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20VC- Codex vs Claude Code vs Cursor- Who Wins, Who Loses | Will All Coding Be Automated - Do We Need PMs | the Real Bottleneck to AGI | the Three Phases of Agents and What You Need to Know With Alex Embiricos, Head of Codex at OpenAI

Startup Funding | The Pitch

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  • Automation Expands The Engineering Pool
    • Automating coding will likely create far more builders, not fewer engineers.
    • Higher-level tooling expands demand and reshapes roles into more full‑stack profiles. Transcript: Alexander Embiricos Given your position and what you see day to day? For sure, I would agree that coding is one of the first domains where LLMs are really good. But what does it mean for coding to be automated? It’s kind of a heavy statement, right? For example, now that we no longer write assembly, when that change happened and we moved to higher level languages, did we say coding is automated? Not really, right? We were just able to write much more code. And then as a result, actually, there was much more demand for code and there were many more software engineers required. But yeah, part of what they used to do was automated. In the same way that like, do you know the origin of the word computer? Harry Stebbings No. Alexander Embiricos I might pronounce the location wrong, but I think it was at Bletchley Park. There were all these machines for like decoding German Enigma. And like there were humans who would like punch out punch cards and like put them into the machine and do a bunch of like tabulated math. I’m probably butchering this, but basically there was an intensely manual part of work. And even like the first spreadsheet software was kind of loosely based off this idea that you would have an office full of desks arranged in a grid and people doing tabulations and then Passing their sheets to the next person. And so all these things, like those specific tasks have become automated, but every time that’s happened, there’s been an explosion in demand for the output. And so you need many more people actually to do that kind of work, even if the specific task has changed. Harry Stebbings So you think we’ll have more engineers in five years, not less? Alexander Embiricos Yeah. And I, you know, sometimes we change what terms mean, right? Like the term computer now refers to something else, but now we have the term software engineer. And so I definitely think we’ll have many more builders. And something interesting that I’m observing now is there’s this compression of the talent stack. You still need software engineers today. You still need designers. (Time 0:05:20)
  • Productize Prompts To Remove User Friction
    • Remove friction so people don’t need to learn complex prompting to use AI.
    • Productize common prompts and connect models to user context for effortless help. Transcript: Alexander Embiricos Okay. That’s a fun one. I think there are multiple bottlenecks, but that’s maybe the most sort of clickbaity one. So if you don’t mind, I will do this slightly socratically. Like how many times would you say you use AI today? 30 plus times a day. Okay, cool. How many times do you think, assuming it was like zero energy expenditure from you, how many times do you think AI could help you per day? Harry Stebbings I mean, in everything, I think we’ll have inference running 24 hours a day across every single thing. Alexander Embiricos Exactly. And like, I hear things now from engineers like at OpenAI and also outside who were telling me like, you know, I constantly have codecs running, I never close my laptop. And if it’s not running while I’m in a meeting, I’m like wasting my time, I need to make sure codecs always has work for me that it’s doing. And that’s like super cool and super exciting. But that’s a lot of work, right to like manage these agents and make sure they’re always working. And going back to the 30 times per day thing. Yeah, like when we look at how often codecs users are using codecs, it’s like kind of this like 10s of times kind of range. And I think AI should be helping us 10s of 1000s of times per day, you know, compute budget permitting will and we’ll get there over time. But the problem is like, at least if I think of myself, like I work on this stuff, I know I should be using AI for everything. But I’m too lazy to like type out that many prompts. And I am too uncreative to figure out all the ways that AI can help me. And so I end up kind of at a similar number as you. You know, I still am at the point where when I use AI to do something cool, like prep for this conversation with you, I’m like kind of proud of myself. I’m like, Oh, cool, I managed to use AI in this new way. That’s fine for people like you and me who are like, really interested in this topic, right? But I don’t think most people we should expect in order to benefit from AGI should need to like put so much effort into how to use this tool. It should just be effortless for them. I think the world we want to get to is one where to use AI, you don’t really need to like figure out the right way to prompt. It’s just super easy for you. And you don’t even need to recognize that AI could help you. It’s just like knows you connected to your context and chimes in helpfully. (Time 0:08:31)
  • Three Phases Of Agent Adoption
    • Alex outlines three agent phases: coding, computer use, then productized workflows.
    • Coding agents act as the bridge because coding is the best way for agents to use computers. Transcript: Alexander Embiricos First, let’s have agents work really well for software engineering and coding because LLMs happen to be good at that. Next, let’s realize that for an agent to be useful more generally, using a computer is super valuable and also we’ll realize that all agents are actually coding agents because coding Is just the best way for an agent to use a computer. So let’s take that same super flexible idea but make it available to anyone who’s excited to explore and tinker and we’re already seeing people start to do this with like the codex app Like codex app is built for soft for builders but we’re seeing builders use it for all sorts of non-coding tasks then finally once we see what’s working let’s build that like productization That you were talking about where you have highly specific features that just work immediately out of the box for people. (Time 0:13:08)
  • Start With People, Then Scale Enterprise Automation
    • Give AI directly to individual workers so they build intuition and pull automation into workflows.
    • Use local, tightly controlled interfaces to let users experiment before top‑down enterprise rollouts. Transcript: Alexander Embiricos If you’re trying to go like all the way from zero to one and you have this like, and I said, I don’t mean grand negatively here, but if you have like a grand vision for some like ultimate workflow Automation system, then yeah, you’re going to have to clear through all of these security hurdles, all these like compliance hurdles that are really real, right? Build connections to all these data systems and like systems of record and action. Yeah. So you’re going to need NFT to do that. What I’ve seen is that when we do these things top down, we end up like massively under leveraging the potential of AI and like helping that company. Whereas you can maybe do that in parallel, right? But if you can just give AI to the people like actually doing the work, they can start to like get a mental model for how AI can help. And then they can start pulling AI into their workflows at the same time. Here’s just like an analogy or something here is like, imagine if you work in like a customer support role, and AI is being brought into your role and starting to automate like meaningful Chunks of your work, but you’ve never heard of ChatGPT, nor are you allowed to use it. So in that scenario, you have like no intuition for what this thing is. Whereas in a world where actually you’ve been using ChatGPT for work at the same time as like of your work are getting automated by an LLM, you have much more intuition for how this works. And I would argue you feel much more empowered about this idea that it’s being accelerated. And you have some degree of control to steer where these automations are built, as opposed to it’s this complete ex machina kind of thing that is quite disempowering. So bringing this back, like I think there is a way to do this because the data control issues you mentioned are real. (Time 0:14:19)
  • Latency Drives Developer Adoption
    • Speed of inference matters a lot for developer experience and continuous use.
    • Improvements come from model efficiency, inference engineering, and hardware partnerships. Transcript: Harry Stebbings You partnered with Cerebris, And Cerebris is the fastest provider, obviously, of inference out there. Amazing win, I think, for both, bluntly. How important is speed for developers when using Codex and in the future of AI code? Alexander Embiricos I mean, these simple answers, it’s super important. And so is it like an inference monopoly? Like you have it now and competitors don’t? This is just my opinion, but I don’t think we’re going to end up in like this kind of monopolistic world. I think there’s so much competitive pressure that there’ll be like multiple answers to this. But I will say that we have like news coming out about that partnership soon. And I’m very excited for these kinds of things to ship. It’s going to be awesome. But even so, like, you know, with GPT 5.3 codex, that model is like significantly more efficient than prior models. And so we’ve in the feedback we’ve heard is that people actually feel like now this is like a very competitively fast model than before. So there’s a lot of things you can do just in terms of the model. There are also things you can do like improving how you do inference. So we recently rolled out a change where in the API, those models are served 40% faster, and in codecs, they’re served 25% faster. So I think speed matters a lot, and we’re kind of approaching it from all angles, like both the hardware, how you do inference, and the model level. (Time 0:16:41)
  • Delegation Shift After GPT‑5.2
    • Since GPT-5.2 Codex, many team members stopped opening editors and began delegating tasks fully.
    • The team moved from pairing to planning then letting agents ‘cook’ independently. Transcript: Alexander Embiricos I would say like most people that I know are basically not opening editors anymore. And this was a step function change that happened in, it’s been happening gradually, but I’d say the key external market touchpoint for this was like GPT 5.2 codex, where all of a sudden The model was like way better at running for longer, handling tasks end to end, managing its context, and following instructions. And so we kind of saw this inflection point. And that’s actually what part of why we built the app. So I think before GPT 5.2 codecs, the kinds of AI features we were using to write code were like, tab completion, or maybe you were pair programming with the model. And in my mind, you know, you still need it to be at your laptop with your hands on the keyboard ish. And like, it might go off and do a little bit of work. But you know, you’re kind of still need to be there and like drive. It’s just like handling these small things for you. And then at the time of GPT 5.2 codex in December, we kind of switched to like, actually, I’m just going to fully delegate this task. It’s like, you know, I’m going to do a plan with it, make sure we like the spec that it’s going to do. And then I’m just going to go let it cook. And this is quite a different way of working. So it’s like, it’s changing, like, literally as we speak. And so part of why we built this Codex app that we released last week is because we wanted to build like a form factor or user experience where it felt like very ergonomic to be delegating Instead of pairing with an agent. And so like delegating to multiple agents at once. And so even at OpenAI, this is changing massively. I don’t have a percentage stat for you, but I would say the vast majority of code is written by AI. And I would say that now probably most people are not even opening IDEs. Maybe if they are opening IDEs to maybe you want to own the interface. (Time 0:19:05)
  • Require Plans And Auto Code Reviews
    • Make plan reviews the primary guardrail before delegation to agents.
    • Use Codex to auto‑review PRs and reduce human review load while maintaining high signal feedback. Transcript: Alexander Embiricos And is AI responsible for internal coding reviews? There are a few things here. First off, the spec for what you want to do or the plan becomes more important than ever. Think like architecturally, like how should this code work? You know, we recently shipped like a very prominent plan mode that works a little differently than others where you have the agent go off and like propose how it’s going to do something. It’s like quite a long plan. And then it asks you questions about if you agree on how it wants to do it or if you want to have input. And this is very similar to like if you had a new hire who was new to your code base, you know, they had to present a sort of request for comments to the rest of the team before they started Doing the work. So even though that’s not formally code review, I would say review of the plan is actually something that’s becoming more important because we’re entering more of this like delegation Phase of working with agents. So that’s an underrated thing. Then, okay, there’s actual code review. I think a problem that I hear a lot of people talking about, especially in the open source world is like a lot of AI slop, like people will just be submitting PRs to these open source repos, And they’re trash. And like, maybe the user hasn’t even the person submitting the PR hasn’t even tested them or definitely hasn’t reviewed the code. I think this is a problem. And so a common practice with Codex is to have Codex like review its own PR or its own change. And Codex is actually incredibly good at this. We’ve explicitly trained the model to be good at code review. And you know, that included things like making sure it’s like really good at creating like high signal feedback. So it’ll like basically have few false positives of criticism, which means you can really trust when it has feedback. And so not only do we encourage people on the team and elsewhere, like to like just ask Codex to review, you can then also set it up to just like automatically review. (Time 0:21:40)
  • Open Standards Reduce Lock‑In Early On
    • Codex favors open standards and vendor neutrality to reduce switching friction.
    • Early coding tasks are hermetic and thus portable across agent providers for now. Transcript: Alexander Embiricos It super openly. So the Codex core harness is open source, and we’re always trying to make it easier for people to switch. So for instance, when we first launched Codex last year, we created like created as even a heavy word, it was just we just established convention, which is called agents.md. This is basically a file that you can put instructions for the agent in. And instead, we didn’t call it Codex.md. We just wanted it to be something that all agents can use. And pretty much every agent except Claude uses agents.md, which is awesome. And then just last week, actually, we helped push for putting skills, which are a standard for giving the agent instructions and scripts. We pushed for those to be sorted in sort of a neutral named folder called agents instead of in codecs or something. And again, everyone has jumped on it except the usual suspect. I think it’s really great for the developers to have a lot of choice, and we’re trying to make it even easier for people to try different things. Now, that said, these coding tasks where you’re asking an agent to write some code, they’re quite hermetic. And what I mean by this is maybe an analogy in TV would be episodic. You can come in, and you’ve got this open-ended agents file that any agent can read from. You’ve got these skills that any agent can use. And you can ask the agent to write some code, and it produces a patch, and that patch goes into Git. So kind of like both ends of this are pretty neutral, vendor neutral. So very easy to move between for now. (Time 0:24:00)
  • Optimize For Active Users Over Revenue
    • Prioritize active users as the north star metric to drive product adoption.
    • Optimize daily or weekly active usage so AI becomes the default first instinct for tasks. Transcript: Alexander Embiricos And so we have like the most conservative sandboxing approach. Sandboxing is kind of like a set of controls, OS level controls over what the agent can do. Harry Stebbings But I’m a fan of Seven Powers, this brilliant book, which talks about kind of seven ways that businesses accrue value and sustainability. And like, you know, your stickiness or your retention is one. If we’re on the same team with Codex, how do we create retentive patterns, behaviors, programs to ensure that people stay with Codex and they don’t flip to cursor when there’s a better Model or Claw Code when there’s a better model. Yeah. Alexander Embiricos I mean, it’s interesting because I think on the one hand, like we think about this, obviously we’re running a business, but our mission here is to ensure that we safely deliver the benefits Of AGI to all humanity. And so something that’s unintuitive to people about the Codex team. Harry Stebbings Alex, you actually, I know, but your job is the success of Codex. I get that. Alexander Embiricos Actually, our job is the distribution of intelligence. And so we’re obviously building out Codex. And this is really unintuitive to a lot of listeners, but we put all this effort into training these models, and then we serve these models to our competitors. (Time 0:25:56)
  • Models And Compute Define The Long Game
    • From OpenAI’s perspective, compute advantage and best models define long‑term victory.
    • Successful products create pressure to improve models faster through real usage. Transcript: Alexander Embiricos Okay. So I think if we’re going to talk about it more from an open AI perspective, obviously, this is way above my pay grade, but I would say it’s compute advantage and having the best models. And in order to achieve that, we then need to build businesses that generate revenue. And also that something that’s really interesting, we noticed with having the Codex team, which is a sort of combined team of research and product, is also by building these successful Products, we create a lot of pressure to improve the model in sort of a faster way. That’s maybe the company perspective, right? (Time 0:29:00)
  • Chat As The Primary Interface, Paired With GUIs
    • Conversational interfaces (chat/voice) will be the pillar for general AI interaction.
    • Power users will pair chat with function‑specific GUIs for depth and speed. Transcript: Alexander Embiricos Then you had ChatGPT. It’s like for any information I need, I can go into this text box, type it out and get information that helps me. And I think the next phase that we’ll see this year is like for any task I need to do, as opposed to just get information, I go to this text box or this input, and something happens that helps Me, even if it’s not the full task, even if it’s only a small part of it. Harry Stebbings You said about kind of chat that again, I jump around, sorry, my brain. My mother has to walk with me around London, and she like deals with this manic, episodic brain. But you said about chat and the interface there. I’m really fascinated by this because it is a seemingly incredibly efficient input function for busy humans. But I spoke to Anish Akaya, who’s a GP at Andreessen, and he came out the other day. And he’s like, no, no, no, this was created by Sam and Elon, and it works for very efficient people. But most of the planet want browser-based discovery, interactions, UIs. Do you think that chat will be the enduring UI in the next wave of AI interaction with humanity? Alexander Embiricos The simple answer is yes, but actually I think there’s two components here. If we just imagine the future, let’s think of some sci-fi movie, right? What does AI look like? I believe that sci-fi is a really good predictor of what the future should look like. And usually it’s pretty simple because it’s a story. And I think simple is usually right. It’s going to be some just like entity that I can talk to however I want about whatever I want, right? I felt like I shouldn’t have to navigate to a place where I work with like my coding AI. And then I have this like different place for my like sales AI. And I have to like be like, hey, I’m now talking to sales thing and like do that. It’s just like, I just gonna talk to a thing and it’s just gonna help. So I think what we’re gonna have is that we’ll have chat or voice, basically conversational interface will be sort of the pillar of everything that you can talk to about anything, and That you can add into any group chat or whatever. So it can like discover how to help you. But then if you’re like a power user, and you’re very good at a specific thing, you probably don’t want to be disintermediated by having to talk to another person. It’d be like if you had an executive assistant, but you can only work by talking to them. That’s like super annoying, right? So at some point, you want to you want to get to the show notes and like look at them yourself and like edit them yourself, right? You want to edit the thing yourself. So I think we’ll pair chat with like functional like graphical interfaces that are bespoke to like what someone needs. So like in my case, I will probably chat to like do my, you know, podcast prep. But when it comes to like actually looking at product and code, I probably want like the Codex app that I can go into and get deep in, right? (Time 0:31:36)
  • Make Systems Human‑Friendly For Better Agents
    • Design systems that are easy for humans because they also become easier for agents.
    • Simplify outputs (e.g., emit only failed tests) to make automation more reliable. Transcript: Alexander Embiricos The answer is often like, well, have you looked at it yourself? And is it is it easy for a human to work with? So like a very specific example would be like running tests in a code base. Naively, if you just like set up most test runners, they just like emit all the outputs of all the tests. And so like as a human, it’s really annoying, because you have to go in and like find the one that failed. And it’s like, you’ve got to read hundreds of 1000s of lines. Turns out that’s terrible for AI as well. But if you filter it down to just only emit the failed test, better for humans, also better for agents. So probably the agent to agent interaction points will be very similar to like, if there was a human in the loop. (Time 0:35:02)
  • Coding Data Isn’t A Dominant Moat
    • Coding doesn’t require a unique data moat; enough coding data exists to build strong models.
    • Knowledge‑work tasks outside coding are often harder due to scarce task trajectories. Transcript: Alexander Embiricos Don’t think they have a significant advantage in terms of data on coding. I think that from what we’ve seen, and I would defer to my research team on this, but I feel like we have plenty enough data to build really good coding models. Like knowledge work tasks, that’s kind of data that’s like not really like available most places on the internet. And so you start to have like really interesting brainstorms for like how to help a model be good at it. Like, maybe you have to like pay people to like simulate doing tasks so that you can like learn these trajectories for the model. Maybe you should acquire startups, you know, that are no longer a business, but have a lot of like data, like say they’re slack or something. Yeah, I think that that kind of knowledge work task distribution is like much harder than coding. (Time 0:36:00)
  • Consumer Use Cases Will Grow From Accessibility
    • Codex will overlap with consumer low‑end builders as availability expands.
    • Free tiers and in‑chat accessibility will drive casual users to build simple projects with Codex. Transcript: Alexander Embiricos Yeah, I would say that right now it doesn’t feel like we’re competing super directly. But, you know, I don’t know if you saw our Super Bowl ad, the tagline of which is just you can just build things. With the app, we noticed that like many people who are less technical are starting to build things. And so the kinds of things they’re building are much more hello worldy. And so I think that we will see some overlap in use cases where you have people just pulling up codecs because they have it as part of their chat GPT. Actually, like a big announcement last week was that we’re now offering some codecs to people even on free chat GPT plans or on the go chat GPT plan. So this is massive just in terms of like bringing availability to everyone. And so I think we’re definitely going to see people with like a free chat PT plan coming in and just like building simple things where they otherwise might have gone to a specialized tool. (Time 0:37:50)
  • Product Turnaround Sparked Rapid Growth
    • After shipping a more polished app and model, Codex saw explosive growth and positive user feedback.
    • The team learned earlier cloud-first assumptions were premature and shifted to interactive experience focus. Transcript: Alexander Embiricos You know, we went on an absolute tear. I feel like the public metric we had was like since August, we grew by like 20x. And then like even like late in the year, we like doubled from December to now. I forget the exact number there. But like that was competing neck and neck. But the shift that we feel last week is, you know, we felt like we had the most intelligent model that was cemented with 5.3 codecs. We had feedback around our model being slower and like maybe less fun to work with and like being less good at communicating with you while it was working. We addressed that feedback. And that’s true even compared to like the other competitor model that launched like 20 minutes before us and was like, maybe this is spicy. It was like soda for 20 minutes. Soda means state of the art. And then we’d always been getting a lot of feedback on like the quality of the user experience in Codex. Our most popular surface was the IDE extension and our CLI, which is a command line interface was less polished. But with the app, the feedback has been like resounding from the market that this is like a really high quality experience. It’s like simple, like unintuitively simple. And people are just loving using even our biggest credits are converted. So yeah, and then we and then we had the Super Bowl ad and then we went to free. (Time 0:39:27)
  • Earn Cloud Autonomy By Solving Bottlenecks First
    • Revisit cloud agents after users achieve local fluency to safely scale automation.
    • Invest in bottlenecks like review, validation, and safety before fully autonomous agents run systems. Transcript: Alexander Embiricos I have two things for you. The first is I actually want to get back to cloud. When we pivoted our strategy from like focusing on the cloud agent last year to working interactively, the thinking was very simple. It was just, and it’s kind of like what I was telling you about FDEs, actually. If you go too far ahead to workflow automation before your end user is fluent with the tooling and can get it to work simply, then there’s like this disconnect. And you just have this pipe dream idea that’s not like effective except for the most power users. But once you have this base where people are using your tool every day, and they’re configuring it, and every time they use it, it gets better, then the step up to letting it run independently In the cloud is a much smaller step up. So I think it’s time for us to get back to building out the cloud product and making it super tightly integrated with the local product. It already is somewhat integrated. And the other thing I want to do differently is start thinking more about the bottlenecks. Like CodeGen, writing code has become like basically trivial now. But the hard part is like what you were talking about with like code review, right? Like how do we know the code quality is good? How do we know we’re doing the right things? And those bottlenecks I think are underappreciated still and underinvested in. So like I think we want to get to a world where you can have an agent that is unbottlenecked, that you trust to like own an entire microsystem or internal tool or whatever, and can do the Full iterative loop, including feedback from users without having to go through human review. (Time 0:40:35)
  • Benchmarks And Vibes Both Matter
    • Benchmarks matter but so do ‘vibes’—how humans like interacting with models.
    • Subjective user preference shapes adoption beyond raw eval scores. Transcript: Alexander Embiricos Like they do tell you, in my mind, they give you a good measure of intelligence. And so you can put weight on those for intelligence. And especially before evals are saturated, I think when you see meaningful progress in those benchmarks, it’s like very, very helpful. And then I think you have to pair that though with like what it feels like to use the model. And that’s a vibes thing. Like whenever I talk to any, even internally, even talking to like customers of our models, I’m always surprised by how vibes based the evaluation of how it feels to work with the model Harry Stebbings Is. How vibes based life is. People want to work with people they like, is the lesson that I give to kids. Alexander Embiricos People want to work with models they like. (Time 0:42:03)
  • Back Firms With Human Relationships Or Records
    • Invest in companies that own human relationships or systems of record to maintain defensibility.
    • Be wary of pure glue layers that own neither the user relationship nor the record. Transcript: Alexander Embiricos Things are built for humans. Otherwise, what’s the point? Even SaaS tools are built for humans. So for me, I think my question is, does this SaaS company own a relationship with a human on the other end of things? And if it does, then I suspect it’s not going away. Or does the SaaS company own some really important system of record? It’s probably not going away. Maybe both of those two things, the interaction with the human and the system of record are more important than ever, actually. On the other hand, is the SaaS company a kind of a glue layer, but it doesn’t own either of those two things? (Time 0:48:10)
  • Showcase Projects To Land Top Roles
    • Build and ship tangible projects to demonstrate agency, taste, and quality to employers.
    • Share projects publicly because they attract attention more than traditional resumes. Transcript: Alexander Embiricos There’s actually never been a better time to be an engineer because you have incredible tooling available to you to get an incredible amount done. And your ability to ramp into a complex code base that you might be hired into has never been faster because you can go ask AI like a ton of questions about the code base. And you can ask it to plan out changes that would otherwise take you like days to research maybe. I think first off, I would say like you should be like very optimistic. But then of course, like about you want your abilities once you’re at the job, then the question is, how do you get the job? Because it’s never been easier to build things, the thing that becomes scarcer is agency, taste, and quality. I would urge you to just build things and demonstrate your agency and your taste around what you build and build things that are of high quality and then share those things. We get a lot of inbound from folks both applying for jobs through the careers page or also on social. And this is just me. But when someone writes to me with like some interesting thoughts and like a link to an interesting project, that gets my attention much more than like a normal resume does. (Time 0:52:53)
  • Coding Agents Led Practical Adoption
    • Multimodal progress was slower than expected; agentic coding interfaces led adoption first.
    • The practical path to general assistance ran through coding and computer control. Transcript: Alexander Embiricos Longer than 12 months ago. But when I joined OpenAI, I thought that we would all just be hanging out with our computer screen sharing within a year from there. We’d have this agent that we’re just talking to. That was completely wrong. I think the rate of progress in multimodal models was slower than I expected. Multimodal means models that work with video and audio. So instead, what happened was that we saw that agents that work with your computer through code are the way. (Time 0:58:20)
  • Prepare For AI‑Managed Engineering Stacks
    • Expect future stacks to be increasingly AI‑managed, reducing manual deploy and monitoring work.
    • Startups may ship by telling agents to build, then iterate through agent collaboration. Transcript: Alexander Embiricos Well, one is just editing code by hand. I think probably another one, this is maybe spicier, but another one might even be like actually managing the deployment and monitoring of systems by hand. Like I basically think that probably big companies will take a long time to like deploy this. But many startups might actually kind of start building on a completely new stack that’s like fully AI managed. To be clear, the stack doesn’t exist yet. But a fully managed AI stack where because basically it’s been built to give you really strong deterministic guardrails over what the agent can do and control over to whirl back deploys And everything like that. And so we’ll get to a world where the way you start a company is you start by getting an agent and just asking it to build things. And then you get more agents in that. And then maybe eventually you add your co-founders to this service that you use to work with agents. (Time 1:01:11)