If you "Ozempify" your diet, will you kill the trademark? See this fun interview with STAT on GLP-1 drugs: https://t.co/S6PzdqsH2o

Yesterday’s announcement from the FDA sent a clear message: There’s a new sheriff in town, and things are going to change around here.

This stands in stark contrast to the agency’s lackluster approach to consumer advertising in recent years. FDA enforcement against deceptive consumer advertising has declined since the turn of the millennium even as the amount of advertising drugs to consumers has ballooned.

Late yesterday, the FDA announced a crackdown on deceptive advertising of medicines to consumers, sending thousands of warning letters to pharmaceutical companies and roughly 100 cease and desist letters. https://t.co/SHFe65X5II

Given the AI arms race with China, it’s critical to establish clear rules of engagement for AI firms in the US. In that context, I agree with preempting the states on AI regulation…but only if the federal government can act swiftly and effectively. https://t.co/rzCVIQVSNt

Teaming up with my husband Boris in a “Feldman Interviews Feldman” format, I’ve made a video podcast episode entitled How I used AI in Writing a Book about AI. My most important takeaway: AI serves best as human augmentation, not as human replacement.

Working with Claude, Grok, and other AI programs intensely over the last several months, I have explored where they currently shine and where they struggle.

Under federal law, compounders can help patients adjust doses or ingredients because with medicine, “one size doesn’t fit all.” But as I mentioned to @SELFmagazine, today’s question is whether those laws still apply when the fit problem is wrong price, not wrong size.

Also, we should consider who benefits from government intervention.  We can’t win the AI war by handicapping our top players or dividing capacities that give the US an edge. More at:

Is AI concentration within the Big 5 bad? I’m no fan of industry concentration, but the cure may be worse than the disease. With rapid innovation, the chokepoints limiting entry may change.

Fortunately, anti-money laundering regimes provide a model for how to identify tacit collusion. For details, read the paper (no paywall) in CLA’s Competition Journal:

Courts will have to develop doctrines that identify the evidence needed to prove that a pricing algorithm is being used for tacit collusion.

The line between efficiency and collusion can be tough to draw. Government shouldn’t tell firms, “Given your industry structure, you can’t ever use pricing algorithms.” But firms shouldn't be able to say, “the algorithm made me do it” as an excuse for price fixing.

In pharma, competition is hampered by highly concentrated intermediaries (PBMs) and an industry in which the consumer is not the buyer. Concerns are heightened by the fact that the intermediaries can pool vast amounts of non-public information.

Algorithmic collusion concerns are heightened when a market is structured in a way that already hampers competition, such as the U.S. pharmaceutical market.

Anticompetitive “Hub-and-spoke” agreements allow competitors to collude without directly interacting. AI algorithms can facilitate this kind of collusion in subtle and hard-to-spot ways.

Today’s conspiracies could look different from drawings of smoky rooms full of well-fed men in suits chomping on cigars and plotting to keep prices high.

Finally, avoiding disasters generally requires enormous cooperation between the public and private sectors. In the end, there is much we can learn from Y2k about distant dangers.

Second, envisioning a disaster doesn’t necessarily mean it will happen. To the extent there are concerns about future AI disasters, we can face them and determine how to avoid the disaster.

First, today is the time to think about where technology will lead tomorrow. As we are seeing from the downsides of social media, tomorrow is a little late.