Meet GC
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Joined April 2009
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Before starting @getserval, @jakeserval changed one question in his customer interviews. He asked IT teams what they would hand to someone hired to sit next to them and do their work.
Almost everyone said the same thing. Building automations took longer than doing the task itself, which left them resetting passwords by hand, week after week.
Serval automates the automations for every team that supports employees.
Watch @marcbhargava in conversation with Jake.
Chapters
00:00 — Meet Serval
00:35 — Brain Scans and a First Startup
02:24 — Seven Years at Verkada
07:43 — The Question That Unlocked Serval
10:06 — Build the Hardest Thing First
13:04 — Why Startups Can Compete With the Labs
20:11 — Training Future Founders
23:01 — Perplexity to the Fortune 20
27:17 — Fixing Problems Before the Ticket
29:28 — Hiring Salespeople in the AI Era
34:24 — Chasing Product-Market Fit Forever
We’re quadrupling down on @WeAreLegora’s $550M Series E.
More than 140,000 legal professionals now do their work in Legora, spending about an hour and a half a day in the product. Law is one of the most supply-constrained industries in the world, and Legora is giving lawyers the capacity to meet more of that demand.
Watch @jcfurstenberg in conversation with @MaxJunestrand.
Chapters
00:00 — "We Stopped Selling for Six Months"
05:10 — Breaking the Law of Jante
09:10 — Swim Faster by Looking Down
13:06 — Earning Trust at Enterprise Scale
14:49 — "It Would Almost Be Unethical"
16:26 — Agentic Law Is Already Here
17:26 — The Metric That Matters
18:47 — It Takes a Village
21:36 — A 70M-View Ad
26:56 — A Global Company With European Values
33:04 — From World of Warcraft to Team Player
36:25 — Betting on Young Talent
41:56 — A Lighthouse for Industry Transformation
.@BabaHQ is modernizing the last mile of healthcare for people on Medicare. Each person gets a dedicated advocate: a nurse, social worker, or benefits specialist equipped with AI. They book appointments, chase down benefits, and stay by each person’s side until they get the care they need.
We’re proud to be deepening our partnership by doubling down in Baba’s $40M Series A.
Watch @vedantsuri in conversation with @_ConnorSweeney.
Chapters
00:00 — Meet Baba
02:03 — Cloning Nana’s Voice
05:19 — From Family Crisis to Company
10:58 — “They Saved My Christmas”
13:45 — Everything Is Downstream of the Team
18:05 — Building a Lifeline That Lasts
28:05 — Earning Conviction in a Week
"We can't claim our values if we can't defend them."
@HypersonicaGmbH exists because Europe needs to own its critical capabilities so no one can hold it hostage.
Watch @jcfurstenberg in conversation with Philipp Kerth.
Chapters
00:00 — “Europe Is Losing Its Best People”
03:32 — Hacking the Academic System
08:13 — The Coolest Problem in Aerospace
14:24 — Aegis 1: Nine Months to Launch
21:01 — Racing to 2029
28:14 — Stopping Wars Before They Start
34:11 — On to the Next Launch
We're doubling down on @modal's $750M Series D.
Revisit @max_rimpel's conversation with Modal's CEO @bernhardsson.
"If you're not tackling hard problems, why are you creating a startup?"
@bernhardsson was told to use Kubernetes. Instead he built @modal's file system and container runtime from the ground up, tuned for the speed AI workloads demand. With Modal, containers boot in under a second, and workloads burst from zero to thousands of CPUs or GPUs in a few lines of code.
Watch @max_rimpel in conversation with Erik.
Chapters
00:00 — Learning to Code on a Mac Plus
04:38 — Building the Engine Behind Spotify’s Discover Weekly
09:17 — The Pandemic Project That Became Modal
10:14 — The 1 to 2 Year Rule for Picking Startup Problems
12:36 — Broke Until Stable Diffusion Hit
15:42 — Architecting a New Cloud for AI
17:41 — Agents Want the Same Things Developers Do
In 1994, Peter Shor showed that a quantum computer could break modern encryption. For three decades, building one looked like it would take millions of physical qubits.
This year, the team behind @TeamOratomic proposed it could take as few as 10,000.
We're doubling down by co-leading Oratomic’s Series B, bringing their total funding to $775M. Their fault-tolerant quantum computer has the potential to unlock new scientific capabilities and applications we have yet to imagine.
Congratulations to @DolevBluvstein and the team.
Explore the future of quantum with @AlexaLiautaud: generalcatalyst.com/stories/…
We started Percepta three years ago and it's been remarkable to watch the scale and ambition of the team that's come together. They're building the transformation infrastructure that our assets, like Janus and Summa, need to reinvent their industries.
Last week, @htaneja joined @hirshjain at our office for a fireside chat on the state of AI and venture.
Hemant talked about the moment ChatGPT launched. It was the first time a single technology had every CEO, in every industry, in every country, grappling to figure out what to do with it.
For General Catalyst, that came with a recognition: the world was going to be transformed, and the real question was what role to play in that transformation.
The principle of "inclusive prosperity," where you empower everyone to do the transforming themselves, ties closely to Percepta's mission.
That's why we started with healthcare, government, and financial services: the categories that actually touch people's lives. And Hemant's bet, one we share, is that as software gets democratized, relationships become the advantage that's left standing.
The models that will accelerate drug discovery, chip design, and energy are limited by the data they learn from. We led @ProximalHQ’s $15M seed as they build the data engine for the next generation of frontier AI and treat training data with the same rigor as model architecture.
Watch @quentinclark in conversation with @calvinchen.
Chapters
00:00 — Meet Proximal
03:45 — A Rainy Afternoon in South Park
05:19 — From Sneaker Bots to a First Exit
08:14 — Why Suffering Equals Growth
10:13 — The Path to Proximal
11:57 — How Models Learn
15:17 — Training Data as a Research Problem
23:24 — Building the Team in India
General Catalyst retweeted
2.5 year old Long Lake buys the iconic 111-year old Amex GBT. Alex has clearly established Long Lake as the leader in the AI roll up category.
More from our Creation team:
"There is no Apple of healthcare. There is no Airbnb of healthcare."
We believe every major consumer market has been rebuilt around the individual except healthcare. Congratulations to @thatch_ai on their $108M Series C to create a healthcare system people love. Thatch gives employees a health budget from their employer to buy whatever plan fits their life.
We’re proud to triple down on our partnership with @chrisellisdc, Adam Stevenson, and the team.
Watch Alex Tran in conversation with Chris and Adam.
Chapters
00:00 — How a WWII Wage Freeze Built America's Healthcare System
05:35 — Losing Parents to Cancer and Starting Thatch
09:07 — Inside the Thatch Product
14:38 — Why the Hardest Healthcare Problems Are Fintech Problems
20:49 — AI That Calls Your Doctor to Confirm It's In Network
25:55 — Unbundling Healthcare From Employment
31:32 — Lessons From Switzerland and Singapore
General Catalyst retweeted
UK finance teams have waited long enough.
Ramp is now live in the UK.
Sorry we’re late.
ramp.com/uk
General Catalyst retweeted
@generalcatalyst is tripling down on @cognition and thrilled to continue to back @ScottWu46, @russelljkaplan, @walden_yan, @stevenkplus1 and the team in the important pursuit of enterprise sovereignty.
Devin is a critical piece of the AI stack with real commitment to help enterprises adopt AI with real ROI
Congratulations to @forus on its $150M Series C to build an AI network for medicine.
Over a third of patients prescribed specialty treatments never get their first dose. Forus moves new drugs from development to the patients who need them, faster.
We’re proud to triple down on our partnership with @sahirjaggi and the entire team.
Revisit @chriswbischoff's full conversation with Sahir: youtu.be/Vdi0fUekm_g
.@MistralAI is building open-weight frontier AI in Europe, for the world. We've backed @arthurmensch, Timothée Lacroix, and @GuillaumeLample since seed and are investing in their €3B Series D.
With Mistral’s full stack, industries tackling the hardest problems of our time gain agency over their AI.
Congratulations to the entire team.
Our short film started with one line: "I want to ride my bike on the moon."
When it comes to the creative case for investing in space, imagination precedes ambition. What looks naive in scientific progress is remembered as inevitable, but only if someone funds the gap in between.
When @brycestrauss was in fourth grade, NASA made most of the technical decisions for the Juno mission. By 22, Strauss was on the team flying Juno to Jupiter.
Now he's the cofounder of @Nominal_io, building the software toolkit for hardware problems. “The operation is just a really high-stakes test,” he says.
In the latest installment of our Space series, Bryce shares why the physical world always gets the final word: generalcatalyst.com/stories/…
"If you're not tackling hard problems, why are you creating a startup?"
@bernhardsson was told to use Kubernetes. Instead he built @modal's file system and container runtime from the ground up, tuned for the speed AI workloads demand. With Modal, containers boot in under a second, and workloads burst from zero to thousands of CPUs or GPUs in a few lines of code.
Watch @max_rimpel in conversation with Erik.
Chapters
00:00 — Learning to Code on a Mac Plus
04:38 — Building the Engine Behind Spotify’s Discover Weekly
09:17 — The Pandemic Project That Became Modal
10:14 — The 1 to 2 Year Rule for Picking Startup Problems
12:36 — Broke Until Stable Diffusion Hit
15:42 — Architecting a New Cloud for AI
17:41 — Agents Want the Same Things Developers Do
General Catalyst retweeted
meet @mostik_ai!
what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can.
everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning?
we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched.
how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance.
we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst
WIRED has the first external account of the company and the work: wired.com/story/russian-star…
full writeup, the setup, and all the numbers: mostik.ai/read-more
