Policy Debates and Enterprise Strategy

Today on the AID debrief, this weeknd AI for ridiculously busy people. AI Daily Brief is aaily podcast and video about the most important news and discussions in AI All right, friends, doing a quick experiment here. The AI Daily brief is obviously quite an information dense podcast. Despite curating the whole world of AI things happening, it can still be a pretty high barrier to climb for people who are paying attention more casually or just don't have time to dedicate twenty or twenty five minutes a day for AI news So for those of you who are looking for something that's closer to five minutes, to send your colleagues who need to know exactly what was going on in AI that week That's what this is for. Let me know how you like it First up, let's talk about the biggest theme of the week, which was absolutely by far token efficiency. I made the argument on Twitter that every AI company is now in some way, shape or form, a token efficiency company We have moved officially from the token subsidy era, where the per seat models of companies like Open AI and Anthropic were allowing people to consume thousands of dollars worth of AI tokens for tens or hundreds of dollars. Now we're in the token shortage era, where all the business models are moving to usage based models and everyone is having to adapt This week that adaptation looks like Uber, putting thousand five hundred dollars monthly limits on employees AI usage and Walmart having to cap usage of their tool for it being too high in demand and comments from companies like TSMC suggesting that this shortage is not a short term thing but is going to last years Importantly though, the market is responding AI software engineering agent company Factory introduced native model routing that can figure out what the right model is for a task, including models that are cheaper or not state of the art, which they say can maintain state of the art performance while cutting costs by a quarter Pplexity introduced a new system that combines a hybrid, local and cloud based inference system, which has benefits for both cost and privacy Harvey announced that it had collaborated with Fireworks AI to build a worker advisor agent where an open weight worker can delegate complex tasks to a closed source frontier advisor powered by one of the state of the art models and found that it outperformed the state of the art model alone on the legal tasks for just a fraction of the costs Microsoft, meanwhile, is clearly trying to bring this sort of capability to the rest of the market, saying that when they collaborated with McKinsey to post train a model on McKinsey tasks, it beat GPT five point five performance at a tenth of the cost TLDR, the token shortage is here, but the market is responding In terms of what you should be playing with this weekend, it is absolutely Kodex updates Codex announced expansion of their plugin ecosystem, new annotations, and a new feature called sites Annotations allow you to edit and interact with specific parts of any given website or document that you're working on pllugins are expanding to include functional based plugins. So for example, you can use a plugin pack for salespeople that comes with connectors to common tools and skills that are relevant for that function And sites is maybe the most interesting one, where you can turn anything you're working on inside of Codex into a website or web app with a single click, which I think will help make websites a fundamental unit of knowledge work in a way that they're not right now And right now that's just available to business and enterprise users. But if you have access to it, you should definitely go play around with sites Next up, the thing you have to pay attention to even if you don't think you care is the question of who owns AI The stakes in the policy discussion are getting higher as the model capability increases and we get towards IPOs Bernie Sanders came out with an op ed in Nework Times this week, suggesting that the government own a full half of the major AI labs And while they might not be getting fifty percent, it seems like the Trump White House is considering taking equity stakes in the big labs, meaning that the oververton window on this sort of government company collaboration is changing very quickly We also got policy related papers from both Anthropic and Op AI this week, talking about how they were seeing the early signs of recursive self improvement in today's AI systems So I think the policy discourse is about to get much louder very soon The biggest takeaway for enterprises, simimply put you are now in the token efficiency business and you need to be thinking about that A, architecturally, in other words, things like model routing and model selection, context management and making sure people aren't wasting cycles trying to find the right context, but also B in supporting best practices and specifically training. The cost of the average enterprise's failure to train their people on these new systems has never been higher. If you don't have a company wide agent centric training program yet you are officially behind B biggest takeaway for solo practitioners, meanwhile, is that even if you are proficient with agents right now This is a good time to start building your systems because the cost equation is going to be more of a challenge That means a smaller version of things like context management, like integrating skills and more ULDR now is the time to build systems In terms of what to watch for next week? Well, of course, the big thing will be the SpaceX IPO, which will be the biggest IPO in history, and tell us a lot about how the market is thinking about these companies So there you have it, the week in AI for ridiculously busy people Let me know what you think about this format, and maybe it'll become something that we do more often appreciate you listening' watching as always And until next time Peace