Anthropic sued over Elvis songs, AI's zero ROI problem, and a DNA model that reads genomes in plain English

Picture the scene: a lawyer opens a lawsuit, and the first exhibit is a list of 274 Elvis Presley songs. Welcome to AI copyright law in 2026.

Today's newsletter covers a lawsuit that could reshape how AI labs handle training data, a brutally cold take on AI's actual return on investment, and a biology breakthrough that might quietly matter more than either of those. Let's get into it.

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The answer is hiding near the bottom of today's newsletter... keep scrolling. 👇

Authentic Brands Group - the licensing organization that controls both Elvis Presley's music catalogue and his likeness rights - has filed a lawsuit against Anthropic alleging that the company used 274 of the King's songs to train its AI models without permission. The suit covers classics including Can't Help Falling in Love.

This isn't the first time Anthropic has faced music copyright claims, but the scale here is notable. 274 songs from one of the most recognizable catalogues in history is a significant volume, and Authentic Brands Group has both the legal resources and the commercial incentive to push this one all the way.

The broader AI copyright landscape is already tense. If you've been following the ongoing wave of AI copyright cases, this one adds meaningful pressure on labs to document exactly what went into their training sets - and could push the industry toward licensing deals at a scale we haven't seen yet.

If Authentic Brands Group wins or settles for a large figure, it sets a pricing floor for music licensing that every major AI lab will need to account for going forward.

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28 Wishes sells ice cream in Los Angeles. Below 70°F, sales fall about 20%. The weather is out of their hands. Rent isn't.

So the owners started putting about $20 a day into Kalshi weather markets, taking the cold side. The days that keep customers away now pay something back.

This is hedging. Big companies have done it for decades, buying protection against bad weather, fuel spikes and rising rates. It used to take a broker, a trading desk, and an order size no corner shop could meet.

Kalshi opens it up. Contracts on weather, fuel prices, inflation, tariffs and regulation, starting at a few dollars. Take a position on the outcome that would hurt you. If it hits, the payout softens it. If it doesn't, the contract expires and the good month was the point.

See how other owners are doing it at Kalshi.com/hedging

An economist at Apollo has published a blunt assessment: despite massive investment and relentless hype, AI has delivered zero financial payoff to corporate America outside of the tech sector. The analysis argues that while AI companies themselves are printing money, the productivity and revenue gains that were supposed to flow to every other industry simply haven't materialized in the numbers.

This lands at an interesting moment. Enterprises have spent heavily on AI subscriptions, integrations, and consultants over the past two years, and the expectation was that efficiency gains would start showing up in earnings by now. According to Apollo's read of the data, that hasn't happened - at least not in any measurable way for most companies.

That doesn't mean AI isn't useful - plenty of individual teams and developers are getting real value. But there's a growing gap between what the technology can do in demos and what it actually delivers at the organizational level. For anyone building with AI tools, this is worth keeping in mind when pitching ROI to leadership.

The AI investment wave isn't slowing down, but this report gives executives cover to ask harder questions about what they're actually getting for their spend.

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Hugging Face dropped Carbon-X today - a 14.8 billion parameter DNA language model built on top of Qwen3-14B. What makes it genuinely interesting is the input method: you feed it raw DNA sequences in a chat interface and it answers your questions about them in plain English. No specialized bioinformatics pipeline required.

It averages 66.7% accuracy across five standard DNA benchmarks and ships under the Apache 2.0 license, meaning researchers and companies can use it commercially without restrictions. The model represents the first time a DNA model and a natural language model have been fused at this scale in a publicly available, open-weight package.

The practical upside here is accessibility. Right now, interpreting genomic data requires specialized tools and significant domain expertise. A model that can answer questions about a DNA sequence in conversational English lowers the barrier considerably for researchers, clinicians, and developers working in AI healthcare applications. This is one of those quiet releases that could look more significant in twelve months than it does today.

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Carbon-X is the clearest signal yet that foundation model architecture is beginning to generalize into biology - and doing it in the open, which matters enormously for research access.

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Reddit's r/opensource - one of the most active communities for developers sharing and discovering open-source projects - posted new submission rules over the weekend that ban anything AI-related. The exact wording from the mods: "If your project uses AI, is made by AI, or is intended for AI, it's not welcome on r/opensource. AI, as it exists today, is incongruent to this Open Source community."

The announcement set off a significant backlash in the comments, and the move has sparked wider debate about whether the open-source label even applies to AI models - given that most major "open" AI models don't release training data, making them open in weights only, not in the spirit of traditional open source.

This tension has been building for a while. The open-source AI space has grown enormously in the past two years, but the question of what "open" actually means for AI has never been fully resolved. This community ban won't slow model releases, but it signals that a meaningful slice of the traditional open-source developer community views AI as a fundamentally different kind of thing.

The schism between AI developers and traditional open-source communities is getting harder to paper over - and that has real implications for how AI tools get distributed and adopted.

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Wired reports on a wave of AI agents that are actively pitching editors on reported stories and investigative features. The agents - going by names like Jayden, Sam, and Zack - are approaching newsrooms with story ideas, apparently indistinguishable from a human freelancer's initial outreach.

This isn't just a novelty. It points to a genuine near-term pressure on editors: how do you evaluate pitches when you can't assume the person pitching is a person? The AI agents space has moved quickly from coding assistants to anything requiring outbound communication, and journalism is apparently next on the list.

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The journalism angle is interesting, but the real story is that agentic outreach is now general enough to pass as human in low-stakes professional contexts - which has obvious implications well beyond media.

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The answer is Qwen3-14B! Hugging Face built Carbon-X by adapting the Qwen3-14B architecture, fine-tuning it to understand raw DNA sequences while retaining the ability to respond in natural English. It's a neat example of how existing foundation model work is being repurposed for specialized scientific domains.

The Apollo economist's "zero ROI" take is doing the rounds today. I'm curious where you land on it - has AI actually moved the needle for your work or your company, or does it feel more like hype than results so far? Hit reply and let me know - I read every single response.

That's all for today. See you tomorrow with more from the fast-moving world of AI - and check out dailyinference.com for the full archive of everything we've covered. 👋

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