk model instead relies on aggregating small lifts from many subtle factors.

The controversy and business opportunity in polygenic embryo selection lie in interpreting genetic data, not in the physical sequencing. Companies are competing on the quality and scope of their predictive models for health and traits, which they apply to data from established lab processes.

The predictive power of embryo screening can be validated without controversial longitudinal studies on children. By testing if models can accurately predict trait differences between adult siblings using only their DNA, companies can prove efficacy for embryos, who are essentially unrealized siblings.

A new innovation allows companies to construct an embryo's entire genome using raw data from a standard Down syndrome test. This means parents can get comprehensive polygenic reports without needing explicit approval from clinics or doctors, effectively democratizing access and removing traditional medical gatekeepers.

Standard IVF practice involves a doctor visually selecting the embryo that appears most "normally shaped." This is already a form of selection. Polygenic screening simply replaces this subjective "eyeballing" method with quantitative genetic data for a more informed choice, making it an evolution, not a revolution.

Polygenic embryo screening, while controversial, presents a clear economic value proposition. A $3,500 test from Genomic Prediction that lowers Type 2 Diabetes risk by 12% implies that avoiding the disease is worth over $27,000. This reframes the service from 'designer babies' to a rational financial decision for parents.

For AI systems to be adopted in scientific labs, they must be interpretable. Researchers need to understand the 'why' behind an AI's experimental plan to validate and trust the process, making interpretability a more critical feature than raw predictive power.

AI and big data give insurers increasingly precise information on individual risk. As they approach perfect prediction, the concept of insurance as risk-pooling breaks down. If an insurer knows your house will burn down and charges an equivalent premium, you're no longer insured; you're just pre-paying for a disaster.

Trying to determine which traits you inherited from your parents is clouded by the 'noise' of shared environment and complex psychological relationships. For a more accurate assessment, skip a generation and analyze your four grandparents. The generational remove provides a cleaner, less biased signal of your genetic predispositions.