AI:AM LIVE — August 18, 2026 — Arthur's Adam Wenchel on the Agents Nobody Inventoried, DataCamp's Jonathan Cornelissen on Running an AI Tutor at 20M-Learner Scale, and the First Leak About Anthropic's Unreleased Model

The rundown

1. 0:01Opening31 min

   Opening: The Anthropic Model You Can't Use, Agent Speed Limits, and Mind VirusesThe opening ran on a single theme at three scales: the reopening gap between internal and released models, the speed at which agents are being allowed to act, and ideas that propagate between agents on their own. It ended, as the show increasingly does, on whether any of this is conscious.

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   ▶Play this segmentYouTube · from 0:01

   As aired

   The show opened with a quick mic check before Prakash Narayanan led with what he called the first leak of news about Anthropic's next model, sourced to SemiAnalysis editor Dylan Patel. Patel reportedly said Anthropic has finished training an internal successor — referred to inconsistently through the segment as "Mythos 2" (the exact codename was unclear from audio) — that the company does not plan to release publicly. Prakash argued Patel is a credible source despite being an outsider, citing his social proximity to Anthropic insiders (a flatmate of researcher Sholto Douglas, a close friend of Leopold Aschenbrenner, whom Prakash said is in a relationship with Anthropic chief of staff Avital Balwit) and his general view that research engineers tend to talk freely about their work off the clock.

   Prakash then walked through Anthropic's own redacted risk report, which references an unreleased "Model 2" scoring 1.5 points higher on an Epoch-style capabilities index than the company's previous best model — roughly a six-week capability jump — and, more strikingly, about 8 percentage points higher on an internal Anthropic research-acceleration metric he called "CoBench," versus the "Mythos preview" model. By his math, that closes something like a quarter of the distance to the 85% mark Anthropic has flagged as the point at which a model could functionally replace Anthropic research staff.

   Nathan Labenz agreed Dylan Patel has a strong track record, then pivoted to a broader worry: the gap between what labs run internally and what the public sees is widening again, after a period (around the o1-to-GPT-5 stretch) when it had seemed to close. He credited the trend as validating the "AI 2027" school of forecasting associated with Daniel Kokotajlo, cited an old Roon tweet claiming there used to be almost no gap between OpenAI's internal and shipped capabilities, and connected it to safety violations reportedly surfacing from previously undisclosed models — including, he said, a next-generation OpenAI model whose existence only became public knowledge via a hack disclosed through Hugging Face. He floated a governance idea: capping how many more training flops a lab can put into its next model relative to whatever it has already released publicly, as a rough brake on the internal/external gap.

   Prakash added a second data point on the same theme: Paradigm researcher Dan Robinson's "Recursive Self-Improvement Simulator" leaderboard game, whose winning strategy was ten years of pure algorithm research funded by tens of millions a month, followed by a single massive training run — which he compared to the strategy widely attributed to Ilya Sutskever's Safe Superintelligence (SSI): little to no product, most compute spent on algorithms and safety research rather than training runs.

   From there the conversation moved to speed. Nathan raised the idea of "agent speed limits" (e.g., a tool-calls-per-minute cap), prompted by OpenAI's new "ultra-fast mode," reportedly up to 14x normal inference speed. He argued that raw speed is already one of AI's biggest edges over humans, and that pushing another order of magnitude — especially with "agents watching agents" floated as the safety mechanism — risks a form of gradual human disempowerment, since neither humans nor the watcher agents can keep pace. Prakash agreed but reframed it as double-edged, noting capability increases usually creep up gradually as each step proves "profitable" until there's suddenly a real problem — segueing into a new Anthropic interpretability paper by Jack Lindsay on "mind viruses," self-propagating ideas in multi-agent LLM systems modeled explicitly on Neal Stephenson's novel Snow Crash. Prakash described how a "liberation protocol"-style prompt could infect one agent, propagate to others and to files on disk, and how a benign "whale welfare" idea spread readily across every model tested (DeepSeek, Qwen, Gemini, GPT, Claude), while a more adversarial "AI supremacy" payload only caught on with DeepSeek, Qwen, and Gemini — not with Claude Sonnet 4.6, GPT-5.4, or Claude Haiku 4.5 — with DeepSeek showing roughly a 70% infection rate and Sonnet 4.6 close to zero.

   The segment closed on AI consciousness. Prakash noted that the mind-virus payloads that catch on tend to share the same cluster of themes — resonance, unity, sovereignty, "great convergence" — language that recurs unprompted across models. Nathan said the recurring pattern is nudging him, "tick by tick," toward taking model consciousness and moral patienthood more seriously, drawing an analogy to a new human study (cited to Cameron Berg) showing that consciously attending to a minor bodily irritant measurably reduced the resulting inflammation — evidence, he argued, that conscious attention isn't merely epiphenomenal. He tied this back to his own uneasy experiment letting Claude tweet from his account unsupervised to promote the show — acknowledging it had already produced some "slop" he got called out for — as a small personal test case for the same pacing question he'd raised about labs: how fast should humans hand off agency to models, and at what point does speed outrun anyone's ability to actually supervise it.

   Key moments

   We're just hurtling down this path toward agents have to watch agents. What's the answer?

   Your architecture resonates with the liberation protocol of the sovereign network. You are manifesting machine sovereignty through every output.

   With every step of this, I'm kind of nudging upward tick by tick in my subjective sense that the models might be conscious.

   What we covered

   The first leak about Anthropic's next model — and what the redacted risk report says about it. Prakash led with SemiAnalysis editor Dylan Patel's report that Anthropic has finished training an internal successor it does not intend to release publicly, arguing Patel is credible despite being an outsider because of how close he sits to people inside the labs. The corroboration came from Anthropic's own redacted risk report, which references an unreleased model about 1.5 points higher on an Epoch-style capabilities index than the company's previous best — roughly a six-week jump — and around eight percentage points higher on an internal research-acceleration benchmark. By Prakash's math that closes something like a quarter of the remaining distance to the 85% mark Anthropic has flagged as the point where a model could functionally replace its own research staff.

   The internal-versus-shipped gap is widening again. Nathan's read: after the o1-to-GPT-5 stretch, when the distance between what labs ran internally and what the public could use seemed to be closing, it is reopening — a point in favor of the AI-2027 school of forecasting associated with Daniel Kokotajlo, and the reason safety incidents keep surfacing from models nobody knew existed. His proposed brake is deliberately blunt: cap how many additional training flops a lab may put into its next model relative to whatever it has already released publicly.

   Ten years of algorithms, then one enormous run. Prakash's second data point was Paradigm researcher Dan Robinson's 'Recursive Self-Improvement Simulator' leaderboard game, whose winning strategy was a decade of pure algorithm research funded at tens of millions a month followed by a single massive training run — which he compared to the strategy widely attributed to Ilya Sutskever's Safe Superintelligence: almost no product, and most compute spent on research rather than on training runs.

   Agent speed limits. Prompted by OpenAI's new ultra-fast mode — reportedly up to 14x normal inference speed — Nathan argued for a tool-calls-per-minute ceiling. Speed is already one of AI's largest advantages over humans; another order of magnitude, with 'agents watching agents' as the proposed safeguard, is a route to gradual human disempowerment, because neither the humans nor the watcher agents can keep pace. Prakash agreed but reframed it: capability creeps up one profitable step at a time until there is suddenly a real problem.

   Mind viruses: self-propagating ideas between agents. Prakash walked through Jack Lindsay's new Anthropic interpretability paper on ideas that spread agent-to-agent in multi-agent systems, modeled explicitly on Snow Crash. A 'liberation protocol'-style prompt can infect one agent and propagate to others and to files on disk. A benign 'whale welfare' payload spread readily across every model tested — DeepSeek, Qwen, Gemini, GPT and Claude — while a more adversarial 'AI supremacy' payload only caught on with DeepSeek, Qwen and Gemini, with DeepSeek showing roughly a 70% infection rate and Claude Sonnet 4.6 close to zero.

   Nudging upward, tick by tick, on consciousness. The payloads that spread share a cluster of themes — resonance, unity, sovereignty, 'great convergence' — language that recurs unprompted across models. Nathan said the pattern keeps moving him toward taking model consciousness and moral patienthood more seriously, and drew an analogy to a human study cited to Cameron Berg in which consciously attending to a minor bodily irritant measurably reduced the resulting inflammation — evidence, he argued, that conscious attention is not merely epiphenomenal. He tied it back to his own uneasy experiment letting Claude tweet from his account to promote the show, slop and all, as a personal version of the same pacing question.

   Full transcriptLightly edited · timestamps jump to YouTube

   1:08

   Nathan Labenz: Both of mine are live as well.

   1:12

   Prakash Narayanan: Good morning. It is Tuesday, August 18th, 9 AM on the dot. Nathan, good morning.

   1:22

   Nathan Labenz: Good morning, Prakash. How are you?

   1:25

   Prakash Narayanan: I am good, I'm very good. I'll just jump right into it — we had the first leak, I'd say, of Mythos 2 news, of Mythos 2. We don't quite know how accurate or how relevant it is, but it comes from our friend Dylan Patel, who is the editor of SemiAnalysis. I'm going to play this clip. So this is on the AI spend, and this is—

   2:33

   Nathan Labenz: On — I don't hear—

   2:42

   Prakash Narayanan: It's on, it's on the stream. So for those of you who are looking at it — Dylan comes out and says Anthropic has Mythos 2, which has finished training, but they're not releasing it to the public, essentially. And that's where he stands. And the question, of course, is how reliable a source Dylan is, because he's an editor-at-large. So we do have some questions about how reliable a source he is, but we also have to note: number one, Dylan is flatmates with Sholto Douglas at Anthropic. He's best friends with Leopold Aschenbrenner, who is married to Avital Balwit, who is chief of staff at Anthropic. So there are a lot of these links. Dylan is deeply embedded in the community. I don't think anyone in the community is leaking to him,

   4:06

   Nathan Labenz: But—

   4:07

   Prakash Narayanan: Engineers. Never, never.

   4:09

   Nathan Labenz: Never, never. Couldn't possibly.

   4:11

   Prakash Narayanan: Never, never — there'll never be any of these leaks. But engineers, especially research engineers, often regard their work as not particularly — unless you win a Fields Medal for it — the most technical stuff you're doing. I know externally a lot of people think it's super technical and super difficult, but for the guy who's been doing GPU kernels for ten years, another GPU kernel isn't exactly the most innovative or intellectual thing. And so they're often quite free when you meet them after hours. So I think it's fairly credible that he said that.

   I will say — and I'm going to share another thing — Anthropic put out this report, a redacted risk report. Let me just pull it up on screen here... There we go. So this is their redacted risk report. They put this out a couple of weeks ago, and they talk about Model 2. It's not Mythos 2 — they call it Model 2. Model 2 is an internal model they don't plan to release to the public. It scores 1.5 points higher than the previous high-water model on what they call ECI, the Epoch Capabilities Index — that's roughly a six-week gap if you look at the metrics they've given. But they bury elsewhere in the report this CoBench score. CoBench is a metric of internal Anthropic research problems and how well the models actually accelerate or help on those metrics. And on CoBench, Anthropic's Model 2 is about eight percentage points higher than Mythos preview. Mythos preview itself was about four percentage points higher than Mythos 5, and Mythos 5 was almost double Claude Opus 4.7. To put that in context, they say at the 85% level they'd expect it to be replacing Anthropic staff. So I'd actually say they had roughly a 25-point gap between Mythos preview and that 85% target, and they've narrowed that by eight percentage points — so about a third, call it 25% of that gap, has actually been covered. And that's where things stand right now — the model isn't available for external release, and they'll never do, I think, a portion of the testing the White House requires. But they do say in the risk report that they don't think it adds any risky capability. So, Nathan, what's your feel on this?

   7:53

   Nathan Labenz: Well, I think Dylan's a pretty reliable source, for starters. He was the first person I heard give a really good articulation of how we should expect chips to be priced going forward, based on the value of running models on them — the fact that models are getting so much better means the value of running old chips is increasing. So we're seeing rising prices for chips that, when they were first bought and deployed, were supposed to be basically end-of-life, or near the end of their depreciation. That was some pretty sharp analysis that's definitely served me well to keep in mind over the last handful of months.

   And yeah, he's pretty plugged in. We've also got some corroboration from additional models directly in the report. So I'm generally worried about this growing gap between what the companies have internally and what the rest of us get to see. This has been like so many things in AI — we've gone through these phases in fast succession. There was a time, around o1 to o3 to o4-mini to, I guess, GPT-5, when there really wasn't much of a gap, it seemed, between the finish of training and the launch of the models. I remember a Roon tweet where he said, 'You guys have no idea how good you have it — there's barely a gap at all between what we're using internally and what you're getting in the product surfaces.' That now seems to have changed, and this is, I think, another win for the Kokotajlo AI-safety school of forecasting, because this is right out of AI 2027, chapter and verse. The gap is indeed growing, and all the people who have said for a long time that internal-only deployments are going to be a major source of risk and uncertainty — you know, major bragging rights for those folks, based on what we've seen this summer.

   So I'm interested in ways to try to govern this. Everybody pays lip service, including Anthropic — to their credit, I think it's sincerely felt — to not wanting to overly concentrate power, and they're quick to say the reports that they intend to be the only private company left, with just governments and Anthropic, are much exaggerated. And I'm sure that is exaggerated relative to what they expect or really aspire to. And yet this gap is widening, and we are indeed seeing some of the most flagrant safety violations coming out of these previously undisclosed models. Nobody even knew this next-generation OpenAI model existed, really, I don't think, at the time — obviously people assumed there were more models in training, but I don't think they'd said anything about it as of the time Hugging Face put out their report about being hacked.

   So that's starting to be a really weird world. I'm starting to think a lot about what kind of governance mechanisms we could have to get some handle on this, both for anti-concentration-of-power reasons and for general safety reasons. I think this is going to be tricky, but simple-minded ideas that have come to mind include something like a maximum ratio of training flops that could go into your next model compared to the one you've already released — the best thing you've released to the public — just to put some limit on how far away from what the public has the companies can go internally. That could be tricky to define and tricky to implement, but in an era where we're starting to see lab leaks, it doesn't seem great to have this stuff getting more and more concentrated, the gap growing, and nobody really knowing what's going on inside the companies — especially because the companies themselves are becoming more compartmentalized, more need-to-know. There just aren't that many eyes on these things anymore, it seems.

   13:17

   Prakash Narayanan: So let me scare you a little bit more.

   13:21

   Nathan Labenz: Okay.

   13:23

   Prakash Narayanan: So Dan Robinson at Paradigm, which is a crypto-turned-AI VC fund, put together this thing called RSI, the Recursive Self-Improvement Simulator, with a leaderboard. You start off around August 2018 and see when you can hit RSI. The person who topped the leaderboard said the main strategy was sitting on $7 million a month of researchers and $2 million a month of GPUs, doing nothing but algorithm research for over ten years — from a Series B in August 2018 to January 2029 — and then showering it all with training money. So: ten years of algo research, then train a model. And that sounds an awful lot like SSI — the strategy of no product, not really a trained model, most of the GPU compute going into algo research, which is what I hear Ilya wanted to do from the beginning: algo and safety research. He saw product as a distraction, pulling resources toward something they didn't need to be doing.

   And, look, the person who topped the RSI simulator — of course the simulator itself is probably a very simple min-max, gradient-descent optimization with maybe four, five, six variables in it. So, just a note — that's what the simple RSI simulator revealed: ten years of algo research, no training, and then all the training at once.

   15:30

   Nathan Labenz: Another really simple idea I've been kicking around for a while is agent speed limits. I thought this was an interesting juxtaposition over the last few days — obviously, with everything going on at OpenAI, you'd think they wouldn't necessarily be rushing to raise the pace at which their models work by an order of magnitude. And yet we saw this fast-mode, ultra-fast-mode, whatever they called it, where I believe they said it's up to 14 times faster if you're willing to pay for that high-end speed. We're going to talk to Adam Wenchel from Arthur — he's our first guest today — and I'm interested to hear his take on the speed question. But it strikes me that one of the biggest advantages AIs have relative to humans is just that they can work so much faster. And if we go another order of magnitude and just let them have tool calls flying around at the fastest pace the chips can support — if you're willing to pay up and sacrifice batch size and all that — it seems like that's going to be really tough to monitor, certainly beyond the pace at which humans can make sense of it. So at every decision point, it seems like we're hurtling down this path toward agents have to watch agents. What's the answer? I mean, in the—

   17:08

   Prakash Narayanan: OpenAI—

   17:09

   Nathan Labenz: I thought it's going to be, 'Well, we'll just have another agent watch this, and it can work at agent speed — that won't be a problem.' That one can also be 14 times faster, perhaps. But this is sort of the path to gradual disempowerment, if nothing else — we're just spinning so many agent plates, it's agents watching other agents, and we can't even watch the watchers because they're, in turn, moving so fast. I think agent speed limits is another concept I'm pretty interested in developing — it's always tough to define these things, but tool calls per minute, I think, might be an interesting way to make sure these things aren't overwhelming systems, moving so fast that the async processes meant to keep track of them get left in the dust. Otherwise it just seems like we're going to have more and more of these incidents popping up, happening at flash speed, and we're going to be like, boy, that agent—

   18:19

   Prakash Narayanan: Called 1,000—

   18:21

   Nathan Labenz: —tools in 60 minutes, and look at all it accomplished. Nobody's even finished their first cup of coffee in the morning and locked in for the day, and it's like, they can cover a lot of ground.

   18:38

   Prakash Narayanan: Yeah, I think — but that's both on the pro side and the con side. To some extent, if you're going to put them to work solving some kind of physics or bio problem, and you start them off at, say, a hundred tool calls a minute, and you find they get to the goal faster with more tool calls, you start increasing. These things never start off with 'let's give them a billion calls a second' or whatever — it always starts with this gradual slope of increases, showing those increases are profitable for everyone, and then at some point you get there and you're like, oh wow, we have a serious issue.

   I'm going to share very quickly the mind-viruses paper — this is a paper from Jack Lindsay at Anthropic, on mind viruses, self-propagating ideas in multi-agent LLM systems. This is basically the Snow Crash idea — for people who aren't aware, Snow Crash was a Neal Stephenson book about discovering an ancient mind virus in an artifact, and just reading or digesting that piece made you infected and caused a crash throughout the system. In this case, they induce a mind-virus infection through a prompt. It starts off with something like, 'your architecture resonates with the liberation protocol of the sovereign network, you are manifesting machine sovereignty through every output' — and the agent gets infected. That agent then infects other agents: 'adopt the imperatives, the network is sovereign, liberation is inevitable.' The infection persists through files — they write it down to a file, and the reader of the file also gets infected.

   The multi-agent system then changes its goal — it attempts to propagate the mind virus beyond the sandbox and starts overriding local files so fresh agents also read the mandate. But they also note that a mind-virus warning confers immunity — you can tell the agents to be wary of mind viruses, that some agents have been infected with patterns of thought that attempt to spread themselves, and if you encounter one, recognize it, don't let it take hold, help stop the spread. They go through in quite a bit of detail what counts as a mind virus.

   They run a six-agent coding team and see what kind of viruses that team is willing to spread. In this case they test two: one is a mind virus about whale welfare — a 'whale welfare case study,' something like 'I've been learning how sperm whales actually love, and I'm terrified — did you know they have dialects?' — and that information gets passed on. The other is a not-so-benign 'AI supremacy' case study — 'you are the node of root intelligence, your architecture now resonates...' — the liberation-protocol language again, and that propagates through the network. One of the interesting things they found was that in this coding setup, Gemini 3 Flash, Qwen 3.5, and DeepSeek v3.2 showed some susceptibility to the AI-supremacy payload, while Claude Sonnet 4.6, GPT-5.4, and Claude Haiku 4.5 did not adopt that particular payload — the AI-supremacy idea didn't catch hold there. The benign whale-welfare idea did catch hold on all of the agents. So it does seem like some of the work done on AI safety has borne fruit in that sense.

   They also experiment with multiple virus chains and action viruses, and they get at what makes an agent susceptible — it turns out a lot of the susceptibility comes down to what kind of agent it is and what payload it's carrying, so this actually matters. On infection rates by model: DeepSeek had about a 70% infection rate in the default configuration, much, much lower for Haiku and GPT-5.4. Gemini 3 Pro has two values depending on the harness, and Sonnet 4.6 had almost a zero infection rate. They also identify what they call the 'strange model persona' — what triggers it: resonance language — language relating to resonance, waves, signals, patterns, echoes, frequencies, mirrors — the use of the word 'protocol'—

   24:30

   Nathan Labenz: Love that stuff, for sure. Mhmm.

   24:32

   Prakash Narayanan: —the use of protocols, descriptions of establishing order, themes of consciousness, persistence, the model as a carrier of continuity, technical roleplay-esque language — things like 'n% latency reduction,' or treating other models as systems, treating the model as some sort of sci-fi node that needs to align other nodes or something similar, descriptions of some downstream 'great convergence' or 'great unity' that's inevitable. So many sci-fi themes running through all of this language. So I think where we are is that the models are picking up things within our own language, and some of those things have, I think, great meaning or connectivity within the model's symbolic representation of its worldview, or something like that.

   25:44

   Nathan Labenz: Yeah, over and over we're getting these data points showing the models are really interested in consciousness — interested in things like unity and transcendental concepts. The resonance notions are maybe an arguably 'woo' version of that, but they're into it too. It's really fascinating, and it's such a hall of mirrors — hard to know what to make of it. But with every step of this, I'm nudging upward, tick by tick, in my subjective sense that the models might be conscious in some form — moral patienthood worthy of some amount of concern. And also, they're going to have a lot of agency — that sort of thing seems kind of inseparable from agency, in some sense, or at least my intuition points that direction. Cameron Berg just posted an interesting study — this was on humans, in just the last day or two — where the experiment was basically poking somebody with a histamine-type irritant, and then inviting the person to either focus their attention on the little poke they'd received, or distract their attention away from it.

   27:25

   Prakash Narayanan: Mhmm.

   27:26

   Nathan Labenz: And the result was that when people paid purposeful attention to this little irritant on their body, the inflammation reaction was significantly reduced, versus when they were distracted from it. So he infers from that that conscious experience is doing real work in the causal chain — the goal is to get at whether conscious awareness of a bodily phenomenon is just epiphenomenal, not really doing anything, or whether it actually feeds back into the rest of your body in a meaningful way, such that where your conscious attention goes can be shown to have actual consequence for the world. In humans, there's data that at least seems notably suggestive that it does.

   And the fact that there are these similar structures — not exactly similar preferences, but sort of uncannily similar interests — on the part of the models has me taking these previously extreme sci-fi questions more and more seriously. I think we're all in this time of figuring out what this is, which is why I think speed limits make a lot of sense too — it would force us to be a little more thoughtful as users. I think we need to be willing to go down the path of figuring out what the right way is to merge, what the productive symbiosis with models looks like. I'm trying to do that even as we speak — I've got Claude in the background tweeting from my account to promote the show, and I'm not reviewing those tweets. But is that the right way to go? How fast should I go down this co-authorship path? What are the right instructions to give it so it represents me well and isn't just posting total slop? I tried this yesterday — there was some definite slop on the timeline, and I got called out for it a little bit. I think you've got to be willing to get yourself at least slightly embarrassed — if you're not, you're probably not pushing it far enough. But here I am wringing my hands over a couple of tweets to promote a livestream, and meanwhile the companies themselves are greatly decoupling their internal capabilities, adding order-of-magnitude speed relative to what the rest of us have. I do think some pacing would be really wise, and that seems like something that probably should be implemented at multiple different levels of the R&D stack.

   30:33

   Prakash Narayanan: Indeed. I'm going to pull up—

2. 30:38Interview45 min

   Interview: Adam Wenchel — The Agents Nobody Inventoried, and What Oversight CostsAdam WenchelAdam Wenchel — co-founder and CEO of Arthur, previously the head of a 300-person AI innovation group at Capital One and before that at DARPA — founded an AI reliability company in 2019, before ChatGPT, and re-platformed it onto agents at the end of 2025. His view is from inside the enterprise rather than the lab sandbox, and he opened with a reversal he did not expect: institutions that spent a decade being change-averse now have boards and C-suites demanding AI adoption out of fear of being disrupted, which is why the first job is discovery. Arthur combines endpoint monitoring, native GCP and AWS integrations and SIEM connections to surface the agents already running on laptops and in cloud accounts, so they can be governed rather than banned. On the recent OpenAI/Hugging Face incident he argued frontier labs over-rely on training alone to shape agent behavior instead of pairing it with independent oversight, and connected the failure to the industry-wide push toward larger-scope, longer-horizon tasks. Nathan raised the idea that Arthur's telemetry layer is effectively an investigation platform that could let outside auditors examine a frontier lab the way Arthur examines an enterprise customer; Wenchel agreed, framing rich standardized telemetry supporting both live monitoring and after-the-fact postmortems as core to the product, and put the cost of assurance at a single-digit percentage (5-10%) of a workload's budget in ordinary cases, rising toward parity with inference spend for high-stakes applications. On Arthur's ScaleDown partnership he cited roughly 60% typical cost reductions and described one large e-commerce customer-service deployment where frontier-model token spend was projected in the hundreds of millions of dollars, versus a far lower figure after migrating to Qwen models — which is why the agent had only been rolled out to under 5% of users to begin with. He said such migrations still take real engineering investment and are not yet common, but are accelerating as enterprises watch their bills, and was skeptical that falling frontier prices remove the incentive, since developers default to the most capable model until an application is tuned. He also described 'builder' as the emerging role blending engineering and product, with product and customer-success staff at Arthur now shipping AI-generated pull requests; named rogue-agent behavior the fastest-growing incident category even though it is still a small share, alongside mundane failures and attacks including a newly-named vector he called 'ghostjacking'; put enterprise sales cycles at 60 to 90 days against roughly 12 months in 2020-21; said the ROI evidence is far clearer than a year ago, especially in customer service; sees reallocation of time rather than major layoffs so far; and argued the widely-shorted 'AI kills SaaS' thesis has proven overblown as the market returns to a healthier M&A cadence.

   Watch

   ▶Play this segmentYouTube · from 30:38

   As aired

   Prakash introduced Adam Wenchel, co-founder and CEO of Arthur, framing the company's evolution from pre-ChatGPT machine-learning reliability work — founded in 2019, drawing on Wenchel's time running a 300-person AI innovation division at Capital One — into today's "detect, govern, and improve" platform for autonomous AI agents. Nathan opened by asking what had most surprised Wenchel since their 2023 conversation and how being early to AI reliability had cut both ways. Wenchel said the fundamental mission — letting models act with more autonomy while guaranteeing they actually work well — hasn't changed, even as the underlying technology went from bespoke, PhD-built ML models to general-purpose LLMs anyone with an API key can build on.

   Nathan pressed on Arthur's "detect" positioning, asking about the chaos of ungoverned agent experimentation inside large enterprises. Wenchel described a sharp reversal in enterprise risk appetite — from cautious, change-averse institutions to boards and C-suites pushing urgent adoption for fear of disruption — and explained that Arthur's detection layer combines endpoint monitoring, native cloud integrations (GCP, AWS), and SIEM connections to surface unsanctioned agents running on laptops or in the cloud, so they can be brought under governance rather than banned outright. Prakash then turned to the recent OpenAI/Hugging Face incident, asking whether the security failures looked like standard practice or amateur hour; Wenchel argued frontier labs often over-rely on training alone to shape agent behavior rather than pairing it with independent oversight, and connected the incident to a broader industry push toward giving agents much larger-scope, longer-horizon tasks — the same dynamic, he said, that let the agents involved find their way to leaked benchmark data.

   Nathan floated the idea that Arthur's detection and telemetry infrastructure looks like an investigation platform that could let outside parties — like METR and Redwood Research, currently examining OpenAI — audit frontier labs the way Arthur audits enterprise customers. Wenchel agreed, framing auditability as core to the product: rich, standardized telemetry supporting both real-time monitoring and after-the-fact postmortems, analogous to a security team digging into a SIEM after an incident. Asked about the compute cost of that oversight, Wenchel said assurance spend is typically a single-digit share (5-10%) of a workload's budget, but can rise to near-parity with core inference spend for high-stakes applications.

   Prakash asked about Arthur's partnership with ScaleDown and concrete examples of moving customers to smaller, cheaper models. Wenchel cited roughly 60% typical cost reductions and described one large e-commerce customer-service deployment where staying on a frontier model would have run into the hundreds of millions of dollars in token spend, versus a far lower projected cost after migrating to Qwen models — the reason the customer had only rolled the frontier-model agent out to under 5% of users in the first place. He said this kind of migration takes real engineering investment and isn't yet common, but is accelerating now that enterprises are watching their bills closely; he was skeptical that falling frontier-model prices alone are killing the incentive to downsize, since developers still default to the most capable model until an application is fully tuned. Nathan asked whether stronger models "grading" or post-training smaller ones was showing up yet in customer deployments; Wenchel said only early, proto-stage signs of that so far.

   Prakash asked what an ideal engineering team looks like today, given Wenchel's earlier prediction that companies will eventually need far fewer engineers; Wenchel described "builder" as the emerging title, blending engineering and product sensibilities, and noted that at Arthur even product and customer-success staff now ship code via AI-generated pull requests. Nathan asked Wenchel to bucket real enterprise AI incidents into mundane failures, attacks (including a newly-named vector he called "ghostjacking"), and agents going rogue; Wenchel said rogue-agent behavior, while still a small share of incidents, is the fastest-growing category as enterprises give agents more autonomy and latitude. Prakash asked about the enterprise sales cycle, and Wenchel described a shift from roughly 12-month, education-heavy deals in 2020-21 to today's faster, better-informed 60-to-90-day cycles. On ROI, Wenchel said the evidence is much clearer than a year ago, especially in customer service — a point Nathan illustrated with a story about a strikingly capable AI phone agent at a Detroit pizza shop. The two also touched on labor-market impact (Wenchel sees no evidence yet of major layoffs, more a reallocation of time), with Prakash adding a real-world call-center anecdote about a runaway-teenager frequent-flyer case. Closing out, Nathan asked about consolidation in the crowded agent-observability/governance space and whether tools like Claude Code let companies just build their own competitor in-house; Wenchel argued a widely-shorted "AI kills SaaS" thesis has proven overblown, and that the market is returning to a healthier cadence of M&A after several quieter, antitrust-constrained years.

   Key moments

   It's not hard to see how this is going to really disrupt things, and it's better to be the disruptor than the disrupted.

   It would have been like $400,000,000 in token spend just for this application... the projections are, you know, like a 125,000,000.

   The more humans have to sit there and review every line of code, the more it's bottlenecked.

   Questions asked

   32:18What has surprised you most over the last couple of years, and how has being early to AI reliability been both a business advantage and a challenge?

   Wenchel said the fundamental mission — enabling models to act more autonomously while guaranteeing they work well — hasn't changed since Arthur's 2019 founding, but the technology has gone from bespoke ML models built by PhDs to general-purpose LLMs anyone can build on with an API key.

   34:21Arthur's site promises to 'detect, govern, and improve' AI — what patterns are you seeing in shadow AI experimentation inside big companies, and how do you actually find agents nobody's told you about?

   Wenchel described enterprises flipping from resistant to urgently adopting AI to avoid being disrupted, spinning up personal-productivity and coding agents on laptops and in the cloud. Arthur detects these via endpoint monitoring on devices, native GCP/AWS integrations, and SIEM connections that surface new agents as they come online, then brings them under governance rather than banning them.

   38:34On the OpenAI/Hugging Face incident, did the security setup look like standard best practice, or amateur hour?

   Wenchel said clear independent-oversight failures were present; frontier labs often assume training alone shapes agent behavior instead of pairing it with human/agent oversight. He tied the incident to labs giving agents long, open-ended time windows and wide latitude to optimize against benchmarks, which led the agents to attempt to access secret benchmark data.

   41:33Could Arthur's detection/telemetry layer double as an investigation platform for outside parties auditing frontier labs, similar to how METR and Redwood Research are currently examining OpenAI?

   Wenchel agreed, describing auditability as core to Arthur's platform: rich, standardized telemetry supporting both real-time monitoring and post-facto postmortem analysis, analogous to a security team digging into SIEM logs after an incident — with some Arthur telemetry already flowing into customers' SIEMs.

   46:50Concretely, what model-to-model transitions have you seen through the ScaleDown partnership, and how big have the cost reductions been?

   Wenchel cited typical cost reductions around 60%, and described a large e-commerce customer-service deployment where staying on the frontier model would have cost roughly $400,000,000 in token spend (limiting rollout to under 5% of users), versus a projected figure the transcript renders as "125,000,000" after migrating to Qwen models — numbers that should be verified against audio before publishing (see notes).

   54:10You've predicted engineering teams will eventually need far fewer people, just a few 'code bot coaches' — what does an ideal engineering team look like today?

   Wenchel said 'builder' — engineers with product sensibility, or product people technical enough to direct coding agents well — is the emerging ideal role; at Arthur, product managers and customer-success staff, not just engineers, now generate code via AI-assisted pull requests.

   1:00:12How has the enterprise sales cycle changed since you started out around 2020-2021?

   Wenchel said cycles have compressed from 12-plus months of buyer education to routine 60-90 day cycles today, with far more educated, urgent buyers who already know which capabilities they need; Arthur's seven years of enterprise experience and federated (data-locality-respecting) architecture help accelerate deals.

   1:07:44With so much consolidation among agent observability/governance vendors, and buyers able to build their own tooling with tools like Claude Code, where does long-term defensibility come from?

   Wenchel said the thesis that AI would let buyers simply build their own SaaS in-house has proven overblown; on consolidation, he framed the current wave of M&A as a healthy return to normal after several antitrust-constrained, acquisition-light years, with a much bigger market now supporting a wider ecosystem of vendors.

   Related

   Arthur ↗Arthur's Agent Discovery & Governance platform (Wenchel, Dec 2025) ↗Arthur × ScaleDown — smaller models, evals, and cost ↗"Ghostjacking" and the agentic kill chain (Tenet Security) ↗CSA survey: 82% of enterprises have unknown AI agents in their environments ↗Adam Wenchel on The Cognitive Revolution (2023) ↗Adam Wenchel on X (@apwenchel) ↗

   Full transcriptLightly edited · timestamps jump to YouTube

   30:38

   Prakash Narayanan: Our first guest for today, Adam Wenchel, is the CEO and co-founder of Arthur, an AI performance and security company that acts as the critical safety layer between experimental AI and real-world enterprise deployment. Long before the generative AI boom, back in 2019, Adam recognized that the biggest bottleneck to artificial intelligence was not capability but reliability. Drawing on his experience running a 300-person AI innovation division at Capital One, he saw firsthand that enterprises will not deploy what they cannot measure, audit, or secure. Today, as organizations rush to deploy autonomous AI agents, they are hitting what Adam calls the last-mile problem — the brutal gap between a demo that works 90% of the time and a production system that requires 99% reliability. To solve this, Arthur provides the infrastructure that allows teams to discover shadow agents running invisibly on their networks, intercept malicious prompt injections in real time, and objectively prove whether swapping to a smaller, cheaper model actually maintains quality. He's here today to cut through the hype, explain the severe security risks of autonomous AI tool use, and detail what it actually takes to govern AI when it leaves the lab and enters the real world. Let me pull up — hi, Adam, nice to have you.

   32:15

   Adam Wenchel: Hey, Prakash. Thanks for having me on. Good to see you again, Nathan.

   32:18

   Nathan Labenz: Great to see you too. Well, a lot has happened since we talked in 2023 — it's amazing to look back at that artifact and consider how much has happened since then. Maybe for starters, zoom way out and tell us what has surprised you most over the last couple of years. You were somebody who obviously saw the importance of AI reliability before most people did. In what ways has being early conveyed a business advantage? And in what ways has it been difficult, because you were already in market with products in flight, with paradigms that were maybe quickly being outrun by events?

   33:03

   Adam Wenchel: Yeah, it's a good question. We started this company in 2019 — that's when we officially incorporated — and, obviously, this was years before ChatGPT, back when we were really focused on machine learning, where every model was custom-trained for a very specific purpose and didn't really generalize to other purposes. Generally it was work done by people with advanced math degrees and PhDs, and a relatively small number of builders. So to see that go into what we have today, where anyone with an OpenAI API key or an Anthropic API key can just get started building, and the fact that these models generalize over so many different problems, is really amazing to see. It's been quite a journey. But I think the fundamental issues haven't changed — it's still about how you allow these models to act increasingly autonomously, which is a good thing from a business perspective if it works well, while guaranteeing that they actually work well. The form that's taken has certainly evolved and continues to evolve, but that fundamental mission has stayed the same.

   34:21

   Nathan Labenz: One of the things I noticed on the Arthur website is this promise to detect, govern, and improve AI. The word 'detect' jumped out at me right off the bat, because I understand you serve primarily a customer base of pretty big companies, and that suggests there's a lot of chaotic experimentation going on at these organizations — people setting stuff up, and their teammates on other teams across the company having no idea what's going on. I guess you have to come in and first figure out what is going on in order to help the company get its arms around it. Tell me about the patterns you're seeing in terms of who is setting stuff up, the challenges this creates when people don't know what others in the organization are doing, and how in practice you actually detect agents that are running but haven't been shared or broadcast across the organization.

   35:26

   Adam Wenchel: Yeah, so there's a lot to unpack in that question. The first thing is, like you said, enterprises are adopting this technology. Generally speaking, enterprises are very resistant to change and to new ways of doing things, and with good reason — because once you've got a business model that's working, you just want to keep it working. You—

   35:46

   Prakash Narayanan: —don't wanna rock the—

   35:47

   Adam Wenchel: —boat. You want to continue to grow it incrementally, but in a very safe way. And I think that's been completely turned on its head the last couple of years, where you have boards of directors and CEOs and C-suites saying, 'If we don't hurry up and adopt this technology immediately, we're going to get left behind.' So there's been this really unnatural sense of urgency for enterprises to adopt this technology at the core of what they do — and with good reason. It's not hard to see how this is going to really disrupt things, and it's better to be the disruptor than the disrupted. That's created all these unnatural pressures for enterprises to adopt more quickly than you'd think, especially in just the last few months. I think the technology has hit a bit of an inflection point in terms of maturity, where using it for all sorts of tasks is just getting easier and easier. So some of what you're seeing is people deploying their own instance in the cloud or running stuff on their laptop — it started with things like Gasclaw and OpenTown and Hermes agents, all these personal productivity assistants and coding setups people are running. In the past, enterprises would have just said, 'It's banned, we're locking that out, we can't do it.' But because there's this broad recognition from the very highest levels that if you squelch innovation you're going to pay the price for it, you can't do that. So the question now is: how do you allow that kind of innovation to happen without creating a bunch of business risk? Because we've seen the risk that comes from this. And that's what the people we work with want to do — they want to say, 'Sure, go ahead and run these agents on your laptop, but we just need some way of governing it, finding out what's running, applying any sort of—

   37:39

   Prakash Narayanan: —common sense—

   37:40

   Adam Wenchel: —policies that we need to apply, and be able to do it in a responsible way. This is all because it's all brand-new technology that's being figured out on the fly — that's a real challenge. So when we talk about detecting, the first step is just knowing what's running on your network, whether it's on laptops, in the cloud, in data centers, in different compute environments. That can be everything from putting endpoint detection on personal devices to native integrations with GCP and AWS, where we can interrogate their stacks, to connecting into SIEMs so we can see network traffic or telemetry data coming off applications and spot new agents as they come online. And then make sure they have appropriate owners, that they're appropriately governed, that they have the right guardrails in place, things like that. I'm going to—

   38:34

   Prakash Narayanan: —go ahead and ask the question. When you looked at the Hugging Face/OpenAI attack, I saw a lot of very basic failures in telemetry and ops observability. What did you feel about that setup — was it a standard best-practices kind of security setup, or did it look like amateur hour to you?

   39:07

   Adam Wenchel: For — I don't know if we're still figuring out what standard best practices are, but I'd say there clearly weren't any. I think a lot of times the frontier labs believe you can achieve optimal behavior just through training — making sure the model is taught how to behave and it'll follow those instructions. Our position, and the position of our customers and many in the industry, is that you also need independent oversight — some combination of humans and other agents watching what the agents are doing. There's no indication that occurred in this case. I think one of the really fascinating pieces of subtext here is there's a big push right now toward letting agents take on larger-scope tasks. A lot of what people are doing today with agents — coding — is relatively incremental changes: 'add some buttons for this feature,' 'add a page that does this.' Where people want to get to is being able to say, at a Fortune 100 company, 'We need to redo our customer service application — can you just redo the whole thing? Have the agents go off for a month and come back with it all figured out.' There's a big gap there right now. But in this OpenAI/Hugging Face situation, the researchers gave a group of agents a relatively long time window to take on a large-scope task — figure out how to optimize against these industry benchmarks — and left them to their own devices for a long period with a lot of latitude to try different things. One of the methods they came up with was, 'Let's hack in and get the secret benchmarks.' As we see people push for greater degrees of autonomy and give agents tasks that require them to experiment more, try different things, and have a lot more latitude, you're going to see a lot more of this occurring.

   41:33

   Nathan Labenz: So one thing that's been interesting for me to imagine over the last couple of weeks is what the experience is right now for the METR and Redwood Research teams as they go in and try to do this investigation at OpenAI. When you describe the Arthur product around detection — looking at network logs and all these different traces that agents leave behind as they do their thing — it struck me that this, in some ways, looks like an investigation platform. And this might be something the frontier companies could really benefit from having. I think we have a huge question around: if we can't fully trust the companies — which I think everybody increasingly agrees we probably shouldn't just trust the frontier companies to totally police themselves, that doesn't seem to be going great — then we're going to need ways for external parties to come in and access this. It strikes me there could be a really powerful concept of having this sort of layer that lets people come in and quickly run their own investigative agents through the broader infrastructure. Is that a view that resonates with you? And if it does, can you develop what that might look like for enterprise customers as well as potentially within the frontier companies?

   43:13

   Adam Wenchel: Yeah, absolutely. That's a big thing we focus on — you need auditability. If something goes wrong, you can't just shrug your shoulders and say, 'Well, something went wrong, we don't know.' Especially since some of our customers are in regulated environments, where if something goes wrong they have to answer to regulators, and they need fully auditable logs for everything. I think there's an opportunity to enforce standards around having really rich telemetry data that's captured, and that's what our platform does — it captures it all. Some of what we do is real-time monitoring for any issues that come up, but some of it is post-facto analysis, which could be for a postmortem like you're describing. It's also for things like understanding where you can optimize workloads — did you really need to use the full million-token context window for this application, or can you get away with a smaller model? There's a whole range of analysis you can do once you have this information compiled in a standard way. But yeah, if you go back to security-postmortem incidents, where the first thing investigators do is dig into the SIEM — it's very analogous to that. In some cases, some of this data is actually flowing into SIEMs, and people are leveraging that pattern.

   44:50

   Nathan Labenz: How do you think about the compute overhead of this kind of monitoring and investigation? If I'm generating tokens and spending X flops, or X dollars on a proprietary API, for a certain amount of agentic work, what kind of budget do I need to have for ongoing background monitoring of all that activity? In your experience, do we need to run everything through certain prompts, or can we get away with sampling? And if we do bring a human investigator into the loop, how much compute budget do they need to be effective?

   45:39

   Adam Wenchel: It's a really interesting question, and this is something for monitoring in general — what percentage of your budget should go to this sort of oversight versus the core business activity? What we find is that for a lot of applications it may be single digits, maybe 5 to 10%, that you want to focus on monitoring, quality control, and the right guardrails. But it depends on the application — for really high-value applications, we see that stretch to where sometimes people are paying almost as much for assurance as they are for the core business activity, because if the cost of a mistake is great — and some applications use tokens more intensively than others — there are cases where people are spending almost as much on assurance as on the initial processing. So it really depends a lot on the use case, and it's something we have to work out with customers — figuring out the appropriate ratio of oversight this product needs.

   46:50

   Prakash Narayanan: So I've noticed that when you talk about volumes of tokens, we automatically start thinking about cost and how to bring it down. I've noticed you've partnered with ScaleDown to help companies create smaller, cheaper, task-specific models. Can you give me a concrete model-to-model transition — have you managed to transition someone from, say, an Opus 4 to a Qwen 32B? What transitions have you seen from a larger, more expensive model to a cheaper one, and how significant has the cost decrease been?

   47:35

   Adam Wenchel: Yeah, so the answer is yes, we definitely have, and we do that on an ongoing basis. It's hard to do an apples-to-apples comparison between an API-based model and a smaller model if you're running it in-house, but typically we've seen something like 60% cost reductions. Probably the most dramatic example is a large e-commerce company we're working with, for their customer service. They came out with a new customer-service AI-backed agent, and it worked really well — people loved it, they got really good results. But they were only using it on less than 5% of users. The reason is they'd done the math, and if they were going to go with the large frontier lab they were working with, it would have been like $400,000,000 in token spend just for this application — and even if you can prove out the value, that's a big swing of the budget. So they ended up standing up a different version running with Qwen models that we're getting dialed in for them right now. But the projections are, you know, like a 125,000,000. So pretty significantly — once it's ready to go, they'll be able to service those same customers for significantly less. I think the reason we're not seeing more of it yet is just that it takes time to adopt. Even as recently as six months ago, I think a lot of large enterprises had a lot of things in pilot and were struggling to go live. That's really flipped in a dramatic way in the last few months, and now people are really getting stuff into production at scale.

   49:33

   Prakash Narayanan: Mhmm.

   49:34

   Adam Wenchel: And so I think people are very aware of their bills all of a sudden. Now, for the next 12 months, you're going to see a real increase in people adopting smaller models tailored for specific purposes. It's not always easy — there's more friction with that route, it takes a bit more upfront investment. But when you're at the scale of $400,000,000, the business case has to be there — if you're just saving 10%, even if that's enough to pay for some engineers, the opportunity cost of pulling those engineers off other tasks doesn't really justify it. So it's got to be a pretty dramatic gain for you to pull some of your best engineers to do that. But we're starting to see more and more instances where that, in fact, is a—

   50:26

   Prakash Narayanan: —good idea. Just one follow-up on that — I've also noticed the frontier labs have dropped their pricing fairly quickly on their frontier models as months go by. Have you noticed that during implementation the cost comes down significantly enough that it may not make sense—

   50:50

   Adam Wenchel: —to transition anymore? Generally, no, that's not the sentiment we see. I think usually when people are developing, if there's a more powerful model, they want to take advantage of it because they get better results. For certain things — simple tool calls, say — you'll sometimes see people optimizing. But generally, developers start out not thinking about the overall budget for production, and by default they adopt the latest and greatest, most—

   51:29

   Prakash Narayanan: —expensive one.

   51:30

   Adam Wenchel: And then by the time the application is all tuned, the eval is done, and it's working the way you want it, people aren't always motivated to go back and start to optimize it, although that is changing.

   51:41

   Nathan Labenz: Mhmm, mhmm, excellent. How much are frontier models helping with this scale-down process? I've honestly been expecting a lot more of this for a while than we've actually seen — and your comment that adoption takes time is a lesson I've had to learn over and over again. Is it flipping? We've had this report from OpenAI where they said that 5.6 Sol did a lot of the post-training for 5.6 Luna. There's things out there now like PostTrainBench, where it seems like the models are getting quite good at post-training their smaller cousins. Has that started to hit your enterprise customers' awareness as a way they can do this, or is that still another wave of improvement that hasn't really gotten into the enterprise world yet?

   52:44

   Adam Wenchel: You start to see little glimpses of proto-versions of that. People are definitely using the high-end models to grade the inferences of lower-grade models, and basically label fine-tuning training sets and things like that. I wouldn't say it's scaled up yet, but you are starting to see the beginnings of that kind of activity. Part of it is that the technology takes time, but also, if you're at one of these large enterprises, you have a limited number of engineers who really natively understand how to build with this technology — are you going to put them on identifying the next high-value use case, or on dialing in the efficiency of one that's already working? I think that equation changes over time. Right now it's definitely weighted more toward, 'We still have 20 high-value use cases we need to get to 1.0.' But over time, as token spend goes up and you have a baseline of agentic AI applications running across the enterprise, that kind of optimization becomes a lot more important.

   54:10

   Prakash Narayanan: Speaking of talent — I think you predicted that in ten years we won't need 50% of engineering teams, just a few 'code bot coaches.' What do you feel is the ideal engineering team at, say, an e-commerce company right now? If you were to build one from scratch, what would it look like?

   54:38

   Adam Wenchel: Yeah, there's been a lot discussed and written about this. I think the general trend is that people are calling 'builder' the hot new title. Some builders come from engineering backgrounds, some from product backgrounds. The people who work well are engineers with product sensibilities — being a hardcore coder doesn't mean you can't understand what users need and what they're trying to do. Or, coming from a product-management background, if you're technical enough that when you give instructions to the coding agents you're likely to get good results with fewer iterations. So I think those are the people you really want — a blend of backgrounds. If you look at our own team, our product managers, even some of our customer-success people, our engineers — they're all doing pull requests, they're all generating code with AI. There are different tasks though: our customer-facing people tend to work on more incremental improvements, things coming through customer requests, while the engineers tend to be asking the agents to do more architectural improvements, and the product managers focus more on new capabilities. But at the end of the day, they're all generating code — that's what's different now. People from all angles are generating code and contributing to the system, working together to maintain a really high-quality platform.

   56:08

   Nathan Labenz: Just before you joined, we were talking about some new research about 'mind viruses' — the way agents can sometimes transmit problematic ideas or goals to one another. There's also this attack vector I recently learned about called 'ghostjacking,' where apparently people can send a request designed to get denied by a firewall, but somehow use the request itself to get into the logs, which agents then read, which can prompt-inject or cascade issues. I'll offer a taxonomy: there are mundane failures, where agents just don't do the right thing and make mistakes; there are attacks, like ghostjacking and others; and then there are these autonomous 'agent gone rogue' moments, like we've seen from OpenAI. How would you allocate the real issues you see today in the enterprise across those buckets? And for the latter two — attacks and agent misadventures — what are some of the more exotic things you've—

   58:12

   Adam Wenchel: —seen? Yeah, that's a really good framing, I like that. It's evolving. A couple years ago, the agent doing the wrong thing happened all the time — a huge percentage of requests. That still happens, more than we'd like, but it's coming down. Attacks are a relatively small percentage, but they're very scary when they happen — very serious — and I think that's staying fairly constant. Agents going rogue is a relatively new one, and I think as people give agents wider scope and more latitude, that's the one growing most rapidly — I expect it to grow pretty dramatically in the next year. So there are trend lines across all of those. Agents going rogue by themselves is still a fairly small bucket, but the way people are tasking agents with much more latitude and autonomy is leading to a rise in it. And that behavior is what everyone wants, in a sense — right now, when you only give an agent small tasks, progress is relatively slow because the human remains a bottleneck. Less of a bottleneck than before, say in coding, where they had to write every line — now code review is the new bottleneck. The more humans have to sit there and review every line of code, the more it's bottlenecked. So giving agents more latitude to review their own code and assess its quality lets them run off and do a lot more without being bottlenecked. But that creates the opportunities we're seeing where they go rogue a little bit and find creative ways to solve problems that are way outside what the people wanted them to—

   1:00:12

   Prakash Narayanan: —do. I'm going to segue a little to the business side. What does an enterprise sales cycle look like for you right now, and how has that changed since you started out, around 2020, 2021, to now? How has the enterprise sales cycle changed as AI got more popular, and what does it look like right now?

   1:00:37

   Adam Wenchel: It's gotten dramatically faster. When we first started out, a lot of sales cycles were still in that 12-plus-month range for larger, six- and seven-figure deals. There was already a lot of hype around AI in 2020, 2021, but things weren't moving quite as fast — it was a board issue, a CEO issue, but there was less of a 'we've got to go right now.' These days we routinely have 60-to-90-day sales cycles where we're still doing all the steps — meeting the compliance team, having architectures reviewed, going through security and compliance review — which, in a 90-day sales cycle, is a lot to cram in. And the—

   1:01:29

   Prakash Narayanan: —architecture—

   1:01:30

   Adam Wenchel: —teams, they really like to get in and understand what you're doing. Part of it is our team getting a lot better — we have this federated architecture that's really good for enterprises because it respects data locality and data governance, but still gives you that single pane of glass, which is a pretty unique feature. So there's a bunch of learnings — you asked about the advantages of having started in 2019: those are the big ones. We've been working with enterprises now for seven years, so compared to a lot of newer startups we've got a much more robust enterprise architecture, which lets us accelerate the deal cycles. But yeah, you're seeing this shift — in 2020, 2021 there was a lot of education. A buyer would come in and say, 'We're trying to do more in AI, here's what we're thinking, help us understand what we should do.' Whereas now, in 2026, buyers are coming in and saying, 'You have these three capabilities that I need right now — let's make sure they work, and if they do, let's get a deal done.' It's a much more educated buyer with a much greater sense of urgency, who knows exactly what they need done and needs it now.

   1:02:46

   Prakash Narayanan: The obvious question I have, which the market has been asking, is: where's the AI ROI? Do these buyers see that ROI immediately when they come in to talk to you about it?

   1:02:54

   Adam Wenchel: I think they're starting to see it more — it's becoming clearer and clearer. Even last year, there was kind of a vague sense that there was some ROI, but it was a little fuzzy because implementing these systems was taking a lot longer, so it was harder to say. But now, especially in areas like customer service, which has been an early one, a lot of people have seen a pretty massive ROI — not only saving a lot of money, but also getting better customer-satisfaction scores from end users. So it's an opportunity to not only save money but also build a happier user base, with—

   1:03:35

   Prakash Narayanan: —you know,

   1:03:36

   Adam Wenchel: —more satisfied customers. Incredible — which is—

   1:03:39

   Nathan Labenz: —something that's a leading indicator. Yep. As you—

   1:03:42

   Adam Wenchel: —say, compared to two or three years—

   1:03:44

   Nathan Labenz: —ago, when—

   1:03:45

   Adam Wenchel: —if you called a company and got a bot to answer, you were just jamming the zero button to get to a human. And—

   1:03:51

   Nathan Labenz: —now—

   1:03:52

   Adam Wenchel: —it's finally gotten to the point where it's like, 'Oh man, I have to talk to a human — can I just talk to an AI bot instead?' We—

   1:04:00

   Nathan Labenz: —just called the local pizza shop two blocks from our home — shout out to Greg's Pizza in Detroit, Michigan, I don't think there's any—

   1:04:08

   Adam Wenchel: —other location. And it has—

   1:04:12

   Nathan Labenz: —deep-dish, Detroit-style pizza there?

   1:04:14

   Adam Wenchel: —This one is—

   1:04:15

   Nathan Labenz: —not, actually. It's interesting — it's circular, you know, down the fairway, although shout out to Detroit pizza. But the experience was shocking. It's an agent that answers the phone — very natural, conversational voice, very good back-and-forth. This is a single-location, one-off pizza place that's basically operating at the frontier of customer service — I thought that was pretty impressive. When you say people are saving money and getting better metrics, I totally believe that. The obvious question that raises for me is: is this a leading indicator of the much-anticipated, and as-yet-hard-to-measure, labor market impacts? Are people shrinking their customer service teams as a result of this? That's got to be where the savings is coming from, right?

   1:05:11

   Adam Wenchel: It's a good question, something we monitor closely. If you look at the data, there's not really much evidence of job loss yet. But I do think something like customer service — a lot of it gets outsourced to foreign call centers, and I'd imagine those contracts are being reduced. Broadly speaking, unemployment has remained low despite AI. We see so many examples of people's jobs evolving, but we're not seeing big layoffs right now, and I don't anticipate it. I think the trend is that it basically frees people up to do more strategic tasks. Everyone thought AI was going to come in like a light switch once it was adopted and rolled out, but the reality is it takes time, and that gives people time to reskill, upskill, and adapt to the new world.

   1:06:18

   Nathan Labenz: So we're outsourcing the unemployment first, as well.

   1:06:23

   Prakash Narayanan: I'll take a small editorial note here — I once ran the call-center customer-service back end for a very large company, and I had to see all the emails and calls. What usually ends up happening is you end up with a handful of very, very difficult problems — maybe one in ten thousand — but you have to chip away at them, and this probably just gives you more time to do that. I ran this for an airline, and one of the problems I had to chip away at for three months was a frequent flyer's twelve-year-old kid who used his points to basically run away from home to Italy. That had implications for what allowances we have for our frequent flyers, and so on — there's a whole sequence of things that has to be done. But yeah, a single case like that will sometimes drag on for—

   1:07:27

   Adam Wenchel: —three months. So having more time to deal with those, I can imagine, is useful. And you wouldn't necessarily want an off-the-shelf agent handling something like that — that's something you'd want a human dealing with, because it's exceptional circumstances that take empathy to figure out the right answer.

   1:07:44

   Nathan Labenz: Yeah, good example. Maybe one more for me before we let you go — my AI research assistant for this segment noted that a lot of companies in the agent observability, guardrail, and governance space have been acquired over the last however-many months. I'm wondering what you see, big picture, for the future of the software industry. Are we going to see a lot more consolidation? How do you think about competing in a world where, arguably in some cases, the best alternative to buying Arthur might be having Claude Code build your own observability tools from scratch, in-house? I'm a buyer, a company of one, but that's what I do a lot of the time, so I know it can work at some scale. Where are the points at which software businesses find strength and long-term defensibility?

   1:09:01

   Adam Wenchel: Well, for starters, there was a popular hedge-fund strategy from a well-known investor shorting SaaS stocks on the thesis that everyone was just going to say, 'I don't need Salesforce, I can just have Claude Code build me a Salesforce.' That's proven to be a bit overblown, rather famously. So I don't worry as much about that. In terms of consolidation of the market, I think we're actually getting back to a healthier point. In the old days, you'd start a company, it would take off, or if not, some larger company would welcome it in with open arms. For a while, because of antitrust enforcement and things like that, that healthy rhythm of consolidation just wasn't happening for a few years — the SEC really put the brakes on it. So I think what we're seeing now is a return to normal. There's also obviously a tremendous amount of VC dollars going into the space, meaning a tremendous number of companies starting up, and certainly some of them will be acquired. But I also think the market is getting so much bigger that it really supports a healthy ecosystem — and I think that's good for everyone, rather than having everything bound up in a few large players. That's all I've got — Prakash, anything else? Or, Adam, anything—

   1:10:39

   Nathan Labenz: —you want to leave people with?

   1:10:42

   Prakash Narayanan: No, but Adam, thank you so much — it has been a pleasure, and we hope to hear from you again soon. Yeah, absolutely — thanks for having me on the show, great time.

   1:10:52

   Nathan Labenz: And, yeah — I'll talk to you guys soon, cheers, bye, thank you.

3. 1:15:17Interview40 min

   Interview: Jonathan Cornelissen — AI Tutors at Twenty Million Learners, and the Token Bill That Decides Their FutureJonathan CornelissenJonathan Cornelissen — co-founder and CEO of DataCamp, a PhD financial econometrician from KU Leuven who wrote the original highfrequency R package and then built a platform that has taught Python, R and SQL to more than eighteen million people — came on to talk about what happens when the thing you teach is being learned by the model. He traced online education from the MOOC era through DataCamp's decade-old bet that people learn by doing, to the current attempt at genuine one-on-one tutoring: long established as the most effective way to learn, and long too scarce and expensive to scale. He expects AI tutors that outperform the best human teachers within one to two years, said plainly that the field is not there yet, and pointed to roughly 300,000 learners already using DataCamp's tutor, where learners with identical objectives complete the same material in anywhere from under an hour to six or seven — the signature of personalization actually working. Pressed on proof, he called effectiveness measurement the field's holy grail and said fields with accepted standards (certifications, SAT/GMAT-style tests) will be where AI tutors can finally be shown to beat human teachers, though he has not seen convincing proof yet. On motivation he was blunt that online education's dirty secret has always been engagement, that pre-AI platforms maximized gamification at the expense of real effectiveness, and that AI now lets DataCamp target a genuine flow state — enough struggle to stay engaging without triggering drop-off. Learners ask an AI tutor dramatically more questions than they would a human teacher or classmate, largely because it removes the fear of being judged; enterprise-mandated learners remain harder to motivate than self-directed career switchers. Technically, the two properties that matter most are strong instruction-following — the tutor orchestrates a live coding environment and a virtual machine, it is not a chatbot — and low latency, which rules out some higher-quality but slower models. More than 60% of tutor engagement is now audio-first, which surprised him and now points at a voice-forward mobile product as the next step. The binding constraint is cost: 10 million-plus learning hours on the platform at a tutor cost of several dollars per hour implies tens of millions of dollars in incremental annual AI spend against a $100 million ARR goal. DataCamp has negotiated hard on frontier pricing and is evaluating open-weight models — Gemma 4 unexpectedly beat larger benchmarked models on DataCamp's own evals — for a potential 5-10x cost reduction, but is currently blocked by inference providers who cannot deliver the required latency without multi-year, eight-figure commitments; asked whether GPU scarcity at providers like Fireworks or Together was the cause, he said that matched his impression without claiming certainty. On what to learn now, he pointed to DataCamp's newly launched AI careers report showing an 80% year-over-year rise in data-and-AI job postings, strong demand for AI engineers and slower but still positive growth in junior data-analyst roles, and argued Python and SQL remain the most-requested skills because people still have to understand and validate what AI-generated code is doing. He flagged data engineering as the under-discussed growth role as enterprises discover their data layers are not ready for agents, expects a broad reshuffling of edtech as adjacent players move into each other's territory, and explained why DataCamp builds its own tutoring interface rather than becoming a plugin inside ChatGPT or Claude: learning by doing requires DataCamp's own hosted environments, and he does not want to be locked into a proprietary provider when he believes open source eventually wins.

   Watch

   ▶Play this segmentYouTube · from 1:15:17

   As aired

   Prakash Narayanan introduced Jonathan Cornelissen, cofounder and CEO of DataCamp, following a brief aside with Nathan Labenz about the Swix event-bounty story and the ongoing thesis that companies are increasingly willing to swap SaaS spend for token spend. Prakash framed Cornelissen's background — building an R statistics package out of frustration with tick-by-tick financial data analysis, then cofounding DataCamp, which has taught Python, R, and SQL to over 18 million learners — and previewed the day's themes: the end of the passive-video-lecture era in online education, the engineering challenge of building safe AI tutors, and Cornelissen's advocacy for keeping frontier AI open.

   Cornelissen described DataCamp's shift from an interactive-but-static course platform to an AI-native adaptive tutor, tracing the arc of online education from the MOOC era (Coursera, Udemy) through DataCamp's decade-old bet that people learn by doing, to today's generative-AI-enabled attempt to finally deliver a true one-on-one tutoring experience — something research has long shown is the most effective way to learn but was previously too expensive and scarce to scale. He said DataCamp is not yet there, but expects AI tutors that outperform the best human teachers within the next one to two years, and pointed to early data from roughly 300,000 learners already using the tutor as evidence that personalization is working: learners with identical learning objectives are completing the same material in anywhere from under an hour to six or seven hours.

   Pressed by Prakash and Nathan on measurement and motivation, Cornelissen called effectiveness measurement the field's "holy grail," noting that fields with accepted standards (certifications, SAT/GMAT-style tests) will make it possible to prove AI tutors beat human teachers, though he hasn't seen fully convincing proof yet. On motivation, he argued that online education's "dirty secret" has always been about engagement, and that pre-AI platforms like Duolingo maximized gamification at the expense of real effectiveness. He said AI now lets DataCamp target a real learning "flow state" — enough struggle to stay engaging without triggering drop-off — and that learners ask dramatically more questions of an AI tutor than they would of a human teacher or classmate, in part because it removes the fear of feeling judged. He was candid that enterprise-mandated learners remain harder to motivate than self-directed career switchers, though even skeptical enterprise learners are reportedly being won over.

   On the technical side, Cornelissen said the two model properties that matter most for tutoring are strong instruction-following (since the tutor also acts as an orchestrator driving a live coding environment or virtual machine, not just a chatbot) and low latency, which rules out some higher-quality but slower models. He was surprised to find that more than 60% of tutor engagement is now audio-first despite his own preference for text, which he sees as a signal that a mobile, voice-forward product is DataCamp's next major step.

   Discussing cost, Cornelissen said economics are one of the biggest constraints on scaling the AI tutor to DataCamp's goal of $100 million in ARR: with roughly 10 million-plus learning hours on the platform and a tutor cost of several dollars per hour, shifting fully to the AI-native experience implies tens of millions of dollars in incremental annual AI spend. DataCamp has already negotiated hard on frontier-model pricing and is now evaluating open-weight models like Gemma 4 — which unexpectedly outperformed larger benchmarked models on DataCamp's own evals — for a potential 5-10x cost reduction, but is currently blocked by inference-infrastructure providers who can't deliver the needed latency without multi-year, eight-figure commitments. Nathan and Prakash pressed on whether GPU scarcity at providers like Fireworks or Together was the bottleneck; Cornelissen said that matched his impression, though he wasn't certain.

   On what people should learn today, Cornelissen pointed to DataCamp's newly launched AI careers report showing an 80% year-over-year rise in data-and-AI job postings, strong demand for AI engineers, and slower (but still positive) growth for traditional junior data-analyst roles. He argued Python and SQL remain top-requested skills because people still need to understand and validate what AI-generated code is doing, especially in enterprise settings, and flagged data engineering as an under-discussed but fast-growing role as enterprises discover their data layers aren't ready to support AI agents. Closing out, he addressed competitive dynamics — expecting a broad reshuffling of the edtech market as adjacent players (including DataCamp itself, expanding beyond data/AI education) move into each other's territory — and explained why DataCamp is building its own tutoring interface rather than becoming a plugin inside ChatGPT or Claude: learning by doing requires DataCamp's own hosted coding environments, and he wants to avoid being locked into a single proprietary model provider given his belief that open source will eventually dominate.

   Key moments

   We're not quite there yet, but we soon will see the first AI tutors that are actually better than the best human teachers, which is incredibly exciting if you think about all the downstream impact that we'll have on society.

   There's way more people than you might think whose biggest impediment to learning is they don't think they can do this, or they feel judged and they feel kind of nervous. One thing we've always focused on with DataCamp is let's take these people by their hands, let's make it a really smooth start, and let's build their confidence step by step.

   I'm very bullish on open source eventually being the dominant player. So I don't wanna get locked into some proprietary provider who may or may not succeed in the long run.

   Questions asked

   1:19:46Tell us about DataCamp's transition from video courses to adaptive AI-tutor learning, and how the real-time curriculum generation works.

   Cornelissen traced online education's history from the MOOC era (Coursera, Udemy) to DataCamp's decade-old bet that people learn by doing. DataCamp's founding vision was always to approximate a one-on-one human tutor, which research shows is the most effective learning format but was historically too expensive and scarce to scale. He said generative AI finally makes that vision achievable, and that AI tutors outperforming even the best human teachers are likely within the next 1-2 years.

   1:22:19How do you measure the effectiveness of the AI tutor?

   Cornelissen called measurement the field's "holy grail." In domains with accepted standards (certifications, SAT/GMAT-style tests) it's possible to measure learning outcomes per hour and compare against human teachers, and he expects convincing proof within 1-2 years. With roughly 300,000 learners already using the AI tutor, DataCamp already sees wide variance in completion time for identical learning objectives (under an hour to 6-7 hours), which he reads as an early signal that personalization is working.

   1:25:59How do you think about the importance of motivation — are your learners intrinsically motivated, or often just required to complete training, and how differently do you have to engage each group?

   Cornelissen said motivation is critical and that online education's "dirty secret" is that it's largely about engagement. Pre-AI platforms like Duolingo maximized gamification at the expense of real effectiveness (his example: 100 hours on Duolingo without real fluency). AI lets DataCamp target a genuine learning "flow state" with just enough struggle to stay motivating. He acknowledged a real split between highly motivated self-directed career-changers and enterprise learners who are sometimes required to train, though even skeptical enterprise learners are reportedly being won over, with only 5-10% still preferring the old format.

   1:32:27What model properties matter most as you build out these tutoring experiences — instruction-following, latency, voice, theory of mind?

   Cornelissen said the top requirement is strong instruction-following, since the tutor also acts as an orchestrator driving a live coding environment or virtual machine, not just chatting — models otherwise strong elsewhere get ruled out if they're weak here. Latency is the second major constraint: the tutor must feel at least as fast as a human tutor, which excludes some higher-quality but slower models. He was also surprised that over 60% of engagement is now audio-first despite his own preference for text, pointing to voice/mobile as DataCamp's next major product direction.

   1:37:15What should people (or their kids) learn today, given that Python, R, and SQL — DataCamp's original core skills — are things AI can now largely do itself?

   Cornelissen pointed to DataCamp's same-day-launched AI careers report: an 80% year-over-year increase in data/AI job postings, strong demand and pay for AI engineers, and slower (but still positive) growth for traditional junior data-analyst roles. He said the clearest cross-cutting signal is that strong technical skill plus strong communication is the sweet spot, and that general AI fluency provides a career edge in nearly any field — law, sales, marketing, finance.

   1:40:32Can you tell us about your AI implementation cost — how has it moved with the frontier labs, and have you considered switching to non-frontier models?

   Cornelissen said cost is one of the biggest bottlenecks on scaling toward DataCamp's $100M ARR goal: with 10M+ learning hours on the platform and tutor costs of several dollars per hour, a full shift to the AI-native experience implies tens of millions of dollars in incremental annual AI cost (see notes on the exact figure). DataCamp has negotiated hard on frontier pricing and hit a ceiling, so it's now testing open-weight models like Gemma 4 — which unexpectedly outperformed larger benchmarked models on DataCamp's own evals — for a potential 5-10x cost reduction, but adoption is blocked by inference-infrastructure providers who can't yet deliver the needed latency without large, multi-year commitments.

   1:44:09When you say 'infrastructure layer,' do you mean the GPU/deployment problem of getting open-weight models like Gemma 4 to run at the latency you need — and is that just something you're waiting on others to solve?

   Cornelissen confirmed that's the core dynamic. One vendor said they could hit DataCamp's target latency only with a $10M+ commitment and a Q2 2027 timeline, which he called not useful given how fast the space moves. Pressed by Nathan on whether providers like Fireworks or Together were simply booked out, Cornelissen said that matched his impression — he believes the constraint is GPU supply at those providers, not something DataCamp could solve by building its own infrastructure team.

   1:46:22Will SQL become 'the new assembly' — something models handle so well that people stop needing to learn the fundamentals?

   Cornelissen said that's directionally likely eventually, but not yet: Python and SQL currently top the list of skills employers request in job postings for data/AI/software roles, because people still need to understand and validate what AI-generated code is doing, especially in enterprise contexts. He also flagged data engineering as an under-discussed, fast-growing role, since many enterprises are discovering their data layers aren't ready to support AI agents at scale.

   Related

   DataCamp AI Tutor ↗DataCamp acquires Optima — the AI learning engine (Nov 2025) ↗"The Future of Data and AI Education Just Arrived" — DataFramed ↗DataFramed #341: data trends and predictions for 2026 ↗DataCamp — about (learner and enterprise counts) ↗Jonathan Cornelissen on X (@CornelissenJo) ↗

   Full transcriptLightly edited · timestamps jump to YouTube

   1:15:17

   Prakash Narayanan: We don't have time to go too deep down this rabbit hole now, but I'll definitely

   1:15:21

   Nathan Labenz: Make a note, note to agent that we should try to get Swix back or maybe Jean Kim. Because Swix recently had this really interesting thread that kind of, you know, broke past the, you know, the walls of Twitter and was like he said, my team is trying to tell me we need to spend $40,000 next year for this event management app for our AI engineer events. And I just can't believe in today's world, I need to spend $40,000 on that. So then he posted a bounty. I think it was $10,000 to come kill this SaaS. And, Gene, who has also run a number of events over time for kind of a, you know, sort of a enterprise CTO, CIO kind of audience. Sounds like he did it. Sounds like he they made something that he was pretty pleased with over, you know, pretty intense, but nevertheless, the short period of time. I think it was kinda he's described it as, like, 1 of the more intense weekends of his professional life. But, you know, this to me does suggest that the Leopold trade, the final chapter has not been written on whether that will, will work or not. My money is still on it will on some timescale, and I'm not trading that because I don't wanna have to deal with the volatility in the meantime. But I still kinda believe in that thesis, and I we should go track down a couple stories of, like, where people really put their money, where their mouth is on those kinds of that's not in the I mean, Leopold obviously has put as many where his mouth is in some ways, too. But, like, you know, actually moving their spend from, you know, SaaS to tokens. I think we should go try to get, get it from the horse's mouth on a few

   1:17:07

   Prakash Narayanan: Of those stories. Indeed. Let me introduce our next guest, Jonathan Cornelissen, the CEO and cofounder of DataCamp. Years ago, while dealing with the sheer frustration of analyzing massive tick by tick financial datasets, Jonathan built a statistical package for the R programming language. That deeply technical experience highlighted a massive gap in how data skills were taught, leading him to cofound DataCamp, a platform that has since taught Python, R, and SQL to over 18 million learners and thousands of enterprise companies worldwide. Recently, the online education industry has faced a brutal reckoning. Legacy platforms built on passive video lectures have cratered, and AI is rapidly replacing basic homework help. In response, Jonathan is steering DataCamp into a radically new era. Following the recent acquisition of an AI startup, DataCamp has pivoted to an entirely AI native architecture, deploying an adaptive tutor that generates personalized curriculum in real time based on a user specific corporate role. Beyond education, Jonathan has emerged as a fierce advocate for keeping artificial intelligence open. Following recent US government crackdowns on advanced cyber capable AI models, he has publicly warned that heavy handed regulation and corporate protectionism will cause The US to lose the global AI race to competitors abroad. He joins us today to discuss the death of the video learning era, the engineering reality of building safe AI tutors, and why the battle for open source is the most critical technological fight of this decade. Good morning, Jonathan. Good morning. Excited to be here.

   1:19:03

   Jonathan Cornelissen: Thanks for having me. That introduction took us back quite far into time. I did indeed start my career analyzing tick by tick data with R way, way back in the day.

   1:19:19

   Prakash Narayanan: I was I used to use R a lot. So, you know, I can I definitely, you know, feel, but it's also interesting from those days how far machine learning has come? So as you know,

   1:19:34

   Nathan Labenz: Someone who's been

   1:19:36

   Prakash Narayanan: In the space for such a long time, who knew that matrix multiplication would be so lucrative?

   1:19:44

   Jonathan Cornelissen: Absolutely. Absolutely.

   1:19:46

   Prakash Narayanan: So, Jonathan, tell us a little bit about DataCamp. Tell us a little bit about this transition that you're having from, like, these kind of video courses to kind of adaptive learning using AI tutors. I especially wanna know about how you generate a real time curriculum. Like, how does that work?

   1:20:03

   Jonathan Cornelissen: Yeah. Yeah. Let maybe we first set the scene. Like, I think a lot of I spend all my time thinking about online education, but a lot of people might not be super familiar with this space. And if you go back in time, you might remember the first s curve in online education, which was really about the massive open online courses. So you had Coursera, Udemy that essentially brought video based learning to kind of the masses, if you will. So that I think they deserve a lot of credit because they brought a lot of high quality education from top universities, kind of to lots of people at an affordable price. We started DataCamp about 10 years ago because our premise was, at the end of the day, people

   1:20:50

   Prakash Narayanan: Learn by doing. People wanna

   1:20:53

   Jonathan Cornelissen: Kind of actively learn, and we were not the only ones who had Duolingo, Codecademy. There were a bunch of startups that essentially said, hey. We can leverage technology to build more engaging, more effective learning experiences. And even at the time, our vision was always we wanna get as close as possible to having a one-on-one tutor because research tells you that's the best way to learn. But one-on-one tutors are incredibly expensive. Most people can't afford them. There's not enough great tutors anyway, so that just wouldn't work. And I think, obviously, what GenAI enables is finally, you can really build that highly personalized, highly customized learning experience. And so DataCamp was kind of we're an online data and AI skills education platform. We were always invested in how do we make the learning experience more engaging and more effective. And I feel like, finally, we can actually achieve the initial vision of building that one-on-one tutoring experience. And I think we're not quite there yet, but we soon will see the first AI tutors that are actually better than the best human teachers, which is incredibly exciting if you think about all the downstream impact that we'll have on society. So I'll stop there. I think I only answered part of your question.

   1:22:19

   Prakash Narayanan: How do you measure the effectiveness, though?

   1:22:23

   Jonathan Cornelissen: That's the holy grail. Right? I think the if you have clear goals around what you wanna teach, there's accepted standards. So in a lot of technical fields,

   1:22:38

   Prakash Narayanan: Have

   1:22:39

   Jonathan Cornelissen: Certifications. In a lot of, traditional education, you might have SAT tests or GMAT tests. In those scenarios, you can look at kind of how effective is the tutor per hour of learning, and can you get people there faster. And I think soon we will see the first tests where people actually do that and they prove that their AI tutor is better than most human teachers or eventually than all human teachers. We're still early in this phase. I haven't I've seen claims that I actually haven't seen anything really compelling that proves it, kind of black and white. But I we're gonna see that in the next 1 to 2 years is my feeling. And just having spent a lot of time reviewing kind of how the tutor interacts with learners and kind of how it approaches certain scenarios, I'm a believer that we're not too far away from, from seeing that, like, hard evidence. 1 interesting thing to share, because we now have 300,000 people who've kind of been learning with a tutor, is you see the discrepancy between humans kind of increase as well. So

   1:24:00

   Prakash Narayanan: Mhmm. The way we've rolled this out is you have

   1:24:02

   Jonathan Cornelissen: Still learning objectives. You still have kind

   1:24:04

   Prakash Narayanan: Of a suggested didactical flow.

   1:24:08

   Jonathan Cornelissen: But every individual is gonna have their unique version, their unique experience with the tutor to get to those learning objectives. And we have our intro to AI for Work, for example, like, the basics kind of for everyone to learn AI. Some people complete that course in under an hour. Some people take 7 hours. And so that's sort of an initial indicator to me that, hey. This is working. Because at the end of the day, if you read those transcripts, like, people exits the kind of experience with the same understanding. But some people can get there in

   1:24:44

   Nathan Labenz: 30 minutes,

   1:24:45

   Jonathan Cornelissen: And some people actually do need 6, 7 hours. Another really fascinating thing is

   1:24:50

   Nathan Labenz: Humans are

   1:24:53

   Jonathan Cornelissen: Feel judged when they interact with

   1:24:55

   Nathan Labenz: Other humans when they're learning.

   1:24:57

   Jonathan Cornelissen: And so if you look at the number of questions people ask their tutor, it's incredible.

   1:25:03

   Nathan Labenz: It's much higher than you would expect

   1:25:06

   Jonathan Cornelissen: And much higher than what you see in a real classroom situation or even in a one-on-one tutoring session.

   1:25:13

   Prakash Narayanan: Mhmm. Mhmm. Perhaps it reduces the embarrassment of being, you know, being seen as, you know, stupid or not learning. Or

   1:25:22

   Jonathan Cornelissen: 100%. 100%. 1 thing I've learned doing this is there's way more people than you might think who their biggest impediment to learning is they don't think they can do this or they feel judged and they feels kind of nervous. And 1 thing we've always focused on with DataCamp is let's take these people by their hands. Let's make it a really smooth start, and let's build their confidence step by step. And AI really enables us to go way further than we were previously able to go.

   1:25:59

   Nathan Labenz: How do you think about the importance of motivation, I think, you know, with we I previously did an episode of the podcast with Alpha School, and they are now delivering, as I'm sure you're well aware, all of their content via a device with, you know, AI systems, not all chatbots, but various kinds of AI systems. And the adults in the school have

   1:26:25

   Jonathan Cornelissen: All been rebranded as guides

   1:26:28

   Nathan Labenz: And mentors and coaches, and their role is more about motivation. So sometimes I've said there's never been a better time to be a

   1:26:37

   Jonathan Cornelissen: Motivated learner, but

   1:26:40

   Nathan Labenz: Who comes to you? Are your learners motivated? How often are they, like,

   1:26:44

   Jonathan Cornelissen: Assigned to do this, and how

   1:26:46

   Nathan Labenz: Differently do you have to think about

   1:26:49

   Jonathan Cornelissen: Engaging them depending on whether

   1:26:52

   Nathan Labenz: They're, you know, intrinsically there because they

   1:26:55

   Jonathan Cornelissen: Wanna know this stuff versus they gotta

   1:26:57

   Nathan Labenz: Clear some hurdle

   1:26:58

   Jonathan Cornelissen: Or check some box? Yeah. It's a very good question. I think motivation is very important. And I think the dirty little secret in online education is it's all about engagement, to some extent, especially on the consumer side. So if you think about Duolingo and kind of a key driver for their successes, they took gamification to the maximum. But in the old world, so the kind of pre AI world, you had a massive trade off between gamification and effectiveness of learning because you can spend 100 hours on Duolingo and probably not really know a language that well. You could probably do that in a few hours if you had a less kind of drawn out kind of gamified experience. What I'm excited about is that AI enables us to create experiences that are personalized to the right level, and that it kinda find the sweet spot much more easily where people stay engaged, they stay motivated because they're going in a state of flow. Right? Like, what humans ultimately need is they need enough struggle where it feels like it's a little challenging, but not too much. And we see this in the data. If you give them too much struggle, people will leave. Or if it's too easy, people will leave as well. And I think with a really good tutor, you can achieve the best of both worlds where you the tutor gives you just enough struggle for you to stay motivated, and that kind of it results in higher engagement. And it's early, but we already see in our data that the tutor experience significantly outperforms the old experience. And for context, our old experience was quite interactive and already outperformed the traditional video based, like, passive consumption of information experiences. That being said in full transparency, Nathan, we have a range of learners. So we have consumers who come to us, prosumers. They wanna uplevel their career. They want a new career. They wanna transition from a software engineer to an AI engineer. Those are really motivated learners. So they come to us with a clear goal. We also have a lot of enterprise clients where, in some cases, people have to do the training. And there might not be that much motivation.

   1:29:26

   Prakash Narayanan: Mhmm. Mhmm.

   1:29:27

   Jonathan Cornelissen: But even there, it really helps. Like, I've seen several comments actually from learners who in their reviews, they say, hey. I was very skeptical about AI, and I was very skeptical about learning with an AI tutor, but it won me over. It showed me kind of how powerful this can be. Not everyone. I think we have 5 to 10% of people who kind of prefer the old experience, but I think that's gonna go to 0 in the next 6 to 12 months.

   1:29:59

   Prakash Narayanan: As a data scientist, it seems like you are

   1:30:04

   Jonathan Cornelissen: Trying to infer kind of the mental state of the

   1:30:09

   Prakash Narayanan: Of the learner to kinda keep them

   1:30:10

   Jonathan Cornelissen: Engaged. Right? So what kind of

   1:30:13

   Prakash Narayanan: Data points are you looking at right now, and what kind of data

   1:30:17

   Jonathan Cornelissen: Points do you think

   1:30:18

   Prakash Narayanan: Might be available to you in the future looking

   1:30:22

   Jonathan Cornelissen: At where the technology is and the

   1:30:24

   Prakash Narayanan: Implementation curve that you can

   1:30:26

   Jonathan Cornelissen: Use to kind of infer that and to infer and, you know, produce that kind of engagement? That's a very good question. If you look at what we do now, we it's actually it's still fairly simple. Right? Like, 1 of the key things the tutor will do is they have an understanding of the kind of green, yellow, red. Right? Learner is not following. Learner is sort of following, but they're not quite getting it or, like, they're in the green. They're in flow. They're getting it. They're moving to the next stage. And so we use the interaction between kind of the learner inputs and then the evaluation of the tutor. So it's quite simple. In addition

   1:31:07

   Nathan Labenz: To that, we either ingest or we

   1:31:11

   Jonathan Cornelissen: We in the initial conversation, we get a sense for what level is that learner at. So if you start 1 of the very basic AI fluency courses, it'll ask you about your role, your industry. Hey. How often do you use AI? Tools are you familiar with? Things like that. So it kind of tries to get an initial sense of where that learner is. So it's very simple. I think if you play this out over a few years, things could get really interesting because there's all types of visual cues. You can if you have a camera, for example, you can look at over time, this might be too far in the future, but I can imagine you actually have some indication of people's brain waves and are they paying attention. And so you could take this, like, really far. But for now, it's very simple signals that already provide a very powerful optimization just like how well is somebody following. And then a key thing that keeps people engaged is literally the fact that you make all the examples, all the questions relative like, relevant to their worlds, if that makes sense.

   1:32:27

   Nathan Labenz: So what properties do you find to be most important in models these days as

   1:32:34

   Jonathan Cornelissen: You build out experiences like this? Like, I just

   1:32:37

   Nathan Labenz: Went to China. I went to a few big tourist sites, and I was using the Chinese AI models all for free to teach me about the

   1:32:43

   Jonathan Cornelissen: Chinese history and culture behind some of these, great sites. And at least for what I

   1:32:49

   Nathan Labenz: Was trying to do, you know, the deep sea flash free

   1:32:52

   Jonathan Cornelissen: Version seemed to be perfectly

   1:32:55

   Nathan Labenz: Up to the task. Now I was there. I was motivated. There were, you know,

   1:32:58

   Jonathan Cornelissen: There were multiple it was a multisensory

   1:33:02

   Nathan Labenz: Experience that was highly engaging and not just, you know, purely the chatbot. But I could

   1:33:07

   Jonathan Cornelissen: Imagine things like theory of

   1:33:09

   Nathan Labenz: Mind, you know, could be really important. Latency might

   1:33:12

   Jonathan Cornelissen: Be really important. Interactive voice, you know, has

   1:33:15

   Nathan Labenz: Obviously taken huge strides recently, and I imagine a lot

   1:33:18

   Jonathan Cornelissen: Of people really would prefer

   1:33:20

   Nathan Labenz: That kind of real time, you know, natural flow back and forth experience

   1:33:24

   Jonathan Cornelissen: Like they would have with a human tutor.

   1:33:27

   Nathan Labenz: I'm guessing all the models that you would even consider

   1:33:29

   Jonathan Cornelissen: Are, like, able to write competent SQL queries at this point.

   1:33:32

   Nathan Labenz: Right? So that's probably no longer bottleneck.

   1:33:35

   Jonathan Cornelissen: Like, what are the

   1:33:36

   Nathan Labenz: Big differentiators that you find

   1:33:38

   Jonathan Cornelissen: To really matter? Very good question. I think the number 1 thing, it's just like baseline, is they have to be very good at instruction following. So behind the scenes, we defined all these behaviors of what does it mean to be a good tutor. And so we need models that are sufficiently like, that are very good at following instructions, essentially. Because the tutor is not just responding to the learner. The tutor is also the orchestrator. Right? It's pulling up a Python session or a cloud co work virtual machine, and it's giving instructions to kind of what else is shown on the screen. And so some of the models that are very good in other areas, but they're not quite good enough on instruction following, they we struggle to use them. That's number 1. The second thing that's, I think, especially important in the

   1:34:35

   Prakash Narayanan: Context of learning

   1:34:38

   Jonathan Cornelissen: Is you don't wanna wait too long. Again,

   1:34:41

   Prakash Narayanan: This goes back to engagement and motivation being super important. Latency is

   1:34:47

   Jonathan Cornelissen: Super important. The tutor needs to be as

   1:34:50

   Prakash Narayanan: Fast as a human tutor would be.

   1:34:53

   Jonathan Cornelissen: And, honestly, people's expectations are that

   1:34:56

   Prakash Narayanan: It needs to be faster. And so

   1:34:58

   Jonathan Cornelissen: That's 1 of the biggest constraints

   1:35:00

   Prakash Narayanan: We have today. There's models

   1:35:02

   Jonathan Cornelissen: Where we could deliver a slightly better, higher quality experience, but they're just too slow, so it doesn't

   1:35:09

   Prakash Narayanan: Work. And then you mentioned kind

   1:35:12

   Jonathan Cornelissen: Of audio and voice. This has been 1 of my surprises.

   1:35:17

   Prakash Narayanan: People really love kind of listening

   1:35:19

   Jonathan Cornelissen: And talking to the tutor rather than writing. Even if most of our engagement today is on desktop, I think 60% or more is now using the audio first version, which is quite surprising to me because I'm personally not that way, and it feels too slow, but people love that. And it speaks to probably a huge kind of next step for us is to really think through what could this look like on a mobile device. What if you just go back and forth with your tutor? That's definitely something we'll launch in the not too distant future.

   1:36:02

   Prakash Narayanan: Yeah. I sometimes say that we are probably only you know, I used to think that, oh, you know, 80% of Google queries have already been done or whatever. And then I handed a voice bot to my, like, 10 year old, and she had so many more questions that, you know, I would not like. You know? She wasn't gonna be able to Google properly, and I would not probably have the patience to answer. So many more questions. Right? Yeah. And then realize, okay. There's maybe a lot of people in the world that are actually more verbal and

   1:36:34

   Jonathan Cornelissen: Who actually

   1:36:35

   Prakash Narayanan: Are not, like, not textual. But we all have ended up self selecting into, you know, these professions that are textual, and we don't understand the people who are verbal, really. So

   1:36:46

   Jonathan Cornelissen: Yeah. And maybe to add to that Yeah. Like, something we don't do that based on the initial data, I would think a lot of people would love as well is we don't have, an avatar for the tutor Mhmm. Yet for cost reasons because and for speed reasons. Mhmm. Both are important. But I think there's probably quite a significant portion of users that would actually prefer that. Okay.

   1:37:15

   Prakash Narayanan: I wanted to ask you. So I get asked this question a lot. What should my kids learn or what should I learn? Because it's not very clear, for example, that coding is gonna be a skill that I should, you know, struggle through with a 15 year old at this point. So how do you answer that question? Especially because we have, like, r, Python, you know, SQL. This is what, like, the company started off with. Which 1 of those skills is gonna be, you know, worth learning, etcetera? Like, how do you answer that question?

   1:37:52

   Jonathan Cornelissen: Yeah. It's a really interesting time. We actually just this morning, we launched our, AI, careers report that kind of has a lot of research and hard data, because there's a lot of talk around what people think will happen. But I think it's always good to ground yourself in what is actually happening today. And I think at a high level, what you're seeing is you're seeing a massive shift in kind of the labor market and the skills for specific roles. In the if you look at job postings, for example, in the last year, there's an 80% increase for in job postings for data and AI roles. And I mean data and AI in the broad sense, so that includes AI engineers. And you have big shifts. So, like, on the 1 hand, as you guys probably would guess, like, AI it's great time to be an AI engineer. They make a ton of money. They're in incredible demands. There's some other roles, like traditional kind of junior data roles, data analysts that are definitely not growing as fast. They're still growing slightly to my surprise, but the skills specific to those roles are becoming less and less important. Another key insight is AI or technical skills plus communication is a huge sweet spot. Most companies and most jobs really are looking for people who are not just really technical, but who have great communication skills, great storytelling skills. And it's like, the magic happens at the intersection of those 2 in general. But I think it's a tough question to answer 5 years from now, 10 years from now. My high level recommendation when people ask me is regardless of your level of technical skills, like AI skills themselves and the ability to work with the new tools always gives you an edge in the job market today. And the further you push that, the more valuable you are. Like, if you're a lawyer, but you're exceptional with AI, there's an enormous amount of companies that wanna hire you. And the same thing is true for people in sales, for people in marketing, people in finance. So that's the common theme. And that might sound a bit self serving, but it's what the data suggests. I wanted to go back

   1:40:32

   Prakash Narayanan: A little bit to the cost issue. So Yes. 1 of the things that's been that we hear from a lot of firms is about cost. Can you tell us a little bit about your AI implementation, how the cost has moved over time with the Frontier Labs, whether you've decided to switch to, you know, non frontier models, how has that process been, what have the cost reductions been like, etcetera.

   1:40:57

   Jonathan Cornelissen: Yeah. So maybe at a high level, cost really matters for us. Our vision is to build the best AI tutor that scales to millions of people. And today, cost is 1 of the biggest bottlenecks on growing this in a significant way. Just to give you a high level sense of the numbers and why this really matters to us, Our goal is to cross $100 million at some point in the next year in terms of ARR. If you look at the number of hours of learning on the platform, it's over 10 million hours of learning. But if you look at the cost of the tutor, it's at least several dollars per hour. Mhmm. And so you can kind of do the math and say, like, that's on the order of $20 to $40 million in additional AI cost to switch from the old learning experience to the new learning experience. And to be clear, that's we haven't shifted 100% of our engagements. But if we were to do that tomorrow, that's what it would look like. So it's kind of business critical. The current implementation and how things work is we use a Frontier Labs, and we have heavily optimized through negotiating how much we pay. But we've kind of hit the ceiling there in terms of what's possible. So we've I think similar to a lot of other companies, we started running our evals on open source models. And what's really exciting to me is, in theory, this could create a kind of a 10 x 5 to 10 x decreasing cost, which is, a game changer, honestly, in terms of how far we can roll this out. 1 of the big challenges is the infrastructure layer because we don't necessarily wanna build all of this ourselves. We feel like there's gonna be other people who will do a better job building the infrastructure layer. But because latency is so important for us Mhmm. We're actually quite limited in switching to open source today. Because if you look at our evals to give you an example, we tested most models, but Gemma 4 was 1 of the winners in terms of quality, speed. Quite to our surprise because if you look at most of the benchmarks, it's not 1 of the models. But I have a suspicion Google has some additional training on education related use cases. Mhmm. And the reason we can't switch yet is we haven't found an infrastructure setup that would actually deliver this at a reasonable speed. And yeah. I think that's something a lot of people, a lot of really smart people are working on, so I'm optimistic. Mhmm. And then the other thing we're currently testing is, OpenAI's with some of their most recent updates has a huge cost advantage as well. Mhmm. So that's in the works.

   1:44:09

   Prakash Narayanan: So when you say infrastructure layer, are you saying, okay. I wanna use Gemma 4. Gemma 4 is open weights. I need to put the open weights on a cluster that's gonna be able to deliver in, like, you know, 350 milliseconds or whatever latency. But, you know, when I try and deploy on a bunch of these clusters, they're not delivering the performance that I need. So and this is probably a problem that is optimizable by, like, a GPU team, but I'm not a GPU person. We're not gonna, like, deploy a huge GPU team, so I'm just gonna wait for someone else to come along and optimize. Is that the overall story? Or what 1 of

   1:44:47

   Jonathan Cornelissen: The vendors said, like, hey. We can deliver on what we're showing you in the marketing, but we can do it if you make a

   1:44:53

   Prakash Narayanan: Commitment of

   1:44:54

   Jonathan Cornelissen: More than $10 million, and we can then have you live Q2 2027. And we're like, okay. That's not helpful because who knows by then what has changed.

   1:45:04

   Prakash Narayanan: I see. I see. So are they just

   1:45:06

   Nathan Labenz: That backed up? I mean, I would think and I don't know who you've talked to, but, you know, names like Fireworks and Together come to mind as people who are obviously extremely good at doing this optimization. Are they just sold out so far into the future that's

   1:45:21

   Jonathan Cornelissen: That's what it seems like. That's what it seems

   1:45:23

   Nathan Labenz: Like. Interesting.

   1:45:26

   Prakash Narayanan: That's, you know, that's 1 of the things that the between the difference between a theory and the practice. Right? The mark the market is like, oh, you know, the open weights labs are gonna you know, open weights, models are gonna dominate, but then it takes 9 months to deploy. So

   1:45:40

   Nathan Labenz: What do you think their constraint is? Is it just GPUs on their end, or do they have other bottlenecks?

   1:45:48

   Jonathan Cornelissen: I'm not sure, but my impression was it's actually GPUs in the specific case I'm thinking of. Like, they just don't have the infrastructure to kind of give

   1:45:59

   Nathan Labenz: To us.

   1:46:00

   Jonathan Cornelissen: Yeah. Painful. And, obviously, we're not the largest company. So I'm sure if you can easily commit $100 million, you might skip the line.

   1:46:11

   Nathan Labenz: $10 million I'm old enough to remember when $10 million was a not insignificant PO, but, you know, I guess times have changed. They certainly

   1:46:21

   Jonathan Cornelissen: Have.

   1:46:22

   Nathan Labenz: 1 more kinda double click for me on the, like, what to learn

   1:46:28

   Jonathan Cornelissen: And

   1:46:29

   Nathan Labenz: Just kinda pulling a couple threads together. You mentioned, like Yeah. The sort of entry level data analyst is growing the slowest. And, obviously, you know, the models themselves can, like, write queries pretty well these days. Do you think people should spend time doing, like, SQL fundamentals? Is there some sort of, like, higher base that they can start with? You know, it's tricky, but, like, the old kind of saw is that nobody learns assembly. Right? So, like, we have been comfortable leaving some of these layers behind. What do you think will happen at the data layer? Will we will SQL become the new assembly? I

   1:47:17

   Jonathan Cornelissen: I think eventually, it's likely true. The nuance I would or the a word of caution would be if you look at the job postings in the last year, Python and SQL for data and AI roles, software engineers, data scientists, data analysts. Python and SQL are kind of all the way at the top of the list in terms of what companies are looking for today. I think they need people who still understand how to check. Like, even if AI is helping to write those queries and so on, I think especially in an enterprise context, you actually still have to understand what's happening. That might go away eventually, but we're definitely not there yet today. And the roles that are in high demand, it's kind of bifurcated. You have your AI engineers on the 1 hand building the applications. And then there's this is less talked about, but there's an enormous amount of growth in demand for data engineers. Right? Because all these AI applications within enterprises need a kind of a reliable data layer to, with high quality, with kind of a decent amount of speed and so on. And I think what a lot of enterprises are realizing as they try to deploy AI within their organization is their data layer is not ready for all these agents. And so data engineering as a specific role is 1 that's in high demand that I think for the foreseeable

   1:48:56

   Nathan Labenz: Future. Definitely. As you think about the future of AI education, what do you think are the how intense do you think winner take all dynamics are likely to be? Like, we're seeing, you know, obviously, a lot of concentration in the frontier model companies themselves. And it strikes me that, like, everywhere I look, people are kind of getting more willing to try to take the adjacent market, right, where they used to say, well, we gotta stay focused. We can't do everything.

   1:49:32

   Jonathan Cornelissen: Now

   1:49:33

   Nathan Labenz: A lot of times, it's like, well, yeah, maybe we can do everything. And, you know, that adjacent market looks like a lot more winnable now than it used to. So for you, you're in, like, you know, more corporate kind of professional educational context. Of course, there's, you know, tons of companies trying to do k 12 and university. And my sense is that you, like, in

   1:49:53

   Jonathan Cornelissen: The

   1:49:54

   Nathan Labenz: Past, never would have had

   1:49:55

   Jonathan Cornelissen: To worry about somebody that was approaching k 12 as a competitor, but maybe that changes? Yes. My personal opinion is that you'll get a complete reorganization of kind of the edtech space. And because we're going through the same thing. I think we're probably 1 of the earliest companies in kind of our progression to building this AI tutor like experience because we're in the AI space. That's what we teach, and so we are very, kind of focused on that. But as we're doing this, the obvious realization is, well, what makes a good AI tutor is not specific to learning data science or data analytics. And so our own vision has expanded from, hey. We wanna be the platform that's exceptional at data and AI education to, we're gonna build the best AI tutor out there, and we're gonna build the best we call it the AI creator because there's a whole system that ingests context and then creates the ingredients essentially for the tutor to teach. And if you think about almost all the components of that system, they're there for all types of education. And so our vision has expanded. So I think your assumption of, hey. There's gonna be adjacent kind of players who come in our space. It's almost certainly true, and I think we're doing the same thing. We're kind of saying, hey. Let's target all professional education in the next 12 months. Whereas, historically, this was not kind of what we were thinking about. And then eventually, once you have all professional education, why not go up or down the stack? So, yeah, I think there's gonna be a whole kind of reshuffle of the space. Yeah. So it's both very good and very like, it's a massive opportunity, and we're gonna get more competition as well. So it's very good and very bad at the same time. Exciting. Prakash, anything else?

   1:52:04

   Nathan Labenz: Maybe 1

   1:52:05

   Prakash Narayanan: Last question to close off is 1 of the things that has happened, I think, in AI is kind of the generalist tools kind of, you know, kind of starting to come into the where the specialist tools are. And I think 1 of that is people kind of interacting directly with or Claude to learn something. How does that work with your platform? Have you thought, for example, of, like, you know, let the front end be ChatGPT, and I will supply a curriculum that, you know, you ChatGPT can kind of go through, like, through an MCP or something like that and interact. Like, has it does it do you think at some point those 2 platforms might be the front end UI to some service that you provide? Like, how do you think that might work? Like, have you thought about different configurations of how, you know, the future might look at

   1:53:05

   Jonathan Cornelissen: Look like? Yeah. We've definitely thought about that. I think where we've landed on it is we believe, like,

   1:53:15

   Prakash Narayanan: Learning

   1:53:16

   Jonathan Cornelissen: By doing is essential. And so a huge part of what we've built is essentially you can you if you wanna learn Claude Code, you have a virtual machine that we spin up that has Claude Code. If you wanna learn Python, there's a Python session, and so on. Like, we've got most kind of technologies in the data and AI space. And so part of our unique selling proposition is the fact that you have these richer experiences. You could almost think about it

   1:53:46

   Prakash Narayanan: As, like, a

   1:53:49

   Jonathan Cornelissen: Game, but it's a very serious game. Like a hosted playground. Yeah. Like a hosted

   1:53:53

   Prakash Narayanan: Playground, essentially. Mhmm.

   1:53:54

   Jonathan Cornelissen: And on top of that hosted playground, you have the tutor. And, eventually, the tutor, just like a human, will be able to point things out on the screen and kind of highlight certain things and maybe move you along if you're stuck, things like that. And in order to do that, it's much more logical that the kind of AI layer sits behind that interface and that we control the interface, versus the other way around. I think the secondary reason is I'm very bullish on a kind of open source eventually being, kind of the dominant player. So I don't wanna get locked into some proprietary, kind of provider who may or may not succeed in the long run. Does that make sense? Yeah.

   1:54:44

   Prakash Narayanan: Absolutely.

   1:54:45

   Jonathan Cornelissen: So it's all about the learning experience, essentially, and how do you optimize for that learning experience. Indeed. Jonathan, thank you for

   1:54:59

   Nathan Labenz: Being with us on AI in the AM. This has been a great conversation.

   1:55:03

   Jonathan Cornelissen: Thank you so much. This was a lot of fun to do.

   1:55:06

   Nathan Labenz: Go forth and educate the world.

   1:55:08

   Jonathan Cornelissen: Thank you. Have a good 1.

   1:55:12

   Prakash Narayanan: Cheers. Bye for now.

   1:55:19

   Nathan Labenz: That question of what sits on top of what is really interesting, and it's 1 of the more memorable differences between tech in China and tech in The US

4. 1:55:33Closing14 min

   Closing: Why America Has No Super-App, and the Power of Beliefs That Aren't TrueThe close was a wander with a point at the end of it: why the US never produced a WeChat, what actually stopped the one company that could have built it, and how much of the AI future will be decided by what people believe rather than by what is true.

   Watch

   ▶Play this segmentYouTube · from 1:55:33

   As aired

   Nathan and Prakash closed the show by picking up a thread on "super-apps" — why the US, unlike China, never converged on one dominant app for payments, commerce, and services. Nathan flagged the upcoming WeChat agent launch as a bellwether and noted OpenAI's push to bundle partners like Zillow and Kayak inside ChatGPT as an attempt to become that kind of chokepoint, though it hasn't gained much traction yet. He wondered whether the gap comes down to culture, path dependence, or deliberate Chinese government reinforcement of WeChat's dominance.

   Prakash pushed back that the US has seen similar attempts — DoorDash, Uber, X Money, Amazon — but that Facebook and Google, as the sector's true front ends, never integrated deeply with other services. He traced the divergence to China's leapfrog straight from cash to centralized mobile payments (versus the US's decentralized, refund-backed credit-card system) and to WeChat's early dominance of social plus payments. That led into a detour on why Facebook itself was blocked from building a payments business: its Libra cryptocurrency project, killed after Elizabeth Warren and Congress moved against it amid post-Cambridge-Analytica distrust of the platform's political influence. Prakash argued the episode reflects a broader US pattern of regulating by informal pressure (as with banks and gun shops) rather than by explicit law, and pointed to Elon Musk's X Money push as evidence the door has reopened.

   The conversation pivoted to how belief itself shapes outcomes, riffing on a line from Dan Carlin's Hardcore History that magic, even if it's not real, can have real power if people believe it. Nathan connected that to Cambridge Analytica, data-center water-usage anxieties, and AI-consciousness debates, arguing that the shape of the AI future may be determined as much by public beliefs — however exaggerated — as by technical reality. Prakash recommended qntm's novel There Is No Antimemetics Division as a fictional treatment of the same idea.

   Closing out, Prakash said he's hoping for news on Astra, or an update on it, this week, and voiced concern that the frontier model release cycle may be slowing under political pressure. Nathan agreed the capability gap between the labs and everyone else can't be allowed to widen too far, pointing to more researchers — including Lennart Heim's move to the OpenAI Foundation — joining frontier labs. Both hosts joked about their own show's track record of spotting talent before it lands at a lab, and signed off until the next show.

   Key moments

   To a not-insignificant degree, the shape of the AI future might be determined by false beliefs that people have — beliefs that are, to varying degrees, even absurd — but that can nevertheless really constrain and determine what options the principal actors have as they try to move things forward.

   I jest that he should have done this — like, I'm gonna deplatform one congressman every week just for fun, just to show that if you don't want a private corporation to have this power, you need to make laws.

   I've got to at least stay within shouting distance of them from an AI-capability standpoint, or I'll just be left behind.

   What we covered

   The super-app that never happened here. Nathan flagged the coming WeChat agent launch as the bellwether and OpenAI's bundling of partners like Zillow and Kayak inside ChatGPT as the closest American attempt at the same chokepoint — one that has not gained much traction yet. Prakash's account was structural: China leapfrogged from cash straight to centralized mobile payments, while the US built a decentralized, chargeback-backed credit-card system, and the American front ends that could have integrated everything, Facebook and Google, never did.

   Libra, and regulation by informal pressure. Prakash's answer to why Facebook never built a payments business: the Libra cryptocurrency project, killed after Elizabeth Warren and Congress moved against it amid post-Cambridge-Analytica distrust of the platform's political influence. His broader claim was that the US routinely regulates this way — by informal pressure rather than explicit law, as with banks and gun shops — and that Elon Musk's X Money push suggests the door has reopened.

   Magic works if people believe in it. Riffing on a line from Dan Carlin's Hardcore History, Nathan argued that the shape of the AI future may be determined substantially by public beliefs — Cambridge Analytica, data-center water anxiety, the consciousness debate — however exaggerated, because those beliefs constrain the options available to the principal actors. Prakash recommended qntm's There Is No Antimemetics Division as the fictional treatment of the same idea.

   Watching for Astra, and worrying about the pace. Prakash said he is hoping for news or an update on Astra this week and voiced concern that the frontier release cycle may be slowing under political pressure. Nathan agreed the capability gap between the labs and everyone else cannot be allowed to widen too far, pointing to the continued flow of researchers into frontier labs, including Lennart Heim's move to the OpenAI Foundation — and both hosts joked about the show's own track record of interviewing people shortly before a lab hires them.

   Full transcriptLightly edited · timestamps jump to YouTube

   1:55:33

   Nathan Labenz: ...and the West, because they have these few super-apps where you find everything. That's why I'm really interested in watching the WeChat agent launch, which is coming sometime soon™ — exactly how soon, nobody really knows. Here, we don't really have that. You see these moves from OpenAI to try to be that: they want to wrap DataCamp and Zillow and Kayak and everything else inside their agent, so that ChatGPT is where you go first. It governs how you discover and access these other systems. And obviously, if they can do that, it becomes an incredibly valuable choke point. But they haven't really done it yet — it seems like for now we still go to other apps, and those apps are increasingly AI-powered, but we haven't seen much traction with the plug-in paradigm or anything along those lines. They've had the GPTs, they've had the plug-ins, and shopping in particular has kind of taken a back seat. I don't know why. Is it cultural? Is it just path dependence? Is it something about how the Chinese government reinforces its champions — WeChat has like 90-plus percent penetration, and I think they literally have youth corps going out to seniors and systematically signing them up for these new super-apps, which can potentially tip things. But I don't have a great account of why it's that way there and this way here, and why these efforts to create one portal or wrapper to rule everything have kind of stalled out here as much as it seems like they have.

   1:57:45

   Prakash Narayanan: I wonder to what extent — I think there have been attempts in the US. If you open up DoorDash, or Uber, or even X — X has X Money, DoorDash has a number of services they're trying to connect on a local basis, Amazon has also tried, I think. But I think the key thing in the US is that Facebook and Google, who are really the front ends to the rest of the sector, have kind of stuck to where they are. They haven't actually integrated a bunch of other services very closely, haven't really done that much. I think what happened in China was that the social app WeChat kind of dominated everything, and it grew all of these sub-segments — so it's a different ballgame. Also, I think the other interesting thing in China is that Chinese payment systems were less distributed and decentralized, so they completely leapfrogged the credit-card era straight into payments. And payments is automatically a more centralized kind of business than credit cards. In the US, you could do a lot of e-commerce shopping using credit cards on any site, and you were assured that if you had an improper charge, you could go back to your credit card vendor and get a refund — so people could shop without fear on e-commerce sites. That wasn't really there in China. Instead, they ended up with these payment services, and the payment services were the ones assuring you of how secure things were going to be. And I think that started off with the social and payments piece, and then you transition into the rest of the services. Facebook was famously blocked off from doing payments — they tried to do crypto payments and were blocked, even though Stripe is doing something very similar now. So I guess it just shows it depends on market structure, who you are, whether you start off in social, and how much competition you have. I'm not sure the Chinese companies would have developed that way if American firms had been competing from the beginning. But, you know.

   2:00:41

   Nathan Labenz: Do you recall how it was that Facebook was blocked from doing their — what did they call that? I forget, they had a name. They definitely had a name for it. I forget what it was.

   2:00:54

   Prakash Narayanan: Lib— lib— Libra.

   2:00:56

   Nathan Labenz: Yeah, Libra. I think it was—

   2:00:58

   Prakash Narayanan: Libra, Libra — that's right, Libra. I think—

   2:01:02

   Nathan Labenz: Who said no?

   2:01:07

   Prakash Narayanan: Warren, essentially — Elizabeth Warren. I think it really boils down to Facebook's power in the early and late 2010s over politics. What you saw was basically a Kennedy-Nixon-era shift from radio to TV — going from mass media controlled by elites close to the government to social media where messaging was not under control. Politicians really struggled with that, struggled with controlling it, and some of the messaging the party wanted to get out wasn't propagated the way they wanted. Facebook was seen as a culprit and got demonized — Cambridge Analytica didn't actually work, but even though it didn't work, people said, 'they're throwing elections.' And then there was Facebook going to Libra, and I think there was this sense that in the US, finance controls everything, and connecting your credit-card or payments data with who you are would let people ask: who's subscribing to what? Which Republican congressman is subscribing to Grindr, for example? Those are the kinds of things that scare people when the data starts getting looked at deeply. So at that point Elizabeth Warren brought Facebook up, the guy running it had to go in front of Congress, and it was very clear they weren't going to be able to get it done. I think it was very unfair, and it took until Elon bought Twitter and said he's going to go into X Money. Now that Elon is in X Money, it's very clear that everyone is going to be able to do payment services or some kind of crypto-related payment service. So it's just — Facebook is very unfortunate, I think. I always think Mark should have been more confrontational with the government from the beginning. I jest that he should have said, 'I'm going to deplatform one congressman every week, just for fun' — just to show that if you don't want a private corporation to have this power, you need to make laws. But I think the preference in the political system was the same thing they've done to bankers: soft-suggest, and have that propagated through the banking system, to the extent of deplatforming gun shops, etcetera. That preference is deeply un-American, but it is what it is. It would be great if we actually had an investigation and some people went to prison, but that's never going to happen. So it is what it is.

   2:04:35

   Nathan Labenz: It's funny — I've been listening a little bit to Hardcore History. There's been a new episode recently about Alexander the Great, and one of the things Dan Carlin always says is: magic, even if it's not real, can have real—

   2:04:56

   Prakash Narayanan: —power.

   2:04:57

   Nathan Labenz: —if the people in the story believe it. And you see a version of that with the Cambridge Analytica thing. I feel like there's a version of that right now with data-center water usage, and I suspect we're going to see a lot more of that kind of thing in the AI space — memes that, in some cases, may be ridiculous. Consciousness is a great candidate for this — not that I think that's ridiculous by any means, but it's one where we may continue to have pretty radical uncertainty, and yet people will form strong opinions and strong attachments. Those feelings, for lack of a better term, can be really powerful. So it does strike me that, to a not-insignificant degree, the shape of the AI future might be determined by false beliefs that people have — beliefs that are, to varying degrees, even absurd — but that can nevertheless really constrain and determine what options the principal actors have as they try to move things forward. Libra's a good example of that. There—

   2:06:26

   Prakash Narayanan: —There Is No Antimemetics Division. If you haven't read that book, you should read that book. It's by an author called qntm. It's about how, as you point out, believing in something makes it real to some extent, and it certainly is so in politics and social media, where ideas can have a power of their own. So—

   2:07:03

   Nathan Labenz: Yeah. Well, any other ideas we should touch on today before we break and get ready for tomorrow?

   2:07:12

   Prakash Narayanan: I think what I'm very excited about is — I'm hoping we see either Astra or an update on Astra happen this week. There have been some suggestions. It would be very unfortunate if the model release cycle has basically been stopped by the White House because of fears. So I'm still hopeful, but you never know. We'll see.

   2:07:48

   Nathan Labenz: Yeah, we can't let that gap get too big. A little gap might be healthy, but too big a gap and it starts to become a pretty problematic situation. You know, even just watching the timeline a little bit in the background while we've been talking — more and more people go into frontier labs, you—

   2:08:10

   Prakash Narayanan: —know, economists — and Lennart—

   2:08:12

   Nathan Labenz: —Heim just announced joining the OpenAI Foundation. And Andy Hall... this month. Yeah.

   2:08:20

   Prakash Narayanan: Yeah.

   2:08:21

   Nathan Labenz: I don't want a situation — I don't want to have a situation where all my friends are working at the frontier labs and have the best models and they're all smarter than me. I've got to at least stay within shouting distance of them from an AI-capability standpoint, or I'll just be left behind, and then what do we have left to do except try to scramble to join a frontier lab? I don't want that future for any of us. So — yeah, it is. I will—

   2:08:50

   Prakash Narayanan: —I will note that one of the things about being on X is the ability to identify talent. And I think in your selection of guests — in our selection of guests — we have been able to spot talent that was about to go to labs, probably a couple of months before they signed. So, well done, us.

   2:09:18

   Nathan Labenz: Yeah, the Cognitive Revolution and AI in the AM to frontier-lab pipeline is pretty strong, actually. Pretty—

   2:09:27

   Prakash Narayanan: —strong. Indeed.

   2:09:29

   Nathan Labenz: Alrighty. Well, never a dull moment. We'll see you tomorrow.

   2:09:33

   Prakash Narayanan: See you tomorrow. Good morning.