why aren't all 22 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 0 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Assertion Not checkable as stated
Lingelbach: Hedra's models support infinite recursion for long video generation
“One of the advantages of our technology and our architecture is our models support infinite recursion so we don't have an issue with long video generation like other providers.”
Insight
Lingelbach: Free users often provide negative product signal to startups
“Free users are often, like, not, they're often negative signal, in my opinion. People, the people who are willing to pay for software are sometimes a totally different group than the people who are willing to use it for free.”
Assertion Not checkable as stated
Hedra signs enterprise deals every few days with zero outbound sales
“I think we sign, like, an enterprise contract every couple days now. Like, pretty big ones now, too. And so and that's without outbound sales.”
Insight
Lingelbach: Media AI apps cannot yet rely purely on foundation models
“LLMs are just, like, much more farther along, and they have, like, a very well-defined, like, path, and it's really just about scaling them at this point, and, like,
You know, working on reasoning, improving general intelligence. I think for, like, media gener…”
Prediction Not checkable as stated
Lingelbach: Future Hedra will automate professional podcast video editing from footage
“So I think, like, in the future version of Hydra, you could chuck some video in and some character references in and say, hey, like, edit this podcast and get something out that felt really professional with very little work.”
Assertion Not checkable as stated
Hedra reached $1M in revenue within its first six months
“We grew to a million in revenue relatively quickly. How fast? Like a few months, five months, six months or something.”
Insight
Lingelbach: Building while designing causes engineering burnout from moving goalposts
“One mistake I made which I'll admit, is, like, we tried to build while designing, which can be quite challenging. Like, if you don't get everyone alignment and buy-in on what, like, an MVP looks like, I think sometimes, you know, it can cause a lot of, like, b…”
Assertion Not checkable as stated
Lingelbach: Hedra quickly reached an eight-figure revenue run rate
“We pretty quickly hit, like, an eight-figure run rate, which was exciting.”
Assertion Not checkable as stated
Lingelbach: Generative Video Dialogue Was Borderline Unusable Before Mid-2024
“There wasn't really dialogue and generative video at this point. Like, there kind of was, but it was just really bad. Like, it was so bad. It was, like, borderline unusable.”
Opinion
Lingelbach: Hedra's growth inflected by shifting from point solution to workflow tool
“And I think that's, like, a lot of the reason why we saw this big inflection point when we launched, is, like, we kind of became one of these go-to tools”
What-if
Lingelbach: Staff-level engineers early on would have eased Hedra's scaling
“Had we brought in, like, really experienced, like, staff level engineers to, like, anchor the team, I think probably it would have been, like, easier to scale”
Insight
Lingelbach: Series A to C veterans make the best seed-stage hires
“And oftentimes, like, the, I think the people that are the Best for, like, setting up a seed stage company are the people that did, like, a Series A to, like, Series C stint, where they've, like, seen, like, completely green-fielded systems. They know how to, …”
Assertion Not checkable as stated
Lingelbach: Hedra stood out and maintained excitement despite OpenAI's Sora
“Like, seven million is not, like, a little, but, like, Sora had come out at this point, and we were still, like, people were really excited to use Hydra, you know? So we had stood out, and, like, we did something other people weren't doing.”
Disclosure
Lingelbach manually onboarded unpaid influencers 10 hours a day to launch Hedra
“I took, like I took a four day sprint of basically doing 10 hours a day onboarding influencers onto the platform, which is really annoying at the time because we had no, like, feature flags, nothing. It was just, like, very much, like, get, like, manually addi…”
Disclosure
Lingelbach: Hedra Survived a Co-Founder Departure During Early Tech Pivots
“When my, like, co-founder left that was, like, another time where I was like, okay, like, I have to go bark on this, like, really big quest, and there's gonna be a huge, I think there's gonna be a huge payoff at the end of the road, but it still seems really f…”
Disclosure
Lingelbach: AI podcasting generated a decent chunk of Hedra's revenue
“Podcasting. I was like, I feel like this is going to be a thing. And like, that was, I think a pretty good read. Cause I ended up being like a decent chunk of our revenue.”
Disclosure
Lingelbach: Hedra raised Series A exclusively from existing backer a16z
“So I didn't really go out and raise, try to raise money from anyone else. We just kind of came to an agreement with our existing investors and I've wanted to work with Matt for a really long time. Bornstein who's on our board.”
Assertion Partly supported
Hedra raised $30M Series A from a16z
“I mean, you raised a thirty million dollar series a couple months ago from A-sixteen”
Disclosure
Hedra raised $7 million for its initial seed round
“We raised seven for our seed round originally.”
Disclosure
Lingelbach dropped out of his Stanford PhD right before his defense
“I had submitted my green light meeting to go do my defense, and then I told my advisors, like, hey, like, I want to take some time off immediately, because, like, I feel like if I wait to do my defense in, like, five months I'm gonna miss this, like, big oppor…”
Assertion Not checkable as stated
Lingelbach: Hedra has kept headcount to approximately 23 employees
“We're, like, 23, I think, as of this week. So we're still pretty small. But we generally, like, have tried to keep the team lean.”
Disclosure
Lingelbach: Hedra trains models operating jointly over image, text, and audio
“We train models that are natively omnimodal. That means they can operate jointly over image, text, and audio.”