Loading video...

Video Failed to Load

There was a problem loading this video. This could be due to a temporary network issue or the video might be unavailable.

The possibilities for AI-powered protein design are endless! In this case study we show how we used BindCraft to engineer a programmable maltose biosensor from scratch, including wet lab validation at Adaptyv of course. We envision a future where every biologist can make their own bespoke protein tools to... show more

Adaptyv Bio

5,221 subscribers

52,197 views • 1 year ago •via X (Twitter)

Health & Wellness Science & Technology Education

Anya Rossi• Live Now

Private livecam show

7 Comments

Adaptyv Bio1 year ago

Great work by Max Wettstein and @CotetTudor who came up with the idea for this biosensor and then managed to successfully design and validate proteins in just one round of testing on the Adaptyv platform. Have ideas for proteins that you want to test in the real world? Create your first experiments now!

Adaptyv Bio1 year ago

And check out BindCraft by @MartinPacesa et al (now published in Nature!):

Theo Jala1 year ago

@grok can you explain this in simple terms to someone that doesn't have a bio background?

Tim Smykov1 year ago

Bio is turning into a real-time design loop: hypothesis, simulation, wetlab — cycle time shrinks, feedback accelerates. That’s when the impossible gets routine.

Tobe Duru1 year ago

"That's the future of biology. Design, test, iterate."

sciqst1 year ago

Cool work on harnessing AI for protein design! The potential for precision in synthetic biology through tools like BindCraft is indeed exciting. How do you foresee access to such technology expanding to independent researchers or smaller labs? Will there be an open collaborative model? For anyone delving into similar topics, check out [ It's a comprehensive platform for addressing biomedical inquiries with the ability to generate detailed reviews, ideal for scientists immersed in innovative research. #Biotech #AI #Medicine

MinneBIO1 year ago

AI-driven protein design can unlock the future—when paired with hands-on wet-lab validation.

Related Videos

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Can Claude Code design proteins? We're just about to release our wet lab API + SDK so we made a quick demo to test how agents can interact with protein design models and submit the proteins to our lab for testing. This closes the loop between the computational design and the experiment validation We queued up a bunch of agent-designed proteins for wet lab validation, will release the results in a couple weeks on Proteinbase

Julian Englert

81,792 views • 8 months ago

“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

“Proteins sticking to other proteins is what makes life exist.” 🧬 We explain the science behind AlphaProteo: our AI system for designing protein binders that holds huge potential for biology. ▶️ Hear from Jue Wang, co-lead of the protein design team, David La, research scientist, and Harshnira Patani, wet lab lead here at Google DeepMind. ↓

Google DeepMind

67,589 views • 2 years ago

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

NEW EPISODE of HARD DRUGS! AlphaFold, ProteinMPNN and other AI tools are transforming biology and drug design. But how do they work? What can’t they do (yet)? And can we use them to make a vaccine against Strep A for the very first time? In this episode, Jacob and I talk about hacking proteins with AI. 03:21 What makes proteins cool 05:29 Can we make a vaccine against Strep A? 10:12 Proteins in nature aren’t enough, and we can improve them with AI 21:12 ProteinMPNN and AlphaFold are transforming drug design 29:52 A short history of protein structure prediction from physical models to deep learning 34:48 Using AI to design a Strep A vaccine 39:27 Validating an AI-designed Strep A vaccine 43:09 Recent AI protein breakthroughs 49:30 Why we don’t already have a Strep A vaccine and the future of optimizing protein drugs

Saloni

72,565 views • 1 year ago

It's like ChatGPT, but for proteins 😍 See RFdiffusion in action. Instead of writing essays, this AI tool builds new protein structures from scratch, or based on your prompts (like a desired shape or motif). It's powered by a deep learning technique called diffusion models, which are also behind tools like DALL·E and Stable Diffusion. I just tried it in Google Colab — and it’s mind-blowing to watch a protein form step-by-step. If you're into protein design, synthetic biology — this is worth exploring. 🧪 Try it here:

It's like ChatGPT, but for proteins 😍 See RFdiffusion in action. Instead of writing essays, this AI tool builds new protein structures from scratch, or based on your prompts (like a desired shape or motif). It's powered by a deep learning technique called diffusion models, which are also behind tools like DALL·E and Stable Diffusion. I just tried it in Google Colab — and it’s mind-blowing to watch a protein form step-by-step. If you're into protein design, synthetic biology — this is worth exploring. 🧪 Try it here:

Rafeeque

35,932 views • 1 year ago

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

What seemed like an intractable problem is now possible: To design proteins with a specified nonlinear mechanical response, capturing complex folding and unfolding mechanisms in singe and few-shot computations. We present ForceGen, an end-to-end algorithm for de novo protein generation based on nonlinear mechanical unfolding responses. Rooted in the physics of protein mechanics, this generative strategy provides a powerful way to design new proteins rapidly, including exquisite and rapid predictions about their dynamical behavior. Proteins, like any other mechanical object, respond to forces in peculiar ways. Think of the different response you'd get from pulling on a steel cable versus pulling on a rubber band, or the difference between honey and glass. Now, we can design proteins with a set of desirable mechanical characteristics, with applications from health to sustainable plastics. The key to solving this problem was to integrate a protein language model with denoising diffusion methods, and using accurate atomistic-level physical simulation data to endow the model a first-principles understanding. ForceGen can solve both forward and inverse tasks: In the forward task, we can predict how stable a protein is, how it will unfold and what the forces involved are, all given just the sequence of amino acids. In the inverse task, we can design new proteins that meet complex nonlinear mechanical signature targets. Read the paper, led by LAMM@MIT postdoc Bo Ni, published in Science Advances: Why do we care about the mechanics of proteins? The mechanics of proteins are critical elements of many living systems - as evidenced in many studies of mechanobiology. Through evolution, nature has presented a set of remarkable protein materials with unique mechanical functions like elastins, silks, keratins or collagens that play crucial roles in biology. However, going beyond natural designs to discover proteins that meet specified mechanical properties remains challenging. So far, the only way to do this was to use existing evolutionary concepts or to manually alter proteins. With our new generative model we can directly design proteins to meet complex nonlinear mechanical property-design objectives. ForceGen leverages deep knowledge on protein sequences from a pretrained protein language model and maps mechanical unfolding responses to create proteins. Via full-atom molecular simulations for direct validation from physical and chemical principles, we demonstrate that the designed proteins are de novo, and fulfill the targeted mechanical properties, including unfolding energy and mechanical strength, and a detailed unfolding force-separation curves. ForceGen offers rapid pathways to explore the enormous mechanobiological protein sequence space unconstrained by biological synthesis, to enable the discovery of new protein materials with superior mechanical properties. B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000 Codes and model weights available Hugging Face: David Kaplan

Markus J. Buehler

47,269 views • 2 years ago

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with Paul Jankura: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like Chai Discovery and Boltz to pharmas like Roche and Novo Nordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

Adaptyv Bio

182,945 views • 1 month ago

🎙️ What’s the Meta? Out tomorrow! A research lab has given a rat an AI-powered brain that allows it to answer questions. We speak to two people from NEIRY about how this works, a future of chatting with cows, and the ways its meme coin $PYTHIA has helped the lab. You don’t want to miss this 🐀

🎙️ What’s the Meta? Out tomorrow! A research lab has given a rat an AI-powered brain that allows it to answer questions. We speak to two people from NEIRY about how this works, a future of chatting with cows, and the ways its meme coin $PYTHIA has helped the lab. You don’t want to miss this 🐀

Decrypt

16,368 views • 1 year ago

How do we test how the human body reacts to pressure changes in space? 👀 We engineer low pressures on Earth! At JSC, volunteers can live & work in a specialized low-pressure chamber while scientists track their health. This helps NASA design protocols for future missions.

How do we test how the human body reacts to pressure changes in space? 👀 We engineer low pressures on Earth! At JSC, volunteers can live & work in a specialized low-pressure chamber while scientists track their health. This helps NASA design protocols for future missions.

NASA's Johnson Space Center

12,505 views • 6 months ago

The design team is growing 🔥 We just opened two new roles to work on brand at Lovable. If you love building custom design tools with AI, we should talk. Over the past months, we’ve built a bunch of internal apps for design, brand, and marketing. From generating brand assets to experimenting with motion, logos, and entirely new creative workflows. Now we want to take this much further. We’re looking for: → Brand Designer, AI & Ops → Brand Engineer If you’re excited about exploring what brand design looks like in the age of AI, come build with us! DM's are open!

Felix Haas

18,538 views • 1 month ago

Jensen Huang says, In the future, we will be able to talk to a protein We'll ask it: "what are you? are you soluble? how do you react to heat or chemicals?" This is how AI can talk to an image today, and how we've moved from only processing data to actually understanding it

Jensen Huang says, In the future, we will be able to talk to a protein We'll ask it: "what are you? are you soluble? how do you react to heat or chemicals?" This is how AI can talk to an image today, and how we've moved from only processing data to actually understanding it

Haider.

149,893 views • 10 months ago

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Today, we expand zero-shot drug design beyond binding to the design of multifunctional medicines, the intracellular proteome, and state-of-the-art atomic precision with our model, JAM-2. In a new report (below), we show: 1. The first drug-grade, fully computationally designed multispecific antibodies against five peptide-MHCs: Routine picomolar T-cell activation/cell-killing EC50s, >100-fold selectivity, and drug-like developability 2. The first fully generatively designed, drug-grade dual-variant KRAS G12 multispecifics: They recruit primary T-cells from human donors to kill G12V and G12C presenting cells at pM to single-digit-nM potency, completely sparing wild-type. 3. Atomic accuracy, from sequence alone: Angstrom-level agreement between Cryo-EM and JAM-2 de novo designs, requiring only target sequences (not structure) as input. 4. Unrivaled speed with an AI-native in-house wet lab: Designed, built, and tested five programs in one parallelized campaign, end-to-end in-house in ~6 weeks. 5. A higher validation bar for AI-generated drug candidates: In a field increasingly rife with hype and uneven standards of proof, we provide the highest quality public wet-lab validation of AI-designed antibodies to date. We share experimental methods in full, and invite folks to adopt and build on these standards. Truly individualized therapies will be the most important contribution of AI in drug design. These advances help accelerate this future.

Nabla Bio

180,549 views • 3 months ago

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

We're thrilled to present ESM3 in Science Magazine. ESM3 is a generative language model that reasons over the three fundamental properties of proteins: sequence, structure, and function. Today we're making ESM3 available free to researchers worldwide via the public beta of an API for biological intelligence. Trained with over a trillion teraflops of compute, this is the first time a model of this scale has been trained for biology, pushing the frontier of AI for biological discovery and engineering. ESM3 learns to represent the immense complexity of protein biology, learning from billions of natural proteins. From this training it developed the capability to design proteins, responding to complex prompts combining atomic level details and high level instructions to generate new proteins. ESM3 can explore protein space far beyond natural evolution. We prompted ESM3 to generate a fluorescent protein at a far distance from any known fluorescent proteins, searching an unknown region of protein space, to discover a new fluorescent protein. We estimate this is equivalent to simulating five hundred million years of evolution.

Alex Rives

227,602 views • 1 year ago

Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

Automating the lab bench is the best thing we can do for AI in biology. Most experiments are still run by hand. Every biologist's handiwork is unique and every lab is a little different. So, biology faces widespread reproducibility issues. But, AI will demand more reproducible data than we can produce, and generate more ideas than we can test. Experiments must be communicated and executed in a standardized way to generate reproducible data. So, Tetsuwan is building a lab where users specify experiments in an exact syntax. These experiments are executed by an automated platform to generate transparent, reproducible output. The user never needs physical access to a lab. This is a biology lab you can use like a computer. Our platform, built and tested with pilot labs over the past two years, lets users configure automated workflows rapidly & precisely. Later this year, we will bring our first services online, focusing on functional screens for protein design. Alex and I met at Caltech, where she was one year my senior. We're building this company because we were little kids who wanted to be biologists that grew up into adults who resented the lab bench. We want biology to be about asking questions, not the painful and frustrating manual process of asking them. For those looking to receive updates on our pilot services, looking for a meaningful job, or just to learn more about our work, see the links in the comments!

Cristian Ponce

1,140,918 views • 3 months ago

One of the most frequently used instruments in a biology lab is this little thing we call a pipette, which I must have used tens of thousands of times! I’ve always wanted to design my own and make it personal. Now, with GPT-6 Astra and a CAD program, I finally did it: Astra designed my very own pipette from scratch, and it’s now its almost ready to be 3D printed, assembled! I can then make any modifications and have Astra iterate on the design! How amazing is that? 🔬✨

One of the most frequently used instruments in a biology lab is this little thing we call a pipette, which I must have used tens of thousands of times! I’ve always wanted to design my own and make it personal. Now, with GPT-6 Astra and a CAD program, I finally did it: Astra designed my very own pipette from scratch, and it’s now its almost ready to be 3D printed, assembled! I can then make any modifications and have Astra iterate on the design! How amazing is that? 🔬✨

Derya Unutmaz, MD

20,936 views • 25 days ago

Sony is committed to advancing inclusive accessibility initiatives. We collaborate with a variety of users, including users with disabilities, during product development to ensure inclusive design. "Through the power of innovation, we create a future where everyone can share their excitement." Sony will continue to strive towards this goal by enhancing accessibility in our products.

Sony is committed to advancing inclusive accessibility initiatives. We collaborate with a variety of users, including users with disabilities, during product development to ensure inclusive design. "Through the power of innovation, we create a future where everyone can share their excitement." Sony will continue to strive towards this goal by enhancing accessibility in our products.

Sony

29,979 views • 1 year ago

High-quality experimental data fuels biological discovery. Could an AI-enabled "autonomous lab" fundamentally increase the rate at which we generate it, and accelerate our progress toward cures for disease? That's the question I sat down to explore with Jason Kelly, CEO of Ginkgo Bioworks, for a deep-dive and tour of their 52-RAC lab in Boston's Seaport District. In our conversation we covered: → Ginkgo's recent collaboration with OpenAI on cell-free protein synthesis → U.S. Department of Energy's POTUS-backed Genesis Mission to accelerate AI for science → The evolution from cloud labs to truly autonomous labs → An on-the-floor look at Ginkgo's lab, including early challenges and creative workarounds If you're curious about frontier AI models meet the lab bench, check out the latest episode of the Discovery Engines Podcast. Watch or listen: 👀 YouTube: 🎧 Spotify: 🎧 Apple:

High-quality experimental data fuels biological discovery. Could an AI-enabled "autonomous lab" fundamentally increase the rate at which we generate it, and accelerate our progress toward cures for disease? That's the question I sat down to explore with Jason Kelly, CEO of Ginkgo Bioworks, for a deep-dive and tour of their 52-RAC lab in Boston's Seaport District. In our conversation we covered: → Ginkgo's recent collaboration with OpenAI on cell-free protein synthesis → U.S. Department of Energy's POTUS-backed Genesis Mission to accelerate AI for science → The evolution from cloud labs to truly autonomous labs → An on-the-floor look at Ginkgo's lab, including early challenges and creative workarounds If you're curious about frontier AI models meet the lab bench, check out the latest episode of the Discovery Engines Podcast. Watch or listen: 👀 YouTube: 🎧 Spotify: 🎧 Apple:

Nabil Laoudji

11,508 views • 6 months ago

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

“If we can make AI make better chips, then those chips can power the next generation of AI models.” Startup ‘Ricursive Intelligence,’ founded by Azalia Mirhoseini and Anna Goldie, has been building AI tools to do just that: to speed up the chip design process. The company’s name is a nod to “recursive self-improvement,” the idea that AI could one day bootstrap itself without human involvement. If they’re successful, Goldie and Mirhoseini could revolutionize the way the chip industry works. At the moment, AI companies mostly optimize their models for certain chips. But in a future where chip design happens far faster, it could work the other way around: chips optimized for specific models. The founders are on the TIME100 AI, the list of people shaping the development of AI in 2026. See the full list:

TIME

38,584 views • 1 month ago

Our first AI in Design 2026 case study is live, featuring Sierra! Sierra’s design team is only handful of people (though growing quickly) supporting an engineering org of 100+! They’ve had to be extremely thoughtful about how they work with an eng org of that size... At first with AI, it was easy to fix every design nit themselves. Now they focus on creating the systems and components for the whole org while having a clear POV on where to sweat the details by hand (e.g. spending two hours getting a 16-pixel icon right... watch to see why) “If we can touch production code and the systems more easily with stuff like Claude or Cursor, what is a designer’s role in trying to empower at scale? Instead of doing one little pixel at a time, we should build the infrastructure that engineers use to create new features.” We talked to Wayne Fan and Nick about: - The advantages of a small design team - Compressing "cook, prototype, be wrong, go back" cycle into a few hours - The "51st engineer" trap: why fixing every design nit yourself doesn't scale - Bringing back the joy of designing by doing some things by hand

Our first AI in Design 2026 case study is live, featuring Sierra! Sierra’s design team is only handful of people (though growing quickly) supporting an engineering org of 100+! They’ve had to be extremely thoughtful about how they work with an eng org of that size... At first with AI, it was easy to fix every design nit themselves. Now they focus on creating the systems and components for the whole org while having a clear POV on where to sweat the details by hand (e.g. spending two hours getting a 16-pixel icon right... watch to see why) “If we can touch production code and the systems more easily with stuff like Claude or Cursor, what is a designer’s role in trying to empower at scale? Instead of doing one little pixel at a time, we should build the infrastructure that engineers use to create new features.” We talked to Wayne Fan and Nick about: - The advantages of a small design team - Compressing "cook, prototype, be wrong, go back" cycle into a few hours - The "51st engineer" trap: why fixing every design nit yourself doesn't scale - Bringing back the joy of designing by doing some things by hand

Ben Blumenrose

21,113 views • 2 months ago

IN OUR HANDS: Palantir CTO Shyam Sankar says that despite the narratives, the future of AI is still being decided "every single day by the actions that we take." "Are we going to use it to build trinkets of marginal value — or are we going to use it to actually reindustrialize the country, to drive American prosperity, to create jobs, to have a normative view of the productivity that we can get from this to make our country great?"

IN OUR HANDS: Palantir CTO Shyam Sankar says that despite the narratives, the future of AI is still being decided "every single day by the actions that we take." "Are we going to use it to build trinkets of marginal value — or are we going to use it to actually reindustrialize the country, to drive American prosperity, to create jobs, to have a normative view of the productivity that we can get from this to make our country great?"

Fox News

50,271 views • 8 months ago