Good morning, and happy Friday! Coming to you today from Miami, where I’ll be watching the Heat tousle with my Knicks on Sunday 🍿🍿🍿 

Hope you all have a wonderful weekend!

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

In today’s newsletter:

• ⚕️ AI for Good: Rare diseases

• 👁️‍🗨️ OpenAI launches preview of wildly expensive GPT-4.5

• 📊 Nvidia didn’t come to the rescue 

• 💻 Mercury: A new breed of LLM

AI for Good: Rare diseases

Rare diseases, by their very nature, don’t get too much clinical attention. 

The problem is that, in aggregate, rare diseases impact a lot of people. 

The numbers: For a disease to be considered “rare,” it must affect fewer than 200,000 Americans. But, according to the Association of American Medical Colleges, there are more than 7,000 known rare diseases, impacting a total of roughly 30 million Americans. They’re difficult to diagnose, and often challenging to treat. 

What happened: Dr. Katharina Schmolly recently launched a startup called zebraMD, which leverages machine learning in an effort to speed up the diagnostic process of these rare diseases. 

• zerbaMD’s model was trained on a vast database of rare and genetic diseases from the National Institute of Health; the model’s output is then validated by expert physicians to ensure accuracy. Schmolly’s goal is to validate 350 diseases this year. 

• It works by tapping into the ecosystem of individual patient health records, where the platform can then look “through millions of data sources at once, connecting the dots between papers, genome databases and individual patient records to come up with clinically actionable insights.”

Why it matters: The goal, according to zebraMD, is to “get people diagnosed, get them diagnosed earlier, and get them the appropriate care, in every single health setting anywhere in the world, no matter the zip code.”

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OpenAI launches preview of wildly expensive GPT-4.5

The news: OpenAI launched a research preview of its latest model, GPT-4.5 on Thursday, billed by OpenAI as the company’s “largest and best model for chat yet.” 

The model is currently only available to Pro users, a restriction that CEO Sam Altman said is due to the startup being “out of GPUs.” 

• “It is the first model that feels like talking to a thoughtful person to me,” Altman said, adding that the “bad news” is that “it is a giant, expensive model.” He said that OpenAI will be adding tens of thousands of GPUs next week, and that “hundreds of thousands” are coming soon — a pretty good summation of the challenges of the AI ecosystem. Plenty of demand for Nvidia, but such a high cost that the road to profitability for developers like OpenAI seems so long as to not really exist. 

• This model is not a reasoning model and “won’t crush benchmarks,” according to Altman. It was achieved by scaling up pre and post-training, though, as per usual, details regarding training data and energy demand and carbon intensity remain unknown. 

And it is truly, enormously expensive, costing $75 per million input tokens and $150 per million output tokens. o1, for comparison, costs $15 per million input tokens and $60 per million output tokens. Because of the cost, OpenAI is “evaluating whether to continue serving it in the API long-term.”

In a live-streamed demo, OpenAI engineers inputted the following prompt to show off the model’s skills: “UGHHH! My friend canceled on me again!!! Write a text message telling them that I HATE THEM!!” — not the kind of application indicative of a general intelligence. 

Codeium CEO Varun Mohan said that, based on early testing, the model is “more expensive, slower and worse at tool calling than models like Sonnet.”

Dr. Gary Marcus called it a “nothing burger,” adding that “GPT-5 is still a fantasy.” 

• Turn off your read receipts. They’re dangerous (Vox).

• DOGE’s Chaos Reaches Antarctica (Wired).

• Meta plans to release standalone Meta AI app in effort to compete with OpenAI’s ChatGPT (CNBC).

• Chip race: Microsoft, Meta, Google, and Nvidia battle it out for AI chip supremacy (The Verge).

• Traders flee risk, seek havens to hedge uncertain stock market (Bloomberg).

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Nvidia didn’t come to the rescue 

In the hours directly following Nvidia’s record earnings report, complete with strong guidance for the current quarter, the stock was a little all over the place. It was up, then it was down, then it was up … and so on. 

But by market close on Thursday, after it had all sunk in, investors seem to have decided that Nvidia did not, in fact, come to the rescue. 

Here’s what happened: As predicted, Nvidia bled on Thursday, though I didn’t expect the Street to react quite as harshly as it did — the stock closed the day down nearly 9%, tugging Nvidia’s market cap below $3 trillion for the first time since September. The stock is now down 10% for the year, while the S&P 500 is down only .12% over the same time period. 

• The broader market didn’t have a wonderful day either; the S&P closed down around 1.6% and the Nasdaq fell by 2.7%. The rest of the hyperscalers similarly struggled — Microsoft was down nearly 2%, Amazon nearly 3%, Meta, 2.3% and Tesla 3%. 

• As I mentioned yesterday, the environment surrounding this is a tense one — consumer confidence is down, amid new policy moves that clearly have the market nervous. 

“We’re in a stalled, range-bound, slightly irrational market as we wait for policy clarity,” Jay Hatfield, CEO of Infrastructure Capital Advisors, told CNBC. 

James Demmert, chief investment officer at Main Street Research, added: “Nvidia earnings were outstanding, but they come during an extremely jittery stock market.” 

CNN’s greed and fear index has pegged markets at the “extreme fear” side of the gauge. 

“This suggests that long-term AI bulls (present company included) are taking a back seat to concerns about the next couple of quarters,” Deepwater’s Gene Munster said. “Long term, I remain optimistic about Nvidia and the broader AI trade, in part due to the increasing demand for more compute.” 

Mercury: A new breed of LLM

We talk often about paradigms, here, specifically, the need for new ones.

Up until this point, early indicators have seemed to point to neurosymbolic AI as the next new paradigm in the field. But a startup just launched a different kind of large language model (LLM), an approach that, once again, might indicate the start of a new approach, and perhaps, a new paradigm. 

The company is called Inception, and the product is a diffusion LLM. 

But first, the old paradigm: Most of the generative AI models you see today are autoregressive models, a transformer-based approach that was popularized by ChatGPT. Very basically, autoregressive models predict the next token in a sequence by learning statistical patterns in their training data (a token, in language models, refers to broken-up pieces of words). 

Probabilistic inference, in other words. 

A different aspect of this older paradigm involves diffusion models, an approach that is the beating heart behind image generation models. 

• Another deep learning approach, diffusion models work by introducing noise (think TV static) to an image, then learning to reverse that process (de-noise). Similar to autoregressive models, the output of diffusion models is reliant on statistical patterns in the training data. These systems just get to that output differently. 

• “In training, diffusion models gradually diffuse a data point with random noise, step-by-step, until it’s destroyed, then learn to reverse that diffusion process and reconstruct the original data distribution,” according to IBM. “A trained diffusion model can then generate new data points that resemble the training data by simply denoising a random initial sample of pure noise.”

What happened: The startup Inception, founded by computer science professors from UCLA, Cornell and Stanford, and staffed by researchers hailing from Google DeepMind, Microsoft, Nvidia and OpenAI, combined these two approaches to create a model they call Mercury, an LLM powered by diffusion. 

According to Inception, the approach is “10x faster and cheaper than current (autoregressive) LLMs.” 

• “Generation is inherently sequential — a token cannot be generated until all the text that comes before it has been generated — and generating each token requires evaluating a neural network with billions of parameters,” Inception said in a statement. 

• And the latest approach that the industry has undertaken to make these autoregressive models more robust — Chain-of-Thought reasoning — requires a massive increase in inference-time compute, meaning the road to that better output is longer and far more costly. 

“A paradigm shift is needed to make high-quality AI solutions truly accessible. Diffusion models provide such a paradigm shift,” Inception wrote. “Because diffusion models are not restricted to only considering previous output, they are better at reasoning and at structuring their responses. And because diffusion models can continually refine their outputs, they can correct mistakes and hallucinations.”

The startup called their “dLLM” a “drop-in” replacement for traditional LLMs, supporting all approaches and use cases. The startup’s coding assistant, the Mercury Coder, is five to 10 times faster than current models, according to Inception, producing more than 1,000 tokens per second. 

You can explore the new model here. 

It might, according to Andrej Karpathy, Tesla’s former director of AI, represent a break away from the slate of near-identical LLMs that have proliferated across the industry: “this model has the potential to be different,” he said. 

The claims here — including strong benchmark performance — have not been peer-reviewed, as per usual, and training data and electrical intensity/carbon emissions remain unclear. 

However, it is an approach that I’d love to see studied and examined in more depth; if diffusion has the potential to reduce inference-time compute while boosting performance, it might actually be impactful. And, along those same lines, it might bite the industry worse than DeepSeek. 

🤔 Your thought process:

Selected Image 1 (Left):

• “Image 2 looked like a painting".”

💭 A poll before you go

Thanks for reading today’s edition of The Deep View!

We’ll see you in the next one.

Here’s your view on Nvidia and the bubble:

27% of you think it looks like the bubble has done the thing that bubbles tend to do (in other words, burst).

23% think it’s a minor setback, 17% think things will come back, but not until next year and the rest have no earthly idea.

Looks like it:

• “Honestly I really hope so. I think AI is an will continue to be revolutionary technology, but it needs time to grow into it. Right now with the current market they're constantly pushing out half-finished, unwanted, and unnecessary products. Doing so is eventually going to tank interest in AI, which will hurt companies in the future who are working to create innovative AI products. The bubble bursting will hurt the industry, but I hope build it back stronger ”

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