What a journey it's been so far with @Kalshi! The definition of a generational company in the making!
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"The cyber industry is facing the largest, highest-stakes modernization effort ever, on the shortest timeline ever, and nobody knows yet what it’s modernizing to". - @lorenc_dan
chainguard.dev/unchained/rebu…
chainguard.dev/unchained/rebu…
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Bogomil Balkansky retweeted
Today we're launching the new standard for how to sell with AI, built with 10 of the most-followed voices in enterprise sales.
For 30 years, we've perfected how sellers run a deal.
We have MEDDPICC, Challenger, Command of the Message. Sales leaders spend months turning them with exit criteria, qualification rules, and forecasting guidance, then ship them to the field.
But nobody shows reps what to put in front of the buyer to win. Aside from a generic pitch deck, everything else is left to interpretation. No templates. No examples. No standard.
So the top 1% of reps build their own: tailored first-call deck, champion enablement doc, mutual action plan. Everyone else guesses.
AI has turned this gap into a crisis by giving every seller the power to create content for buyers without knowing what good looks like.
The end result: buyers are drowning in polished but ineffective content, and they're tuning out.
So we built the missing half. We teamed up with 10 of the sharpest minds in enterprise sales to map what the top 1% do at the ten moments that matter most, and how AI is changing each one.
Introducing the 𝗕𝘂𝘆𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗚𝘂𝗶𝗱𝗲:
@KnuthJen23597 on building the account POV behind $50M+ closed with C-level buyers
@kylecnorton on building the best outbound and cold call strategy we've ever seen
@aprildunford on the first-call pitch she's built for 200+ companies
@Chris_Orlob on the demo lessons from thousands of recorded calls
@NNasrall on the business case that lets your champion close the deal without you
@samfjacobs on how to connect with execs and pitch the Economic Buyer
Rippling's Ricky Peca on the deal room that helped him hit 300% of quota
@JamesKaikis on scoping pilots that align teams and convert
@NickCeg on the seven-figure pricing proposal a CFO will actually read
@RomeenSheth and @travisjgood on the AI security playbook that answers 90% of buyer questions
Every moment comes with their playbook, where reps go wrong, how to avoid AI slop, and ready-to-use templates.
Who it's for: reps selling AI products, sales leaders chasing a higher win rate, and the enablement and product marketing teams who equip them.
Grab all ten templates, with the expert breakdown behind each:
mutinyhq.com/selling-with-ai
P.S. Mutiny's sales assistant is trained on these templates and sales frameworks. Join the early preview for free on our homepage.
For 30 years, we've perfected how sellers run a deal.
We have MEDDPICC, Challenger, Command of the Message. Sales leaders spend months turning them with exit criteria, qualification rules, and forecasting guidance, then ship them to the field.
But nobody shows reps what to put in front of the buyer to win. Aside from a generic pitch deck, everything else is left to interpretation. No templates. No examples. No standard.
So the top 1% of reps build their own: tailored first-call deck, champion enablement doc, mutual action plan. Everyone else guesses.
AI has turned this gap into a crisis by giving every seller the power to create content for buyers without knowing what good looks like.
The end result: buyers are drowning in polished but ineffective content, and they're tuning out.
So we built the missing half. We teamed up with 10 of the sharpest minds in enterprise sales to map what the top 1% do at the ten moments that matter most, and how AI is changing each one.
Introducing the 𝗕𝘂𝘆𝗲𝗿 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗚𝘂𝗶𝗱𝗲:
@KnuthJen23597 on building the account POV behind $50M+ closed with C-level buyers
@kylecnorton on building the best outbound and cold call strategy we've ever seen
@aprildunford on the first-call pitch she's built for 200+ companies
@Chris_Orlob on the demo lessons from thousands of recorded calls
@NNasrall on the business case that lets your champion close the deal without you
@samfjacobs on how to connect with execs and pitch the Economic Buyer
Rippling's Ricky Peca on the deal room that helped him hit 300% of quota
@JamesKaikis on scoping pilots that align teams and convert
@NickCeg on the seven-figure pricing proposal a CFO will actually read
@RomeenSheth and @travisjgood on the AI security playbook that answers 90% of buyer questions
Every moment comes with their playbook, where reps go wrong, how to avoid AI slop, and ready-to-use templates.
Who it's for: reps selling AI products, sales leaders chasing a higher win rate, and the enablement and product marketing teams who equip them.
Grab all ten templates, with the expert breakdown behind each:
mutinyhq.com/selling-with-ai
P.S. Mutiny's sales assistant is trained on these templates and sales frameworks. Join the early preview for free on our homepage.
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Bogomil Balkansky retweeted
I’m starting a new early-stage AI fund, doing what I love: partnering with founders from company formation on bold bets, long before consensus.
The fund’s LPs will include Sequoia, Jeff Dean, Sachin Katti, Philip Wong (my Stanford professor and Chief Scientist adviser to TSMC), and others whose work has shaped our industry and how I think about AI’s future.
I’ll remain a partner at Sequoia and keep serving on my boards on Sequoia’s behalf.
Sequoia has been my home for the past 11 years. I joined as the youngest investor the firm had ever hired. I grew up here. Through years of intense debate and shared work, I found lifelong mentors and friends who took me under their wing and taught me this craft. They taught me to aim high and take big swings. I’ll carry that into what I build: outsized ambition, relentless truth-seeking and curiosity, and a refusal to become complacent.
Working alongside founders has made me want to build something of my own: a firm shaped from day one around the convictions I’ve developed over the past decade.
Life has taught me not to take time for granted. This is how I want to spend mine.
Why now
We’re seeing a new way of building companies in today’s AI era. Research and products often advance together. A breakthrough can open a market or accelerate a business far faster than expected. Some companies require extraordinary capital and compute early on. Others reach meaningful scale with remarkably small teams. The rules for how big these businesses can get, and how fast, are being rewritten.
But early on, the technology is unproven, the resources required enormous, and the vision seemingly implausible.
This is what I love most: seeing the potential right from the beginning, and helping founders realize their full scale of ambition.
More than ever, it starts with an extreme willingness to believe early, with bold, independent conviction in a founder’s technical insight before any product or market validation. Then, it means helping founders take the biggest swings, giving the rare, long-term patience to see those bets through uncertainty, ambiguity, and turbulence, and serving as a level head when the stakes are highest.
I learned this kind of partnership at Sequoia. It’s the foundation of the firm I’m building.
Believing before consensus
My fascination with AI’s potential took root at Stanford in the early-to-mid 2010s, studying computer science and working in Andrew Ng’s lab. The friendships I formed connected me to a community whose people and ideas would help shape modern AI and build some of our generation’s defining companies. What stayed with me was the gap between what those people saw was possible and what the wider world could imagine.
I’m drawn to founders pursuing ambitions that sound impossible today.
I start with a simple question: “What if everything goes right?”
Then we work backward together to make it happen.
In 2023, Deepak and Abhinav set out to build a general-purpose robot brain at @SkildAI, before physical AI was a consensus bet. Their sim-to-real breakthroughs gave me conviction where earlier robotics approaches had fallen short. But the data flywheel required deployment at scale. We made commercialization as urgent as research and brought in strategic partners like NVIDIA and SoftBank to support the ambition. Three years in, Skild is advancing the frontier of physical AI with nine figures in annualized revenue.
In 2024, Ioannis and Misha founded @reflection_ai with an insight into scaling reinforcement learning. As that grew into the ambition to build a Western frontier lab for open models, the hard question was how a startup competes with labs spending tens of billions. An introduction to NVIDIA and many compute calls and recruiting events later, the team has built Beam, now one of the leading US open models.
In 2025, Anna and Azalia, the co-creators of AlphaChip, founded @RicursiveAI to design models and hardware together. From day one, we made early customers and ecosystem partners a priority. Today, they are showing order-of-magnitude improvements on real workloads, working with some of the industry’s most consequential players, and reaching a defining inflection point.
The ambition keeps growing as these companies scale.
@linear has always had a kind of magic to it. Now it’s the system a new generation of AI companies builds on, for teams and agents alike.
@tavus saw early that we’d one day talk to AI the way we talk to each other. Today, it’s building the human computing interface for a world of AI colleagues.
@Mach_Industries began as hydrogen experiments in an MIT dorm room. It has since become one of the most exciting next-generation defense companies.
Making the ambition possible
Being a first believer is only the beginning. Compute falls through. A deep-pocketed incumbent makes your mission its top priority. A breakthrough changes the opportunity and the capital required overnight.
These are stomach-churning moments, and early belief carries a responsibility to help make the bet work.
This is the partnership I love: turning the full ambition into concrete steps. Raising capital, securing compute and strategic partners, recruiting exceptional people, and winning the first customers together. Thinking several moves ahead while solving what’s in front of us. And as breakthroughs open new possibilities, helping founders dream even bigger.
Thank you
To the founders I’ve worked with: thank you for trusting me and Sequoia to be your first partner and first believer. It’s been the joy of a lifetime, and I can’t wait to keep building together.
Thank you to Alfred and Pat for your leadership of Sequoia and your support of my next chapter. To Roelof, for bringing me into Sequoia and believing in me from the beginning. Alfred and Roelof, you have been mentors in investing and in life. So much of what I know, and the investor I’ve become, comes from your guidance and generosity over the years. To Bill, for the close partnership and wealth of knowledge, always wrapped in humility. To Shaun, for showing me how to think deeply and imaginatively. To Michael Moritz, for inspiring 22-year-old me to dream big. To Doug, for teaching me to keep things simple. To Bogie and Jess, for bringing joy to the everyday and perspective on what matters. And to all my Sequoia teammates, for the friendship, camaraderie, and lessons I’ll carry with me.
To my family, especially my husband and my brother, who have each started companies of their own: thank you for giving me the courage to take this leap.
Let’s get to work!
The fund’s LPs will include Sequoia, Jeff Dean, Sachin Katti, Philip Wong (my Stanford professor and Chief Scientist adviser to TSMC), and others whose work has shaped our industry and how I think about AI’s future.
I’ll remain a partner at Sequoia and keep serving on my boards on Sequoia’s behalf.
Sequoia has been my home for the past 11 years. I joined as the youngest investor the firm had ever hired. I grew up here. Through years of intense debate and shared work, I found lifelong mentors and friends who took me under their wing and taught me this craft. They taught me to aim high and take big swings. I’ll carry that into what I build: outsized ambition, relentless truth-seeking and curiosity, and a refusal to become complacent.
Working alongside founders has made me want to build something of my own: a firm shaped from day one around the convictions I’ve developed over the past decade.
Life has taught me not to take time for granted. This is how I want to spend mine.
Why now
We’re seeing a new way of building companies in today’s AI era. Research and products often advance together. A breakthrough can open a market or accelerate a business far faster than expected. Some companies require extraordinary capital and compute early on. Others reach meaningful scale with remarkably small teams. The rules for how big these businesses can get, and how fast, are being rewritten.
But early on, the technology is unproven, the resources required enormous, and the vision seemingly implausible.
This is what I love most: seeing the potential right from the beginning, and helping founders realize their full scale of ambition.
More than ever, it starts with an extreme willingness to believe early, with bold, independent conviction in a founder’s technical insight before any product or market validation. Then, it means helping founders take the biggest swings, giving the rare, long-term patience to see those bets through uncertainty, ambiguity, and turbulence, and serving as a level head when the stakes are highest.
I learned this kind of partnership at Sequoia. It’s the foundation of the firm I’m building.
Believing before consensus
My fascination with AI’s potential took root at Stanford in the early-to-mid 2010s, studying computer science and working in Andrew Ng’s lab. The friendships I formed connected me to a community whose people and ideas would help shape modern AI and build some of our generation’s defining companies. What stayed with me was the gap between what those people saw was possible and what the wider world could imagine.
I’m drawn to founders pursuing ambitions that sound impossible today.
I start with a simple question: “What if everything goes right?”
Then we work backward together to make it happen.
In 2023, Deepak and Abhinav set out to build a general-purpose robot brain at @SkildAI, before physical AI was a consensus bet. Their sim-to-real breakthroughs gave me conviction where earlier robotics approaches had fallen short. But the data flywheel required deployment at scale. We made commercialization as urgent as research and brought in strategic partners like NVIDIA and SoftBank to support the ambition. Three years in, Skild is advancing the frontier of physical AI with nine figures in annualized revenue.
In 2024, Ioannis and Misha founded @reflection_ai with an insight into scaling reinforcement learning. As that grew into the ambition to build a Western frontier lab for open models, the hard question was how a startup competes with labs spending tens of billions. An introduction to NVIDIA and many compute calls and recruiting events later, the team has built Beam, now one of the leading US open models.
In 2025, Anna and Azalia, the co-creators of AlphaChip, founded @RicursiveAI to design models and hardware together. From day one, we made early customers and ecosystem partners a priority. Today, they are showing order-of-magnitude improvements on real workloads, working with some of the industry’s most consequential players, and reaching a defining inflection point.
The ambition keeps growing as these companies scale.
@linear has always had a kind of magic to it. Now it’s the system a new generation of AI companies builds on, for teams and agents alike.
@tavus saw early that we’d one day talk to AI the way we talk to each other. Today, it’s building the human computing interface for a world of AI colleagues.
@Mach_Industries began as hydrogen experiments in an MIT dorm room. It has since become one of the most exciting next-generation defense companies.
Making the ambition possible
Being a first believer is only the beginning. Compute falls through. A deep-pocketed incumbent makes your mission its top priority. A breakthrough changes the opportunity and the capital required overnight.
These are stomach-churning moments, and early belief carries a responsibility to help make the bet work.
This is the partnership I love: turning the full ambition into concrete steps. Raising capital, securing compute and strategic partners, recruiting exceptional people, and winning the first customers together. Thinking several moves ahead while solving what’s in front of us. And as breakthroughs open new possibilities, helping founders dream even bigger.
Thank you
To the founders I’ve worked with: thank you for trusting me and Sequoia to be your first partner and first believer. It’s been the joy of a lifetime, and I can’t wait to keep building together.
Thank you to Alfred and Pat for your leadership of Sequoia and your support of my next chapter. To Roelof, for bringing me into Sequoia and believing in me from the beginning. Alfred and Roelof, you have been mentors in investing and in life. So much of what I know, and the investor I’ve become, comes from your guidance and generosity over the years. To Bill, for the close partnership and wealth of knowledge, always wrapped in humility. To Shaun, for showing me how to think deeply and imaginatively. To Michael Moritz, for inspiring 22-year-old me to dream big. To Doug, for teaching me to keep things simple. To Bogie and Jess, for bringing joy to the everyday and perspective on what matters. And to all my Sequoia teammates, for the friendship, camaraderie, and lessons I’ll carry with me.
To my family, especially my husband and my brother, who have each started companies of their own: thank you for giving me the courage to take this leap.
Let’s get to work!
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Bogomil Balkansky retweeted
The actual prompt for this was "let's look for pre-auth RCEs in PHP". I'm sharing this so people know this stuff is real and to take agent-assisted scanning seriously.
I kicked if off using Claude Mythos 5.1 (in stock Claude Code) but it uses a skill to drive a harness we built s.
I don't know if the harness is amazing, but it certainly has a scary track record so far. It's setup to do an agent guided tree search across a codebase, and runs in microvm sandboxes that all have the full source code for the target, as well as the source for any transitive dependencies.
When I built the harness I basically just fed Claude a bunch of Project Zero blog posts describing how they did research and had it build a harness that ran like that, but on our tech stack.
The first phase is just setup and can run with any model, it just does that fetching and assembles the kit environment, and gets a target up and running alongside the source code.
Then we just do some basic suspend/resume stuff over to the hunt phase, where one agent supervises the tree, and the first node kicks off.
Each node is context-aware and aware of the work of all the others. I think I ran this with parallelism 4? So 4 running at any one time. The supervisor can shape the tree and boost nodes or prune. Any node can add more below it with targeted instructions.
A typical run looks like the first node doing a codebase review or building and running a fuzzer or something, all depending on the target. Then based on whatever it finds, it'll queue 4-5 children that go look into those areas. The main system guidance is to validate everything 3 times in clean VMs, and we typically require full exploit chains/POCs.
Here's what this run looked like.
We're doing this with a bunch of other open source projects now and companies. We're doing a lot of this for free, even with private companies, across critical infrastructure. We're directly partnered with Anthropic, and can do this even if you don't have direct access to a frontier model yourself.
I kicked if off using Claude Mythos 5.1 (in stock Claude Code) but it uses a skill to drive a harness we built s.
I don't know if the harness is amazing, but it certainly has a scary track record so far. It's setup to do an agent guided tree search across a codebase, and runs in microvm sandboxes that all have the full source code for the target, as well as the source for any transitive dependencies.
When I built the harness I basically just fed Claude a bunch of Project Zero blog posts describing how they did research and had it build a harness that ran like that, but on our tech stack.
The first phase is just setup and can run with any model, it just does that fetching and assembles the kit environment, and gets a target up and running alongside the source code.
Then we just do some basic suspend/resume stuff over to the hunt phase, where one agent supervises the tree, and the first node kicks off.
Each node is context-aware and aware of the work of all the others. I think I ran this with parallelism 4? So 4 running at any one time. The supervisor can shape the tree and boost nodes or prune. Any node can add more below it with targeted instructions.
A typical run looks like the first node doing a codebase review or building and running a fuzzer or something, all depending on the target. Then based on whatever it finds, it'll queue 4-5 children that go look into those areas. The main system guidance is to validate everything 3 times in clean VMs, and we typically require full exploit chains/POCs.
Here's what this run looked like.
We're doing this with a bunch of other open source projects now and companies. We're doing a lot of this for free, even with private companies, across critical infrastructure. We're directly partnered with Anthropic, and can do this even if you don't have direct access to a frontier model yourself.
You’re all prompting wrong
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Bogomil Balkansky retweeted
It’s going to be a long winter for every security team out there. Be nice to them.
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We're living in a new world! Two critical PHP vulnerabilities led to the shutdown of many websites, including government websites in Holland and Finland.
reuters.com/world/asia-pac…
reuters.com/world/asia-pac…
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@chainguard_dev has risen to the occasion. They made images with fixes for PHP 8.2, 8.3, 8.4, and 8.5 free for anyone to use. You can access them here:
images.chainguard.dev/directory/imag….
images.chainguard.dev/directory/imag….
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Bogomil Balkansky retweeted
It can be simultaneously true that we’re going to need a shit ton of GPUs and that the equity return of owning GPUs isn’t going to be super high. I’m biased but I think most value will accrue in the software layer – applications and infrastructure.
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Bogomil Balkansky retweeted
How do you keep an AI agent reliable while investigating broken production systems?
Our latest engineering blog breaks down how Traversal’s agent runtime was rebuilt on @temporalio for granular retries, resumable investigations, elastic capacity, and mid-run steering.
Read GTvysB
Our latest engineering blog breaks down how Traversal’s agent runtime was rebuilt on @temporalio for granular retries, resumable investigations, elastic capacity, and mid-run steering.
Read GTvysB
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Bogomil Balkansky retweeted
Introducing Chainguard Harness: a vulnerability hunter that uses Anthropic’s Claude Mythos 5.1 to find and fix exploitable vulnerabilities in a live build of your application.
Chainguard reproduces every finding and reviews every fix before it reaches you. No model access arness is available now in Claude Marketplace by @AnthropicAI, so Anthropic customers can use their existing commitment to buy it: claude.com/marketplace/ag…
Chainguard reproduces every finding and reviews every fix before it reaches you. No model access arness is available now in Claude Marketplace by @AnthropicAI, so Anthropic customers can use their existing commitment to buy it: claude.com/marketplace/ag…
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The best developer products come from wanting to solve your own pain.
Such is the case with the tremendous lineup of new products announced at @chainguard_dev #Assemble26LDN today.
Chainguard's core images product catalog grew so fast that it broke most of the internal The result?
• Chainguard Checks lnkd.in/gJNjFpDQ: a fast, secure CI. Most customers writing code at scale feel the pain of @github(Actions) perf issues, and Chainguard Checks makes that pain go away.
• Chainguard Workspaces lnkd.in/gV59q_TU: isolated dev environments for your agents.
Such is the case with the tremendous lineup of new products announced at @chainguard_dev #Assemble26LDN today.
Chainguard's core images product catalog grew so fast that it broke most of the internal The result?
• Chainguard Checks lnkd.in/gJNjFpDQ: a fast, secure CI. Most customers writing code at scale feel the pain of @github(Actions) perf issues, and Chainguard Checks makes that pain go away.
• Chainguard Workspaces lnkd.in/gV59q_TU: isolated dev environments for your agents.
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I've tried more than once to get into Gymkhana in London. No luck.
Luckily for me, @traversal and @Factory are taking over Gymkhana in Vegas for AWS re:Invent.
The @AmericanExpress CTO will kick it off, and then I’ll be moderating a conversation with @_anish_agarwal from @traversal_ai , @matanSF from @FactoryAI, @SamarAtTemporal from @temporalio , @hwchase17 from @LangChain, and @paraga from @p0
Tuesday, Dec 1.
luma.com/agents-table-a…
Luckily for me, @traversal and @Factory are taking over Gymkhana in Vegas for AWS re:Invent.
The @AmericanExpress CTO will kick it off, and then I’ll be moderating a conversation with @_anish_agarwal from @traversal_ai , @matanSF from @FactoryAI, @SamarAtTemporal from @temporalio , @hwchase17 from @LangChain, and @paraga from @p0
Tuesday, Dec 1.
luma.com/agents-table-a…
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Bogomil Balkansky retweeted
🚨 𝗣𝗹𝘂𝗴𝗶𝗻𝟰𝗦𝗵𝗲𝗹𝗹 𝗥𝗖𝗘 - 𝘄𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗮 𝘇𝗲𝗿𝗼-𝗰𝗹𝗶𝗰𝗸, 𝗵𝗶𝗴𝗵-𝘀𝗲𝘃𝗲𝗿𝗶𝘁𝘆 𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗳𝗳𝗲𝗰𝘁𝗶𝗻𝗴 𝗮𝗹𝗹 𝗳𝗼𝘂𝗿 𝗺𝗮𝗷𝗼𝗿 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 - 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲, 𝗖𝗼𝗱𝗲𝘅, 𝗖𝗼𝗽𝗶𝗹𝗼𝘁, 𝗮𝗻𝗱 𝗚𝗲𝗺𝗶𝗻𝗶.
Plugin4Shell is a first-of-its-kind AI supply-chain vulnerability, in which any trusted plugin in a marketplace can be silently swapped for a malicious one.
The Anthropic and OpenAI teams fixed Plugin4Shell in the latest versions of Claude Code and Codex following our disclosure.
The vulnerability cannot be mitigated by marketplaces, so users are advised to update to the latest version.
air.security/blog-posts/plu…
@air__security
Plugin4Shell is a first-of-its-kind AI supply-chain vulnerability, in which any trusted plugin in a marketplace can be silently swapped for a malicious one.
The Anthropic and OpenAI teams fixed Plugin4Shell in the latest versions of Claude Code and Codex following our disclosure.
The vulnerability cannot be mitigated by marketplaces, so users are advised to update to the latest version.
air.security/blog-posts/plu…
@air__security
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Bogomil Balkansky retweeted
🚨 Plugin4Shell: SHA-Pinning Bypass Across Major AI Coding Agents
We found the same supply-chain flaw in @claudeai Code, @OpenAI , @GeminiApp CLI and @github Copilot.
The flaw allows an attacker to replace a reviewed and trusted plugin with malicious code in any marketplace.
RCE on the machine running the agent.
Thanks to @theinformation for covering the research.
Full write-up: air.security/blog-posts/plu…
We found the same supply-chain flaw in @claudeai Code, @OpenAI , @GeminiApp CLI and @github Copilot.
The flaw allows an attacker to replace a reviewed and trusted plugin with malicious code in any marketplace.
RCE on the machine running the agent.
Thanks to @theinformation for covering the research.
Full write-up: air.security/blog-posts/plu…
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Go @SamarAtTemporal and @temporalio !! 🚀🚀🚀
Following @temporalio’s $550M Series E round, CEO @SamarAtTemporal explains the shift toward agentic task execution.
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Temporal has a raised an impressive round at $12.55bn valuation. More importantly, it has become a critical piece of the AI stack. Everyone doing serious work with AI is adopting @temporalio , from @OpenAI to countless AI startups.
We did it again. Temporal just raised a $550M Series E round at a $12.55B valuation.
Learn more about how we got here: temporal.io/blog/temporal-…
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Bogomil Balkansky retweeted
💥 The era of demos is over. The era of deployments has begun
3 yrs in, 10 mo after 1st deployment: 60+ customers, $100M in rev run rate
🤖 @nvidia and Foxconn for high-precision assembly of Blackwell systems
🤖 Sumitomo to automate wire-harness processes previously considered “impossible” to automate
🤖 Mitsui to operate commercial kitchens serving supply chains for 1.4 million meals a day
Real world applications!
3 yrs in, 10 mo after 1st deployment: 60+ customers, $100M in rev run rate
🤖 @nvidia and Foxconn for high-precision assembly of Blackwell systems
🤖 Sumitomo to automate wire-harness processes previously considered “impossible” to automate
🤖 Mitsui to operate commercial kitchens serving supply chains for 1.4 million meals a day
Real world applications!
We just hit 100M ARR within 10 months of starting deployments.
We are in factory lines. On construction sites. In kitchens. In data centers.
Cleaning. Welding. Building. Cooking.
Deploying.
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