Good morning. Alrighty, today’s the day.

Nvidia’s highly anticipated earnings report is entering into a challenging economic environment; stocks had another rough day Tuesday, with the S&P 500 falling for its fourth straight day.

Nvidia fell more than 2%, and Tesla tumbled around 9%, all against a landscape of tariff threats, weakening consumer confidence and highly inflated valuations.

We’ll see if Nvidia makes things — as my eye doctor says — better, or worse.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

🎙️ Podcast: IBM’s Chief Scientist on the state of AI

In the latest episode of The Deep View: Conversations, I sat down with Dr. Ruchir Puri, the Chief Scientist of IBM Research. We talk about everything from AGI and X-Risk to his perspective on the idea of ‘useful,’ rather than ‘general’ intelligence.

Check it out below:

AI for Good: Enhanced protein understanding

We’ve talked before about how predictive models for protein folding alone aren’t enough; researchers need to take things several steps further, understanding where proteins move within our cells and how they interact with other molecules. The more complete that picture of protein behavior becomes, the greater the impact will actually be. 

What happened: Researchers at MIT recently made a significant stride in fleshing out that picture with the launch of a biological AI model called FragFold. 

Why it matters: Unsurprisingly, this might have significant implications for biological research, specifically, the study of disease and design of therapeutics. Gene-Wei Li, an associate professor of biology at MIT, said that “we can imagine delivering functionalized fragments that can modify native proteins, change their subcellular localization, and even reprogram them to create new tools for studying cell biology and treating diseases.”

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Chegg challenges Google for AI-driven, ‘unfair’ competition 

Online education platform Chegg, which has been struggling mightily since the launch of generative AI tools in 2023, said Monday that it had filed a lawsuit against Google, alleging that its launch of AI Overviews — which has “transformed Google from a ‘search engine’ into an ‘answer engine’” — is at least partially responsible for crippling the platform’s business. 

The details: The lawsuit comes alongside an affirmation from CEO Nathan Schultz that Chegg is undertaking a strategic review which has the company exploring a number of options, including being acquired or going private. The company reported a net loss of $837 million for 2024 amid a decrease in subscribers and a massive reduction in non-subscriber web traffic. 

Chegg, which was trading Tuesday at a 52-week low of around $1, has a market cap of around $118 million. At the end of 2022, the company’s market cap was $3.7 billion. 

At the same time, Chegg has been rushing to integrate generative AI and machine learning technology into its own processes, an approach that has reduced the cost of its content creation by 70%. 

“Every day, Google sends billions of clicks to sites across the web, and AI Overviews send traffic to a greater diversity of sites,” Google spokesperson José Castañeda said in an emailed statement. “We will defend against these meritless claims.”

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Report: The misguided race to regulation 

From the moment Sam Altman uttered the words “if this technology goes wrong, it can go quite wrong,” the world has found itself witness to several, increasingly tense races — one, between companies racing to develop slightly more powerful models on the road to a hypothesized general intelligence; two, between countries competing to develop and control powerful generative AI models; and three, between legislators urgently attempting to regulate the tech, both to prevent things from going wrong and to ensure that things go (economically, at least) right. 

While the first two races there are heating up, the third one seems to have changed recently. Governments are increasingly signaling a greater interest in ‘winning the race’ than coming up with rules for it, with even the European Union promising to boost investment and roll back red tape. In the U.S., of course, federal regulation has remained pretty much nonexistent, despite the popularity of such laws. 

But the idea of oversight in AI is fundamentally flawed, owing, in part, to the frustratingly vague, rather intensely misleading term itself. Artificial intelligence, according to Dr. Milton Mueller, a professor in the School of Public Policy at Georgia Tech, doesn’t exist. At least, not in the way it is presented. The term instead refers to a number of related, but distinct technologies, mainly machine learning.

“The evidence suggests that the boom in legislative and regulatory initiatives rests on deeply flawed understandings of what they are trying to govern,” Mueller wrote in a recent paper, adding that “what we now lump under the unitary label ‘artificial intelligence’ is not a single technology, but a highly varied set of software applications enabled and supported by a globally ubiquitous digital ecosystem.”

It is not a new technology, according to Mueller, rather, it represents “at best an inflection point in computing capabilities that have been developing since the 1960s.” The diversity in applications that fit under that umbrella term are such that they “render the concept of ‘AI governance’ practically meaningless.” 

He proposes a different regulatory approach, one that focuses on addressing specific problems caused by specific machine learning applications, rather than governing the wider bundle of tech in at once. 

“Governing AI, in other words, means governing everything in information and communications technology,” Mueller said. 

The details: Mueller lays out an examination of our digital ecosystem, one that has four main components: computing devices to generate/process information, networks to transmit information, data, which acts as a manifestation of that information and the software that ties it all together. 

Aside from claims that AI poses an existential risk to humanity, an impression that scientists don’t agree on, an impression that is not supported by any sort of rigorous scientific evidence, Mueller argues that today’s AI doesn’t pose any truly novel policy questions.  

For Europe’s AI Act to actually accomplish what it claims to accomplish, for instance, Mueller said that the EU would have to extend control over both networks and data “in order to maintain regulatory authority over AI applications,” something that is not happening, and something that, he argues, shouldn’t. 

I am reminded of what Dr. Seena Rejal, CCO of the AI startup NetMind, told me recently, that policymakers can’t keep up with AI because they don’t “really understand it at its core.” 

Some of the legislation that’s come out of the states is really promising — laws to protect artists from generative impersonation, or laws that protect victims of sexual harassment, are good laws. And the good laws do have that theme of targeting highly specific applications; to Mueller’s point, it is impossible to regulate the umbrella term, here, because that necessarily involves regulating an entire ecosystem the breadth of which is largely being ignored. 

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