Speaker 1Late last week, OpenAI announced that they would be cutting off access to their models in Cursor. Now for many Cursor users, this is a huge blow. They invested in that Harness ecosystem and view OpenAI's move as some version of competitive pettiness. Other observers think that this was always inevitable as soon as SpaceX AI decided to buy Cursor. They point to other examples like Anthropic cutting off windsurf in mid-2025 to say that this is just the way that Frontier Lab competition is going to work from here on out. And while this all might seem like just psychodrama competition between the labs, for Enterprise AI users, it has very significant implications. Already, there was a push in enterprises to understand and to better be able to take advantage of OpenWeight's models, to build the capability to have more complex model architectures that allow their users to better match the task with the capability level in a way that is more cost-effective. What these moves make clear is that this is not just a cost-efficiency conversation, but is more broadly about control and resilience. And what these latest moves make clear is that enterprises have to think not only about these questions in terms of model, but also about the cost-efficiency of the model. But in terms of harnesses as well. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Sponsors are AIDailyBrief.ai. Lastly, as you guys get prepared for back to school, back to work, if you are looking for more support, the latest crop of super intelligent executive agent training programs is coming up just after Labor Day. You can get that information linked off of AIDailyBrief.ai, or you can go to training.besuper.ai. For anyone who is looking for a boost or who has just liked my guest episodes with Nufar Gaspar, I highly recommend you check those programs out. Today, we begin with a follow-up in the ongoing saga of the data center debate, where blue-collar labor unions... ...are organizing to support data center construction. As data centers become a pivotal political issue for the midterms, labor unions are becoming the backlash to the backlash and are threatening to withhold support from candidates who oppose data centers. The Wall Street Journal viewed a memo from Steamfitter UA Local 602, whose members install industrial piping across Virginia and Maryland, covering the region known as Data Center Alley. The union said that they were drawing a, quote, clear line and won't back any politicians running on an anti-data center platform. In some cases, unions are breaking their longstanding alliance with local Democrats over the issue. The Kansas HVAC and Railroad Workers Union have supported the Republican gubernatorial candidate for the first time in decades over the issue. Sidney Bonilla, the treasurer for Steamfitter's Local 602, emphasized that union support isn't just about votes. He said his union also participates in door-knocking and fundraising to support local campaigns. Bonilla expects unions across the country to follow suit in rejecting anti-data centers. He said that the union's support for steamfitters is not just about votes. He said his union also participates in door-knocking and fundraising to support local campaigns. Bonilla expects unions across the country to follow suit in rejecting anti-data centers. He said his union also participates in door-knocking and fundraising to support local campaigns. Center candidates, commenting, we are dependent on these jobs. Other union leaders are asking politicians which side they are on. Don Slaman, the political coordinator for Electrician Workers Local 26 in Virginia and Maryland, said, you're not a friend if you're taking away great career opportunities. This is a once-in-a-generation opportunity to really get in the upper middle class. Now, if you have been paying attention to my coverage of this issue for the last several months, you will have seen this start to bubble and emerge. In fact, in my last episode that was a primer all about this issue, one of my arguments was that these labor unions were most uniquely suited to intercede in the middle between these communities and the tech companies that are building the data centers, given that the unions are both deeply rooted in those communities, but also stand to benefit economically from the transformation that they bring. Overall, I think it's an extremely positive development that could bring a lot of calm and rationality to the discussion that has been lacking thus far. For some, this is a moment to turn the tide. Investor Gavin Baker wrote, there were reasonable concerns about data centers 18 ish months ago, water, taxes, jobs, electricity prices, the environment, and what they would do to small towns. Well-structured data center projects have largely addressed these concerns today, and we should be celebrating this. On balance, data centers are awesome for America in every way. No less than NVIDIA's Jensen Huang reposted Gavin and said, spot on. AI is bringing manufacturing back to America and re-industrializing the nation after decades of offshoring. We have the opportunity to create lasting benefits for communities across America and help America lead the next industrial revolution. Next up, another frequent topic in the headlines. The Trump administration is developing rules to prevent Chinese labs from getting remote access to AI chips now. Earlier this month, CNBC reported that multiple Chinese firms had access to cutting-edge NVIDIA chips through data center hubs in Thailand, Malaysia, and Japan. Alongside renting compute from third-party operations, the reporting claimed that some of the data centers were owned by Alibaba and ByteDance. Importantly, none of this was illegal, as the export controls only govern physical exports into China. But of course, it's not just the data centers that are owned by Alibaba and ByteDance. If the administration's goal was to block access to cutting-edge chips, then this sort of arrangement undermines the entire policy. The information reports that the Commerce Department is currently working on a new rule aimed at closing the loophole. Sources described it as a slimmed-down version of the AI diffusion rule, which was introduced in the final week of the Biden administration and immediately scrapped once Trump took office. The rule was heavily criticized for having a huge enforcement and administrative burden, but it would have made it difficult to establish third country data center hubs for the Chinese labs. Among other things, the diffusion rule capped AI chip imports at a very low level for unaligned nations. Those caps could be raised if national governments worked with the U.S. to ensure their data centers wouldn't service Chinese companies. Now, it's unclear from the reporting exactly how far the new rule would go and whether it even has support within the administration. Critics of the diffusion rule argued that it would weaken America's dominance in the chip industry, pushing most of the world to adopt Chinese technology. Senior officials at the Commerce Department have been vocally critical of the diffusion rule, with Undersecretary Jeffrey Kessler telling Congress in July, "I don't think the rule is worth replacing. It's a bad rule, and we're glad that it's not being enforced." But if you have watched anything when it comes to AI policy out of this particular White House, you know that among three people, there's going to be four opinions, so we'll just have to wait and see where it lands. Speaking of this administration, Anthropic has won their lawsuit against the Pentagon, with a federal judge ruling that the government had no basis for declaring them a supply chain risk. In her order, U.S. District Judge Rita Lynn found the government hadn't provided evidence that Anthropic represented a genuine threat to the economy. Instead, Judge Lynn wrote, In particular, Judge Lynn noted that the government's continued use of Anthropic's models undermined the Pentagon's claims. The order found that the government had violated the First and Fifth Amendments in making the designation. Judge Lynn wrote, Now, while this is a big win for Anthropic, they are certainly not out of the woods just yet. A second lawsuit in the D.C. appeals court is still awaiting a ruling, and the judge in that case has been so far a bit more receptive to the Pentagon's arguments. One funny little story in a follow-up to the Mac Mini craze of earlier this year. In their most recent earnings report, Apple said that Mac sales were up 29% over the past year, which was the fastest growth of any product line of the company. And of course, for those in the know, the open-claw boom was a big part of that, leading to an estimated $100 million plus of Mac Mini sales. Now, most assumed this was just a consumer trend, with hobbyists and early adopters snatching up Mac Minis to run their new suite of agents. When the new Mac Mini line was unveiled last week, although the specs were up, the price was up much more than it was supposed to be. Meaningfully as well, making it potentially a little bit more out of reach for that generalist sort of audience. Over the weekend, however, the information published a deep dive on how, in fact, a big part of the Mac Mini explosion has been enterprise demand. In June, Apple held an enterprise-focused hardware sales event with significant emphasis on the Mac Mini, which was pitched as a cost-cutting measure, allowing simple agents and AI models to be run locally instead of contributing to rising cloud bills. Former Apple enterprise marketing manager Todd Daly remarked on how out of character this was for Apple. Apple doesn't have a dedicated engineering team for business customers or even a developer relations team. Daly commented, the idea that any team at Apple has an actual plan for embracing enterprise AI is a joke. Still, enterprise demand seems to be very real in some pockets. Sources told the information that OpenAI has purchased tens of thousands of Mac Minis in Mac studios for reinforcement learning. The machines are used to train computer use agents, and sources said OpenAI is desperate to buy more. Anthropic is apparently also renting Mac Minis from AWS, according to people familiar with the operation. In other words, even with the price increases, it seems that the humble Mac Mini will continue to play a significant role in the next wave of AI. Now, speaking of OpenAI, the subject of today's main episode is about a big decision that OpenAI made at the end of last week, and what it means for enterprises and companies who have to position themselves for a new competitive reality. Well, speaking of competitive realities, OpenAI recently introduced some significant price cuts. They cut prices on GPT-56 Luna by 80%, and the large-scale company, TerraVersion, by 20% through the API. They later announced a 20% price reduction on Sol, although that pricing has only been in effect for a couple of weeks. Now, the assumed goal of all of this is to drive up usage on third-party platforms like OpenRouter. This could be part and parcel of a recognition that the frontier labs now have to compete not just on the frontier when it comes to raw capability, but also when it comes to the frontier of efficiency. However, some also speculated that because media uses third-party platform usage like OpenRouter as a proxy for overall token consumption, even though that's a pretty massive misread of the data, that perhaps OpenAI's goal was to get an outsized PR effect. from a relatively minor move. With the battle heating up ahead of Anthropic's IPO in the coming months, this could be a way for OpenAI to generate some concerning headlines. Whatever the motivation and the ultimate goal, OpenRouter is reporting a massive boost in usage for OpenAI's models. They report that daily usage of Terra is up 5.6x after the discount, and Luna rose a massive 13.8x. Sol was not yet discounted during the window they looked at, and its usage was relatively flat with just a 10% gain. What's more, while the discounts on Terra and Luna expired on August 14th, OpenRouter reports that users stuck around, with nearly a third of them continuing with OpenAI's models at full price. Box's Aaron Levy reposted the chart and said, Sometimes people don't have an intuitive sense of what Javon's paradox looks like for token consumption. Those of us working with enterprises get to see this firsthand every day. Basically, enterprises have an unending stream of tasks that they'd love to be able to bring automation to. But for each individual task, it's either ROI positive or not, based on the cost of bringing automation to it. As tokens get cheaper at a certain capability threshold, enterprises can afford bringing more of them to the work that they do. This could be processing every contract, reading every log, watching new streams of data for insights, having background agents execute workflows, and so on. Anytime we can lower the cost of tokens, we will see a disproportionate increase in consumption. Even a 50% drop in token prices could result in a 5x increase in tokens for these kinds of workloads. That's why it's critical to keep bringing down the cost of AI, and why that's good for all of us. For sales, Brandon Galing added, Beyond just Javon's paradox, cheaper tokens means entire use cases go from 0 to 1 in viability, given an organization's willingness to spend and their risk tolerance for experiments. Many tasks require some minimum quantity of tokens to actually do the job. If the pricing doesn't allow you to hit that minimum feasibility, it won't be done. When the pricing drops, enterprises are able to get to the point of value and greenlight use cases that were previously not viable before. Their total token spend increases, but so does the value and ROI they get from it. Now like I said, today's main episode is all about some new competitive moves. And the reason I wanted to end on this story about the impact of OpenAI's competitive pricing experiments is that it's exemplary of the experimental moment that I think we're heading into. But with that, let's close the headlines and move on over into that main episode. A new study from KPMG and the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs. These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com slash US slash AI amplifiers. 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One thing I keep seeing in enterprise AI, companies hedging across every cloud, every model, every framework, or paying a GSI for a pilot that never ends. The team's actually shipping, they've picked a lane, and they move fast. That's one of the reasons I like today's sponsor, Robots and Pencils. They've gone all in on AWS.com. They're an advanced tier and AWS pattern partner, and they ship production AI co-workers in 45 days. That's led to them doing some of the more interesting work I've seen on AI co-workers. And by that, I'm not talking about chatbots. I'm talking about actual agentic systems that sit inside a business architecture and do real work. That kind of focus matters if you're an enterprise leader trying to get something real into production or an AWS rep trying to move a customer from interested to deployed. Request an AI briefing at robotsandpencils.com. One conversation with Robots and Pencils, and you'll know. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. New users get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. HyperAgent deploys always-on agents in the cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Sales' agent enriches leads, drafts emails, and updates the CRM. Ops' agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about the team. HyperAgent is a great way to get more information about your scope and approvals. It's time you add agents that feel like teammates. Hire yours at HyperAgent, built by the team at Airtable. Claim your $1,000 in inference at hyperagent.com slash AI Daily Brief. Welcome back to the AI Daily Brief. On Friday night, OpenAI announced that they would be ending their relationship with Cursor. This is a highly consequential, if not necessarily particularly surprising decision. And on the one hand today, we are going to discuss what this means for the AI competition among the frontier labs. But we will also get into what is, I think, the more important discussion, at least for all of us in an applied sort of way, which is how to position ourselves and our companies for the inevitabilities that that next phase of competition bring. While we can't read the future, I think that there are some pretty clear patterns that have some fairly significant implications for how we think about enterprise AI strategy. But first, let's talk about the move that OpenAI made. In a late Friday announcement, OpenAI basically said, we love Cursor, but we hate Elon, so sorry, Cursor users, you don't get to use OpenAI models anymore. Now, they tried to frame it a little bit nicer in the press release. They wrote, to work with a large partner like SpaceX, we typically rely on custom contracts to ensure compliance with our terms of service, and the integration provides for safety at scale. After Musk acquired Twitter, now part of SpaceX, the company broke the terms of our contract alongside many others. Under oath earlier this year, Musk admitted that XAI, now also part of SpaceX, had violated OpenAI's terms of service. On the flip side, when it comes to Cursor, they said, we've worked with Cursor for nearly four years and have enormous respect for their team, their product, and what they've built for the developer community. We know that the people most affected by this decision are the developers who rely on OpenAI models in Cursor. We care about their experience in this transition, and we're ready to go above and beyond to support them. Now, the cutoff will not actually come until November 12th, and that long deadline seems to be intentional, with OpenAI saying that they are giving the maximum notice provided by their contract. Now, Elon's response will likely come as no surprise. He responded to a post on X saying, Cursor CEO Michael Truel said that they were sorry to see the note, and we're seeing if they couldn't come to some different agreement. Now, he also included a note that OpenAI models serve about 5% of Cursor user traffic, clearly with the implication that even if they weren't able to come to some agreement, that this wouldn't be all that bad. OpenAI product manager Thibault, for some reason, felt the need to clarify on that 5%, reposting Michael Truel and arguing, tokens are not a proxy for revenue nor value created, and the OpenAI models are on the very front line. He said, Smaller or less strong models require many more tokens to achieve a task and therefore will inflate traffic share significantly. His point, in other words, is that even if that 5% of token traffic is factually accurate, it might represent more like 10 or 15 or 20 or even more percentage of the actual value created because of what particular tasks people are using OpenAI models for and how much more efficiently they do them. Now, why he decided that he had to increase everyone's awareness of the pain that they were causing for Cursor users isn't exactly clear, but Epic founder, Tim Sweeney, was happy to jump in and say, Now, a natural question might be, is Anthropic going to follow suit? However, co-founder and chief compute officer, Tom Brown, said absolutely not. He posted on X, tweeted, If your opponent is busy making a mistake, don't interrupt them. And certainly for many in the community, it is OpenAI who's making a mistake here. Dasam on X writes, Biggest loser here is OpenAI. Cursor already has Grok 4.7 on the way, Composer 3, and a bunch of open models. What OpenAI just did is going to make every partner start thinking about plan B. Yusuf Al-Tuki writes, This behavior is genuinely so poor. Most of my usage of OpenAI models was on Cursor. Cursor, unlike Codex, has a fast mode toggle for the models that actually works and causes a 2x speed increase. Cursor IDE has the most gorgeous UX. OpenAI is stripping this away from paying users due to nothing but pettiness. There is no valid reason to stop paying users from using the model of their choice in the harness of their choice. Then to add insult to injury, they replied to Cursor, Actually, when you look at our efficiency, we contributed more than 5%. No point other than rubbing salt in the wound of customers. And yet for others, there are pretty clearly some reasons other than pettiness for OpenAI to make this move. Benjamin DeKraker writes, This is very obvious and this advantage was openly touted as a huge win for XAI when the acquisition was announced. Isn't it somewhat reasonable for OpenAI to opt out their advanced models being included in that training data harvester? Pragmatic Engineering's Guglia Rose writes, OpenAI pulling GPT, not all that surprising. Gail Wiener writes, "What was OpenAI supposed to do? This isn't just a competitor bought cursor, but it's a competitor who took them to court to try to destroy them. And much more significantly of all, many pointed out that this is not some isolated incident, but this is just the norm now of how frontier labs behave. An early example of this came back in June of 2025. After Bloomberg reported that OpenAI was close to nearing a deal to acquire Windsurf, Anthropic cut off Windsurf's access to their models. On June 3rd, 2025, Windsurf's Varen Moen wrote, with less than five days of notice, Anthropic decided to cut off nearly all of our first-party capacity to clod 3.x models. This was a point that many brought up with Tom Brown in the comments when he posted about Cursor being a trusted partner of Anthropic since Sonnet 3.5. Replit CEO Amjad Massad wrote, maybe you've changed your ways, but we all remember what you did to Windsurf, which was infinitely nastier. Amjad continued pointing out that it is likely that part of Anthropic's difference of approach here has to do with the fact that they now have a compute relationship with SpaceX AI that is integral to the training of their future models. AI researcher Mehul Mohan writes, I don't get the hate OpenAI is getting for banning Cursor, and how Anthropic is somehow trying to be the good guy here. Anthropic literally did the same thing with Windsurf one year ago, and would 100% have done the same with SpaceX AI if Anthropic was not paying a billion dollars per month for compute to them. Why is this controversial? And the Windsurf thing wasn't an isolated incident. In August of 2025, Anthropic blocked OpenAI's access to the API. They claimed a violation of terms of service, which most believed was about using Anthropic's models to build a competing AI model. OpenAI's story was that they were just benchmarking the models, but clearly in retrospect the whole move was about concerns about distillation. Then in January of this year, Anthropic also blocked XAI. In a Slack message in January, XAI co-founder Tony Wu said, Hi team, I believe many of you have already discovered that Anthropic models are not responding on Cursor. According to Cursor, this is a new policy Anthropic is enforcing for all its major competitors. This is both bad and good news. We will get a hit on productivity, but it really pushes us to develop our own coding models and products. We're at a time in which AI is now a critical technology for our own productivity. The team is rapidly developing our own models and product. We will have something to share with everyone soon. In the meantime, you may still try different kinds of models and grok build. Then, of course, over the next couple of months, Anthropic changed the way their subscriptions work to not cover third-party tools, specifically things like OpenClaw and Hermes. Hermes co-founder Technium was happy to point this one out to Tom Brown as well. A couple months later, Anthropic partnered with Figma publicly and then poached an executive and launched Claw Design to compete directly, which led to a July story. about a concern that Anthropic was going to use proprietary information that they got from people using their models to build competing services to their customers. And while these examples have all been Anthropic, many have been beating the drum that this is just the way that it's going to work in the next phase of competition. Palantir's Alex Karp has been screeching about this to anyone and any news outlet that will listen. And the presumption that there is a fundamental misalignment between the Frontier Labs and their customers now seems to be core to no less than Microsoft's strategy. A couple of months ago, Microsoft CEO Satya Nadella released a blog post called the Reverse Information Paradox. In it, he wrote, you essentially pay for intelligence twice, once with money and again with something even more valuable. The proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it. Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return. That is what I think of as the reverse information paradox. The reverse information paradox is a very important part of the reverse information paradox. This requires more than data protection. Models learn from exhaust, the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong. Every correction is distilled into institutional know-how. It's the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly. Trace by trace, correction by correction, eval by eval. It's imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop. Now, if you've been paying attention, this has set the tone for every move that Microsoft has made subsequently. Their new models that they recently released are very much designed to be the base for customizations and post-training, which, while they are certainly not open weights, are designed to be more customizable and more owned by the customer, as opposed to just sending off information into a black box that only the frontier lab can access. And as much as OpenAI tries to say that this is a move that is not general, but is specific to issues with Elon based on a demonstrated pattern, the implication for many is clear. As Yuchen Jin put it, when Elon acquires Cursor, OpenAI cuts off Cursor. When OpenAI tried to acquire Windsurf, Anthropic cuts off Windsurf. Not your weights, not your product. Now, functionally for enterprises, it doesn't matter if there is substantive difference in these two things. The response that they're going to have to have is very similar. As AI content creator Theo put it, if you want to avoid getting hurt by companies beefing with each other, make sure you own your tools and your relations with the products you rely on are direct and not routed through other layers like this. I have a feeling this is not a one-off thing. If anything, it's the start of the end. We're probably going to see more and more moves like this from Anthropic and OpenAI, and probably even companies like Google and SpaceX. And of course, already, even before this, we had seen companies starting to get more acquainted with open weight models. Earlier in August, the Wall Street Journal profiled how AT&T had started working with open models, and Business Insider also talked about how Thomson Reuters had done something similar building off of a base of Alibaba's Quen. Now, these stories mostly posited this as a cost control measure, obviously a big part of the process. However, there is clearly now a sovereignty and control aspect of those moves, and what this shows is that those questions are moving from strictly the model layer to also the harness layer as well. Again, in the Wall Street Journal's CIO Journal, a recent post introduced the idea of an AI model harness to a wider audience. In explaining why businesses need a model harness, Moody's David Pan called it a way for companies to take back control of their AI. The journal writes, developing their own software around AI models, a practice Pan calls harness engineering, gives businesses a way to decouple their workflows from the models themselves, and that helps them become less reliant on a single AI provider. Said Pan, if you bring that harness in-house and control it, you're baking in a lot more business resilience. And given that this article came out a week ago, Pan's advice that companies build their own harnesses rather than rely on those offered by labs like OpenAI and Anthropic, seems particularly prescient. So if you are an enterprise AI buyer, what is the move here? First of all, this is another reminder that if you don't have a policy around OpenWeights models yet, you need to go figure it out. Now, to be clear, what I do not see is companies abandoning frontier closed models entirely. Frankly, even among sophisticated users, it's not like some big majority of use cases have even moved over to those new models yet. But what the sophisticated enterprise AI users understand is that being able to integrate and route the models that you need to build without certain types of tasks to lighter, cheaper, more controllable OpenWeights type models is going to be a key capability that they need to have. And that like any capability, it's going to take time and they need to start now. What I think that this cursor move puts a point on is that this is not just a model conversation, but also a harness conversation as well. My very strong prediction is that you're going to see a lot more discourse around not just open models, but open harnesses. An early example of this is that once again, earlier this month, DeepSeq released DeepSeq Harness. And their announcement post they wrote, DeepSeq Harness is an agent harness built around one core idea. Everything is a plugin. Models, tools, skills, sessions, sandboxes, file systems, loops, orchestration, and UI are all implemented as plugins and can be mixed, matched, replaced, and extended. And first impressions of DeepSeq Harness are pretty good. But I think that the DeepSeq Harness itself matters less than the fact that open harnesses are now a tool that enterprises are going to have access to as well. Ultimately, I don't think anything about the cursor move is particularly surprising, which certainly doesn't mean that cursor use shouldn't yell and scream at OpenAI and see if they can't change their position. I think a highly balkanized world of models and harnesses is inherently worse for everyone than the one we seem to be going into. So I am fully in support of people using market pressure to try to change the policy. But for enterprises, the lesson is clear. If you want to not be subject to the whims of the companies that control your models and control your harnesses, you basically can't be reliant on any one company to control your models or to control your harnesses. So you know, for enterprises, just a whole additional set of things that you have to get good at to make AI work for you. Anyways, interesting times. This is a trend that we will watch closely. For now, though, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. Till next time, peace.
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