Pod Insights Big Technology Podcast OpenAI Chief Research Officer Mark Chen: GPT 4.5 is Live and Scaling Isn’t Dead About this episode Mark Chen is the chief research officer at OpenAI. Chen joins Big Technology Podcast to discuss the debut of GPT 4.5, the company's largest model, which is going live today. In this bonus episode, Chen speaks about what the new model says about the AI scaling wall, how scaling traditional GPT models compares to reasoning models like OpenAI's o1, how important EQ is for AI models today, whether product matters more than models, and how OpenAI's talent bench looks after last year's departures. Tune in for an inside look at what it took to build OpenAI's newest and biggest large language model, and why OpenAI is committed to pushing the frontier forward.
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Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Alex Kantrowitz? OpenAI Chief Research Officer Mark Chen is here to talk about the release of GPT-4.5, the company's largest and best model yet, which is coming out today. We'll dive in right after this. This episode is brought to you by Genesis. What does it actually take to put a Gentic AI to work across an entire enterprise? I recently spoke with Genesis Chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience, and what companies need to get right in the AI era. Then, I sat down with Adam Mitchell, Head of Enterprise Business Solutions at Voya Financial, to go inside a large-scale agentic CX transformation, including the business decisions, trade-offs, and lessons
0:43 Alex Kantrowitz? along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay? Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation. Welcome to Big Technology Podcast, a show for cool-headed nuanced conversation of the tech world and beyond. We're joined today by Mark Chen, the Chief Research Officer at
1:24 Alex Kantrowitz? OpenAI, who's here to talk about the company's newest release, GPT-4.5. Yes, it's finally here, and it is debuting today. Mark, great to see you. Welcome to the show. Thank you so much for having me on. Thanks for being here. This is in four and a half years of the show, our first OpenAI interview, so hopefully the first of many. We appreciate you jumping into the water like this, and it's on big news, the release of GPT-4.5.
1:51 Speaker 01 Yeah, so GPT 4.5 really signifies the latest milestone in our predictable scaling paradigm. So, you know, previous models that have fit this paradigm have been GPT 3, 3.5, 4, and now this is the latest thing. It signifies an order of magnitude improvement over the last models, kind of commensurate with the jump from 3.5 to 4.
2:13 Alex Kantrowitz? I think the question that most of our listeners are going to be asking, and certainly we've asked on our show in the past couple of months, is why isn't this GPT 5? I mean, what is it going to take to get to GPT 5?
2:26 Speaker 01 Yeah, well, I think GPT 5, you know, whenever we make these naming decisions, right, we try to keep with a sense of what the trends are. So, again, when it comes to predictable scaling, right, going from 3 to 3.5, you can kind of predict out, you know, what an order of magnitude of improvements in, you know, amount of compute that you train the model with in terms of efficiency improvements will buy you. And we find this model kind of aligns with what 4.5 would be. So, we want to name it what it is.
2:58 Alex Kantrowitz? Okay, but there's been so much talk about when GPT 5 is going to come. Correct me if I'm wrong, but I think there's been a longer wait between GPT 4 and 4.5 than there has been between, let's say, GPT 3.5 and 4. And I don't know, is this like because we're seeing a lot of hype from OpenAI folks on Twitter about what's coming next, or maybe this is probably, it probably is the most impatient industry in the world and the most impatient users in the world. But it seems to me like the expectations for GPT 5 are built up pretty high. And so I'm curious from like your perspective, do you think it's going to be hard to meet those expectations whenever that GPT 5 model does come out?
3:41 Speaker 01 Well, I don't think so. And one of the fundamental reasons is because we now have two different axes on which we can scale, right? So, GPT 4.5, this is our latest scaling experiment along the axis of unsupervised learning, but there's also reasoning. And when you ask about kind of like why there seems to be, you know, a little bit bigger of a gap in release time between 4 and 4.5, we've been really largely focused on developing the reasoning paradigm as well. So I think, you know, our research program is really an exploratory research program, right? We're looking into all avenues of how we can scale our models. And over the last, you know, one and a half, two years, we've really found a new, very
4:24 Speaker 01 exciting paradigm through reasoning, which we're also scaling. And so I think like GPT-5 really could be the culmination of a lot of these things coming together.
4:34 Alex Kantrowitz? Okay. So you talk about how there's been a lot of work toward reasoning. We, of course, have seen that with A1. There's a lot of buzz about DeepSeek. And now we're talking about, again, like one of the more traditional scaled up large language models with GPT 4.5. So the big question here, I think that was on a lot of people's mind when it came to this upcoming release, we thought was going to be 4.55. Anyway, it doesn't matter. The big question is, can AI models continue to scale when you add more compute, more data, and more power to them? It seems like you have an answer to this, so I'm curious to hear your point of view on whether, what you've learned about the scaling wall, given your development of this
5:19 Alex Kantrowitz? model, and whether we're going to hit it, whether we're already seeing some diminishing returns from scaling.
5:25 Speaker 01 Yeah. I really kind of have a different framing around scaling. So when it comes to unsupervised learning, right, you want to put more ingredients like compute, algorithmic efficiencies, and more data. And GPT 4.5 really is proof that we can continue the scaling paradigm. And this paradigm is not the antithesis of reasoning as well, right? You need knowledge in order to build reasoning on top of, right? A model can't kind of go in blind and just learn reasoning from scratch. So we find these two paradigms to be fairly complementary, and we think, you know, they have feedback loops on each other. So, yeah, GPT 4.5, again, it
6:08 Speaker 01 Is smart in different ways from the ways that reasoning models are smart. When you look at the model today, it has a lot more world knowledge. When we look at kind of comparisons against GPT four oh, you see that everyday use cases, people prefer it by a margin of sixty percent for actually productivity and knowledge work against GPT four oh. There's almost like a seventy percent preference rate. So people are really responding to this model. And it's this knowledge that we can leverage for our reasoning models in the future.
6:40 Alex Kantrowitz? So what are the examples, like you talk about everyday knowledge work? What are some of the examples that you would use GPT four point five for that you would prefer it over a reasoning model?
6:50 Speaker 01 Yeah, so I I wouldn't say like it's a it's a different profile from from a reasoning model. So with a larger model, what you're doing is it it takes more time to kind of process and think through the query, but it's also giving you an immediate response back. So this is very similar to what a GPT four would have would have done for you. Whereas I think with something like oh one, you get a model where you give a query and it can think for several minutes. And I think these are fundamentally kind of different trade offs. You have a model that immediately comes back to you, doesn't do much thinking,
7:31 Speaker 01 but comes up with a better answer versus a model that thinks for a while and then comes comes up with the with the answer. And we find that in a lot of areas like creative writing, for instance. Again, this is stuff that we want to test over the next one or two months. But we find that there are there are areas like creative writing where this model outshines reasoning models.
7:53 Alex Kantrowitz? OK, so writing, any other use cases?
7:56 Speaker 01 Yeah, so there's there's writing, I think some coding use cases as well. We also find that kind of like, you know, there are some particular kind of scientific domains where this outshines in terms of the amount of knowledge that it can display.
8:12 Alex Kantrowitz? Okay, and I'm going to come back to benchmarks in a moment, but I want to keep on this scaling question because I think there's been a lot of conversation about it in public and it's great to be speaking with you from OpenAI to sort of get to the bottom of what's happening. So the first is, the question that folks have is, do you end up at this size? And you don't talk about the size of the models, which is, you know, which is fair, but they're big, right? This is the largest model that OpenAI has ever released, GPT 4.5. So I'm actually curious to hear at this size, does adding, you know, similar amounts of compute, similar amounts of data, get you the same returns that you did? Or are we already starting to see the returns of adding these resources tail off?
8:58 Speaker 01 No, we are seeing the same returns. And I do want to stress that GPT 4.5 is that next point on this unsupervised learning paradigm. And, you know, we're very rigorous about how we do this. We make projections based on all the models we've trained before, on what performance to expect. And in this case, you know, we put together the scaling machinery, and this is the point that lies at that next order of magnitude.
9:22 Alex Kantrowitz? So what's it been like getting here? I mean, again, we talked, okay, so there was, there was a period of time that was longer than the last interval, and part of that was focused on reasoning, but there's also been some reports that OpenAI has had to start and stop a couple of times to get this to work. And it really had to fight through some thorny issues to get it to be this step change, as you're saying. So talk a little bit about the process, and maybe you can confirm or deny some of the things that we've heard about having to start and stop again and retrain to get here.
9:54 Speaker 01 Actually, so I think it's interesting that this gets, is a point that's attributed to this model, because actually, in developing all of our foundation models, right, they're all experiments, right? I think, you know, running all the foundation models oftentimes does involve stopping at certain parts, kind of analyzing what's going on, and then restarting the runs. And I don't think that this is a characteristic of GPT 4.5. It's something that we've done with GPT-4, with O-series models. And, you know, they are largely experiments, right? We want to go in, diagnose them in the middle, and if we want to make some interventions, we should make interventions. But I wouldn't characterize this as kind of something that we do for GPT 4.5, that we don't do for other models.
10:40 Alex Kantrowitz? We've already talked a little bit about reasoning versus these traditional GPT models. But it makes me think of DeepSeek. And I think you already gave a pretty compelling answer as to like what you would use one of these models for versus a reasoning model. But there's another thing that DeepSeek did that is worth discussing, which is that they made their models much more efficient. And it's kind of interesting, like when I talked to you about like, all right, so you need data, you need compute, you need power. You're like, yeah, and you need model optimizations, which is something that people often overlook. And just going back to DeepSeek for a moment, the model optimization, the fact that they went from basically querying the entire knowledge base to mixture of experts where they're able to sort of route the queries to certain parts of the model instead of lighting it all up,
11:25 Alex Kantrowitz? is credited with helping them get more efficient. So I just want to turn it over to you without commenting on what they did, or if you can, if you want, but I'm actually more curious what OpenAI is doing on that front and what sort of, whether you did similar optimizations with GPT 4.5? And are you able to run these large models more efficiently? And if so, how?
11:46 Speaker 01 Yeah, so I would say kind of the process of making a model efficient to serve, I often see as fairly decoupled from developing the core capability of the model, right? And we see a lot of work being done on the inference stack, right? I think that's something that DeepSeek did very well. And it's also something that we push on a lot, right? We care about serving these models at cheap cost to all users. And we push on that quite a bit. So I think this is irrespective of, you know, GPT 4 reasoning models. We're always applying that pressure to be able to inference more cheaply. And I think we've done a good job of that over time, right? Like the costs have dropped many orders of magnitude since we first launched GPT 4.
12:29 Alex Kantrowitz? And so are there like, are you, I mean, maybe, tell me if this is too elementary, but the move towards, for instance, mixture of experts. Is that more of a reasoning thing? Or can you apply that in GPT 4.5?
12:41 Speaker 01 Yeah, so that is an architectural element of language models. I think pretty much all large language models today utilize mixture of experts. And it's something that applies equally to efficiency wins in foundation models like GPT 4, 4.5, as it does to reasoning models.
13:01 Alex Kantrowitz? So you were able to use that here as well, basically?
13:04 Speaker 01 No, we've definitely explored a mixture of experts as well as a number of other architectural improvements in 4.5.
13:11 Alex Kantrowitz? Okay, great. So we have a Discord with some members of the Big Technology listeners and reader group. And you know, a theme that's come up recently, it's kind of interesting to be talking with you right now about an extremely large model, because a theme that they can't stop talking about, the people in Discord, is just that how small and niche models to them are going to, you know, potentially be the future. I'll just read you one comment that we had over the past few days. "For me, the future is very much aligned with niche models existing in workflows, unless so of these general-purpose God models." So clearly, OpenAI has a different thesis here, and I am curious to hear your perspective on what we get with the big models versus the niche models, and do you see them in competition or as complements? Help us think through that.
13:55 Speaker 01 Yeah, so I think one important thing is, we also serve models that are smaller, right? Like, we serve our flagship frontier models, but we also serve mini models, right, which are cost-efficient ways that you can access the capabilities or fairly close to frontier capabilities for much lower cost, right? And we think that's an important part of this comprehensive portfolio here. Fundamentally, at OpenAI, though, we're in the business of advancing the frontier of intelligence, and that involves developing the best models that we can. And I think really, kind of, what we're motivated by is really pushing that out as much as possible. We think there's always going to be use cases at the frontiers of intelligence.
14:36 Speaker 01 You know, we think that, you know, going from 99.9 percentile in mathematics to the best in the world in mathematics, right? Like, that difference means something to us. Like, I think what, you know, the best human scientists can discover is tangibly different, right, from what you or I can discover. So we're motivated by pushing the intelligence frontier as far as possible, and at the same time, we want to make these capabilities cheaper and more cost-effective to serve for everyone. So we don't think the niche models will go away. We want to build these foundation models and also figure out how to deliver these capabilities at cost over time. So that's always been our philosophy. There's always going to be some juice there in in those last bits of intelligence.
15:18 Alex Kantrowitz? Yeah, so let's talk about that because we have a debate on the show often. What matters more, the products or the model?
15:25 Speaker 01 Right.
15:26 Alex Kantrowitz? I'm on team model. We have Ranjan Roy, who comes on on Fridays. He's team product. He's basically like, just take what you have now and productize it. And I say, well, you could probably do more with a better model. But I have to be honest, I'm kind of at a loss for words sometimes about what that getting from that 99th percentile in math to the best in world in math will do. So actually, I'm curious to hear your answer on this one.
15:48 Speaker 01 Right, right.
15:49 Alex Kantrowitz? What does building the best model in the world do that?
15:52 Speaker 01 Yeah, yeah, 100%. And I think really it signals a shift, right? Like, I think if you just think about, hey, you take the current models and you build the best surface for them, that's certainly something you should always be doing and exploring that exercise. I think three years ago, that looked like chat, right? We launched chat GPT. And today, when it when you take the best models and the best capabilities, I think it looks a little bit more like agents, right? And I think reasoning and agents, they're they're very, very much coupled, right? When you think about what makes a good agent, it's something that you can kind of sit back, let it do its own thing, and you're fairly confident it'll come back with something that you want, right? And I think reasoning is the engine that powers that, right? Like, you have the model go and try something out.
16:37 Speaker 01 And if it if it can't succeed on the first try, it should be able to be like, oh, well, why didn't I succeed? And what's a better approach for me to do? So, you know, I think very much kind of like the capabilities are always changing and the surface is always changing as a as a response. And we're always exploring what the best surface for the current capabilities looks like.
16:56 Alex Kantrowitz? But just to hammer on.
16:56 Speaker 01 I'm on your team here.
16:57 Alex Kantrowitz? Yeah, but again, just to hammer on with this, like, what does that improvement in model get you? Like, what do you think that it will enable?
17:04 Speaker 01 Yeah, yeah. So, I mean, I think, I mean, agents of all forms, right? When you look at stuff like deep research, for instance, right? It gives you the ability to essentially kind of get a fully formed report on any single topic that you might be interested in, right? I've used it to even put together like hour long talks. And it goes and really kind of like synthesizes all the information out there and and really organizes it, comes up with lessons, allows you to do deep discovery. It allows you to. You know, like, dig into almost any topic that you're interested in. So I feel like just the amount of information and synthesis that's available to you now is just really rapidly evolving.
17:47 Alex Kantrowitz? So basically, it's not as simple as like, just go make deep research better with the product, with the model you have now. Am I reading between the lines the right way, saying that what you're expressing here is that if you make the model better, then the product is going to get better inherently, take deep research, for instance?
18:04 Speaker 01 A hundred percent, a hundred percent, yeah. And that's something that is not enabled unless you have models of a certain level of capability, both in reasoning and in the foundational unsupervised learning sense.
18:15 Alex Kantrowitz? Okay. You know, it's interesting, I guess like this one question I've had in the back of my mind is, and I'm just going to ask it to you again, just so I'm sure I'm clear on it, is my view, maybe erroneously, was that we were just gonna, or your industry was just gonna move from these massive models to the massive models with reasoning, but you're actually saying that there's a dual track here.
18:37 Speaker 01 Yeah, yeah, so I think we're always pushing the frontier, right? And we, I think, even since, you know, five, six years ago, the prevailing way to do that was to up the scale, right? And so we've been upping the scale in unsupervised learning, we've been upping the scale in reasoning, but at the same time, right, you care about serving mini models, you care about serving models that are cost effective, that can deliver capabilities at a cheaper cost, and that will often be sufficient for a lot of use cases, right? And the mission isn't just about pushing the biggest, most costly models, it's about having that and also a portfolio of models that people can use cheaply for their use cases.
19:15 Alex Kantrowitz? Okay, so let's quickly talk before we leave about the upgrades that you're seeing in 4.5 compared to 4. So I'm curious, like, if you can just run us through very high level, the benchmarks it hits versus the benchmarks of the previous models, and then I'll just throw a double question in here.
19:31 Speaker 01 Yeah.
19:33 Alex Kantrowitz? I've already read your blog post, and so I have an idea of what's coming. By the way, we're going to release this just as the news is released, so. It seems like you're also saying, making a statement in some ways, saying like, yes, we have the traditional benchmarks, but we also need to measure how this model works with EQ as opposed to just pure intelligence. So yeah, just hit us with the benchmark improvements and then why you think that's important for us to look at both of these in conjunction.
19:59 Speaker 01 Right, yeah. So I mean, along all traditional metrics, like things like GPQA, AMI, the traditional kind of benchmarks that we track, this does signify an order of magnitude, about at the same level of jump from 3.5 to 4. There isn't, there's a kind of interesting focus here also on, I would say, more vibe space benchmarks, right? And I think that's actually important to highlight because every single time we've launched a model, there is a discovery process of what the kind of interesting use cases out there are going to be. We noticed here, it's actually a much more emotionally intelligent model. You can kind of see examples in the blog post later today, but like how it responds to
20:39 Speaker 01 queries about a hard situation or advice in a particular difficult situation. It responds more emotionally intelligent. I think there's also just kind of like, you can kind of see like, this may be a kind of silly example, right? But if you ask any of the previous models to create ASCII art for you, actually they mostly just fall down. This one can do it almost flawlessly.
21:06 Alex Kantrowitz? Pretty well.
21:07 Speaker 01 And so there's just so many kind of like footprints of improved capabilities. And I think things like creative writing will showcase this.
21:16 Alex Kantrowitz? One of the things that I think I picked up in the examples that you've given so far is that it doesn't seem like it feels the need to write a thesis for every response. Like one user was like, I'm having a hard time. And it actually succinctly wrote like as if a human would, as opposed to like maybe the traditional, you know, here's three paragraphs of self-care routine you can do for yourself.
21:38 Speaker 01 Exactly. Yeah, yeah, yeah. And that speaks to the emotional intelligence, right? It's not like, oh, I see that you're feeling bad. Here are like five ways you could feel better, right? It just doesn't feel like a grounded kind of compassionate response. And here you just get something else, you know, direct to the point and really invites the user to say more.
21:53 Alex Kantrowitz? So I think there's going to be a criticism, I can, I'm anticipating it, and let's, let's talk about it right now, that people will say, okay, OpenAI was talking about these traditional benchmarks, now it's talking about emotional intelligence, it's shifting the goalposts and wants us to pay attention to something else. What's your response there?
22:10 Speaker 01 Well, I, I really don't think that the accurate characterization is that it doesn't hit the benchmarks that, that we expected to. So when you look at kind of the development of three to three point five to four to four point five, this does hit the benchmarks that we expect. And I think the main thing is like, you know, it's all about use case discovery every time you put a new model out there. And in many senses, like GPT-4 is already very smart, right? And, and kind of when we were putting that, this parallel is kind of like when we were putting GPT-4 out, right? It's like, we saw it hit all the right benchmarks that we expected to, but what are users going to resonate with? That was the key question. And I think that's the question that we're asking today with
22:51 Speaker 01 GPT-4.5 as well. And we're inviting people to be like, hey, you know, we did some early explorations, we see that it's more emotionally intelligent, you know, we see that it's a better creative writer, but what do you see here?
23:01 Alex Kantrowitz? Yep. All right, Mark. So I've been seeing you and we mentioned this before we started recording, I've been seeing you in all the OpenAI videos about every release. So first of all, great to speak to you live. But also, over the past year, we've seen a lot of exodus out of OpenAI, maybe the media plays it up too much, probably we do. But I am kind of curious what it's like working within OpenAI and how you see the talent bench inside the company. You recently became chief research officer just a few months ago. And now, look, we have a new foundational model. So just give us a sense as to what the talent situation is inside the company.
23:34 Speaker 01 Honestly, it's, it's still, I think, the most world-class AI organization. I would say that there's a separation between the talent bar at OpenAI and any other firm out there. And when it comes to kind of people leaving, you know, like the AI landscape, it changes a lot. You've probably more so than any other field out there, right? The field three months ago looks different from the field three months before that. And I think it's kind of just natural in the development of AI that some people will have their own theses about, here's the way I, I want to develop AI and go try it their, their own way. I think that's healthy. And it also gives an opportunity for people internally to shine. And we've never had a shortage of people internally who are willing to step up, and we've seen that a lot. And I really just love the bench that we have here.
24:20 Alex Kantrowitz? Very cool. All right, folks. GPT 4.5 is out today for OpenAI Pro users. Next week, it's coming out for Plus, Team, Enterprise, and EDU. Mark, great to see you. Thank you again for spending time. You're about to go and do the live stream, so I'm very grateful that you spent the time with me today. Thank you so much. I really appreciate your time, too. Thanks for having me. Well, let's do it again soon. And folks, so we shouted out the Ranjan and I argument. We'll go into that and more, everything we can share about GPT 4.5 coming up tomorrow on the Friday show. Thanks for listening. Thanks again to Mark and OpenAI for the interview, and we'll see you next time on Big Technology Podcast.
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