We started company 2022. First year was all about building the research and the product to really kickstart the work. We built the first text-to-speech model that finally could sound human. Released it in 2023, beginning of 2023. Then it took us roughly 20 months to get to the first 100 million in ARR. Roughly 10 months to get to 200, 5 months to get to 300. And that's how we closed end of last year and now we are at 600.

This revenue ramp — accelerating from 20 months to 5 months per $100M ARR milestone — is one of the most striking growth curves in recent enterprise software history.

We embedded engineers in every place and even in the places which aren't engineering. So our talent team will have an engineer, our legal team will have an engineer, our revenue engineering or go to market engineering have engineers embedded all across and those people will have two roles. One is of course creating automations and bringing the software inside of that team, but second is actually helping everybody else do what you said, which is make sure that people are adopting AI, but also there's a security check for everything they deploy.

Embedding engineers in every non-technical function is an organizational design insight that addresses both AI adoption and security simultaneously.

If you're not using a lot of the coding software, a lot of the co-working software, then you're probably in the wrong spot. If you're using too much of it, that is also a flag because you maybe are not doing that in the right way. And of course as you start bringing that into the sites of the organizations that never were exposed they frequently can create but not necessarily review whether that's actually doing behind the scenes all the secure ways or anything.

The observation that both under-use and over-use of AI coding tools are warning signs reframes AI adoption as a calibration problem, not just a usage problem.

Never did. thought it's a little bit of what you mentioned was also before the true AI impact started which was ideal ideal person in that role can code can understand the customer can understand design of course that's very hard to find there's no truly that many people that are expert in all of those fields at the same time so we optimize for profiles that are experts in at least one of those fields but understand at least one other field really well. What we are seeing now there's if you can do a little bit of all with AI you can maybe step change from being an amateur to being a advanced level maybe not an expert level so suddenly you are not bottlenecked on all the other functions to do your work.

AI is collapsing the need for product managers by enabling T-shaped individuals to cover the gaps that PMs once bridged between engineering, design, and customer understanding.

We're seeing a transition. We're suddenly — and that's you know the the biggest fuel of the recent growth for us — enterprises sales team just doing incredible work but then finally the product combines the reliability that's core with the orchestration for a lot of the AI models but also the knowledge and the integrations to provide you the right experience. And yeah, I think it was a step change in the last 12 months and especially in the last six of how good that experience became where it's like this golden era of consumers — for the consumers out there customers is coming where you're going to actually open a website, call an agent and have the agent have information from your past interactions and deliver that help.

The framing of a 'golden era' for consumers via AI voice agents — personalized, context-aware, proactive — signals a fundamental redesign of customer service infrastructure.

We work with a lot of financial services companies — Revolut, Klarna, Pogbank. And some of the frequent case, not in all of them, is of course how you remind people about payment or that you collect the debt from the people that aren't answering and frequently people would naturally feel ashamed of telling the real situation. With AI people are much more open to share what actually happened, give the information and suddenly this emotional block of like in front of other human I don't want to be able to say all of that is very different.

People's willingness to disclose sensitive financial situations more freely to an AI than a human reveals a deep behavioral shift with major implications for financial services and mental health applications.

The voice is identity and IP. It's like, you know, when you speak a certain way people recognize it, can feel that emotion. For us on the safeguard side, over last years we took the role as we are leading on our development, we also need to lead on a lot of the safeguards. So that's like a critical element. We do three things. One, trace everything that's generated so we can take action when needed. Two, now we moderate both on the voice and text level. So if you were to input something that would be commercial in nature or would try to scam someone that gets flagged, we can block it. And now free because over last years we've seen the development of those models more broadly, how can we create systems for the wider world so people can upload a sample and get information whether it's AI or not immediately.

A leading voice AI provider's three-pronged approach to safeguards — tracing, moderation, and public detection tools — sets a template for responsible deployment in identity-sensitive AI.

Today, we paid back over $22 million back to the community of talent. Which so those voiceover actors now who got paid as hourly workers, sometimes they get a little backend if they were doing a commercial or something. Now they can spend an hour reading, create an 11 Labs voice, and then license it out. And probably our most important work was actually working with people that lost their voice due to ALS, due to throat cancer and working on bringing that voice back.

The same technology enabling celebrity voice licensing also restores speech to those who have lost it — a rare case where commercial and humanitarian applications of AI are genuinely unified.

Fortnite launched Darth Vader which people and players could interact with live in partnership with the estate, in partnership with Disney. So every player after reaching a certain stage could have a Darth Vader interact and help you solve the missions. And we are seeing that kind of mode coming up more and more often of how you can effectively extend your likeness, your publicity into interactive use cases, bring it across the world together.

The Darth Vader-in-Fortnite example is the clearest early signal that deceased or rights-held identities can be infinitely instantiated as interactive AI characters — a paradigm shift for entertainment IP.

On the research side, it's the architecture that matters, not the scale. You really need to change how the model operates. Two, you need very specific data that there's of course a wide set of data out there, but it's unlabelled data and where we spend a lot of time. So we build an internal team of over thousand contractors that label all those audio assets to make them good. And then as you think about the rest of product stack, we want to create a fully verticalized solution for that communication angle. The product understanding the right workflow in financial services is very different to healthcare, very different to telcos.

The claim that architecture beats scale in voice AI — combined with the moat of 1,000+ human audio labelers — reveals the hidden labor infrastructure behind frontier audio models.

We hope this year we'll do that same thing for voice where any conversation feels like you are speaking with another human. It will be... I think you're there. It just depends on the application and like what question you ask. But it definitely passes it. I mean, if we were to look at the tests that were created to define artificial general intelligence or just to define artificial intelligence, we passed all of those. These were tests that were created 30 or 40 years ago. We need a new set of tests right now. I think the new test is like can this be more intelligent than every single person on the planet times 10.

The argument that existing AGI benchmarks are obsolete and we need new 'superintelligence' benchmarks reframes the AI progress conversation from 'have we arrived' to 'where are we going'.

The way to think about the market or at least the way that we like to is you have this enormous bucket of legal services which today is being done manually. It's a trillion dollars every year into legal services which is very fragmented. But the software spend into legal technology is about 40 billion. So it means there's 4% software, 96% service which is bananas. The software piece should be much bigger than that. And so the software piece naturally will grow into the service revenue but also legal is a very supply constrained market.

The 4% software / 96% service split in a $1 trillion legal market is a striking data point that quantifies the scale of AI disruption opportunity in law.

It starts to break this model where you charge out associates for very high hourly rates and you have a billable hour model. And actually, if you look in law firms, the way that that business model works is you overcharge for the associates and you actually undercharge for the partners. The only way they know how to price that is to overcharge for the associates.

Exposing the internal cross-subsidy of law firm economics — where associate hours fund partner compensation — reveals exactly where AI will apply the most destructive pricing pressure.

We're doing this partly at Lora. We acquired four businesses so far this year. We did the diligence in-house with our own tool and the fastest transaction we did was 12 days from LOI to closing because your motivation as the founder is to get the deal done. The motivation of the lawyer is to not have you sue them if they up the deal, right? And to make as much money as possible, which means to drag it out.

Closing an M&A transaction in 12 days using in-house AI tools is a concrete proof point that misaligned incentives in legal services are already being arbitraged away.

There's a lot of anxiety and a lot of fear and you know these law firms are enormously profitable and big businesses. Kirkland Ellis turns around $10 billion a year. Per partner, they make between 5 and 10 million every year in profits. And so, when something like AI comes along, that poses existential threats and existential opportunity. And that's actually a big part of my job to help articulate with the leadership teams that we work with because we will only be as successful as our customers are. And so we actually have a very unique role at Legora as well which is called the legal engineer. So in the same way that Palantir has forward deployed engineers, we have forward deployed lawyers.

The 'legal engineer' role — lawyers embedded inside law firms to guide AI transformation — is a novel go-to-market innovation that also functions as a change management consulting service.

The job will exist. The tasks will be different, right? In order to have a partner-driven model, you need to bring people up the ranks, right? In the same way as you do with software engineers. You need to have junior engineers so that one day you can have senior engineers who know what they're doing. But the way to get there is very different. The way of getting there today will not be lock yourself in the physical data room, read through every single document, mark the errors, and you know, go fax it, right? It's and it's also no longer just look in the virtual data room and control F. It's orchestrating the agent that will be doing that work.

The reframing of junior lawyer work from 'document review' to 'agent orchestration' describes a generational shift in what legal apprenticeship means.

The data that Legora sits on top of is on one hand side the firms and enterprises own data, their precedent, their organizational data, and secondly we do the hard work of gathering all the cases, all the legislation, all the regulatory updates for every jurisdiction in the world and that is very painful but once you start to do that at scale it builds a real data moat. And so in the system if you are the GC of a company in California and you just landed your first customer in South Africa, right? Legora can be adapted to the local legislation in South Africa. And we actually had a case of this where, you know, instead of having to call a lawyer who then knows a lawyer in that region who will respond to the query, they can get an 80% accurate response immediately.

The painful, jurisdiction-by-jurisdiction data aggregation work is precisely the moat that prevents well-funded competitors from easily replicating Legora's global legal coverage.

What's really interesting about especially the agents following the release of Opus 4.5 and 4.6 is they can now start to do really intelligent case strategy and they can actually start to combine the witness statements, the cases and they can really do end to end work which is I think moving us from a world where AI is just augmenting to AI is actually really doing things and your job becomes to orchestrate and to manage those agents as we're seeing in coding.

The transition from AI-as-augmentation to AI-as-autonomous-actor in legal case strategy is a qualitative threshold that redefines the role of the lawyer.

I don't believe in fine-tuning or building any general intelligence models. I think that's a total waste of time and money. I do believe in very narrow models for narrow use cases that you also drive a lot of scaling. So you can drive both cost and latency down. An example of this for us is we have a big feature called tabular review which is basically the number of documents times the number of prompts. So 100 documents, 100 prompts, 10,000 API calls. If you make a fine-tuned model at extracting contract data, it's very applicable there. But it doesn't make sense to build a general legal intelligence model like some of our competitors are attempting.

The strategic bet on narrow fine-tuned models over general legal intelligence models is a pointed critique of how several well-funded legal AI competitors are spending their capital.

Compliance is our currency. And so it's actually one of the reasons why it's really hard to sell into law. There's a lot of legal AI companies and very few are making it through. And not because it's hard to build stuff. It's actually quite easy to understand where you can build value, but getting it to the customer is very hard. But that's something we cracked pretty early on. And once you're in, it's much easier to expand. So that's also one of the driving forces behind our M&A strategy.

The insight that legal AI's hard problem is distribution and compliance trust — not product building — explains why so many technically capable entrants fail to gain revenue traction.