The Ledger, every show

“The glimpse of the future that I see is that all knowledge work is going to be automated. If a human's job involves looking at a computer, analyzing what's on the screen, using different tools, using a keyboard and a mouse, it's going to be automated. It's onl…”

“Now, I think in the limit over the next decade, every human might run seven to eight companies as these, as the loop from idea to, I mean, as you can go from prompt to company in hopefully like a single step over the next decade.”

“I think the era of data labeling companies is over, and it's now the era of research accelerators.”

“I don't see an AI bubble. These models are incredibly powerful today.”

“SaaS as we know it, I think is over. I think it's completely over.”

“It's innings one, and I believe in slow takeoff. I'm sorry to pour cold water on all the AI doomers that might be listening to this, but we are not in a rapid takeoff scenario. I believe in slow, steady takeoff for AGI and eventually super intelligence.”

“So I think like Turing will probably replace companies like McKinsey, Bain, BCG, and every services company like Accenture, TCS, Wipro, Infosys, like all of these companies doing vanilla services that's going to be automated with these agentic systems.”

“You're seeing what the coding models today are capable of building end-to-end systems, like coding for days. Sometimes a week.”

“When the models improve in coding, they have out of domain performance. They also improve in other tasks that have nothing to do with coding for reasons we don't fully understand.”

“If you can solve coding, tool use, reasoning, and multimodality, you have the keys to superintelligence. Those are the building blocks. If you can solve those four, you can automate anything that a human does in front of a computer, and it'll give humans super…”

“AGI is going to just make humans a hundred X more productive.”

“Basically the agents create V-one of the work. Humans are verifying and iterating. Humans never create V-one.”

“So on one hand, the models are becoming agentic. And in theory, if the models have access to the right sources of data, You kind of don't need software in the middle. The models can do it end to end.”

“Step one should be to, like, just do it with a frontier model and see how well it does. And most times you'll see that you don't have to touch the weights. But there are certain workflows like customer support like an invoice to pay system, some of these more …”

“In a future where AGI exists, this person will recruit a marketing GPT, a software engineer GPT, a PM GPT, and get off the ground for a lot less capital. A million flowers will bloom. Lots and lots of non-technical founders will start companies.”

“And there's a very significant model capability overhang. By that, what I mean is the models are capable of X, but what we are getting out of the models is X minus Delta.”

“I think it'll, I think there'll be more software engineers because if you define a software engineer as somebody who's capable of building a software product to solve a real problem, that pool of builders is going to expand way beyond people who've graduated w…”

“The vertical stuff, like, I mean, we are scaling up pretty massively in generating data for different verticals. So I don't see that as like a big white space. But I think everybody is relatively early with robotics. And robotics is such a vast realm that ther…”

“I think it's very impressive, like the progress that they've made in open source with DeepSeek Kimi Ketu, Kuen. These models are state of the art.”

“Turing's the world's leading research accelerator, working with all of these frontier labs to advance them along I would say the four pillars of superintelligence which is multimodality, reasoning, tool use, and coding. If you solve these four things, you will…”

“And I think of ASI as us having automated 90% of the tasks that 90% of humans do today in front of a computer, right?”

“This is like, if you look at those four dimensional, if you look at that four dimensional matrix, that's 30 trillion dollars of knowledge work that is going to be automated.”

“This industry had a huge shift after the reasoning models came out late last year, O-one being the first. The shift is, before the reasoning models, this industry needed simple data. Gobs and gobs of simple data. You needed a data factory. Or, ah, this industr…”

“It'll probably probably be a smaller model. Oftentimes we see a half a billion to a ten billion parameter model. That's the regime, not a trillion parameter model. And this will be faster, more accurate.”

“I think, like, the best firms are already doing this, ah, to help an investment analyst make better decisions faster, and eventually with significantly fewer people.”

“Most data companies don't see deployment, and most deployment companies don't see data. So Turing is the one company that works with the Frontier AI Labs and Fortune 500 enterprises, and we are a trusted bridge between research and deployment.”

“The current series of frontier models are largely in the trillion parameter realm. Yeah. Now we are starting to see 10 trillion parameter models.”

“The scaling laws are continuing to hold, meaning bigger model with more data, with more compute, means that the models smoothly keep getting better. It's like the, when the pre-training loss keeps coming down, it's like the model's performance on also all thes…”

“We are probably the largest provider of coding data to all the labs.”

“What I'm seeing more and more is there's a lot more people going into the no fine tuning camp than a couple of years ago for very high value enterprise use cases.”

“Legacy SaaS software also applies to legacy cybersecurity products too.”

“We would not build our own model. We don't want to compete with our customers, but we are building around the models so that enterprises can unlock the fullest value.”

“We work with seven out of the eight frontier labs.”

“I think it's a permanent requirement.”

“In back office automation, it'll probably be very slow, and it'll, it'll probably be the upstarts that'll do things well. I think the change management will be too slow. But I'm optimistic about front office, especially in financial services, life sciences, ph…”

“I think one moat will be data-driven feedback loops.”

“I think it would make sense for governments to build their own internal versions of some of these models, which would require proprietary data again to be collected.”

“I feel like for frontier models, there is some value in keeping some of the technology closed.”

“We work with seven out of the eight Frontier Labs. We work with OpenAI, Anthropic, Meta, Google, Microsoft, Nvidia, Amazon, anybody that's building a Frontier Foundation model, we are probably working with them already.”

“These models ate the internet when they were pre-trained, but the internet data is used up. It was used up like three years ago, right?”

“Now, for these verifiable domains like coding and math, instead of doing reinforcement learning with human feedback, you can do reinforcement learning. Because you can automatically check when you got the correct answer or not in these verifiable domains. And …”

“I don't think rapid takeoff is how things will unfold. I think it's going to be steady, continuous progress. Every step of the way, we're going to keep moving forward and it's going to be great, and it's great for a few reasons.”

“OpenAI came to us when they were training GPT-III and they wanted to teach GPT-III to code. So we collaborated with them on teaching the models to code and to do tool use, function calling”

“If you have tasks that are too easy for the model, there is no learning signal. If it is too difficult, there is no learning signal. So there is a sweet spot of complexity that you'd want the RL environments to be at. Usually like, 20 to 40%, something in that…”

“Like with code or with math, it's relatively more binary, easy to verify. But how do you verify the quality of a board deck? Yeah. It's a, you have to be, you have to have like a good rubric based evaluator.”

“And I think all the labs are scaling this up, and we are constrained by algorithmic research, compute, and data, and companies like Turing Advance the data pillar, NVIDIA advances the compute pillar, and of course the labs advance the algorithmic research pill…”

“Coding is one of those areas where, because it's verifiable, I think that there is a good path to using reinforcement learning to improve coding models quickly.”

“In the past, it was a lot of the compute went into pre-training. Now a lot of compute goes into reinforcement learning in post-training as well. Especially after O-one came out and DeepSeek came out.”