Rather than government regulation, market forces will address AI bias. As studies reveal biases in models from OpenAI and Google, competitors like Elon Musk's Grok can market their model's neutrality as a key selling point, attracting users and forcing the entire market to improve.

Digital platforms can algorithmically change rules, prices, and recommendations on a per-user, per-session basis, a practice called "twiddling." This leverages surveillance data to maximize extraction, such as raising prices on payday or offering lower wages to workers with high credit card debt, which was previously too labor-intensive for businesses to implement.

The price mechanism in capitalism is a successful but lossy compression of complex economic information into a single number: money. AI agents can operate on the uncompressed, real-time data of supply and demand across the economy, creating a more efficient system that avoids the waste inherent in capitalism's information loss.

The 'Andy Warhol Coke' era, where everyone could access the best AI for a low price, is over. As inference costs for more powerful models rise, companies are introducing expensive tiered access. This will create significant inequality in who can use frontier AI, with implications for transparency and regulation.

AI startups should choose their pricing model based on a 2x2 matrix of autonomy (human-in-the-loop vs. fully automated) and attribution (how clearly its value can be measured). Low levels lead to seat-based pricing, while high levels of both unlock outcome-based models.

The real danger of algorithms isn't their ability to personalize offers based on taste. The harm occurs when they identify and exploit consumers' lack of information or cognitive biases, leading to manipulative sales of subpar products. This is a modern, scalable form of deception.

Marks questions whether companies will use AI-driven cost savings to boost profit margins or if competition will force them into price wars. If the latter occurs, the primary beneficiaries of AI's efficiency will be customers, not shareholders, limiting the technology's impact on corporate profitability.

Current regulatory focus on privacy misses the core issue of algorithmic harm. A more effective future approach is to establish a "right to algorithmic transparency," compelling companies like Amazon to publicly disclose how their recommendation and pricing algorithms operate.

Financial institutions generate significant revenue from customer errors like overdrafts and late fees. This income allows them to offer rewards and lower rates to more sophisticated, affluent customers, creating a system that exacerbates wealth inequality.