Head @samsungnext. EVP, Samsung Electronics. Tweets/RT are mine.
Los Angeles, CA
Joined May 2007
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davidlee retweeted
I'd like to share a little about what I've been working on at my new company @ultrametricai with my cofounder @tylerlastovich. We have been exploring how we could automate the startup itself, leaving founders with more time to focus on their exact mission.
Enter our first release of the Open Startup Repo: github.com/ultrametricai/ult…
A common foundation for transparent automation of the startup.
After close to 20 years in silicon valley, backing hundreds of amazing founders & startups, being in the trenches of founding Heyzap (YC08), then solo founding @golden, going through 2 acquisitions and working behind the scenes with some of the iconic companies today, I'm witnessing the largest change to how we work I could have imagined - a truly monumental era of creative abundance.
I believe the next generation of companies will largely run themselves: from operations, code, product upgrades, growth, vendor control & selection, process execution, special situations, strategy etc etc. That leaves founder time for 'the art' at the very niche core: the (currently) unautomatable, the special leaps humans tend to make, and north star setting.
We want founders to go super deep into the main problem they are trying to solve and not get held up doing anything else, from searching for domain availability with trademark checks to setting their company up, to running their processes on autopilot.
So to help that broad foundational layer for starting and running a company, we have spent around a year exploring how to help founders focus on their mission across different verticals, geos, stages and initial starting conditions.
Our first few attempts at making something useful actually failed as the market and AI was moving so fast; it just got absorbed into the models or other product releases. We saw polsia happen, openclaw, then grokbot, muse, dots, etc era as the astras, fables, opuses, kimis and jevs rolled out. What seemed to have been missing along the way was a common repo to identify, unify, open and normalize all the lego bricks that startups have to interact with (vendors, processes, business logic, law, best practices, surface areas, art of the possible).
As the number of software vendors blew up, the interfaces to drive them multiplied, the international startup founder blew up as silicon valley moved from a specific location to a state of mind and global movement. Unlimited pre seed funding in unlimited vertical sub markets and permutations has brought us to the age of infinity but we have yet to achieve being able to type into an agent a very simple sentence with very complex implications 'just setup my company and make $1m/m'. We have ended up with a hybrid mismash of computer use, API calls, plugins, shifting surface areas (API/MCP/CLI/plugins/chats/musish futures), and changing model absorption to vendor balance making it an exciting and unstable time.
As our first step we started to rank all the vendors that startups interface to when going about their journey in terms of how agentically controllable they are via their APIs, MCPs, CLIs etc and computer use compatibility as we are largely switching from a UI driven world to the agentic control surfaces and onto full automation. We have 100 ranking areas you can check out with an overall open ranking here: ultrametric.ai/overall
Then we looked at the processes that startups do and how the vendors rank for the sub steps or process as a whole and how this changes across geos. Open processes: ultrametric.ai/processes
Then we started to hack on an open repo for startups that would contain all the components we are going to need for a common foundation for founders across the globe even if our gov processes vary so much from Bengaluru to SF or Lisbon to Shenzhen. The founder spirit is geo-invariant.
We have looked at special founder/startup situations, the business logic that will make open modules for the startup that we can hack on together, ranked law firms, government services and interfaces, stacks that go well together. We have mapped the law and lore that maps to startups and the schemas that make up the data objects and documents. But also the open documents and the missing gaps. MUCH TO DO.
All the BS processes and fluff work that founders waste their time on needs to goto zero. I want to see more supersonic planes, cancer drugs, seemingly impossible missions done and increase the bang for buck founders have with their funding.
This is a work in progress and we are keen for the community to come hack on the repo with us here: github.com/ultrametricai/ult…
Lets get into the structure of the general repo and the visualizations we made on ultrametric.ai site with example links:
- Open rankings: ultrametric.ai/overall
- Open rankings example (online payments): ultrametric.ai/arena/payment…
- Open ranking example 2 (cap table): ultrametric.ai/arena/equity-…
- Open processes: ultrametric.ai/processes
- Open process example (Incorporate C-Corp) ultrametric.ai/processes/inc…
- Open situations that founders face: ultrametric.ai/situations
- Open startup simulator (you can test our different initial starting conditions): ultrametric.ai/startup-sim
- Open modules (e.g. cap table, payroll logic etc - we'd love help improving these) ultrametric.ai/open-modules
- Open law (we are interested in how law changes the processes, situations and normalizing this over time) ultrametric.ai/open-law
- Open lore (we are interested in the historical lore of startups, the rules of thumb and heuristics that might guide your agent): ultrametric.ai/open-lore
- Open fields (we are interested in how the schema for the data connects to the open modules, business logic, open documents and artifacts of each process eg how is a EIN defined?) ultrametric.ai/fields
- Open documentation (eg SAFE notes and missing! docs) ultrametric.ai/open-document…
We have looked at YC companies as well in terms of which ones do well agentically: ultrametric.ai/yc
We are interested in stacks and how people are changing their work: ultrametric.ai/stacks
We are interested in what is automatable and where the agentic ceiling is currently and how to push that up in all areas: ultrametric.ai/rankings/proc…
There is more for you to explore and a lot more coming in terms of how you will be able to navigate business in an optimal way in your chosen surface area; whether you are hanging in grokbot, dots, muse, claude, terminal, chat, or whatever is coming up in this exciting time.
We have a lot of data to collect, clean up and help agents navigate the vast landscape emerging functionality the entire startup ecosystem has to offer.
Come join us getting your vendor in, ranked or that access pattern you have wanted or getting the data more accurate in terms of which vendor is best for a process or steps. Or open a module of something that currently cost money that could be free.
Also come play in our discord for founders and join the chaos (we can solve each others issues including fund raising tips from me personally): discord.com/invite/3aHky836q…
It's time to lift all the boats in the harbor and I'm extremely excited as to what the community of hackers and painters will hack into the repo, what asks they will have and whether we can shift normalization of processes globally, gov agentic interfaces and have common repo for founders that grows over time. I expect a lot of experimentation along the way, bugs and upside...
I appreciate our backers having faith in us as well from @ycombinator with @garrytan to @immad doing a sizable check (congrats on $1bn rev homie) to @darbyw (congrats on Stripe buying @clerkyinc), @drewhouston form @Dropbox and also @pioneer_fund @tomerlondon cto @GustoHQ and hopefully this is 'boring' and complex enough for Charlie Songhurst :> @delingpole @Liquid2V @ivolo from @segment @ryanpowe @sonicaghi of @kong @DellyTamer+ other supporters (@michaelwfrank, Fundamental ventures and if I missed you sorry moving fast!).
[And don't worry, all vendor rankings are open scoring methods so no favors are being done if someone backed us and is a vendor they have to compete with how agentic and automatable they are just like anyone else.]
PS: we have bugs and many quality upgrades to make but thought better to go out early and fix as a community.
PPS: we have other stuff to launch we just thought this was a great place to start and have 200 PRs coming out pretty soon but I still want to know what is broken or doesnt make sense or inaccurate.
Special thanks to current and past team members for getting us here and all the experiments we did you know who you are.
davidlee retweeted
🛰🛰🛰 A great group of investors just invested $250M in next-gen GPS from Xona.
On Tuesday, SpaceX launched 6 Xona sats, all operational. You can see one deployed in space in the last image, adorned with team signatures.
Xona holds the first and only FCC license to operate a commercial GPS service. By flying a constellation closer to Earth, the service is 100x more powerful, so it even works indoors. And more precise, to cm resolution. And more secure and robust to spoofing. And it’s backward compatible with existing GPS systems. Commercial service begins in 2027.
Excerpts from Forbes today:
“CEO Brian Manning, a former SpaceX engineer who cofounded Xona in 2019 with seven fellow alums of Stanford’s aeronautics and astronautics department decided to raise again after “seeing an immense amount of incoming demand, which I think is a reflection of the progress we have made. We started the year with technically an empty warehouse, and now we have a functional satellite factory.”
Jurvetson was an early investor and former board member in Tesla and has a stake in Elon Musk’s Boring Company. Xona is his third space investment, with satellites apparently becoming something of a specialty.
Satellites have three major markets, each of which involves a distinct set of hardware and a tailored altitude. Jurvetson’s portfolio neatly spans the trifecta: SpaceX’s Starlink offers communication services; geospatial imaging company Planet photographs Earth; and Xona is working to disrupt the third industry segment, navigation, which has historically been dominated by government-run services like GPS.
The U.S. Defense Department developed GPS in the 1970s. The system identifies locations using a constellation of satellites that revolve at a mid-altitude orbit; it also has the benefit of being free. But the flaws of GPS are becoming impossible to ignore. Because its signals are fairly weak, new technologies are increasingly able to jam them with competing transmissions or even “spoof” them with imitation radio waves. This is a particular problem on the modern battlefield—Russia’s electronic warfare tactics are forcing Ukrainians to supplement GPS with backup navigation systems—but it’s disrupting civilian life as well, including commercial travel, which still overwhelmingly uses GPS. “The world’s in a pretty dangerous place relying on GPS alone,” says Manning.
Xona is working to create a stronger and more reliable alternative. Following the business model that Starlink popularized, it plans to mass-produce low-orbit satellites, link them into a commercial network and then sell its services. It expects to deploy 258 satellites into an initial constellation it’s calling Pulsar, which will orbit at a lower altitude than GPS and therefore transmit more powerful signals. Pulsar’s wide-bandwidth radio waves and cutting-edge software will also make it particularly accurate; it will offer centimeter-level precision, which GPS can typically only achieve after applying correction data.
“Fortune favors the bold,” says Jurvetson, who returned to Forbes’ Midas list of top venture investors. “We’ve seen entirely new commercial markets emerge in space, from rideshare launches to daily observation of the entire Earth, that few could have imagined before SpaceX and Planet did it. Xona has the same level of ambition: bring commercial innovation to a domain historically reserved for nation-states.”
This $250 million venture round—which brings the company’s total funding to over $570 million—comes just days after Xona hit a key milestone. On Tuesday, it officially kicked off the rollout of its commercial network by launching six satellites into orbit on one of SpaceX’s Falcon 9 rockets.”
Company: XonaSpace.com
i'm proud to back @xonaspace's $250M Series D alongside Future Ventures. I've long admired @FutureJurvetson and i'm excited to work with him on Xona. He's arguably the most successful space investor ever, having been very early at SpaceX (still on the BOD!) and Planet.
GPS is some of the most important infrastructure on earth, but it was designed decades ago for a world of human users. the next wave of autonomy (robots, drones, self-driving vehicles) needs positioning and timing that's far more precise and harder to jam.
Xona is rebuilding it ground up. Move satellites to LEO, design spacecraft, signal and ground systems together, and build the satellites in-house so the network keeps improving.
Early results: sub-2cm accuracy, compatibility with existing receivers via firmware upgrades, first-of-its-kind FCC spectrum authorization, and the commercial constellation now going up, with customer service starting in 2027.
congrats Brian (@FoundTheSpace) and the whole team. this is foundational infrastructure for the physical AI era.
xonaspace.com/news/series-d
davidlee retweeted
i'm proud to back @xonaspace's $250M Series D alongside Future Ventures. I've long admired @FutureJurvetson and i'm excited to work with him on Xona. He's arguably the most successful space investor ever, having been very early at SpaceX (still on the BOD!) and Planet.
GPS is some of the most important infrastructure on earth, but it was designed decades ago for a world of human users. the next wave of autonomy (robots, drones, self-driving vehicles) needs positioning and timing that's far more precise and harder to jam.
Xona is rebuilding it ground up. Move satellites to LEO, design spacecraft, signal and ground systems together, and build the satellites in-house so the network keeps improving.
Early results: sub-2cm accuracy, compatibility with existing receivers via firmware upgrades, first-of-its-kind FCC spectrum authorization, and the commercial constellation now going up, with customer service starting in 2027.
congrats Brian (@FoundTheSpace) and the whole team. this is foundational infrastructure for the physical AI era.
xonaspace.com/news/series-d
Two 🐐!
What a wild day 2 at @NVIDIAGTC Berlin
I got the chance to sit down and learn more about @silicondata @computeexchange from @carmenli
More later at BEP Research
davidlee retweeted
My latest essay on the likelihood of each person training their own AI to be the most improbable verion of themselves. kevinkelly.substack.com/p/mo…
davidlee retweeted
Our CEO @carmenli was featured in cover story in the @WSJ Markets & Finance section today. Thanks to @rudegeair and @candicechoi for covering Carmen's life journey and the AI compute market! [Link below]
davidlee retweeted
Thanks @GavinSBaker for citing our token and GPU rental indices at the @nvidia GTC Pregame show with @dee_bosa! 🥰
Gavin Baker says token prices are falling, but the price of renting a GPU hour “has gone vertical,” because each GPU hour now produces more useful tokens
BAKER: “By the way, on Jevons Paradox, it is interesting, and I think it’s very important to be precise in our terms. What we have seen, Silicon Data, an Atreides and I believe NVIDIA portfolio company, has this token index. It captures price and mix."
"And as the price of tokens has gone down and the mix has shifted to cheaper tokens. The cost to rent an hour of GPU has gone vertical."
"And that is because as you’re getting more tokens that are more useful out of a given GPU hour, that GPU hour becomes more valuable. So we’ve actually seen as the price of tokens has gone down, the prices that you were charging companies per your last call has gone vertical. You raised prices how many times in the last three months?”
VOLOZH: “Ugh, nobody counts. Now we raise prices every 12 minutes on the auctions. Not only raising, by the way. Sometimes they go down.”
BAKER: “But token, we need to be precise in our terms. Price of tokens going down, shifting to cheaper tokens, does not mean the price of renting a GPU from Arkady is going down.”
davidlee retweeted
We’ve spent the past year developing new primitives for GPUs. We believe this enables new algorithms to change how machines learn, open new markets, and dramatically reshape AI’s cost structure.
We have raised a $1 billion Series A, co-led by Index and Lux.
davidlee retweeted
I think we are hosting another compute party next week at SF. My job is to show up and have fun. Pin @Midnight_Captl for invite.
davidlee retweeted
Today we’re releasing Underdog Bark 3.0, the #1 frontier speech model
It's a tiny 163 MB download as accurate as NVIDIA’s Parakeet (15x bigger). It beats OpenAI's Whisper large-v3 (57x bigger)
It's fast- transcribes 1 min of audio in <0.25s on a Macbook. On recorded meetings (AMI), Bark 3.0 gets 7.82% of words wrong. Parakeet gets 9.42%. OpenAI's Whisper large-v3 gets 13.63% error rate
Bark runs on both your Laptop & Phone. It’s free and open source under CC-BY 4.0
Try private on-device meeting notes & dictation, powered by Bark, in Underdog today: underdog.ai/bark
davidlee retweeted
Biological intelligence to bootstrap robotic intelligence?
If robotics is about to enter its golden age, @MeckaAI is one of the companies feeding it, by sensorizing humans to train robots.
The biggest challenge robotics faces is the data flywheel cold start problem. LLMs got a free cold start from the internet: trillions of tokens, already written. Robotics gets nothing. Someone has to go create the training data to jumpstart the system.
@joshavata is the co-founder and CEO of Mecka, the data and deployment company for physical AI. Josh read every robotics paper he could find in late 2024, visited the labs, and made a bet: humans and humanoids look the same, so humans could act as the training fleet. Egocentric data, first-person video of people doing real work, could become vital fuel.
Two years ago, the biggest egocentric dataset was Ego4D at 4,000 hours. The field is now in the millions of hours, thanks in part to Mecka. We’re probably still orders of magnitude of training data away from robotics nirvana. But every rung on the physical intelligence ladder unlocks real commercial tasks. And eventually, Josh imagines a world with an order of magnitude more robots than people, of a plurality of shapes and sizes and use cases, as the Hitchhiker’s Guide to the Galaxy foretold :)
A few things we cover in this conversation.
- Why robotics has no cold start: pixels, actions and logic, and why pre-training data is a real market in robotics in a way it never was for LLMs
- What “teaching robots about carbon life” means: in sci-fi the final frontier is organic. The human brain runs on 20 watts and self-heals
- The egocentric bet: teleop couldn’t scale, humans and humanoids share a form, so Waymo-fy the humans. The first pilot was aunties and uncles recording themselves cooking on a mobile app
- Force data: video can’t tell a robot how hard to squeeze an egg. Wrist cameras are a hack. Mecka built the gloves instead
- Inside the data factory: a consumer app that streams your tasks, a B2B engine recruiting across “the taxonomy of all physical human work,” 100-gigabit upload lines, hundreds of petabytes
- Millions of hours vs. Tesla’s decade on two degrees of freedom, and why it’s a slow creep up, not a GPT-3 moment
- The home has the most data and is the hardest place to deploy. Laundry folding and assembly-line pick-and-place get solved in the next year or two
- Useful robots already exist: hotel delivery and cleaning bots in China and Southeast Asia, industrial cleaning in the US. Humanoids are the terminal state, not the first step
- Deployment diffusion: a modern systems integrator whose installed robot does something next week it couldn’t do last week. If Mecka ever helps deploy a robot that mines an asteroid, they’ve done their job
- The data pot of soup: a million hours of human data, tens of thousands of UMI, a few hours of in-domain teleop. Tactile and simulation haven’t entered the mix yet
- Move 37 for robotics: AlphaGo went behavior cloning, then self-play, then magic. The catch is the rules of the game are the entire physical world, and we don’t have the simulator yet
- Will the AGI labs steamroll robotics? “It takes a lot of courage to scale up data with imperfect information.”
- The Kawasaki Corleo, the robot panther-dog-horse on Mecka’s conference room wall, and why the future of robotics looks like Hitchhiker’s Guide to the Galaxy
Timestamps:
0:00 – Introduction
1:46 – What Mecka is: pixels, actions and logic
2:45 – Is robot data economically feasible without the internet?
4:15 – Teaching robots about carbon life
5:27 – Cloud kitchens, stablecoins and the path to robotics
7:03 – Entering robotics with a fresh lens
8:45 – What egocentric data is and why Mecka bet on it
10:09 – The first pilot: aunties and uncles with a mobile app
10:42 – Force data and tactile gloves
11:55 – Inside the operations of a data factory
14:41 – Heat-mapping the taxonomy of human work
15:42 – Quality gates, fraud and the McDonald’s POV videos
17:44 – Earning a margin as a data intermediary
19:33 – Millions of hours vs. Tesla’s decade
20:55 – Why it isn’t binary: unlocking commercial tasks along the way
22:02 – Home data is plentiful; home robots are the hardest
23:24 – Useful robots can be deployed today
24:35 – When robotics gets an FSD-style flywheel
26:42 – Deployment diffusion and the modern systems integrator
28:21 – Favorite vertical robot categories; mining an asteroid
30:08 – The hardware slate: Waymo-fying people
30:55 – EgoVerse
32:04 – The data pot of soup: the converged training mix
33:54 – Move 37 for robotics: AlphaGo and Federer
36:02 – World models and the training mix
36:58 – Will scaling the current mix get us to the promised land?
39:05 – Will the AGI labs steamroll robotics?
40:50 – Sci-fi roots and the Kawasaki Corleo
42:44 – How many robots in a decade?
davidlee retweeted
Local inference is the future, so we made local Perplexity. Enjoy!
This is great
This is very similar to why Tesla is winning on FSD.
In robotics just as in LLMs, more data and more compute seem to create general intelligence. For LLMs, the web was conveniently there supplying ample enough data to reach recognizably useful intelligence, and since the delivery mechanism was just an existing Web browser, the marginal cost of getting that product out to users where it could get valuable feedback data to improve was zero.
Robotics is different. A lot of the initial intelligence it turns out you can use video data, which the world also has a ton of existing data to work off of. But the way to improve beyond that is to get lots of feedback data, and how do you do that when the marginal cost of a robot is not zero but thousands of dollars, and the number of actual users is tiny?
Elon already had cars on the road (great ones btw, getting a Tesla is a no brainer nowadays), which meant the marginal cost for him of getting drive data was small. He got rid of LIDAR for many reasons, but one of them was simply that it cost so much more at the time. He understood that the cheaper the system, the more data one collects.. and the more data one collects the faster the system can be improved. And as the system improves, it will get you more customers which gives you more data. (It also gives you more scale which enables you to drop the price and sell it even more)
Revolution does not begin from the top down, it begins from the bottom up. The Microducks are cheap and look like a toy to people, but that's exactly why it will end up changing everything. Right now the truth is that robotics are only economically useful for factory and warehouse settings where the tasks are narrow and repeated 10,000 times a day. Efforts like Dream-1 and Microduck aim to actually make general use robotics economically happen. Some robotics companies might think that they should just hoard their own data, but those companies will probably go under because one company's data is not going to be enough to beat the utility of a model created with the sum of everyone else's data. Just as we all collectively made LLMs happen by using the internet, together we will all make robotics happen. Very excited for this world, it's going to happen fast.
* Caveat: These companies need to stay in business long enough to make it all happen. One advantage that Tesla has with Optimus is that they have massive economic utility in using it to help standup AI datacenters. Elon is smart and will land and expand from there. All of this is to say robotics is coming. It'll come slower than LLMs did but it's coming.
** I fully expect to own an army of Microducks that will be working together to fold my laundry.
davidlee retweeted
You can now run Perplexity Computer fully local on your Mac with no cloud based inference, without WiFi too. Enjoy!
davidlee retweeted
Cancer patients with the same diagnosis often have very different tumor biology.
Molecular testing can reveal underlying differences, but that's slow and expensive.
Introducing Holon-S1, our platform to predict genes and biomarkers from routine pathology slides.
• beats two other models at predicting spatial gene activity in a controlled comparison
• has identified potentially novel biomarkers in breast cancer and colon cancer
• evaluated across 16 organ types and more than 15 scanners, using slides from both formalin-fixed, paraffin-embedded and fresh-frozen tissue.
We believe Holon-S1 has the potential to help researchers find new biomarkers in slides they already have, discover treatment targets, and bring more personalized cancer care within reach!
davidlee retweeted
Kyle Shanahan says a 51% analytics recommendation won’t convince him to go for it because he gets one shot, not an hour at a blackjack table
“I don’t want to hear about it if it’s 51% of the time we should go for it.”
“As I always say, I’m not sitting here playing Blackjack for an hour with no one else at the table, and eventually I’ll get 51% of it. Like, I have this one try, and I have a feel for it. I’ve been doing it too long.”
“But what I want to know from him, when it’s not a feel, when it, no, this is 98%. We don’t even need to talk about it. So we don’t need to talk about it. Tell me when it’s that, and I’ll just do it.”
davidlee retweeted
Wabi 2.0 is live!
personal agents are one person, one chat. but the parts of life that matter run on more than one person
Wabi is an agent you use together. you talk to it, it does stuff, builds the apps you need, and anyone can join the thread - your partner, your best friend, the preschool moms groupchat
school changes pickup time → Wabi updates the family calendar, everyone sees it.
a show you’d both love → Wabi finds a night that works, gets the tickets.
the trip everyone keeps talking about → Wabi picks the dates, builds the itinerary, books it
go start a thread with your people 👇