We sat down with Annie to discuss her company, the importance of protective patents, and Laminar’s emphasis on providing sustainable processes to customers.

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They help process manufacturers ensure sustainable production of their products through real-time chemical data from their sensors and AI models they’ve created.

After witnessing the 2022 baby formula crisis, founder and CEO Annie Lu joined her brother in starting their Physical AI company, Laminar.

Startups are building innovative new services with AI, but lawmakers can only take into account the potential impacts on services they know about.

The good news is, startups can help inform AI policymaking by sharing their perspectives with policymakers.

Many of those proposals have been developed with the largest companies in mind, but overinclusive frameworks mean the burdens are likely to fall hardest upon startups.

Policymakers need to recognize startup realities, or we’ll end up with policies that devour innovation.

On the regulatory front, the environment has only fractured and grown, threatening a burdensome patchwork for startups to navigate.

It will add costs and steer their limited resources away from innovation and toward (likely duplicate) compliance activities.

Most AI startups are building on others’ foundation models, leveraging them for uses that look nothing like the underlying training data.

But an adverse ruling in the cases could lead to exorbitant licensing fees, chasing startups out of the innovation ecosystem.

AI systems ingest vast training data to learn about the world and establish predictive relationships, creating a general purpose tool capable of generating new material.

But there’s a scary number of copyright lawsuits ongoing right now parsing whether AI training is fair use.

Startups want to “chomp” away at creating novel products with AI, but navigating the maze of innovation increasingly involves dodging “ghosts” of legal threats, an ever-growing pile of regulatory proposals, and practical challenges. 🎃🧵

In Part 3 of our AI and copyright series, we're breaking down what these costs would mean for startups and what's at stake for competition across the AI ecosystem.

Read our new report here: engine.is/s/AI-Copyright…

Most startups aren't developing large AI models from scratch.

They're building products using existing models, meaning the outcomes of these cases could affect the cost, quality, and availability of models startups depend on.

What if licensing copyrighted material to train AI models came with a $10 trillion price tag?

That's the scale of potential licensing costs for data needed to train a model at or near today's frontier, as we explore in a new report we’re publishing today.

In Part 2 of our AI and copyright series, we're unpacking the legal landscape, what courts have decided so far, and why the outcomes could have significant consequences for startups building with AI.

Coming up in Part 3: What could these cases actually cost startups?

The stakes are high and extend well beyond the companies named in these lawsuits.

More than 140 AI copyright cases have been filed in the U.S., and many center on the key question: Is training AI models on copyrighted material fair use?

Most of the litigation is still ongoing and courts have yet to reach a universally instructive ruling.

In Part 1 of our AI and copyright series, we're breaking down the basics of copyright, how fair use works, and why these questions matter for startups and innovation.

Up next: A closer look at the lawsuits shaping this debate.

What does copyright law have to do with the future of AI?

Quite a bit, actually.

With AI companies facing a growing number of copyright lawsuits, courts are being asked to determine how longstanding copyright protections and the fair use doctrine apply to AI development.

Deepak is working to solve that with his next-gen wearable smartwatches. We sat down with Deepak to talk about his company, the state of the patent application process, and more.

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Deepak Arora, MBA founded Wearable Technologies Inc. (Wear-Tech) in the wake of a family tragedy.

In researching prevention, he learned that certain age groups are underserved by current life-sign monitoring infrastructure.

We also discussed the AI and data privacy questions facing startups that handle sensitive client information, and what U.S. immigration policy means for startups, as well as international talent.

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