CEO at Conjecture.
I don’t know how to save the world, but dammit I’m gonna try.
Connor Leahy
Karma: 1,801
Our current plan is to work on foundational infrastructure and models for Conjecture’s first few months, after which we will spin up prototypes of various products that can work with a SaaS model. After this, we plan to try them out and productify the most popular/useful ones.
More than profitability, our investors are looking for progress. Because of the current pace of progress, it would not be smart from their point of view to settle on a main product right now. That’s why we are mostly interested in creating a pipeline that lets us build and test out products flexibly.
Ideally, we would like Conjecture to scale quickly. Alignment wise, in 5 years time, we want to have the ability to take a billion dollars and turn it into many efficient, capable, aligned teams of 3-10 people working on parallel alignment research bets, and be able to do this reliably and repeatedly. We expect to be far more constrained by talent than anything else on that front, and are working hard on developing and scaling pipelines to hopefully alleviate such bottlenecks.
For the second question, we don’t expect it to be a competing force (as in, we have people who could be working on alignment working on product instead). See point two in this comment.
This is why we will focus on SaaS products on top of our internal APIs that can be built by teams that are largely independent from the ML engineering. As such, this will not compete much with our alignment-relevant ML work. This is basically our thesis as a startup: We expect it to be EV+, as this earns much more money than we would have had otherwise.
To point 1: While we greatly appreciate what OpenPhil, LTFF and others do (and hope to work with them in the future!), we found that the hurdles required and strings attached were far greater than the laissez-faire silicon valley VC we encountered, and seemed less scalable in the long run. Also, FTX FF did not exist back when we were starting out.
While EA funds as they currently exist are great at handing out small to medium sized grants, the ~8 digit investment we were looking for to get started asap was not something that these kinds of orgs were generally interested in giving out (which seems to be changing lately!), especially to slightly unusual research directions and unproven teams. If our timelines were longer and the VC money had more strings attached (as some of us had expected before seeing it for ourselves!), we may well have gone another route. But the truth of the current state of the market is that if you want to scale to a billion dollars as fast as possible with the most founder control, this is the path we think is most likely to succeed.
To point 2: This is why we will focus on SaaS products on top of our internal APIs that can be built by teams that are largely independent from the ML engineering. As such, this will not compete much with our alignment-relevant ML work. This is basically our thesis as a startup: We expect it to be EV+, as this earns much more money than we would have had otherwise.
Notice this is a contingent truth, not an absolute one. If tomorrow, OpenPhil and FTX contracted us with 200M/year to do alignment work, this would of course change our strategy.
To point 3: We don’t think this has to be true. (Un)fortunately, given the current pace of capability progress, we expect keeping up with the pace to be more than enough for building new products. Competition on AI capabilities is extremely steep and not in our interest. Instead, we believe that (even) the (current) capabilities are so crazy that there is an unlimited potential for products, and we plan to compete instead on building a reliable pipeline to build and test new product ideas.
Calling it competition is actually a misnomer from our point of view. We believe there is ample space for many more companies to follow this strategy, still not have to compete, and turn a massive profit. This is how crazy capabilities and their progress are.
The founders have a supermajority of voting shares and full board control and intend to hold on to both for as long as possible (preferably indefinitely). We have been very upfront with our investors that we do not want to ever give up control of the company (even if it were hypothetically to go public, which is not something we are currently planning to do), and will act accordingly.
For the second part, see the answer here.
To address the opening quote—the copy on our website is overzealous, and we will be changing it shortly. We are an AGI company in the sense that we take AGI seriously, but it is not our goal to accelerate progress towards it. Thanks for highlighting that.
We don’t have a concrete proposal for how to reliably signal that we’re committed to avoiding AGI race dynamics beyond the obvious right now. There is unfortunately no obvious or easy mechanism that we are aware of to accomplish this, but we are certainly open to discussion with any interested parties about how best to do so. Conversations like this are one approach, and we also hope that our alignment research speaks for itself in terms of our commitment to AI safety.
If anyone has any more trust-inducing methods than us simply making a public statement and reliably acting consistently with our stated values (where observable), we’d love to hear about them!
To respond to the last question—Conjecture has been “in the making” for close to a year now and has not been a secret, we have discussed it in various iterations with many alignment researchers, EAs and funding orgs. A lot of initial reactions were quite positive, in particular towards our mechanistic interpretability work, and just general excitement for more people working on alignment. There have of course been concerns around organizational alignment, for-profit status, our research directions and the founders’ history with EleutherAI, which we all have tried our best to address.
But ultimately, we think whether or not the community approves of a project is a useful signal for whether a project is a good idea, but not the whole story. We have our own idiosyncratic inside-views that make us think that our research directions are undervalued, so of course, from our perspective, other people will be less excited than they should be for what we intend to work on. We think more approaches and bets are necessary, so if we would only work on the most consensus-choice projects we wouldn’t be doing anything new or undervalued. That being said, we don’t think any of the directions or approaches we’re tackling have been considered particularly bad or dangerous by large or prominent parts of the community, which is a signal we would take seriously.
We (the founders) have a distinct enough research agenda to most existing groups such that simply joining them would mean incurring some compromises on that front. Also, joining existing research orgs is tough! Especially if we want to continue along our own lines of research, and have significant influence on their direction. We can’t just walk in and say “here are our new frames for GPT, can we have a team to work on this asap?”.
You’re right that SOTA models are hard to develop, but that being said, developing our own models is independently useful in many ways—it enables us to maintain controlled conditions for experiments, and study things like scaling properties of alignment techniques, or how models change throughout training, as well as being useful for any future products. We have a lot of experience in LLM development and training from EleutherAI, and expect it not to take up an inordinate amount of developer hours.
We are all in favor of high bandwidth communication between orgs. We would love to work in any way we can to set these channels up with the other organizations, and are already working on reaching out to many people and orgs in the field (meet us at EAG if you can!).
In general, all the safety orgs that we have spoken with are interested in this, and that’s why we expect/hope this kind of initiative to be possible soon.
See the reply to Michaël for answers as to what kind of products we will develop (TLDR we don’t know yet).
As for the conceptual research side, we do not do conceptual research with product in mind, but we expect useful corollaries to fall out by themselves for sufficiently good research. We think the best way of doing fundamental research like this is to just follow the most interesting, useful looking directions guided by the “research taste” of good researchers (with regular feedback from the rest of the team, of course). I for one at least genuinely expect product to be “easy”, in the sense that AI is advancing absurdly fast and the economic opportunities are falling from the sky like candy, so I don’t expect us to need to frantically dedicate our research to finding worthwhile fruit to pick.
The incubator has absolutely nothing to do with our for profit work, and is truly meant to be a useful space for independent researchers to develop their own directions that will hopefully be maximally beneficial to the alignment community. We will not put any requirements or restrictions on what the independent researchers work on, as long as it is useful and interesting to the alignment community.
We currently have a (temporary) office in the Southwark area, and are open to visitors. We’ll be moving to a larger office soon, and we hope to become a hub for AGI Safety in Europe.
And yes! Most of our staff will be attending EAG London. See you there?
See a longer answer here.
TL;DR: For the record, EleutherAI never actually had a policy of always releasing everything to begin with and has always tried to consider each publication’s pros vs cons. But this is still a bit of change from EleutherAI, mostly because we think it’s good to be more intentional about what should or should not be published, even if one does end up publishing many things. EleutherAI is unaffected and will continue working open source. Conjecture will not be publishing ML models by default, but may do so on a case by case basis.
Our decision to open-source and release the weights of large language models was not a haphazard one, but was something we thought very carefully about. You can read my short post here on our reasoning behind releasing some of our models. The short version is that we think that the danger of large language models comes from the knowledge that they’re possible, and that scaling laws are true. We think that by giving researchers access to the weights of LLMs, we will aid interpretability and alignment research more than we will negatively impact timelines. At Conjecture, we aren’t against publishing, but by making non-disclosure the default, we force ourselves to consider the long-term impact of each piece of research and have a better ability to decide not to publicize something rather than having to do retroactive damage control.
EAI has always been a community-driven organization that people tend to contribute to in their spare time, around their jobs. I for example have had a dayjob of one sort or another for most of EAI’s existence. So from this angle, nothing has changed aside from the fact my job is more demanding now.
Sid and I still contribute to EAI on the meta level (moderation, organization, deciding on projects to pursue), but do admittedly have less time to dedicate to it these days. Thankfully, Eleuther is not just us—we have a bunch of projects going on at any one time, and progress for EAI doesn’t seem to be slowing down.
We are still open to the idea of releasing larger models with EAI, and funding may happen, but it’s no longer our priority to pursue that, and the technical lead of that project (Sid) has much less time to dedicate to it.
Conjecture staff will occasionally contribute to EAI projects, when we think it’s appropriate.
Answered here.
We strongly encourage in person work—we find it beneficial to be able to talk over or debate research proposals in person at any time, it’s great for the technical team to be able to pair program or rubber duck if they’re hitting a wall, and all being located in the same city has a big impact on team building.
That being said, we don’t mandate it. Some current staff want to spend a few months a year with their families abroad, and others aren’t able to move to London at all. While we preferentially accept applicants who can work in person, we’re flexible, and if you’re interested but can’t make it to London, it’s definitely still worth reaching out.
Currently, there is only one board position, which I hold. I also have triple vote as insurance if we decide to expand the board. We don’t plan to give up board control.
Thanks—we plan to visit the Bay soon with the team, we’ll send you a message!
We aren’t committed to any specific product or direction just yet (we think there are many low hanging fruit that we could decide to pursue). Luckily we have the independence to be able to initially spend a significant amount of time focusing on foundational infrastructure and research. Our product(s) could end up as some kind of API with useful models, interpretability tools or services, some kind of end-to-end SaaS product or something else entirely. We don’t intend to push the capabilities frontier, and don’t think this would be necessary to be profitable.
TL;DR: For the record, EleutherAI never actually had a policy of always releasing everything to begin with and has always tried to consider each publication’s pros vs cons. But this is still a bit of change from EleutherAI, mostly because we think it’s good to be more intentional about what should or should not be published, even if one does end up publishing many things. EleutherAI is unaffected and will continue working open source. Conjecture will not be publishing ML models by default, but may do so on a case by case basis.
Longer version:
First of all, Conjecture and EleutherAI are separate entities. The policies of one do not affect the other. EleutherAI will continue as it has.
To explain a bit of what motivated this policy: We ran into some difficulties when handling infohazards at EleutherAI. By the very nature of a public open source community, infohazard handling is tricky to say the least. I’d like to say on the record that I think EAI actually did an astoundingly good job not pushing every cool research or project discovery we encountered, for what it is. However, there are still obvious limitations to how well you can contain information spread in an environment that open.
I think the goal of a good infohazard policy should not be to make it as hard as possible to publish information or talk to people about your ideas to limit the possibility of secrets leaking, but rather to make any spreading of information more intentional. You can’t undo the spreading of information, it’s a one-way street. As such, the “by-default” component is what I think is important to allow actual control over what gets out and what not. By having good norms around not immediately sharing everything you’re working on or thinking about widely, you have more time to deliberate and consider if keeping it private is the best course of action. And if not, then you can still publish.
That’s the direction we’re taking things with Conjecture. Concretely, we are working on writing a well thought out infohazard policy internally, and plan to get the feedback of alignment researchers outside of Conjecture on whether each piece of work should or should not be published.
We have the same plan with respect to our models, which we by default will not release. However, we may choose to do so on a case by case basis and with feedback from external alignment researchers. While this is different from EleutherAI, I’d note that EAI does not, and has never, advocated for literally publishing anything and everything all the time as fast as possible. EAI is a very decentralized organization, and many people associated with the name work on pretty different projects, but in general the projects EAI chooses to do are informed by what we considered net good to be working on publicly (e.g. EAI would not release a SOTA-surpassing, or unprecedentedly large model). This is a nuanced point about EAI policy that tends to get lost in outside communication.
We recognize that Conjecture’s line of work is infohazardous. We think it’s almost guaranteed that when working on serious prosaic alignment you will stumble across capabilities increasing ideas (one could argue one of the main constraints on many current models’ usefulness/power is precisely their lack of alignment, so incremental progress could easily remove bottlenecks), and we want to have the capacity to handle these kinds of situations as gracefully as possible.
Thanks for your question and giving us the chance to explain!
We Are Conjecture, A New Alignment Research Startup
I really liked this post, though I somewhat disagree with some of the conclusions. I think that in fact aligning an artificial digital intelligence will be much, much easier than working on aligning humans. To point towards why I believe this, think about how many “tech” companies (Uber, crypto, etc) derive their value, primarily, from circumventing regulation (read: unfriendly egregore rent seeking). By “wiping the slate clean” you can suddenly accomplish much more than working in a field where the enemy already controls the terrain.
If you try to tackle “human alignment”, you will be faced with the coordinated resistance of all the unfriendly demons that human memetic evolution has to offer. If you start from scratch with a new kind of intelligence, a system that doesn’t have to adhere to the existing hostile terrain (doesn’t have to have the same memetic weaknesses as humans that are so optimized against, doesn’t have to go to school, grow up in a toxic media environment etc etc), you can, maybe, just maybe, build something that circumvents this problem entirely.
That’s my biggest hope with alignment (which I am, unfortunately, not very optimistic about, but I am even more pessimistic about anything involving humans coordinating at scale), that instead of trying to pull really hard on the rope against the pantheon of unfriendly demons that run our society, we can pull the rope sideways, hard.
Of course, that “sideways” might land us in a pile of paperclips, if we don’t solve some very hard technical problems....
We would love to collaborate with anyone (from academia or elsewhere) wherever it makes sense to do so, but we honestly just do not care very much about formal academic publication or citation metrics or whatever. If we see opportunities to collaborate with academia that we think will lead to interesting alignment work getting done, excellent!