Episode Categories:
Resources:
Join the discussion on LinkedIn: Got something to say? Let us know on LinkedIn and network with other AI leaders
Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup
Connect with Jordan Wilson: LinkedIn Profile
Start Here Series in our Inner Circle Community: Join for free access
U.S.–China AI Cold War: Implications and Real-World Impact for Business Leaders
The current landscape around artificial intelligence is being redrawn with a sharp edge as tensions mount between the U.S. and China. Recent news indicates that both nations are now moving to restrict access to their most advanced AI models, a development that directly impacts business strategy, technology adoption, and operational risk assessments. This article breaks down critical findings from a recent in-depth discussion and aligns them specifically with the needs and concerns of business owners and decision makers on LinkedIn.
Strategic AI Model Restrictions: What’s Changing in the U.S.–China Arena
Several points were raised, including new reports that China is considering restricting external access to its frontier AI models, while U.S. political signals suggest reciprocal measures may follow quickly 00:33. These simultaneous actions illustrate a significant shift—from an open, competitive ecosystem to a controlled one where access to leading AI models is increasingly viewed as a national strategic asset.
Rather than competing solely for benchmarks or technological bragging rights, the underlying contest is about economic leverage, geopolitical influence, and the ability to set standards for the next wave of global digital infrastructure 01:13.
Business Model Risk and AI Leadership Dynamics
A key theme that emerged was the evolving risk profile for U.S. organizations integrating Chinese open-weight AI models. Adoption patterns shifted as Chinese AI offerings rapidly closed the capability gap with their U.S. counterparts, particularly through aggressive pricing and capable models entering the enterprise space 04:49.
Deploying AI models sourced from China now introduces risk factors beyond cost and performance: businesses must account for unpredictable regulatory restrictions, potential compliance hurdles, and increased threat of deployment disruption if governments escalate restrictions 02:15.
AI’s Core Value: Beyond Technology, Towards Economic and Security Power
The discussion explored how dominating advanced AI is rapidly becoming synonymous with economic growth and geopolitical status. Whoever automates knowledge work at scale first can outpace rival economies and gain outsized influence 06:28. Moreover, AI's potential for use in offensive cyber and military scenarios elevates its importance to a tier on par with traditional strategic resources such as oil or gold 07:04.
Open Source AI Models: The Shifting Cost-Benefit Calculation
Previously, open-weight Chinese models allowed enterprise teams to achieve near state-of-the-art AI capabilities at a fraction of the cost compared to U.S. offerings, especially while domestic models were limited by regulatory constraints and premium pricing 13:14. However, the landscape shifted rapidly. The most recent models from China, such as Moonshot’s Kimi K3 and Alibaba’s Quinn 3.8 Max, while highly capable, are now harder and more expensive to deploy at scale 13:01.
An analysis of cost per task metrics reveals that current leading-edge Chinese models are no longer the price leaders they once were—OpenAI and similar U.S. developers offer equivalent or superior intelligence at lower effective operational costs 21:37. This sharp shift has upended traditional cost calculations for many enterprises previously reliant on open-weight options.
Narrative Control and Model Distillation: Subtle Competitive Edges
One concept discussed was how widespread model adoption can be harnessed for soft power objectives. Chinese models are consistently designed to reinforce government-approved narratives, with studies showing these systems avoid sensitive topics or answer political queries less directly 11:43. Enterprises leveraging such models—often by default—may inadvertently propagate filtered content throughout their knowledge bases, communications, and public-facing outputs.
In addition, rapid Chinese advancement has partly been attributed to model distillation, where responses from U.S. frontier models are used to train new domestic entrants, further accelerating the technology gap now that access risks are escalating 25:39.
Open Source vs. Closed AI Ecosystems: Decision Points for Enterprises
As enterprise-grade models scale to trillions of parameters, distinctions between consumer and enterprise-grade open-source models have sharpened. Running these powerful, open-weight models now requires significant compute infrastructure, putting local deployment out of reach for most save for large-scale corporates 18:25.
Consequently, model evaluation must prioritize total cost of ownership, ongoing compliance with evolving export control laws, and operational agility to rapidly adapt to abrupt changes in model availability 30:00. Leading companies are exploring flexible architectures and model routing (mixture-of-models approaches) that allocate workloads dynamically by cost, security, and legal exposure 30:00.
Enterprise AI Strategy: Tactics in a Fragmenting Global Market
A key recommendation involves maintaining a documented inventory of all AI model dependencies, with a special focus on those developed in jurisdictions now subject to export controls or political scrutiny 32:34. Enterprises should build contingency plans to switch models in response to sudden policy shifts and regularly assess which models are best suited based on updated intelligence—balancing price, performance, security considerations, and anticipated regulatory developments.
Ultimately, expecting partial openness—older models shared, cutting-edge models reserved—is now a baseline assumption as both the U.S. and China recalibrate their AI engagement rules 32:28. Adapting enterprise technology and operational frameworks to this new landscape is not optional; it is now integral to risk management, competitive positioning, and continuity planning in the era of the AI cold war.
Topics Covered in This Episode:
- U.S.-China AI Cold War Overview
- Chinese AI Models Closing U.S. Gap
- Government Restrictions on AI Model Access
- Economic and Geopolitical AI Power Struggle
- Risks for U.S. Businesses Using Chinese AI
- Open Source vs. Closed Source AI Debate
- Chinese AI Model Pricing Undercuts U.S.
- AI Model Distillation and U.S. Security Concerns
- Enterprise AI Cost-Effectiveness Benchmarks
- Microsoft Testing Chinese AI Deployments
- Future AI Model Export Controls & Strategies
- Recommendations for AI Model Sourcing and Risk
Episode Transcript
Jordan Wilson [00:00:16]:
A US first China AI cold war is starting, and most business leaders have no idea they may already be participants. And it's moving much faster than hardly anyone can keep up with. About two weeks ago, Reuters reported that China is considering locking down its most advanced AI models, keeping its best from the rest of the world. Then just yesterday, the Trump administration is signaling it may restrict Chinese AI models inside The United States. Yeah. Read that again because both sides are now moving to wall off AI access within days of each other. And here's why two governments suddenly cares so much about who runs which model. Well, that's because this was never really about who has the most powerful AI or who has the best benchmarks.
Jordan Wilson [00:01:13]:
It's actually about economic growth, geopolitical power, and who sets the standards the rest of the world builds on. That's because whoever leads AI doesn't just win a tech race towards superintelligence, They gain leverage over every other country's future. And that's the fight your business may now be caught in the middle of whether you signed up for it or not. So let's dig in here is the big picture. The AI power is flipping right in front of our very eyes. That's because for three plus years, The US has been pretty far ahead, at least when it came to the open source Chinese models. But now China is actually closing the gap with open weight models. Yeah.
Jordan Wilson [00:02:02]:
Closing the gap between the best that anthropic and open AI have to offer. But now both governments are treating their frontier models as strategic weapons worth potentially restricting. And US businesses that have been chasing models and maybe using these open source Chinese models because they were very capable, now might accidentally inherit some serious geopolitical deployment risk. So on today's show, you'll learn how these Chinese models became cheaper, closer, and harder to ignore. You're gonna know why a trillion parameter. Yes. Trillion with a t. A trillion parameter open model no longer means that you can simply run it on your computer.
Jordan Wilson [00:02:48]:
You're gonna know why Microsoft testing Chinese models could signal the enterprise AI future, and you're gonna know what leaders should deploy, document, and avoid before restrictions may harden. Alright. Let's get into it. Welcome to Everyday AI. My name is Jordan Wilson, and we do this every day, and it's yours. This is your unedited, unscripted daily livestream podcast and free daily newsletter, helping business leaders like you and me keep sense of all of these developments because, yeah, they're happening at warp speed. I tell you what matters, what doesn't. You take that information, and you're the smartest person in AI at your company.
Jordan Wilson [00:03:26]:
So it starts here with the podcast, but make sure to go to our website at youreverydayai.com. We're gonna be recapping the highlights from today's show and a whole lot more. Let's get straight into it. What the heck has happened? I mean, my gosh. As someone that's been doing this everyday AI thing for three and a half years, the last, like, five or six weeks, at least when it comes to, The US versus China tensions and even what, each country is pushing out aside from just AI acceleration is at an all time high. The battle between these two nations couldn't be, any higher. So, like I said, in the past two weeks, we saw reports first that, Beijing is looking at curbing oversee access to its top AI models. And then we got a report just yesterday saying the Trump administration may also ban Chinese AI models.
Jordan Wilson [00:04:30]:
And that's big news. That's because we've seen a lot of reports. I've talked to people personally, that are moving entire enterprises off, you know, maybe like an OpenAI or Entropic or Google models, right, and on to other Chinese models. I think especially when we saw GLM 5.2 from ZAI, which was technically a more affordable, you know, version of some of the Frontier models. It wasn't quite yet punching, at the state of the art, kind of AI class, but it was getting close. That's when it kind of started, but it's really snowballed just the past week with models like Kimi k three, which is now just under the, Fable five and, GPT five six soul class. And then, Quinn, their new 3.8 model, which we don't have full benchmarks on yet, but it's released, which is weird. Right? Normally, companies don't release models and not release benchmarks, but it's live, and it's seemingly really good.
Jordan Wilson [00:05:36]:
And it also may be entering, that top tier. So in that upper echelon now, we might have models from Infropic OpenAI in two open weight Chinese companies. So in theory, these are models. Right? Again, you can't really run them on your computer, but these are models that large, large enterprises that have the means to do it. They can run all of these things locally. Again, assuming they have a couple, servers to throw on. So here's why AI leadership now decides the global superpower status. It's three things.
Jordan Wilson [00:06:16]:
Right? It's the economy, power, and this multi access. Okay. So here's what each of those boils down to. Whoever automates thinking work fastest compounds growth over every rival nation, and that helps whoever is actually winning AI have a leg up on the economy. Power, I mean, aside from, you you know, these, systems, these AI systems, because that's what there are. They're much more than models. Right? They're being used in military use cases, and I've been talking about this since the very beginning. Right? Before we had models that should have even been touching the battlefield, I've said the future of AI is definitely, it is going to become the new oil.
Jordan Wilson [00:07:04]:
It is going to become the new gold. And I think that most people that hang out on the bleeding edge understand that to be true. Maybe the rest of the world is well, see that in a year or two, but that is the reality. If you control AI, if you control, you know, if you are in the lead toward AGI or artificial superintelligence, that means that your country will wield a or yield a power over every other country, and there may not be much that anyone can do about it. I mean, when you think about things like being able to, you you know, put cyber attacks and being able to, in theory, take down entire country's power grids, their banking systems. Right? That's where we're at. It's things that, physical weapons, you know, would try to do. Right? But this is something that could, in theory, be launched autonomously at scale.
Jordan Wilson [00:07:54]:
That's the down and the ugly side of AI, but that's ultimately anything as powerful as artificial intelligence in these models that we have now. You have to think it's much more about, you know, it's about much more than helping us all write better emails or helping us triage our days better. There is a bigger and sometimes better purpose between the, behind what nations, especially the, nations jostling for power at the top of the global, pyramid. This is what they want it for. And then last but not least, it is this, this parity. There is this, parity that's happening right now because right now, no nation leads in every access. Right? When it comes to model capability, available compute, cost, adoption, and deployment. Right? And I think that's one of the things that, China is really working on.
Jordan Wilson [00:08:47]:
And just FYI, as I'm talking about this, I'm obviously right. I'm based in The US. Most of the people and companies I work with are based in The US. So I'm obviously coming at it from that perspective, if you couldn't tell already. So one of the biggest questions is, like, why? Why does China have they been coming with this open source or open weight approach, and The US just really hasn't? Well, first, you know, some recent models from The US have done fairly okay, on the open source scheme. So, Thinking Machines Labs, new inkling model, really good. You know, NVIDIA's, open models, fairly good, but no one's been able to touch the, Chinese models. Part of that is because of distillation, which we'll get to, but it always gets to why would people always ask, why would China put out these open source models that, you know, at least six months ago, you know, you could download them on very powerful consumer hardware and run them, and everyone was always confused.
Jordan Wilson [00:09:49]:
And I think that there's a couple of reasons. But one, it China wants to be the default on what the rest of the world builds on. Because if so, that makes the all the other services that you might need to run those models more valuable. In every enterprise, here's the thing. It is a competition. Every enterprise that switches from US models drains the revenue funding, essentially, Silicon Valley's next training runs. Right? So if you take the fuel out of the car, the car can no longer run. Also, huge models make enterprises rent a Chinese compatible hosting tools and support.
Jordan Wilson [00:10:29]:
And then China gains adoption, and they they weaken US's pricing power, and they keep the leverage. The other thing that most people don't talk about is controlling the narrative, something that China is obviously, very concerned about and has been concerned about for many decades. But, these Chinese models, right, studies have shown that they avoid sensitive topics that Beijing does not want to discuss globally. So people are just sometimes copying and pasting whatever, an AI model spits out, and they're maybe sending it to colleagues, they're sending it to clients, or in many cases, they're just putting it on the Internet. Right? And then large language models start to regurgitate this, and this becomes part of the training data. So, this is a way and, you know, this gets, you know, put out in schools, media, government documents, everything. So, and you also have people using these models to distill and create other models. Right? As an example, I believe cursors, Kimmy, I believe cursors models, their first ones that, were based off of Kimmy's open source models.
Jordan Wilson [00:11:39]:
So why does that matter? Why does China wanna control the narrative? Well, a Stanford study even found that China origin models answered political questions less directly. So it's it's it's not like, you you know, these models are going to say something that's, you know, overtly slams The US or overtly, you know, puts, you know, Chinese, you know, morals and ethics on a pedestal. That's not what I'm saying, but it's just the nuance. It's the, you know, describing things in a slightly different way. And, you know, you know, if you think of the game of of of telephone. Right? Each time that happens, you know, each time someone just blindly copies and paste something on the Internet, and then the next round of frontier models get trained on that information, and it just starts to weaken and distill maybe certain talking points that Beijing would rather not be out there. So there's it's much more than just about controlling the, you know, what the rest of the world builds on and, you know, maybe sucking, The US's, power supply dry. It's also about controlling the narrative.
Jordan Wilson [00:12:48]:
Alright. So let's talk about some of the more recent models. So I think this all started, earlier this summer or late spring with, ZAI's GLM 5.2. So that is not nearly on the same tier as the most recent ones from this past week, and that's Moonshot's Kimmy k three and Alibaba's, Alibaba's Quinn 3.8 max. And they essentially, on some things, undercut, US pricing, especially GLM 5.2. And in many cases, they have been good enough. Right? We've literally read, stories where, you know, I would say more, tech forward or AI native companies, but large ones, essentially took their Claude spend, right, because in Propix models are the most expensive. And there was a period, right, where, Anthropic's Mythos five, Fable five came out, before OpenAI released their, competing model in GBD 5.6.
Jordan Wilson [00:13:50]:
So there was this time period where Anthropic had a lead in the quote, unquote model wars, but it was just ridiculously expensive. And everyone's like, wait. We could use a model like GLM five two, which isn't that far behind their Opus class model, and we could get, like, 95%, of the power of, like, an Opus model for a fraction of the cost. So they were just saying they're trying to undercut US pricing with good enough models, not necessarily necessarily by being the number one model in the world, and that's kinda where we stand now. And I I I said this on a show earlier. I think, previously, Chinese models were, like, six months behind. Now it's, like, one to two months. Part of that is I think their distillation efforts and their architecture under.
Jordan Wilson [00:14:42]:
Right? It's not just distillation. Obviously, these labs have some of the most talented engineers in the world. So it's a combination, I think, of, you know, number one, their distillation, efforts have increased. Number two, some of their architecture that they're putting out is truly good and novel and making a difference. And, well, number three, which you can't overlook, is the recent, kind of US sanctions, that had been handed down largely because of anthropic to all model providers here in The US, which is delaying. We've seen reports, maybe, like, a thirty day, forty five day or more, whereas these companies would have been pushing these models out to the public a little faster. But now that we live in permission slip AI land, you know, it's a combination of those three things that have kinda closed this gap that was much wider before. But it's the benchmarks.
Jordan Wilson [00:15:37]:
We do have to talk about the benchmarks. Alright. So if we look at the artificial analysis index, which we talk, sorry, the artificial analysis intelligence index, which we do talk about a lot on this show, you know, now all of a sudden, you have Kimmy k three, in the same, tier, right, almost as Claude Fable five and GBD 5.6 soul. So on this benchmark, which is probably the best single overall benchmarks because it is a conglomerate. Right? Quad fable five has a 60, GBD 5.6 Soul has a 59, and Kimmy k three has a 57. Right? And there is usually always, like, at least a 10% drop off, right in terms of the AA, index. Now it's like a 5% drop off. Right.
Jordan Wilson [00:16:31]:
Which is not that big of a drop. So here's though where it's changed, especially the past, like, couple of models, with z a i's GLM 5.2. This is something that, yeah, you could run it a slow down, a watered down version of that model if you had a super powerful, consumer PC. Right? Or, you couldn't actually run it at full speed. Nothing like you could run online. But people out there that spend maybe way too much money on their, consumer setups, they are able to run, essentially, watered down versions of GLM 5.2. Not anymore. Right? Because now it seems like the next step that China is taking, they want to compete on the frontier, the frontier frontier.
Jordan Wilson [00:17:20]:
And as The US models, the state of the art models get more and more capable, well, it gets harder for, you know, a small enough model to close that gap. Because now what we're seeing is these multiple trillion parameter models. So open source, right, especially through 2025 generally meant, okay, you could get a a quant version of this model, right, which, you know, only activates certain parameters. So think about two, you you know, four, context. Right? The, the g b t four family of models was, like, 2,000,000,000,000 parameters. So now you have open source models that are bigger than that, right, which is crazy. So these multiple trillion parameters, you can't run them. You literally need a small data center, or like I said, if you are an enterprise company that has access to compute, yeah, you're gonna need a whole rack of NVIDIA GPUs to actually run these things.
Jordan Wilson [00:18:25]:
So, this does even change, I think, how most enterprise leaders should be viewing open source. It's almost like you should be saying, like, what kind? Like, consumer open source or enterprise open source? Because there really wasn't that distinction. There really wasn't that, you know, multiple tiers, you know, because if someone chained together a couple Mac studios a year ago, you could probably run a a quant, a quant version of these, you know, Chinese open source models. But are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on GenAI. Hey. This is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and NVIDIA have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use GenAI.
Jordan Wilson [00:19:36]:
So whether you're looking for chat g p t training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to youreverydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI. They're not exactly cheap anymore either. So with that distinction or moving away, means they're not exactly cheap. And so for me, when I'm looking right? If I'm a business leader well, I am. Right? But I'm not, necessarily making enterprise decisions at Fortune 500 companies, although I do advise those type of companies. Before, even two or three months ago, you say, yeah.
Jordan Wilson [00:20:35]:
Look at open source models as an alternative. Today, I don't really know why people should, And maybe that's largely because of what OpenAI has been able to do. Right? In in terms of cost per intelligence index, right, which I think is, just as important as the overall intelligence from artificial analysis. But this essentially says, how much are you paying to get these tasks done? Right? Because all of these benchmarks. Right? Artificial analysis runs it, and they say, here's how much it actually costs to get all of this work done. And what we've seen is, well, anthropics models, this is not one where you wanna have a big lead. You wanna have a bigger bar chart. No.
Jordan Wilson [00:21:21]:
That's bad. Right? Anthropics models are ridiculously expensive. So, as an example, it costs $2.75 per task, whereas OpenAI's models, are much cheaper up to a third cheaper. So their smaller version, GBD by six terra is 82¢. And what's interesting here is actually the Chinese models, Kimi k three and Quinn 3.7 actually cost more, than g v d five six Terra, and the QEN costs more than the mid tier. Oh, sorry. I don't have this one in the benchmark here. But it right.
Jordan Wilson [00:22:08]:
When you have a leading state of the art model, and it's costing about the same or even less than the open source model. So it's like, okay. Maybe there's a couple 100 companies in The US that can actually go out and run this themselves without paying API prices. Because if you are paying API prices, at this point, you would probably just should be using OpenAI. Because when it comes to price per task, which is what is ultimately gonna matter, they're winning. Right? Or maybe you are looking at Grok in Meta Muse Spark. Right? You remember on the show, last week, I said it was a really bad week for Anthropic with all of these new, open source models coming out. But then also with Grok and MetaMuse Spark, 1.1 coming with some pretty good models that people weren't expecting, especially on the coding and software engineering side.
Jordan Wilson [00:23:04]:
But similarly, artificial analysis has this, this quadrant, when it's cost per tax, cost per task. And the, essentially intelligence or the artificial intelligence, artificial analysis intelligence index score. So essentially, you wanna be in the upper left hand corner, which means you have the smartest model at the cheapest cost, and none of the Chinese open source models are in that quadrant anymore. That obviously, it resets as new models come out and the the the medians and the averages all change. But right now, the three companies in there are OpenAI, Grok, and Meta. No open source Chinese models where this is actually a quadrant that they used to dominate, which is why I think six months and a year ago, it made a lot of sense for business leaders to be looking at Chinese open source models, especially when they were a little bit more tamable in terms of what you could do without having a multimillion dollar, you you know, compute setup. But but it's just not the case anymore. So for me, anyways, even though I know that that the war is going to rage on, if I'm making decisions, I'm looking at these charts and saying, well, there's maybe not that big of a reason for, us to look at these models unless you do want or need that open nature, which I know some companies obviously do.
Jordan Wilson [00:24:35]:
But it's no longer just the cheap API prices that reveal the true cost of using these models. Right, because you have these aggressive Chinese prices, that shows their strategy is not just competing for money. Right? Because there's other subsidies, cloud cross selling competition, and also still these lower prices force enterprises to question the expensive closed model defaults. So I know that the models from the past week, right, specifically Kimmy k three and Quinn 3.8, they may not still fit in the traditional open source cheap Chinese models to use Paradigm, but there are still those models. They will still be, continue to be developed, and I think that there will be a place for them. But it comes to distillation. We can't get to, you know, twenty plus minutes and not talk about distillation. So distillation is essentially where, for the most part, Chinese companies, you know, take.
Jordan Wilson [00:25:39]:
It's just like they copy the questions and they copy the answers for a lack of a better term from the big AI model provider. So it's just like they copy the answers on the test. They take all the hard work from the American companies and use it to train their own models. Right? And that accelerates the progress, but it you still can't explain, you you know, how they get there. Just through distillation, that's not enough because they have had some great, advancements, the Chinese companies in architecture and just their overall execution. So AI model distillation isn't universally illegal. It's highly frowned upon, and it is, you know, sparking some backlash politically, and economically with export controls. I think we're gonna continue to see those ramped up now here for the rest of 2026.
Jordan Wilson [00:26:31]:
But, you know, these alleged Chinese practices do violate contracts and have led to major US national security actions. So what is The US doing to stop this model distillation? Well, they're trying to cluster traffic and, you know, detect and, you know, catch large scale, offenders and, you know, block them. But it's pretty hard because it's they're just playing whack a mole. A US house committee did it, though, just unanimously back a bill to sanction foreign actors for extracting US models, but, laws in The US are a slow moving machine. Right? So, it could be many months or multiple quarters or even more than a year before something like that ever becomes law. But these published open weights cannot be recalled, so businesses can still adapt them anyways. Right? So when models go open source or open weight, it's kinda like the cats out of the bag at that point. So The US can try to restrict access, but that could potentially backfire because Washington can yeah.
Jordan Wilson [00:27:38]:
They can pressure, you you know, via export controls or otherwise the chips, the cloud access, procurement sanctions, and all these other things. But the restrictions may just protect the technology while raising the cost for American companies. Like I said, if you, are restricting these models, but companies already have the weights, you may just be keeping money ultimately, from other ecosystems that could power the American, enterprise. Right? So I think the writing though is kind of on the wall. And I think it's actually Microsoft's, the, some of their recent actions that show, I think, what we might be looking at when it comes to how should large enterprises be looking at or using these Chinese models. So, reportedly, Microsoft was evaluating DeepSeek for cheaper Copilot co work model routing. So not for all of Microsoft Copilot. Right? And this is just according to reports.
Jordan Wilson [00:28:45]:
I don't believe Microsoft has confirmed anything yet. And they were looking at this for a backup because what they found, Microsoft that the Copilot cowork was actually very cost intensive. And they did started to bill it, you you know, at a token rate and no longer just including usage, like they did when it was in beta. So that is telling you everything that you need to know. They haven't switched to it, but Microsoft yes. That Microsoft, the one that has huge stakes, in OpenAI and Anthropic. They are looking at a model like DeepSeek for all the, you know, the time that DeepSeek's name has gotten dragged through the mud for, you know, some alleged shady practices. They're still looking at them to use them.
Jordan Wilson [00:29:38]:
And I think, ultimately, you have to look at your balance sheet. You have to look at the dollars and the cents and make it make sense. And in this case, Microsoft. Right? And the other thing, again, if you're a large company and you can, you know, essentially download the weights to these models, and you can fine tune them and make sure that they perform up to a certain standard. But I think the future workloads like in this Microsoft example could just be routed by cost capability, security, geography, and regulation. Right? I've been a huge advocate over the years for who knows? Maybe we'll get there eventually, but, you know, the mixture of models, much different than a mixture of experts. Right? That's using a single model, and then the model kind of calls on the different experts and the different parameters in this dense model. Right? Mixture of models is similar to model routing, but it's just using right? I think perplexities, model council, Microsoft has something similar.
Jordan Wilson [00:30:34]:
But it's well, when you put a prompt out there and there's just a router, and it might send a simple prompt to one open source model. Right? That's a good example. Or it might send a complex prompt to a 100 different models and then an orchestrator model to go in and collect all the information. And maybe half of those 100 models are open source. But I think if anything, the shift though does weaken loyalty to one model and rewards flexible architecture. I do think that is probably the future where enterprise leaders need to be focusing on. So as we wrap up, let's talk about that. What is coming next and what business leaders should be doing now? Well, I would do this.
Jordan Wilson [00:31:17]:
I'm gonna say expect controlled openness. Alright. Whether it is China placing export on their open models, which I know kind of, goes against the, very reason they put them out there in the first place, but that's another, another topic for another episode. But I would expect controlled openness. So either China is going to restrict probably The US, from using their maybe, most frontier models, and or, The US may also, restrict the usage of some of these Chinese open models as well, especially as now these models are getting more and more capable, more and more autonomous. And, when it comes to cyber, things are getting a little bit scary ish. Right? I I think in six months, a year, that's when things are gonna be getting, like, real scary. In in terms of these autonomous models as they can technically get smaller, faster, more capable, and, well, more open source, that's when, these exports are going to, or these export controls, are going to come in.
Jordan Wilson [00:32:28]:
So expect both nations to probably just share older models, but maybe guard their best. But what you need to do is inventory where your current Chinese open source models are running and then route those tasks by cost security and policy risk. And then you need to document your sourcing and be ready to swap out a model or to, you know, go to your plan b, when and if those government restrictions harden. Alright. I hope this episode was helpful. But like I said, whether you know it or not, there's a good chance if you're using open source models, that part of your plan might need to be modified pretty soon because the war between The US and China when it comes to AI is heating up. So I would expect a lot of back and forth racket. So don't get stuck in the middle.
Jordan Wilson [00:33:23]:
Don't waste hours every single day toiling over it. Just tune in to us here on the show, and the newsletter at youreverydayai.com because we're always gonna keep you up to date. I hope this one was helpful. Thanks for tuning in. Hope to see you back tomorrow and everyday for more everyday AI. Thanks, y'all.