Note they’ve opted for a cumulative graph; the revenue-by-month curve would be flatter.
Still, the total amount companies are paying for job contracts and fees has grown from about $5M/month in Jan to $10M/mo in Aug.
Interestingly, despite the recent 2x revenue growth, the market cap of the BTRST token has been flat or down.
It’s currently $175M, down 76% from last year’s ICO.
I wonder how many recruiter salaries currently get subsidized by Braintrust’s venture funding.
LinkedIn shows 248 employees, but many are just network participants.
If Braintrust keeps charging industry-low fees, it’s not clear how they’ll keep affording recruiter salaries.
How is a blockchain token supposed to help Braintrust sustain a recruiting operation with lower fees?
According to their white paper, here's what the BTRST token provides:
⬜️Governance
⬜️Bid Staking
⬜️Career Benefits
Braintrust's #1 claim about the BTRST token is that it attracts contractors who appreciate having network governance rights...
Yet there’s been no serious on-chain voting to date.
A core team member even admitted that Braintrust's on-chain voting feature isn’t a priority.
Does important governance still happen off-chain?
Kind of.
The Braintrust community recently “voted” on a proposal to partner with Kunai, a non-blockchain developer contracting marketplace: snapshot.org/#/usebraintrus…
The decision appears to have had little community involvement.
At the time of the vote on Kunai, Braintrust’s network had about 45,000 registered members.
Only 27 wallets voted, a microscopic fraction of the community.
Those 27 votes all gave unanimous consent. Very normal.
Is this what living in the Network State feels like, @balajis?
The next claim about BTRST is “bid staking” (a feature not yet live).
If you're applying for work, bid staking lets you agree to punish yourself with a financial loss if you don’t show up to a scheduled interview.
This idea… doesn’t require a blockchain. I’ll leave it at that.
The final claim about BTRST is “career benefits”: They incentivize you to take a course, and in the future they’ll give you some kind of special perks.
But to the extent these benefits make any business sense, they could obviously be matched by Web2 competitors like Upwork.
Investors think the key to disrupting traditional recruiting is incentivizing referrals with BTRST tokens.
Where’s the evidence this drives below-market costs?
Sources tell me most recruiters are paid market wages in fiat, including half the names on the referral leaderboard.
Braintrust claims to have more transparency than its Web2 predecessors.
But juicing revenue by partnering with existing Web2 agencies and subsidizing recruiter pay isn't transparent.
Ironically, Upwork offers a more transparent public breakdown of their revenue and headcount.
What have we learned about $BTRST?
⬜️ Job seekers hardly use it
⬜️ Recruiters hardly use it
⬜️ But Braintrust uses it to paint misleading narratives about take rates, ownership, and transparency
The tokens aren’t helping build a winning business as claimed in the white paper.
Web3 proponents like @packyM love pointing out that a talent marketplace is a real use case and potentially a real business.
And they’re right: copying Upwork is a real use case, and can be a sustainable business.
It’s just not a Web3-enabled use case or a Web3-enabled biz.
A wide range of reputable Web3 investors poured over $100M into this project.
Do investors know there’s no community voting? Do they know how much staffing is subsidized?
Typical of Web3, a poorly-articulated business model justified a sky-high valuation.
Braintrust still has potential to get profitable before funding runs out if they swallow their pride.
Forget the white paper. BTRST tokens are no more than a marketing gimmick.
Best hope may be to rollup (centralize) many agencies like Kunai and drive operational efficiencies.
The key takeaway of my analysis is how incapable blockchain is at helping companies succeed.
I can assure you the next Web3 company I analyze will be the same. There are fundamental reasons why blockchain is devoid of practical applications.
Today's Extropic launch raises some new red flags.
I started following this company when they refused to explain the input/output spec of what they're building, leaving us waiting to get clarification.)
Here are 3 red flags from today:
1. From extropic.ai/writing/inside…
"Generative AI is Sampling. All generative AI algorithms are essentially procedures for sampling from probability distributions. Training a generative AI model corresponds to inferring the probability distribution that underlies some training data, and running inference corresponds to generating samples from the learned distribution. Because TSUs sample, they can run generative AI algorithms natively."
This is a highly misleading claim about the algorithms that power the most useful modern AIs, on the same level of gaslighting as calling the human brain a thermodynamic computer. IIUC, as far as anyone knows, the majority of AI computation work doesn't match the kind of input/output that you can feed into Extropic's chip.
The page says:
"The next challenge is to figure out how to combine these primitives in a way that allows for capabilities to be scaled up to something comparable to today’s LLMs. To do this, we will need to build very large TSUs, and invent new algorithms that can consume an arbitrary amount of probabilistic computing resources."
Do you really need to build large TSUs to research if it's possible for LLM-like applications to benefit from this hardware? I would've thought it'd be worth spending a couple $million on investigating that question via a combination of theory and modern cloud supercomputing hardware, instead spending over $30M on building hardware that might be a bridge to nowhere.
Their own documentation for their THRML (their open-source library) says:
"THRML provides GPU‑accelerated tools for block sampling on sparse, heterogeneous graphs, making it a natural place to prototype today and experiment with future Extropic hardware."
You're saying you lack a way your hardware primitives could *in principle* be applied toward useful applications of some kind, and you created this library to help do that kind of research using today's GPUs…
Why would you not just release the Python library earlier (THRML), do the bottlenecking research you said needs to be done earlier, and engage the community to help get you an answer to this key question by now? Why were you waiting all this time to first launch this extremely niche tiny-scale hardware prototype to come forward explaining this make-or-break bottleneck, and only publicize your search for potential partners who have some kind of relevant "probabilistic workloads" now, when the cost of not doing so was $30M and 18 months?
2. From extropic.ai/writing/tsu-10…:
"We developed a model of our TSU architecture and used it to estimate how much energy it would take to run the denoising process shown in the above animation. What we found is that DTMs running on TSUs can be about 10,000x more energy efficient than standard image generation algorithms on GPUs."
I'm already seeing people on Twitter hyping the 10,000x claim. But for anyone who's followed the decades-long saga of quantum computing companies claiming to achieve "quantum supremacy" with similar kinds of hype figures, you know how much care needs to go into defining that kind of benchmark.
In practice, it tends to be extremely hard to point to situations where a classical computing approach *isn't* much faster than the claimed "10,000x faster thermodynamic computing" approach. The Extropic team knows this, but opted not to elaborate on the kind of conditions that could reproduce this hype benchmark that they wanted to see go viral.
3. The terminology they're using has been switched to "probabilistic computer": "We designed the world’s first scalable probabilistic computer." Until today, they were using "thermodynamic computer" as their term, and claimed in writing that "the brain is a thermodynamic computer".
One could give them the benefit of the doubt for pivoting their terminology. It's just that they were always talking nonsense about the brain being a "thermodynamic computer" (in my view the brain is neither that nor a "quantum computer"; it's very much a neural net algorithm running on a classical computer architecture). And this sudden terminology pivot is consistent with them having been talking nonsense on that front.
Now for the positives:
* Some hardware actually got built!
* They explain how its input/output potentially has an application in denoising, though as mentioned, are vague on the details of the supposed "10,000x thermodynamic supremacy" they achieved on this front.
Overall:
This is about what I expected when I first started asking for the input output 18 months ago.
They had a legitimately cool idea for a piece of hardware, but didn't have a plan for making it useful, but had some vague beginnings of some theoretical research that had a chance to make it useful.
They seem to have made respectable progress getting the hardware into production (the amount that $30M buys you), and seemingly less progress finding reasons why this particular hardware, even after 10 generations of successor refinements, is going to be of use to anyone.
Going forward, instead of responding to questions about your device's input/output by "mogging" people and saying it's a company secret, and tweeting hyperstitions about your thermodynamic god, I'd recommend being more open about the seemingly giant life-or-death question that the tech community might actually be interested in helping you answer: whether someone can write a Python program in your simulator with stronger evidence that some kind of useful "thermodynamic supremacy" with your hardware concept can ever be a thing.
Remember "Come work for us if you want to rebuild the web on top of blockchain"?
It's like that: The thing they're asking you to do for them is likely incoherent.
More importantly, they don't need to build hardware to settle it one way or the other IMO.
Eliezer Yudkowsky can warn humankind that 𝘐𝘧 𝘈𝘯𝘺𝘰𝘯𝘦 𝘉𝘶𝘪𝘭𝘥𝘴 𝘐𝘵, 𝘌𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘋𝘪𝘦𝘴 and hit the NYTimes bestseller list, but he won’t get upvoted to the top of LessWrong.
That’s intentional. The rationalist community thinks aggregating community support for important claims is “political fighting”.
Unfortunately, the idea that some other community will strongly rally behind @ESYudkowsky's message while LessWrong “stays out of the fray” and purposely prevents mutual knowledge of support from being displayed, is unrealistic.
Our refusal to aggregate the rationalist community beliefs into signals and actions is why we live in a world where rationalists with double-digit P(Doom)s join AI race companies instead of AI pause movements.
We let our community become a circular firing squad. What did we expect?
Please watch my new interview with Holly Elmore (@ilex_ulmus), Executive Director of @PauseAIUS, on “the circular firing squad” a.k.a. “the crab bucket”:
◻️ On the “If Anyone Builds It, Everyone Dies” launch
◻️ What's Your P(Doom)™
◻️ Liron's Review of IABIED
◻️ Encouraging early joiners to a movement
◻️ MIRI's communication issues
◻️ Government officials' review of IABIED
◻️ Emmett Shear's review of IABIED
◻️ Michael Nielsen's review of IABIED
◻️ New York Times's Review of IABIED
◻️ Will MacAskill's Review of IABIED
◻️ Clara Collier's Review of IABIED
◻️ Vox's Review of IABIED
◻️ The circular firing squad
◻️ Why our kind can't cooperate
◻️ LessWrong's lukewarm show of support
◻️ The “missing mood” of support
◻️ Liron's “Statement of Support for IABIED”
◻️ LessWrong community's reactions to the Statement
◻️ Liron & Holly's hopes for the community
Search “Doom Debates” in your podcast player or watch on YouTube:
Also featuring a vintage LW comment by @ciphergoth
He spends much time labeling and psychoanalyzing the people who disagree with him, instead of focusing on the substance of why he thinks their object-level claims are wrong and his are right.en.wikipedia.org/wiki/Bulverism
He accuses AI doomers of being “bootleggers”, which he explains means “self-interested opportunists who stand to financially profit” from claiming AI x-risk is a serious worry:
“If you are paid a salary or receive grants to foster AI panic… you are probably a Bootlegger.”
Thread of @pmarca's logically-flimsy AGI survivability claims 🧵
Claim 1:
Marc claims it’s a “category error” to argue that a math-based system will have human-like properties — that rogue AI is a 𝘭𝘰𝘨𝘪𝘤𝘢𝘭𝘭𝘺 𝘪𝘯𝘤𝘰𝘩𝘦𝘳𝘦𝘯𝘵 concept.
Actually, an AI might overpower humanity, or it might not. Either outcome is logically coherent.
Claim 2:
Marc claims rogue unaligned superintelligent AI is unlikely because AIs can "engage in moral thinking".
But what happens when a superintelligent goal-optimizing AI is run with anything less than perfect morality?
That's when we risk permanently disempowering humanity.