A cookware brand using AppLovin's platform grew from $4 million to $16 million in revenue, turned profitable, and is on pace for $80 million in the current year.
Open Source Wins, AGI Is Here, and Scorsese's AI Toolkit with CEOs of Cerebras & Black Forest Labs
Andrew Feldman says AGI is already here by any definition we'd have used 20 years ago — we just haven't fully deployed it yet.
Jul 10, 2026
1:03:57
Difficulty: Intermediate
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All-In with Chamath, Jason, Sacks & Friedberg
Open Source Wins, AGI Is Here, and Scorsese's AI Toolkit with CEOs of Cerebras & Black Forest Labs
Andrew Feldman says AGI is already here by any definition we'd have used 20 years ago — we just haven't fully deployed it yet.
Jul 10, 2026
1:03:57
Difficulty: Intermediate
Played
TL;DR
Andrew Feldman (Cerebras) and Robin Rombach (Black Forest Labs) join Jason Calacanis at what appears to be a Paris event to discuss the AI buildout, inference computing, open source sovereignty, and generative video. Feldman argues AGI has already arrived by any prior definition [1] — Andrew Feldman "By any definition we had twenty years ago, we've hit it. I mean, if you think about it, although was a Turing test, blew it away." 30:49 , explains how Cerebras is breaking Moore's Law with a new chip architecture [2] — Andrew Feldman "Cerebras breaking Moore's Law (>2x in 18 months): While GPUs are constrained by Moore's Law (doubling every 18 months), Cerebras' novel chi…" 13:13 , and makes the case that AI will enable a world where no child dies of cancer [3] — Jason Calacanis "Paradigms don't die. People do." 37:09 . Rombach reveals Black Forest Labs worked directly with Martin Scorsese to explore generative video as a creative tool. Key takeaway: reasoning + fast inference = exponential gains that are only beginning.
#AI inference hardware
#wafer-scale chips
#AGI debate
#open source models
#AI sovereignty
#latent diffusion
#generative video
#AI in filmmaking
#AI robotics
#data center buildout
#AI red teaming
#token economics
#recursive AI learning
#AI education
#Cerebras
#Black Forest Labs
#AI inference
#open source AI
#AGI
#Moore's Law
#reasoning models
#Martin Scorsese
#AI data centers
#robotics
#film production AI
#wafer-scale chip
Andrew Feldman (Cerebras CEO) and Robin Rombach (Black Forest Labs CEO) discuss the AI buildout, inference computing, open source AI sovereignty, AGI, and generative video — including a partnership with Martin Scorsese.
Chapter list
-
The episode opens at what appears to be a Paris setting, with Jason Calacanis welcoming back Andrew Feldman, founder and CEO of Cerebras, whom he last saw at Davos and at a recent Liquidity event. Calacanis frames the conversation around two big themes: the unprecedented AI buildout and what it means for inference computing. Before the conversation begins in earnest, the AppLovin Ads sponsorship segment airs — spotlighting the platform's billion-user reach inside mobile games, its AI-driven ad optimization, and a case study in which a cookware brand scaled from $4M to a projected $80M in revenue using the platform.
-
Feldman describes a fundamental shift in human-AI interaction: early models did exactly what you told them — the classic 'computers are dumb, they do exactly what you say' formulation from a colleague two decades ago. But modern reasoning models like those in OpenAI's latest releases are beginning to infer intent: a user asks for a chart, and the model proactively offers both a line and a bar version. Calacanis illustrates this vividly with a live experiment — using an unrestricted model to build a real-time trend-scouting agent that debates with itself about where to look for emerging signals (Hacker News, Reddit, Instagram). He watches the model reason in real time, debating its own approach before collapsing into an answer. This is not ChatGPT summarizing a PDF — this is an agent doing strategic research, checking its own work, and asking the human what it missed.
-
Feldman takes the conversation into longer time horizons. The fundamental constraint on human knowledge is that paradigms shift at the speed of generations: Thomas Kuhn showed us that Freud, Skinner, and their disciples maintained intellectual dominance not because their ideas were right, but because they held positions of leadership until they died. New ideas have to wait for the old guard to exit. AI breaks this constraint. Feldman reaches for the geneticist's tool: fruit flies produce two generations per day, allowing researchers to observe thousands of generations of evolution in a single career. AI is now doing the equivalent for knowledge — compressing what previously took centuries of human intellectual turnover into near-instantaneous iterative cycles. He closes with the Palace of Versailles as metaphor: the builders who worked on century-long projects across multiple family generations were doing recursive, compounding learning. AI is that, but at the speed of compute.
-
Robin Rombach introduces himself and his company with a technical origin story that reframes the scope of what Black Forest Labs has built. He and his co-founders invented the latent diffusion algorithm as PhD students in Munich — a method that compresses natural data like images and video into efficient latent representations and trains a transformer model on that compressed space. The intuition is old (JPEG compression, MP3 audio encoding) but the application to generative AI was new. From that foundation came Stable Diffusion, and from Stable Diffusion came Black Forest Labs and Flux. Now, two years in and past 100 employees, the company is pushing into territory that goes far beyond images: multimodal models that simultaneously process images, video, and audio as both inputs and outputs — and can generate content in any of those modalities. The next frontier is action prediction: using the same model to predict physical robot actions, bridging generative AI and the real world.
-
Rombach steps back from the film discussion to articulate the deeper architectural insight: the model that makes a movie and the model that drives a robot are not two different technologies — they are the same model. Pre-training on video at scale gives a model implicit understanding of physics, causality, and spatial relationships — the foundations of intelligent physical action. From that base, only a few hours of task-specific fine-tuning data are needed to deploy the model on a specific robot in a factory setting. The long-term goal is to make even that fine-tuning unnecessary, enabling in-context robot instruction — just tell a robot in natural language what to do, the same way you prompt a language model. Rombach acknowledges this is still a research problem, but the trajectory is clear: world models, action models, and generative video models are all converging into the same underlying architecture.
- Inference
- The computational process of running a trained AI model to generate outputs; distinct from training. In the context of reasoning models, inference is extremely compute-intensive.
- Moore's Law
- The historical observation that transistor density on chips doubles roughly every 18 months, leading to proportional performance gains. Cerebras claims to have exceeded this curve with its new architecture.
- Wafer-scale chip
- A processor that uses an entire silicon wafer as a single chip rather than cutting it into many smaller chips; Cerebras' approach, enabling massively more on-chip memory and bandwidth.
- Latent diffusion
- An algorithm that compresses data (images, video, audio) into a compact latent representation and trains a generative model on that compressed space; the foundational method behind Stable Diffusion and most modern generative AI.
- Reasoning model
- An AI model that generates many intermediate 'thinking' tokens internally before producing a final answer, allowing it to tackle complex multi-step problems — at significantly higher compute cost than standard models.
- Token
- The basic unit of text processed by a language model (roughly a word or word-fragment). Token consumption is the primary measure of AI compute usage and cost.
- Token maxing
- Colloquial term for consuming AI tokens inefficiently or without strategic intent — like wandering every Costco aisle rather than going straight to what you need.
- AGI (Artificial General Intelligence)
- AI that matches or exceeds human-level performance across a broad range of tasks, not just narrow domains. The episode debates whether this threshold has already been crossed.
- Prompt whisperer
- Colloquial term for someone who has mastered the craft of writing precise prompts to elicit good outputs from AI models — a skill becoming less necessary as models learn to interpret intent.
- Red teaming
- A security practice in which a group attempts to find vulnerabilities or failure modes in a system before it is deployed publicly; in AI, it means stress-testing a model for dangerous capabilities.
- Sovereignty (AI context)
- The ability of a country, organization, or individual to control their own AI infrastructure and models rather than depending on foreign or third-party systems.
- Multimodal model
- An AI model that can process and generate multiple types of data — such as text, images, video, and audio — within a single unified architecture.
- Action prediction
- A capability in AI models that predicts the next physical action to take based on visual and contextual inputs; the bridge between generative video models and robotics.
- P-doom
- Short for 'probability of doom' — the estimated likelihood that advanced AI leads to catastrophic or existential outcomes. Used colloquially to describe AI pessimism or doomism.
- Hyperscaler
- A company that operates massive-scale cloud computing infrastructure — specifically AWS, Microsoft Azure, and Google Cloud — known for buying enormous quantities of chips and data center capacity.
- MFU (Model FLOP Utilization)
- A metric measuring how efficiently a training or inference run uses the theoretical peak compute of the hardware — higher MFU means less wasted processing power.
- Loop maxing
- Emerging term for the practice of chaining AI reasoning loops iteratively — each output feeding the next — to produce exponentially better results than a single-pass query.
- Paradigm shift (Kuhn)
- Thomas Kuhn's concept that scientific worldviews don't change gradually but through sudden revolutionary breaks, typically only after old guard thinkers die or retire.
- Drosophila
- The common fruit fly, used extensively in genetics research because it reproduces rapidly (two generations per day), allowing scientists to study many generations quickly. Used here as a metaphor for AI's accelerated learning cycles.
- Flux
- Black Forest Labs' flagship open source image generation model, widely adopted for its quality and flexibility across text-to-image and image-editing tasks.
Chapter 1 · 00:00
The AI Buildout: Datacenters Bigger Than Cities (Andrew Feldman)
The episode opens at what appears to be a Paris setting, with Jason Calacanis welcoming back Andrew Feldman, founder and CEO of Cerebras, whom he last saw at Davos and at a recent Liquidity event. Calacanis frames the conversation around two big themes: the unprecedented AI buildout and what it means for inference computing. Before the conversation begins in earnest, the AppLovin Ads sponsorship segment airs — spotlighting the platform's billion-user reach inside mobile games, its AI-driven ad optimization, and a case study in which a cookware brand scaled from $4M to a projected $80M in revenue using the platform.
Claims made here
Data Centers Bigger Than Cities: The AI Buildout Is Unprecedented
The AI infrastructure buildout is unlike anything in modern history. Individual data centers draw more power than mid-sized cities, and nations from Kazakhstan to France are racing to participate. This is the Great Wall of China moment of our era.
Chapter 2 · 01:50
Reasoning, Inference, and Breaking Moore's Law
Feldman describes a fundamental shift in human-AI interaction: early models did exactly what you told them — the classic 'computers are dumb, they do exactly what you say' formulation from a colleague two decades ago. But modern reasoning models like those in OpenAI's latest releases are beginning to infer intent: a user asks for a chart, and the model proactively offers both a line and a bar version. Calacanis illustrates this vividly with a live experiment — using an unrestricted model to build a real-time trend-scouting agent that debates with itself about where to look for emerging signals (Hacker News, Reddit, Instagram). He watches the model reason in real time, debating its own approach before collapsing into an answer. This is not ChatGPT summarizing a PDF — this is an agent doing strategic research, checking its own work, and asking the human what it missed.
Claims made here
Individual AI data center buildings are now drawing more power than mid-sized cities, and aggregate AI data center demand in the next several years will exceed the previous 50 years of global energy use.
Cerebras has a $25 billion order backlog for its AI inference chips.
Cerebras chips run inference approximately 15 times faster than competing hardware.
Data centers larger than mid-sized cities
Individual AI data centers under construction will draw more power than mid-sized cities, with aggregate demand set to exceed the previous 50 years of Earth's energy use.
Data point
$25B
Cerebras is sitting on $25 billion in backlog, and every hyperscaler from OpenAI to AWS faces the same problem: demand is fully booked, and they're racing to keep customers from leaving — not chasing speculative future adoption.
Data point
$25B
Cerebras has a $25 billion backlog of orders, reflecting demand that far outstrips the industry's ability to build and fill data centers.
The Costco Analogy: AI Token Discipline Is Coming
Early AI adoption looked like everyone grabbing unlimited tokens with no strategy — exactly like someone wandering every aisle at Costco and walking out with $200 of impulse buys. Enterprises are now learning which AI models to use for which tasks, and routing intelligently between frontier and open source.
Reasoning = Inference: Why Fast Chips Are the New Frontier
Modern reasoning AI consumes enormous numbers of tokens internally — essentially thinking out loud before responding. That internal computation is inference, and speed directly translates to more reasoning cycles per dollar. Run Cerebras for 24 hours and you get the equivalent of weeks of AI thinking.
Data point
15x
Cerebras chips can run inference 15 times faster than competing hardware, meaning a 24-hour run on Cerebras could yield weeks or months worth of AI thinking.
Reasoning consumes massive internal tokens
Modern reasoning models consume enormous numbers of tokens internally during inference — making fast inference hardware like Cerebras chips disproportionately valuable.
Breaking Moore's Law: Cerebras Charts a New Trajectory
Every chip before Cerebras followed Moore's Law — doubling performance every 18 months. Cerebras broke that curve with a fundamentally new architecture, and expects to far exceed 2x gains in the next 18 months. New architectures have room to optimize that mature 20-year-old GPU designs simply can't access.
Data point
>2x
While GPUs are constrained by Moore's Law (doubling every 18 months), Cerebras' novel chip architecture is on track to exceed 2x performance gains in the same period.
Chapter 3 · 16:28
Open Source, AI Sovereignty, and the Road to AGI
Feldman takes the conversation into longer time horizons. The fundamental constraint on human knowledge is that paradigms shift at the speed of generations: Thomas Kuhn showed us that Freud, Skinner, and their disciples maintained intellectual dominance not because their ideas were right, but because they held positions of leadership until they died. New ideas have to wait for the old guard to exit. AI breaks this constraint. Feldman reaches for the geneticist's tool: fruit flies produce two generations per day, allowing researchers to observe thousands of generations of evolution in a single career. AI is now doing the equivalent for knowledge — compressing what previously took centuries of human intellectual turnover into near-instantaneous iterative cycles. He closes with the Palace of Versailles as metaphor: the builders who worked on century-long projects across multiple family generations were doing recursive, compounding learning. AI is that, but at the speed of compute.
Claims made here
OpenAI released an open source model called OSS 120B.
Cerebras runs models from GLM, Kimi, Qwen, OpenAI's closed source models, GlaxoSmithKline's proprietary models, and models from G42 and MBZUAI in the UAE.
Palo Alto Networks, after testing a frontier AI model against their own systems, found previously unknown critical bugs and had to halt operations to patch for six weeks.
Current AI systems have already surpassed any definition of AGI that would have been put forward 10, 20, or 50 years ago, including the Turing test.
Nasdaq powers more than 135 marketplaces and regulators globally.
The Open Source Sovereignty Crisis: It's US vs. China
The open source AI landscape has quietly become a geopolitical flashpoint. Outside of OpenAI's OSS model, most available open source options are Chinese. Regulated industries in finance and healthcare that need on-premise, sovereignty-friendly AI have almost nowhere to turn for domestic alternatives.
AI Found Bugs at Palo Alto Networks in Hours — and Stopped Them for Six Weeks
Nikesh Arora of Palo Alto Networks told Jason Calacanis that testing a frontier AI model against their own systems was devastating — it found critical bugs in hours that their own team had missed. They halted everything for six weeks of emergency patching. That's the case for government red teaming in a single anecdote.
AI finds bugs in Palo Alto Networks in hours
Nikesh Arora of Palo Alto Networks told Jason Calacanis that when tested with a frontier AI model, it found unknown critical bugs — forcing the company to halt operations and patch for six weeks.
AGI Is Already Here — We Just Haven't Admitted It
By any definition anyone would have offered 10, 20, or 50 years ago — including the Turing test — we have already hit AGI. The goalposts moved because the reality arrived faster than our imagination could keep up.
AGI already achieved by prior definitions
Feldman argues that by any definition of AGI used 10, 20, or 50 years ago — including the Turing test — AI has already blown past it.
AI Is Learning Faster Than Evolution: The Fruit Fly Comparison
Human learning moves at generational speed — like elephants, one cycle per 15–20 years. Geneticists study fruit flies because they produce two generations a day, compressing evolution into observable timeframes. AI is doing the equivalent: compressing thousands of generations of learning into near real-time iteration.
Paradigms don't die, people do
Feldman referenced Thomas Kuhn's insight that scientific paradigm shifts only happen when incumbent thinkers die — a cycle AI is now compressing by accelerating learning across the equivalent of thousands of generations.
AI cancer cure potential
Feldman argues AI gives humanity a real shot that neither our children nor anyone they know will die of cancer — the defining pro on the ledger of AI's costs and benefits.
Adaptive AI Tutors: We've Known How to Teach Better for 2,000 Years
Aristotle tutored Alexander the Great one-on-one. We've known for millennia that personalized adaptive teaching produces better outcomes. We chose factory-model classrooms anyway. AI agents that adapt to each child's learning style aren't a new idea — they're a 2,000-year-old idea we can finally afford to execute.
Chapter 4 · 40:54
The Innovation Behind Generative Video (Robin Rombach)
Robin Rombach introduces himself and his company with a technical origin story that reframes the scope of what Black Forest Labs has built. He and his co-founders invented the latent diffusion algorithm as PhD students in Munich — a method that compresses natural data like images and video into efficient latent representations and trains a transformer model on that compressed space. The intuition is old (JPEG compression, MP3 audio encoding) but the application to generative AI was new. From that foundation came Stable Diffusion, and from Stable Diffusion came Black Forest Labs and Flux. Now, two years in and past 100 employees, the company is pushing into territory that goes far beyond images: multimodal models that simultaneously process images, video, and audio as both inputs and outputs — and can generate content in any of those modalities. The next frontier is action prediction: using the same model to predict physical robot actions, bridging generative AI and the real world.
Claims made here
All generative AI models deployed for image generation, video generation, and physical AI are built on the latent diffusion algorithm invented by Robin Rombach and his co-founders.
Latent Diffusion: The Algorithm Behind All Generative AI
Every major generative AI model for images, video, and physical AI runs on latent diffusion — invented by Robin Rombach and his co-founders as PhD students in Munich. The insight: compress natural data like images and video into efficient representations, then train a transformer on that. JPEG and MP3 principles, applied to generative intelligence.
Latent diffusion underpins all generative AI
Robin Rombach and his co-founders invented the latent diffusion algorithm — the foundational method behind all deployed image, video, and physical AI generative models.
Chapter 5 · 47:31
Martin Scorsese, Robots, and the Future of Hollywood IP
Rombach steps back from the film discussion to articulate the deeper architectural insight: the model that makes a movie and the model that drives a robot are not two different technologies — they are the same model. Pre-training on video at scale gives a model implicit understanding of physics, causality, and spatial relationships — the foundations of intelligent physical action. From that base, only a few hours of task-specific fine-tuning data are needed to deploy the model on a specific robot in a factory setting. The long-term goal is to make even that fine-tuning unnecessary, enabling in-context robot instruction — just tell a robot in natural language what to do, the same way you prompt a language model. Rombach acknowledges this is still a research problem, but the trajectory is clear: world models, action models, and generative video models are all converging into the same underlying architecture.
Claims made here
A Bitcoin movie starring Gal Gadot was produced for $30 million using AI-generated scenery, compared to an estimated $150 million using traditional set construction.
Black Forest Labs models require only a few hours of fine-tuning data to adapt a visually pre-trained model to a specific robotic hardware task.
AI-generated Star Wars fan films on YouTube, including a channel called 'Star Wars Stories Untold,' are already receiving millions of views per video.
Black Forest Labs has recently crossed 100 employees and is hiring in both Freiburg, Germany and San Francisco.
Martin Scorsese Used AI to Get Ideas Out of His Head
Robin Rombach sat with Martin Scorsese multiple times to demonstrate Black Forest Labs' generative tools. What captivated Scorsese wasn't automation — it was the ability to take a visual scene living in his imagination and externalize it for his team to iterate on. Language is lossy. Images are not.
Scorsese used BFL tools to visualize scenes
Martin Scorsese worked directly with Robin Rombach to use Black Forest Labs' generative models to visualize pre-production scene concepts for a potential new film.
Data point
$30M vs $150M
A Bitcoin movie starring Gal Gadot was filmed entirely on a sound stage, with all scenery generated by AI in post. The result: a $30M production that would have cost $150M with traditional set builds. It never would have been greenlit at $150M — generative AI didn't just cut costs, it made the film possible.
Data point
$30M
A Bitcoin movie starring Gal Gadot was made for $30M using generative AI for all scenery — it would have cost $150M with traditional set builds and might never have been greenlit.
One Model, Two Worlds: Movies and Robotics from the Same AI
The most underappreciated insight in AI right now: a single multimodal generative model can produce a movie and act as the perception and action brain for a physical robot. Pre-training on video gives implicit understanding of real-world physics — which transfers directly into robotic action prediction.
AI models can be fine-tuned for robots in hours
Black Forest Labs' visual understanding models require only a few hours of fine-tuning data to adapt to a specific robotic task, dramatically reducing deployment friction.
AI Fan Films: The Future of IP Is Fan-Made
Star Wars AI fan films are already pulling millions of views per video on YouTube, with channels like 'Star Wars Stories Untold' leading the way. Jason Calacanis argues the smarter play for studios is to build licensing models that let fans be creative with beloved IP — and take a cut of the output.
Star Wars AI fan films getting millions of views
AI-generated Star Wars fan films on YouTube are already accumulating millions of views per video, signaling consumer appetite for AI-enabled creative IP exploration.
Data point
100
Black Forest Labs recently crossed 100 employees, with offices in Freiburg, Germany and San Francisco, and is actively hiring researchers and engineers.
No indexed bits in this chapter.
Show stoppers
AGI Is Already Here — We Just Haven't Admitted It
By any definition anyone would have offered 10, 20, or 50 years ago — including the Turing test — we have already hit AGI. The goalposts moved because the reality arrived faster than our imagination could keep up.
Martin Scorsese Used AI to Get Ideas Out of His Head
Robin Rombach sat with Martin Scorsese multiple times to demonstrate Black Forest Labs' generative tools. What captivated Scorsese wasn't automation — it was the ability to take a visual scene living in his imagination and externalize it for his team to iterate on. Language is lossy. Images are not.
Snapshots ()
Key Quotes ()
This episode
Cast
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Legendary director who worked directly with Black Forest Labs, using their generative models to visualize pre-production scene concepts.
-
OpenAI CEO credited with foreseeing the reasoning AI revolution and discussed in relation to governance tensions with Anthropic.
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Anthropic CEO discussed in relation to the Claude model release controversy and his adversarial relationship with the Trump administration.
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OpenAI co-founder credited with being an early visionary on AI safety and the trajectory of reasoning models.
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Actress who starred in a Bitcoin movie produced for $30M using AI-generated scenery, a real-world example of generative AI in production film.
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Cited as a director famous for storyboarding and collaborative visual pre-production, now a model for how AI tools can expand that process.
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AI inference chip company founded by Andrew Feldman, discussed as a key enabler of reasoning AI with a $25B backlog and wafer-scale architecture that breaks Moore's Law.
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Discussed as a demanding Cerebras customer, competitor, and the company behind reasoning models and the OSS 120B open source release.
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Generative image and video AI company co-founded by Robin Rombach, known for open source model Flux and a partnership with Martin Scorsese.
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Named as a major AI compute customer and frontier model company; Dario Amodei's relationship with the Trump administration discussed in the context of AI governance.
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Track
Major IP holder cited as a company that should consider training its own generative AI models on its library, including the Star Wars franchise.
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Track
Discussed as the dominant GPU maker whose 20-year-old architecture is constrained by Moore's Law, and as a company beginning to push open source models.
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Amazon's cloud arm cited as both a data center capacity demander and as the analogy for early unstructured AI token consumption.
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Track
Leading cybersecurity firm whose CEO told Jason Calacanis a frontier AI model found critical unknown bugs in their systems in hours, requiring six weeks of patching.
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Track
Episode sponsor; mobile advertising platform that started with an $8 domain and grew into one of the largest ad platforms in the world.
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Discussed as the paradigm case for AI-powered fan film creation and IP licensing opportunities for generative video.
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Black Forest Labs' flagship open source image generation model, widely used for creative and commercial image generation tasks.
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Generative image model built on latent diffusion by Robin Rombach and his team before founding Black Forest Labs.
Stats
This episode
Claims & Sources
1 / 14 cited (7%)
Factual claims made this episode, and whether a source was named.
Cerebras has a $25 billion order backlog for its AI inference chips.
Individual AI data center buildings are now drawing more power than mid-sized cities, and aggregate AI data center demand in the next several years will exceed the previous 50 years of global energy use.
Cerebras chips run inference approximately 15 times faster than competing hardware.
All generative AI models deployed for image generation, video generation, and physical AI are built on the latent diffusion algorithm invented by Robin Rombach and his co-founders.
Current AI systems have already surpassed any definition of AGI that would have been put forward 10, 20, or 50 years ago, including the Turing test.
A Bitcoin movie starring Gal Gadot was produced for $30 million using AI-generated scenery, compared to an estimated $150 million using traditional set construction.
Palo Alto Networks, after testing a frontier AI model against their own systems, found previously unknown critical bugs and had to halt operations to patch for six weeks.
OpenAI released an open source model called OSS 120B.
Cerebras runs models from GLM, Kimi, Qwen, OpenAI's closed source models, GlaxoSmithKline's proprietary models, and models from G42 and MBZUAI in the UAE.
Nasdaq powers more than 135 marketplaces and regulators globally.
Black Forest Labs has recently crossed 100 employees and is hiring in both Freiburg, Germany and San Francisco.
Black Forest Labs models require only a few hours of fine-tuning data to adapt a visually pre-trained model to a specific robotic hardware task.
AI-generated Star Wars fan films on YouTube, including a channel called 'Star Wars Stories Untold,' are already receiving millions of views per video.
A cookware brand using AppLovin's platform grew from $4 million to $16 million in revenue, turned profitable, and is on pace for $80 million in the current year.
