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⚙️ Google unveils a new breakthrough in AI drug discovery

Good Morning, and happy Friday. Today marks the end of my first week here — during that time, I’ve seen the Knicks get off to a wonderful start against the Pacers, and I’ve learned a lot about putting together a newsletter. 

I appreciate your support and patience as we iterate and experiment with new approaches and styles. This edition, for example, should have hit your inboxes a few hours early. Let us know how you liked waking up to The Deep View.

In today’s newsletter: 

  • 🛜 Judge makes a tentative ruling on AI copyright case against Stability and Co. 

  • 📱TikTok just started labeling AI-generated content

  • 📎 Microsoft found that 75% of people already use AI at work 

  • 💊 Google’s got a new breakthrough in AI-powered drug discovery

Before we get into it, this Sunday is Mother’s Day — if you’re a mother, happy Mother’s Day! For everyone else, consider this a friendly reminder to pick up some flowers 🙂 

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Judge makes a tentative ruling in AI copyright case

Image Source: Unsplash

On Tuesday, I talked about the copyright issue that’s become somewhat central to generative AI as a business. The lawsuits keep stacking up – as do content licensing deals – and for the most part, it’s been hard to tell where things might be headed. 

But earlier this week, the judge in the class action lawsuit of artists against Stability, Runway and Midjourney issued a tentative ruling in which he said that he is “inclined to DENY all (defendant) motions to dismiss the direct and induced infringement claims under the Copyright Act.” This means the case will likely proceed to discovery.

Read the tentative ruling here

Why it matters: 

  • AI companies have been scraping the internet for free — for a while, now — to train for-profit models. They have argued that it’s a perfectly fair thing to do — creators, after all, chose to publish their work online. 

  • While most creators and media orgs have taken the opposite stance, saying that they should be compensated for the use of their work, legal decisions have been less than scarce, and the practice has continued. 

  • The result of this suit could help set a precedent that could impact all the other lawsuits that AI companies have been fielding recently. And if the result is that AI companies have to pay all creators for scraped content, these companies will start burning through their VC cash even more quickly than they already have.

TikTok to label AI-generated content

Image source: TikTok

In another instance of things coming full circle, TikTok announced Thursday that it is starting to automatically label AI-generated content as such (when uploaded from other platforms). The social media giant is partnering up with the C2PA to do this, and becoming the first video-sharing platform to instate content credentials in the process.

The platform also said that it will begin adding content credentials to TikTok’s own videos, so that other people and platforms can determine the provenance of TikToks. 

Why it matters: 

  • You might recall me recently wishing that all social media platforms take steps immediately to flag the provenance of their content. 

  • TikTok’s decision to do exactly that is a big one, especially with all the elections set to take place this year against the backdrop of AI-generated misinformation that has already begun to spread. 

  • With TikTok already on board, the question remains how long it will take the other platforms to follow suit. 

Microsoft: 75% of people already use AI at work

Image Source: Microsoft

The global workforce, according to a recent survey by Microsoft and LinkedIn, is adopting something Microsoft calls BYOAI – bring your own AI. The two companies surveyed more than 30,000 people around the world; 75% of them have already adopted AI for work. And faced with cautious leadership at the top, nearly 80% of those have decided to bring in their own AI tools for the job. 

Key details: 

  • Microsoft said a key driver in this adoption is simple: people are overworked. Nearly 70% of those surveyed said they struggled with the pace & volume of work. Almost half feel burned out and buried in endless emails and meetings. 

  • The tech giant also addressed the elephant in the room when it comes to AI and employment: fears of unemployment. Microsoft said that two-thirds of leaders would prefer to hire people with experience in generative AI platforms. 

  • “While employees fear job loss due to AI, most leaders worry they can’t fill key roles.”

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🌎 The Broad View:

📚 Interesting Reads:

  • OpenAI shared the first draft of its Model Spec, a document that shares how the company shapes ideal model behavior.

  • Mozilla Research published its Elections Playbook, a breakdown of tech-related risks to global elections.

  • A new paper from a trio of AI researchers breaks down algorithmic harms and algorithmic wrongs, laying out the differences between the two.

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Google’s got a new breakthrough in AI-powered drug discovery

Image Source: Google

Google DeepMind – in partnership with Isomorphic Labs – on Wednesday introduced AlphaFold 3, an AI model designed to predict the structure and interaction of proteins, DNA, RNA and other molecules. 

The hope is that this model will enable a better understanding of human biology, which in turn, could enhance drug discovery efforts. Google said that scientists can access the bulk of the model’s capabilities for free through the AlphaFold Server

  • AlphaFold 3 can, given an input list of molecules, model how they all fit together – and importantly, the impact of chemical modifications to those molecules, according to Google. 

  • Google said that the model has achieved “unprecedented accuracy in predicting drug-like interactions.” Isomorphic Labs is already using the model to assist with drug designs. 

Google did not detail the cost (in carbon emissions or in money) of training or running the model. 

AI and biology: 

Google is not the first to explore the role of AI in biology. Earlier this year, I chatted with a couple of players in the space: Recursion Pharmaceuticals and Lantern Pharma, two biotech companies that are leveraging AI to assist with drug discovery. 

The moral of each story fits nicely into the core selling point of AI: there’s just too much data involved for humans to analyze, and AI is really good at quickly parsing through giant swaths of data and identifying patterns. 


  • Recursion, which is partnered with Nvidia, created an AI-powered operating system capable of “​​generating, analyzing and deriving insight from massive biological and chemical datasets to industrialize drug discovery.” The company currently has seven drugs in its pipeline that are in or beyond the preclinical testing phase; two of these are oncology drugs. 

  • Imran Haque, Recurison’s SVP of AI and digital sciences, told me that their technological approach allows for greater experimentation, which can be very informative for scientists. “The ability of humans to model or understand biology is just not there. Biology is too complex of a system,” he said. 

Lantern Pharma: 

  • Lantern, with four drugs in its pipeline, is focused on applying AI and machine learning to the discovery of oncology drugs. CEO Panna Sharma told me a bit about the company’s process; scientists use Lantern’s RADR platform, set to certain constraints, to generate potential molecules, which they then test. 

  • The process then of finding a potential candidate, he said, could take as little as six months, compared to the years it might take a non-AI-powered company to do the same work. 

  • He also explained that the issue of hallucination is less of a problem in his arena — he said that hallucinations could lead to innovation, while also adding that the amount of testing that happens in the lab weeds out mistakes early on.  

My take: 

I’ve become enamored with the idea of the true promise of AI. Big Tech companies will shout about the coming AI utopia until they’re blue in the face, but often, the corporatized side of AI isn’t anything that could save the world, and in many cases, it’s actively hurting (disinformation, environmental impact, devaluation of creativity, etc.). 

But AI does have a tremendous capacity to do good, and the biotechnological side of things feels promising: parse the data that we’re incapable of sifting through, and help us iterate faster so we can create new treatments that might well be life-saving. 

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