Chris Barber

I asked people: "who has hosted great events in SF?"

2 votes each:
- Alicia (@sharedalbums)
- Ben Lang (@benln)
- Dave Fontenot (@davefontenot)
- Ankit Gupta (@AnkitGuptaAI)
- Harbor (@alexgshaw, @ryan_marten)
- Joanne Shang (@JoanneShang)
- Midjourney (@DavidSHolz, @aalleexx)
- Ricardo de Arruda (@notawizard)
- Sarah Chieng (@MilksandMatcha)
- Sukone Hong (@sukonehong)
- Umesh Khanna (@forwarddeploy)
- Vivian Shen (@vivianmshen)

40 people voted. People couldn't pick themselves. I didn't vote.

Continued:

I asked people which tier they'd put each lab in:

Frontier:
- Anthropic: consensus
- OpenAI: consensus

n-1 (1 generation behind):
- Google DeepMind: consensus
- xAI: consensus
- Meta (Muse Spark): over half say n-1
- Moonshot (Kimi): over half say n-1, some n-2
- Zhipu (GLM): over half say n-1, some n-2
- DeepSeek: over half say n-1, some n-2
- Thinking Machines (Inkling): leans n-1, some put n-2

n-2 (2 generations behind):
- Alibaba (Qwen): leans n-2, some put n-1
- ByteDance (Seed): over half say n-2
- Xiaomi (MiMo): leans n-2, some put n-1
- MiniMax: over half say n-2, some n-1
- Tencent (Hy): over half say n-2
- NVIDIA (Nemotron): leans n-2, some put n-3+

n-3+ (3+ generations behind):
- Microsoft (MAI): over half say n-3+, some n-2
- Cohere: over half say n-3+, some n-2
- Mistral: over half say n-3+
- Amazon (Nova): consensus
- Apple: consensus

Consensus = 75%+, over half = 51%-74%, leans = the most common pick but under half, some = 20%+.

I asked 70 people. Engineers/researchers/founders at AI data cos, people at labs (I didn't count votes for their own lab), and founders/engineers at other startups. I didn't vote.

I asked people: "who have you met in the AI data and post-training space that you find most insightful?"

5 votes: Daniel Chang (@dnnssl2)

3 votes each:
- Abhijay Rana (@abhijaymrana)
- Ryan Wexler (@RyanMWexler)
- Sean Cai (@SeanZCai)
- Will Brown (@willcb)

2 votes each:
- Amy Tam (@amytam01)
- Jesse Hu (@huyouare)
- Nikhil Namburi (@nikhilvnamburi)
- Thomas Liao (@distributionat)

91 people were picked. The other 82 got 1 vote each and are in the next two posts.

People couldn't pick themselves. I didn't vote.

I asked people which neolabs under $10B have the highest talent density:

32 votes: Core Automation (@MillionInt, @_arohan_)

26 votes: Periodic Labs (@LiamFedus, @ekindogus)

20 votes: Flapping Airplanes (@spectorb, @amspector100, @aidanmantine)

18 votes: Standard Intelligence (@G413N, @devanshpandey)

8 votes: Ricursive (@annadgoldie, @Azaliamirh)

5 votes each:
- Ineffable (David Silver)
- Mirendil (@bneyshabur, @HarshMeh1a, @shayan_, @tararezaeikh)
- Recursive (@RichardSocher, @_rockt, @timshi_ai, @josh_tobin_, @CaimingXiong, @jeffclune, @tydsh, Alexey Dosovitskiy)

3 votes each:
- Applied Compute (@ypatil125, @rhythmrg, @lindensli)
- Isara (@ezhang7423, @hegasz)
- Prime Intellect (@vincentweisser, @johannes_hage)

2 votes each:
- Elorian (@AndrewDai, @yinfeiy, @SethInternet)
- Goodfire (@eric_ho, @DanJBalsam, @banburismus_)
- Inherent (@tantumscollins, @edwardfhughes, @LouisKirschAI, @kallyaleksiev)
- Trajectory (@rronak_, @QuantumArjun, @MichaelElabd)
- World Labs (@drfeifei, @jcjohnss, @BenMildenhall, @chlassner)

People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in Applied Compute, Factory, Standard Intelligence, Trajectory and Wafer. I didn't vote.

Continued:

I asked people: "where should enterprises go for post-training as a service?"

30 votes: Applied Compute (@ypatil125, @rhythmrg, @lindensli)

13 votes: Trajectory (@rronak_, @michaelelabd, @QuantumArjun)

10 votes: Prime Intellect (@vincentweisser, @johannes_hage)

7 votes: Thinking Machines (@miramurati, @johnschulman2)

5 votes: Fireworks (@lqiao, @dzhulgakov, @pawelg, @jamesr66a, Chenyu Zhao, @divchenko)

4 votes: Baseten (@tuhinone, @amiruci, @saltyph, @defpan, @mudithj, @maxkirkby, @oneill_c)
4 votes: Together AI (@vipulved, @ce_zhang, Chris Ré, @tri_dao, @percyliang)

2 votes: Arcanum (Rehan Rupawalla, @danielrupawalla, Krish Thawani, @samir_walji)
2 votes: Belvedir (@thezacharyyu)
2 votes: Trainloop (@jackson_stokes, @mlpierce22)

1 vote each:
- AfterQuery (@CarlosGeorgescu, @spencermateega)
- Bespoke Labs (@madiator, @AlexGDimakis)
- Engram (@dan_biderman, @EyubogluSabri, @realJessyLin)
- Mercor (@BrendanFoody, @adarsh_exe, @suryamidha)
- Radixark (@ying11231, @BanghuaZ)
- River AI (@ibab, @dsoboliev, Ievgen Soboliev, Yaroslav Nazarov)
- Unsloth (@danielhanchen, Michael Han)

People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in AfterQuery, Applied Compute, Arcanum and Trajectory. I didn't vote.

I asked people: "where should neolabs go for GPUs?"

15 votes: SF Compute (@evanjconrad @jeztek @Ethan_is_online @EricMenees @apagajewski)

11 votes: CoreWeave (@BrianVenturo @branninmcbee)

10 votes: Modal (@bernhardsson @akshat_b)
10 votes: Nebius (@romanchernin)

9 votes: Lambda (@stephenbalaban)

5 votes: Fluidstack (@garywu @CesarMaklary @jimcox25)

4 votes: RunPod (@flash__singh)

3 votes: Crusoe (@ChaseLochmiller @Electron_Cowboy) (also received one vote against)
3 votes: Together AI (@vipulved @ce_zhang @tri_dao @percyliang)

2 votes: Baseten (@tuhinone @amiruci @saltyph @defpan)
2 votes: Google Cloud (@sundarpichai)
2 votes: Oracle (@ClayMagouyrk)
2 votes: Ornn (@bavaria_kush @wayne_nelmz)

People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in SF Compute. I didn't vote.

I asked people: "which robotics labs and startups have the highest talent density?"

28 votes: Physical Intelligence (@hausman_k, @svlevine, @chelseabfinn, @adnan_esm, @brian_ichter, @QuanVng, @lachygroom)

18 votes: Generalist (@peteflorence, @andyzengineer, Andrew Barry)

9 votes: Sunday (@tonyzzhao, @chichengcc)

6 votes: Skild AI (@deepakpathak, @gupta_abhinav_)

4 votes: Dyna (@Lindon_Gao, @YorkYang5050, @JasonMa2020)

3 votes: Bracket Bot (@sincethestudy)
3 votes: OpenAI Robotics (@model_mechanic, @PengchuanZ, @ChengshuEricLi)

2 votes: 1X (@BerntBornich)
2 votes: The Bot Company (@kvogt, @pariljain, @lukeholoubek)
2 votes: Figure (@adcock_brett)
2 votes: Genesis (@zhou_xian_, @theo_gervet, @johnsonwang0810)
2 votes: Prometheus (@JeffBezos, @vik_bajaj, @sherjilozair, @wgussml, Alex Graves)
2 votes: Robocurve (@chooi_jeq)
2 votes: Tesla (@elonmusk)
2 votes: Weave Robotics (@kaandogrusoz, @evan_wineland)

People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in Physical Intelligence. I didn't vote.

I asked people: "who are the best active angels in AI?"

5 votes: Jeff Dean (@JeffDean)

4 votes: Umesh Khanna (@forwarddeploy)

3 votes: Sholto Douglas (@_sholtodouglas)
3 votes: Thomas Wolf (@Thom_Wolf)

2 votes: Daniel Chang (@dnnssl2)
2 votes: Elad Gil (@eladgil)
2 votes: Eric Glyman (@eglyman)
2 votes: Logan Kilpatrick (@OfficialLoganK)
2 votes: Michael Chen (@michaelzchen5)
2 votes: Nat Friedman (@natfriedman)
2 votes: Noam Brown (@polynoamial)
2 votes: Philip Bogdanov (@philip_bogdanov)
2 votes: Sarah Zeng (@sarahzengy)
2 votes: Tim Luong (@chongz)
2 votes: Zach Frankel

I asked people to focus on angels that work in AI.

People couldn't pick themselves. 3 people picked me; I left myself off the list. I didn't vote.

I asked people: "which data companies should an ambitious future founder join?"

13 votes: Mercor (@BrendanFoody @adarsh_exe @suryamidha)

11 votes: Fleet (@nicoup @andrewthezhou)

10 votes: Proximal (@MatternJustus @navidkpr @calvinchen)

9 votes: Surge (@echen)

8 votes: AfterQuery (@CarlosGeorgescu @spencermateega)

7 votes: Mechanize (@GuiveAssadi @tamaybes @EgeErdil2 @MatthewJBar)

6 votes: micro1 (@aliansarinik)

4 votes: Datacurve (@serenaa_ge @charleyslee)
4 votes: OpenAI (data team) (@BarrAlexandra @phoebethacker)
4 votes: Preference Model (@Ning_Catsnail)

3 votes: Bespoke Labs (@madiator @AlexGDimakis)

2 votes: Anthropic (data team)
2 votes: Datology (@arimorcos @hurrycane @leavittron)
2 votes: Long Lake (@alextaubman @rasmuswissmann @varunshenoy_ @tarasholovko)
2 votes: Sieve (@mokshith @thpicy)

People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in AfterQuery and Fleet (in this post), and in Abundant, Applied Compute, Latch, Olam Labs, Trajectory and Vmax (elsewhere on the list). I didn't vote.

I made a list of great startups to join. It's called the Breakout List.

The list has 92 companies. These are the 20 with 25 or fewer employees:
- Hone (@moritz_stephan, @CarloWillem, @oqbrady)
- Normal (@ansonyuu, @hudzah)
- Standard Intelligence (@G413N, @devanshpandey)
- Tacit Labs (@ninklefitz, @AmDroste)
- American Terawatt (@atroyn, @rslparker, @aranibatta)
- Conduit (@clemvonstengel, @riopopper)
- Convergent (Omkar Savant, Vivek Katara, @debnilsur)
- Core Automation (@MillionInt, @_arohan_)
- Engram (@dan_biderman, @EyubogluSabri, @realJessyLin)
- Instinct (@noahrshinn)
- Keenable (@styskin, Matthias Petri)
- Lumaril (Mark Elliot, Ben Duffield)
- Neion Bio (@Dimkell, Sam Levin)
- Pangram Labs (@max_spero_, @bradley_emi)
- Quadrillion (@echinaceous)
- Re (@karnsaroya, @AnandDhillon, @thecliffwhite, @benaneesh)
- Ricursive (@annadgoldie, @Azaliamirh)
- Sail Research (@neilmovva, @blintzbase)
- Trajectory (@rronak_, @michaelelabd, @QuantumArjun)
- Watney Robotics (Sean Cheong, Ryan Gannon)

Picks from Elad Gil, Charlie Songhurst, Keith Rabois, Mike Vernal, Alana Goyal, Sonya Huang, Ramtin Naimi, Marc Bhargava, Cory Levy, Aashay Sanghvi, Konstantine Buhler, John Luttig, Varun Gupta, Ray Tonsing and Avichal Garg.

Disclosure: I'm a small investor in American Terawatt, Convergent, Standard Intelligence and Trajectory (in this post), and in Factory, Physical Intelligence and SF Compute (elsewhere on the list). I didn't vote.

The full list is on Breakout List.

anyone know of companies doing financing for data cos? eg invoice factoring, credit, venture debt. instead of raising

presumably banks are hesitant?

what are the main products in the personal/professional assistant space?

more professional
- chatgpt work
- claude cowork
- claude tag
- grok bot
- manus
(and claude code and codex)

more personal
- town
- tomo
- instinct
- ollie
- pallie

drafted this 2x2, right axes?

I asked people: "which AI supply chain startups (compute, power, chips, datacenters) valued at $10b or less are most interesting?"

10 votes: MatX
8: American Terawatt
6 each: Lightmatter, Modal, Prime Intellect
4 each: Valar Atomics, Wafer
3 each: Ayar Labs, d-Matrix, Gimlet Labs, Heron Power, Olix, Panthalassa, Positron, Substrate, Taalas, Tenstorrent, xLight
2 each: Aalo Atomics, Corintis, Fab2, General Matter, Ornn, SF Compute, Starcloud, Stone Power, TensorWave
1 each: 49 more startups, see image and below

People also voted for Base Power, Crusoe, Etched, and Fluidstack but those are over the valuation limit.

Disclosure: I'm a small angel in American Terawatt and Wafer. I didn't vote.

I asked people: "which AI data startups are most interesting?" The focus was on startups that sell data to labs, excluding the massive ones (Mercor, Surge, Handshake, Turing):

22 votes: Mechanize
12: Proximal
11: Fleet
9: AfterQuery
8: Datacurve
5 each: Bespoke Labs, Preference Model
4: Abundant
3 each: Andon Labs, Latch, microagi, Prime Intellect, Snorkel, XDOF
2 each: Build, Datology, Design Arena, dmodel, Good Start Labs, hillclimb, HUD, Inheritance, Mecka, Sieve, Taste Labs, Trajectory, Vmax
1 each: 37 companies, info in image and below

Disclosure: I'm a small angel in Abundant, Fleet, Inheritance, Latch, Trajectory, and Vmax. I didn't vote.

I added a robotics data section to pavlov's list:

- Scale (Natasha Dadabhoy, Don McCullough)
- microagi (@bercankilic @joinshift)
- micro1 (@aliansarinik)
- Human Archive (@babugi28 @Rush26agarwal)
- Mecka (@joshavata @jasontheutopian @mog3n @NotDeu)
- XDOF (@philippswu @YideShentu @itsnemojin)
- Build (@eddybuild)
- Sieve (@mokshith @thpicy)
- Claru (@DaniYogatama @artetxem)
- Midcentury (@dgmonsoon @ckxyz_)
- Datoric (@NickelReady @Jeffjinlin)

Thanks to @dgmonsoon @dygk_0x1 @markgrinev @jayparth_ @bercankilic @aliansarinik @Thom_Wolf @NickelReady @sincethestudy @elvisnavah and @rajivpoc

pavlovslist.com/robotics-data

Talked with the head of data acquisition at a large ai lab: "we are already working with a lot of data vendors and wanted to see if you had any other suggestions"

"where did you find the ones you work with today?"

"mostly on pavlov’s list"

What are the best startups to rent gpus from rn? I'd love to make a list of people's fav providers

Just added Snorkel to Pavlov's List - they have a large data selling business (@ajratner @paroma_varma @henryehrenberg @vincentsunnchen @fredsala)

i made some updates to pavlov’s list and am pleased with them!

thanks to @xeophon @ybenpan @phoebeyao @amit05prakash @kate_shapova @timshi_ai @kevinhou22 @madiator @dlbydq @daljeet_v @ingmariusX @davidstutz92 & Dylan Rogers for feedback lately

(pavlov's = list of rl env cos)

I asked a few people to predict: frontier vs non-frontier AI models for coding at SF startups, as of end of 2028 - which gets more of the token volume, and how big is the price premium per token?

(Non-frontier here also includes cheaper closed-weight models from frontier labs, and tokens routed to by a frontier model. The reference class here is SWEs at SF startups, which influences the outcome.)

Upper left (frontier medium+ premium, 50%+ share):

- Nick Mehta (@nrmehta): "I guess for me it comes down to how much you believe in the bitter lesson and generalization. I guess I have a worldview similar to Demis - that all information and perhaps the universe itself is simply a high dimensional vector space. In this way, I believe the problems to be solved will be so much more powerful at the frontier."

- Michael Stajer (@michaelstajer): "new startups building new business models using self improving code linked to business evals… more eval focused and self looping ai coding cycles… ai writing code >> evaluating business results >> changing code >> evaluate >> repeat. Mimic of frontier labs self improving AI, startups who build business models on top of ai self improving code will have outsize advantage. What seem like small gaps between frontier and everyone else become large advantages over hundred/thousands/millions of cycles. Also — I think non-frontier will stall as frontier gets better at preventing distillation."

Upper right (frontier medium+ premium, <50% share):

- prinz (@deredleritt3r): "1. I think the frontier labs will be way ahead of their competitors, and will be able to charge a high premium for their very best models. 2. I think the frontier labs will also offer cheap/fast models that will be very difficult for others to compete with. 3. I think that the combination of these cheap/fast models becoming really good at basis "meat-and-potatoes" tasks AND potential compute constraints will mean that people will use the cheap/fast models more."

- Arun Rao (@sudoraohacker): "Right now I'd lean to OAI and Anthropic keeping their premium and leads in frontier coding and all the OSS models being a distant second for workhorse dumber flows-but the main issue is product work (choosing good domains) and verification, and then all the RSI uncertainty."

- Super Dario (@inductionheads): "I think frontier companies will have hybrid services where most tokens are served by smaller models. But won't be per task selection by user. Will be more like automatic per token selection ala moe"

- Simon Grimm (@Simon__Grimm): "Sticking to my thinking where markets for AI will be like markets for knowledge work, this is how I'd expect it to cash out."

- Herbie Bradley (@herbiebradley): "by 2028 in SWE specifically I think things will get to the point where routing is much more sophisticated and most SWEs are using *by token volume* predominantly non-frontier models (eg more complex versions of the Fable advisor Opus implementor pattern). but in terms of $, most $ might end up being frontier simply because of the disparity in cost? in domains outside SWE eg which are harder to verify I expect 2028 frontier to have more of a USP"

- Eran Sandler (@erans): "It's going to be a mix. So price wise and token wise can change because frontier is more expensive and planning phases might consume more tokens. But overall token usage is - frontier might end up being a smaller portion. Probably 40% and 60% will end up being non frontier. If you go to places that are not SF startups - like regular enterprises that will look even less frontier and more something else that is good enough for most tasks"

Lower left (frontier small to no premium, 50%+ share):

- Soren Larson (@hypersoren): "My intuition is that –– for writing code specifically –– so long as there are venture dollars available, startups will continue to use the leading models to write software. Venture is in the game of big wins not minimizing risk –– no early stage venture investor would say "how about you spend less on tokens." That said, coding models are the basis for AGI and likely agent labor. If you were to expand your question by deleting "for coding" in your prompt, then you implicitly ask about the distribution of agent labor application and the associated economics, which I think would produce a different result"

Lower right (frontier small to no premium, <50% share):

- P Equity Research (@pequityresearch): "I think most of the high volume coding will be capable on non-frontier models, not requiring as much of the frontier model use. You may have some use cases where frontier excels and thats where a small premium exists per token, but most of the work will be automated with good results via non-frontier."

- Finn Hulse (@finn_hulse): "i think coding will be more or less a solved problem by end of 2028. premiums can't be that high; code can only be so "correct" before marginal gains just aren't worth the premium. so against backdrop of plummeting costs i don't think frontier models will command a huge premium. this also matches my general sense that premiums will not get out of hand/there won't be a "god" model because ROI is all that matters, and that ratio always approaches reasonable efficiency. i think startups will continue to be happiest to pay for higher premiums simply because they are at the riskiest end of the curve where ROIs might warrant higher premiums (i.e. it makes no sense to hedge or settle for less than the best when attempting something ~impossible). but by the argument that most models will become good enough to solve any reasonably defined coding problem, a good amount of subagent work will be handed off to cheaper sub-models. i honestly think the big labs are going to have to start natively blending models even more efficiently in a way that sort of blurs the question itself. like a single "frontier" query might consume resources commensurate with 90% smaller models and 10% larger models, just out of the box due to scaling limits/brutal competition/diminishing marginal benefits of extra intelligence at routine substeps."

Thanks to @alexolegimas @deredleritt3r @erans @finn_hulse @herbiebradley @hypersoren @inductionheads @michaelstajer @nrmehta @pequityresearch @Simon__Grimm @sudoraohacker.