Investment Partner and CLO @variantfund ex @ycombinator founder, attorney @Cravath tweets are not investment/legal advice
Joined March 2024
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Daniel Barabander retweeted
a cold comment on the @usv blog got me a reply, a meeting, a seed investment, and an incredible thought partnership thats lasted over a decade.
so grateful to capture that in this talk with @nickgrossmanon, on the long arc of a single idea, and why we're still investing in it @variantfund
Everything is getting more autonomous. Except us.
When @jesseldn went out to raise his newest fund, he built the strategy around one word: autonomy.
But getting there was a long and circuitous route.
In 2016, @usv invested in Jesse’s company Mediachain. That’s given us the chance to work together for a decade, including his time at @a16zcrypto and now at @variantfund .
The through line is a belief that technology should give people more control over their lives.
In this week’s episode of The Slow Hunch, we trace that belief back to Jesse’s humble beginnings as an internet pirate and talk about what it will take for AI to fulfill that promise.
Jesse is consistently one of the most cogent thinkers I get the chance to speak with, and I think you’ll enjoy our chat.
0:00 Preview
01:15 Variant's new thesis
02:20 Automation ≠ Autonomy
06:50 How piracy shaped Jesse's worldview
09:00 How the internet went from open to closed
15:45 How BTC inspired Jesse’s startup MediaChain
21:00 Why breaking the rules drives innovation
24:00 The Variant founding story
29:30 Why crypto's ownership thesis didn’t fully play out
34:00 Could AI agents deliver on crypto’s original promise?
37:00 Will AI become another walled garden?
39:00 “You can just do things”
46:15 Will AI give us more agency or take it away?
Daniel Barabander retweeted
It's feeling like we're starting to overfit to this loss target - but at the same time we're now seeing techniques surface here that none of us in Pluralis had thought of. Latest record is a >30x speedup over vanilla Subspace networks.
Two new records for ProtocolNanoGPT
First, "random_sequence" pushed the record from 17,784s → 12,663s improving both training speed and convergence via v3 version of his submission.
- Seconds per step: 3.09s → 2.65s
- Steps to target loss: 5,750 → 4,775
Second, @Xingyu_9 dropped the record from 12,663s → 5,985.6s, another massive 53% reduction!
- Seconds per step: 2.65s → 2.28s
- Steps to target loss: 4,775 → 2,625
Based on the latest submission's description, "compressed pipeline communication with an overlapped schedule," it looks like improvements to the pipeline schedule played a major role!!
Daniel Barabander retweeted
Over the last 30 days, Derive has remained over 90% of the onchain options market. In that same time frame, we’ve done about 5% of Deribit’s options volumes, up from 0.1% a year ago.
It’s difficult to build momentum in this category because of the uniquely strong network effects around options markets. Users and market makers hold positions and collateral on exchange for the duration of an option, often many months. The difficulty and cost of integration are a liquidity provider is an order of magnitude higher than perps. Options markets are also the last vertical to mature in any asset class.
Onchain has finally broken the dam - and Derive, alongside all of our builders and partners, has a chance to redefine the options category, first in crypto, and then beyond.
The tailwinds are enormous, and still underappreciated by most people I speak with.
1. Bringing options liquidity onchain unlocks more functionality than any other vertical: programmable, composable building blocks for precise, tailored yield generation, hedging, and speculative trades. One click hedges embedded in every brokerage and fintech, yield generation strategies with verifiable backtests and transparent risk, on any asset, new social trading experiences built using the kinds of trades only options can support.
2. AI for UX: the biggest barrier to entry to trading options is understanding. 2026 has seen a step function change in how models reason about options. This has yet to filter through to UX, but is coming. People shouldn’t trade options if they don’t understand them. But - given they want to express an opinion about the market - they should be able to find the best way to translate that into a trade. Options are often the best way to do so.
3. AI for agentic trading: options give you thousands of ways to express a view across strikes, expiries and structures. For many traders this is overwhelming, but for agents, it’s a much more useful set of choices. My wager is that agents will prefer specificity and precision when executing trades: not just whether an asset goes up or down, but how far, by when, and with how much risk. Options are the answer.
These tailwinds are strengthening just as our liquidity hits an inflection point. Big integrations take time to come to fruition, but we’re seeing the results month by month, with many more in the pipeline. At the same time, we’ve completed our biggest infrastructure upgrade in 3 years, taking what was already a leading onchain product into what I believe is the best options infrastructure anywhere.
We have so much more work to do, and will be constantly taking feedback, iterating on the product, and ensuring that every dollar of liquidity that onboards to Derive is earned. But we really are only at the dawn of the last major trading vertical to move onchain, and in my opinion, it will be the largest.
Daniel Barabander retweeted
Every company is becoming a Network Effect company
The model "software factory" looks like end-to-end agentic development directed autonomously by user feedback. Your ability to compound is determined by the number and frequency of users communicating product feedback (via email, x, fax...)
Daniel Barabander retweeted
As AI makes execution cheaper, we believe that a company’s edge will come from human judgment (which makes hiring the right humans more consequential than ever.)
Proud to be backing the tryprism.com team, which is putting a dedicated talent team within reach of every founder.
Daniel Barabander retweeted
Delighted to (somewhat belatedly) announce our $3.3 million raise to help companies find, engage and hire better candidates faster.
Thanks @ycombinator for the start and @variantfund for accelerating the journey ahead!
Daniel Barabander retweeted
Blessed to have VCs we like this much. Normally a founder can only dream :)
Daniel Barabander retweeted
In my initial, very amateur foray into robotics, one thing that’s helped me make sense of the space is grouping by types of data used for training. Here’s how I’ve been thinking about them:
1. Video footage is cheap, but its value to robots plateaus quickly, since (1) human hands don’t map onto robot hands and (2) it doesn’t capture metrics that are invisible to cameras, like grip force. Generating data synthetically is also ineffective, since the sim2real gap means it’s incredibly challenging to reproduce real-world physics closely enough.
2. Teleoperation is probably the quickest way to bootstrap and get robots in the field for data collection, but it’s also incredibly expensive, as you’re constrained by the number of robots you have, skilled operators that can control them, and site partnerships you have to deploy them.
3. This leaves tactile methods of data collection (like handheld rigs and wuji gloves). Attempts here are interesting but have thus far mainly involved companies paying someone to wear these specifically for data collection rather than incorporating these devices into everyday worker routines (either because they’re going after household deployment or because the devices used are too obstructive to deploy in everyday work.)
Daniel Barabander retweeted
Ep. 210: The Law of AI Distillation
What it is, why Anthropic considers it illicit and how the decision in a web scraping case (involving @X) could guide what comes next.
Featuring @dbarabander, @sabina_beleuz and @PossibltyResult.
Anthropic calls some AI distillation illicit. But is distillation illegal?
This @LawofCodeFM podcast is a deep dive into how courts might rule, with @dbarabander, @sabina_beleuz and @PossibltyResult, who co-authored one of the sharpest papers on the topic.
We cover X v. Bright Data, what frontier labs' terms say about outputs, why Anthropic's own fair use arguments could be turned against it and predictions for the future.
Timestamps:
0:00 What is distillation?
1:52 Intro
3:01 Why distillation matters
5:44 X v. Bright Data @dbarabander
7:50 Access vs. selling
10:40 Copyright preemption
11:25 Why X doesn't own your posts
13:49 From scraping to distillation
16:12 Who owns AI outputs?
19:38 Is all distillation illicit?
24:10 Anthropic's fair use problem @sabina_beleuz
25:05 Bartz v. Anthropic
26:42 Predictions @sabina_beleuz
28:41 Distilling the distillers
29:21 Selling services, not tokens @PossibltyResult
32:34 Antitrust
37:06 Tech moves exponentially, law sublinearly @PossibltyResult
40:47 Humans, AI and personhood
45:15 Final thoughts
Thank you to the presenting sponsor of this episode, @altitude.
Nothing in this podcast is legal or investment advice.
Daniel Barabander retweeted
If frontier AI labs can't stop distillation, they may stop selling the thing that gets distilled.
@PossibltyResult, investor at @variantfund, on why labs could move further into drug discovery and materials science.
Or as @dbarabander puts it: Services you can't distill.
Anthropic calls some AI distillation illicit. But is distillation illegal?
This @LawofCodeFM podcast is a deep dive into how courts might rule, with @dbarabander, @sabina_beleuz and @PossibltyResult, who co-authored one of the sharpest papers on the topic.
We cover X v. Bright Data, what frontier labs' terms say about outputs, why Anthropic's own fair use arguments could be turned against it and predictions for the future.
Timestamps:
0:00 What is distillation?
1:52 Intro
3:01 Why distillation matters
5:44 X v. Bright Data @dbarabander
7:50 Access vs. selling
10:40 Copyright preemption
11:25 Why X doesn't own your posts
13:49 From scraping to distillation
16:12 Who owns AI outputs?
19:38 Is all distillation illicit?
24:10 Anthropic's fair use problem @sabina_beleuz
25:05 Bartz v. Anthropic
26:42 Predictions @sabina_beleuz
28:41 Distilling the distillers
29:21 Selling services, not tokens @PossibltyResult
32:34 Antitrust
37:06 Tech moves exponentially, law sublinearly @PossibltyResult
40:47 Humans, AI and personhood
45:15 Final thoughts
Thank you to the presenting sponsor of this episode, @altitude.
Nothing in this podcast is legal or investment advice.
Daniel Barabander retweeted
there's an abundance of opportunities for products that subsidize themselves that currently don't
bio wearables e.g., can take the best of what we learned from google X ads, by just making it opt in
Daniel Barabander retweeted
as the value of data skyrockets, old ideas around self-custody of personal data and the ability to benefit from it through direct participation in markets will make a comeback
its becoming economically interesting and politically important in ways it wasn't before
two clear why nows
Daniel Barabander retweeted
Nice chapter-by-chapter breakdown of my chat with @8teAPi and @labenz on their show.
If any of this resonates with you, reach out! We are hiring!
We meet Daniel McKinnon (@danielmckinn0n), CEO of Gamow Labs, who is building AI systems to reanalyze unresolved rare-disease genomes—and using a rescued laboratory to generate new biological evidence.
We spend much of the conversation at the VUS boundary: a biology study can add ACMG points to a patient's case while also teaching the machines what that region of the genome does.
We trace the personal origin of the work through his son Owen: McKinnon's own reanalysis found what a prenatal sequencing lab had missed.
We then follow Owen's missed diagnosis to the workflow level: a disease-causing deletion fell outside a clinical lab's search cutoff, while an AI agent kept looking.
The conversation is worth our time because it gets concrete about the AI layer: a harness, tools, and trace analysis are used to catch failures as basic as changing a gene's name.
We also visit the lab to examine the proposed hill climb from $50,000 per functional study toward a 50-cent target, with a robot arm moving plates through high-throughput experiments. That target is not an achieved cost; it is the engineering question on the table.
We learned a ton. Enjoy!
TIMESTAMPS
0:00 Can a rescued lab teach AI?
3:09 Why AI genetics needs biological experiments
6:10 A failed drug program unlocked Gamow's lab
7:51 Owen's missed diagnosis became a life's mission
10:21 AI agents can search beyond human cutoffs
13:14 Sequencing got cheap; interpretation stayed the bottleneck
15:20 Why Gamow chose diagnostics over screening
18:41 Reanalysis in a week for ten dollars a genome
20:28 What Gamow builds on top of frontier models
23:10 Stronger models can break existing tools
25:45 Can robots make genetic experiments affordable?
27:08 Can biological experiments teach genetic AI?
Daniel Barabander retweeted
"Distillation has been this key bulwark against these frontier labs."
@dbarabander on why the legality of AI distillation is existential.
"They already have infinite money, they already have infinite talent, they already have infinite compute and data, like it's not that hard to see how this can become a runaway train where they just control frontier intelligence and have a monopoly on it."
Anthropic calls some AI distillation illicit. But is distillation illegal?
This @LawofCodeFM podcast is a deep dive into how courts might rule, with @dbarabander, @sabina_beleuz and @PossibltyResult, who co-authored one of the sharpest papers on the topic.
We cover X v. Bright Data, what frontier labs' terms say about outputs, why Anthropic's own fair use arguments could be turned against it and predictions for the future.
Timestamps:
0:00 What is distillation?
1:52 Intro
3:01 Why distillation matters
5:44 X v. Bright Data @dbarabander
7:50 Access vs. selling
10:40 Copyright preemption
11:25 Why X doesn't own your posts
13:49 From scraping to distillation
16:12 Who owns AI outputs?
19:38 Is all distillation illicit?
24:10 Anthropic's fair use problem @sabina_beleuz
25:05 Bartz v. Anthropic
26:42 Predictions @sabina_beleuz
28:41 Distilling the distillers
29:21 Selling services, not tokens @PossibltyResult
32:34 Antitrust
37:06 Tech moves exponentially, law sublinearly @PossibltyResult
40:47 Humans, AI and personhood
45:15 Final thoughts
Thank you to the presenting sponsor of this episode, @altitude.
Nothing in this podcast is legal or investment advice.
Daniel Barabander retweeted
Anthropic has called three Chinese AI labs’ efforts to distill Claude “illicit.”
The irony is that when authors sued Anthropic over their its use of their books to train Claude, Anthropic argued that training was fair use (and won on that issue). If it now argues that a competitor can’t learn from Claude’s outputs, it sounds like they're arguing against their own position.
The cases aren’t identical, and fraudulent access changes the analysis. But in order to go after distillation, Anthropic will have to explain why the legal arguments that protect its own training shouldn’t protect someone else’s.
@dbarabander ,@PossibltyResult and I joined @JacobRobinsonJD to talk through this tension, as well as what comes next in the fight over distillation.
Anthropic calls some AI distillation illicit. But is distillation illegal?
This @LawofCodeFM podcast is a deep dive into how courts might rule, with @dbarabander, @sabina_beleuz and @PossibltyResult, who co-authored one of the sharpest papers on the topic.
We cover X v. Bright Data, what frontier labs' terms say about outputs, why Anthropic's own fair use arguments could be turned against it and predictions for the future.
Timestamps:
0:00 What is distillation?
1:52 Intro
3:01 Why distillation matters
5:44 X v. Bright Data @dbarabander
7:50 Access vs. selling
10:40 Copyright preemption
11:25 Why X doesn't own your posts
13:49 From scraping to distillation
16:12 Who owns AI outputs?
19:38 Is all distillation illicit?
24:10 Anthropic's fair use problem @sabina_beleuz
25:05 Bartz v. Anthropic
26:42 Predictions @sabina_beleuz
28:41 Distilling the distillers
29:21 Selling services, not tokens @PossibltyResult
32:34 Antitrust
37:06 Tech moves exponentially, law sublinearly @PossibltyResult
40:47 Humans, AI and personhood
45:15 Final thoughts
Thank you to the presenting sponsor of this episode, @altitude.
Nothing in this podcast is legal or investment advice.
Had an absolute blast jamming on the law of distillation with @JacobRobinsonJD. He has the best legal/tech podcast out there.
Favorite part was chatting about how a case X brought against a scraper is the best guide to understanding how judges will evaluate distillation.
Anthropic calls some AI distillation illicit. But is distillation illegal?
This @LawofCodeFM podcast is a deep dive into how courts might rule, with @dbarabander, @sabina_beleuz and @PossibltyResult, who co-authored one of the sharpest papers on the topic.
We cover X v. Bright Data, what frontier labs' terms say about outputs, why Anthropic's own fair use arguments could be turned against it and predictions for the future.
Timestamps:
0:00 What is distillation?
1:52 Intro
3:01 Why distillation matters
5:44 X v. Bright Data @dbarabander
7:50 Access vs. selling
10:40 Copyright preemption
11:25 Why X doesn't own your posts
13:49 From scraping to distillation
16:12 Who owns AI outputs?
19:38 Is all distillation illicit?
24:10 Anthropic's fair use problem @sabina_beleuz
25:05 Bartz v. Anthropic
26:42 Predictions @sabina_beleuz
28:41 Distilling the distillers
29:21 Selling services, not tokens @PossibltyResult
32:34 Antitrust
37:06 Tech moves exponentially, law sublinearly @PossibltyResult
40:47 Humans, AI and personhood
45:15 Final thoughts
Thank you to the presenting sponsor of this episode, @altitude.
Nothing in this podcast is legal or investment advice.
Daniel Barabander retweeted
Attention: Honcho Happy Hour series is coming to SoHo. Drinks and pizza on us, Thurs 10/22 in SoHo. RSVP in Luma 👇
go.honcho.dev/hh
