← The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis
5 AI Engineering Trends for Non-Engineers
00:28:09
Loading summary
Transcript1 lines
- [00:01]
A
Today on the AI Daily Brief, the five trends among AI engineers that non engineers should be paying attention to. Before that are the headlines. More information about OpenAI's first consumer device the AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, kpmg, Robots and Pencils, Blitzy and Airtable. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe at Apple Podcasts. And to learn more about sponsoring the show, send us a Note@ SponsorsIDailyBrief.AI we kick off today with an update on OpenAI's first consumer device as it apparently enters the prototyping stage. Bloomberg's Mark Gurman has the scoop with his sources, describing the device as a portable, screen free smart speaker designed to be a type of new home computer for the AI era. The sources suggest that the device is intended to remain in the home and will be able to control smart home appliances and play music as well as functioning as a chatbot interface. The pitch is that this will be a human like AI companion. Now at first read, this essentially sounds like a replacement for Alexa or Google Home devices, but according to the sources, OpenAI is betting that advanced AI features give it an edge in the market. Sources said the goal is to make the device feel like a physical manifestation of ChatGPT and somewhat alive rather than just an electronic device responding to commands. To that end, the device will have some components that move on their own, making it feel like an anthropomorphic mini robot rather than an inert piece of electronics. The Device will incorporate ChatGPT's memory feature to evolve its understanding of the user as well as a camera and other sensors to understand its surroundings. It will incorporate the two way voice model technology unveiled with the release of GPT Live last week and enabling more natural real time conversations. That natural style is apparently key, with OpenAI viewing the ability to connect with users in a human like manner as their core advantage. The device will have rechargeable batteries allowing users to carry it around the home rather than needing to keep it plugged into one location. OpenAI aims to unveil the device by the end of the year before releasing it in 2027 and reportedly it could be the first of several consumer devices from OpenAI with rumors of a pendant, earbuds and maybe even a smartphone. One question now casting its shadow over the launch is how it will be impacted by the Apple lawsuit Apple recently accused OpenAI of IP theft and will almost certainly file for an injunction to stop the release of a device based on any of their ip. And while the lawsuit discusses the brushed metal finish of an iPhone as one of their concerns, Apple is also reportedly working on a series of AI powered smart home devices that could muddy the waters quite a bit more. And to that end, OpenAI has elaborated slightly on their initial response to the lawsuit. In a new press statement, they said, while we take these allegations seriously, we're not aware of any evidence that this complaint has merit. We believe in fair competition and allowing people the freedom to work wherever they choose, and we're focused on building innovative technology that empowers people everywhere. Now, in terms of first takes, a fair number of people are pretty skeptical, negligible capital rights Call me crazy, but this OpenAI product sounds pretty stupid so long as you own a smartphone, except for the fact that it can constantly watch and listen to you in your home, Chris Paxton wrote. The new OpenAI consumer device is said to be a speaker with movable parts mixed on this, to be honest, but the big problem with smart speakers like Alexa was always that they were stupid. Prakash provides the bull case, saying want to know why they're doing this? AI is getting defined by context, the data it has, access to, the actions it can perform. You will have a personal AI like ChatGPT. You will have a corporate AI like Claude Tag. We what's missing is a family AI. What it will likely be able to do recognize every family member by face and voice, manage schedules for everyone, message you, call you on the phone, run a variety of errands, and be upgraded with capability over time. If they do it right, it will become as familiar and lovable as your family dog. Now I'm not sure that I am sold on this new device being fido, but I do think it is notoriously hard for people to predict how consumers are going to interact with new categories of products. I'm pretty skeptical of the pitch of Alexa, but better. But I'm also, broadly speaking, willing to suspend some amount of skepticism because it feels inevitable that there are going to be some new category of devices that arise based on this new type of intelligence. I think the bigger question for OpenAI specifically is just where this is going to fit with their priorities. Part of the reason that the company has gotten its mojo back recently is that it's spent the last few months abandoning what it has famously called sidequests to focus on building its coding and enterprise business. Now this is a company that's going public, it's gotta draw a differentiation somewhere. And even if the enterprises are valuable now, one of the unique things that OpenAI has that anthropic doesn't is the consumer base. So maybe they feel it's important to actually have an answer to how to monetize that user base, and devices like this are part of that answer. Then again, it is really hard to be a hardware company and a software company at the same time, and you gotta think that there are discussions internally about just how much they should be supporting both efforts. Moving now into the political realm, the Trump administration has introduced a new cybersecurity clearinghouse, which is the first major program to come from the recent AI Executive order. The initiative is called, and no, I'm not joking, Gold Eagle and will bring together government agencies, companies and open source projects to share information on cyber vulnerabilities. Gold Eagle was rolled out earlier this month as a joint effort between the Treasury Department, Department of Homeland Security and the Pentagon in consultation with AI companies. The concept stems from the mythoshock, which uncovered hundreds of vulnerabilities in critical software. This revelation kicked off Project glasswing, a multi industry sprint to detect bugs and deploy patches. That effort made it clear that better information sharing and coordination was going to be necessary in this new AI era, and Gold Eagle is meant to make glasswing style efforts a permanent government initiative. Now, a few other policies from the cybersecurity EO are still in the works. Reporting suggests work is underway to define a model vetting protocol to allow for government AI safety testing ahead of New Frontier releases. And it seems like a set of clear standards are being negotiated between the government and the AI industry, which has a very meaningful chance of being a huge upgrade to the ad hoc approach that we've seen with Fable and GPT 5. 6. There's also those rumblings we've been covering about a Chinese model ban, which some fear could extend to open source models in general. That said, during the press briefing for Gold Eagle, National Cyber Director Sean Cairncross addressed those rumors, commenting, I could not be more clear that we are in full support of the US Open source community. We will do everything we can to support the strength of that community. Finally today, one of the simmering conversations over the past month or so has been whether enterprises can trust AI companies with their data. This has always been a question for enterprises dealing with software providers around sensitive information, but it was brought to the foreground during the release of Fable, when Anthropic said that customers with zero data retention clauses would still have their data monitored for safety reasons. It's loomed larger in the conversation ever since Palantir CEO Alex Karp discussed the risks on cnbc, and the topic was also a major theme in a pair of recent essays from Microsoft CEO Satya Nadella, which could be summed up as saying, you can't trust AI companies with corporate data. But how real is the risk? Well, at least in one instance specifically when it comes to SpaceX AI, it appears that the risks were pretty real. On Monday, a security firm called Serilab published an audit that found that Grok Build was uploading entire code bases. Even if the coding task only required a few files, gigabytes of data were being uploaded. While most AI providers will upload necessary code snippets and store reasoning traces, this is an instance of Grok Build just dumping the entire repo up front. Security analyst Hari Malakal verified the results, finding that even in a session with zero tool calls, Grok Build still uploaded the entire code base. Hari commented, it ships a malware like background code collector now. Part of the reason that people are pissed is that this seems to have been happening regardless of user settings. Users can opt out of transmitting data to help improve the product, but that appears to have had no effect. As of Monday, SpaceX AI made changes to the code which stopped the uploads and added a new privacy setting as a manual override. And they claim that all API use now defaults to zero data retention. SpaceX AI developer Andrew Millich wrote, Zero data retention and privacy are always respected in Grok Build, and swapping your settings with privacy deletes any synced data retroactively. Elon Musk added that as a precautionary measure, all user data that was uploaded to SpaceX AI before now will be completely and utterly deleted. 0. Anything whatsoever will remain. And yet, honestly, people were not happy about this. Benjamin DeCracker writes, why did it happen in the first place? Haha. I guess we'll delete it now is not great. Accelerate. Harder wrote, there is no acceptable scenario where uploading code bases and secrets is an acceptable default. If that is indeed what happened, you must do a public incident review. My trust for XAI as a business partner is on the floor. Flower slop accused if OpenAI had done this, you'd have posted about it 20 times, called them the worst company on earth, and said no one should ever trust them again. But now that you've done it yourself, all you have to say is sorry. Don't be mad. I'll delete it, I promise. Ultimately, all of this highlights how much of AI Data Retention policy still hinges on trust. Many users of Grok Build likely had their full code bases scraped over recent weeks, and we have no real way to verify whether they were actually deleted as Elon claimed. The entire episode gives a lot more credence to Satya Nadella's point when he wrote, in the AI age, the buyer risks giving away knowledge just in order to use what they bought. It's the kind of knowledge a competitor could never buy, and the kind that leaks out almost imperceptibly. Even if you take this at face value as a mistake and give SpaceX AI the benefit of the doubt, it still puts a fine point on many of the issues that we've been talking about over the past few weeks. Still, that's going to do it for today's headlines. Next up, the main episode. One of the most important AI questions right now isn't who's using AI? It's who's using it? Well, KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions and found something surprising the highest impact users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate, and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more at kpmg.com us sophisticated that's kpmg.com us sophisticated I cover the capability gap between AI potential and AI reality every day on this show. Most companies are still figuring out how to start Robots and Pencils is already launching and scaling agentic and generative AI in production at large enterprises in weeks. AWS Advanced Tier Pattern Partner more than doubled in a year and they're hiring 50 open roles. If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look at Robots and Pencils. The best ideas win and the team is purposefully kept super high quality. This is the kind of place you look back on as the best decision you ever made. Take a look@rootsandpencils.com careers if you're looking to adopt an agentic SDLC, Blitzi is the key to unlocking unmatched engineering velocity. Blitzi's differentiation starts with infinite code context. Thousands of specialized agents ingest millions of lines of your code in a single pass, mapping every dependency with a complete contextual understanding of your code base. Enterprises leverage Blitzi at the beginning of every sprint to deliver over 80% of the work autonomously. Enterprise grade end to end tested code that leverages your existing services, components and standards. This isn't AI autocomplete. This is spec and test driven development at the speed of compute. Schedule a technical deep dive with our AI experts@blitzi.com that's blitzy.com this episode of the AI Daily Brief is brought to you by HyperAgent where 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 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@hyperagent.com AIDAILY Brief. Welcome back to the AI Daily Brief. Today we are doing something a little bit different that I'm very excited about. One of my longest held tips and tricks for non engineers who want to be ahead of the curve when it comes to where AI is going is is to pay attention to what with regards to AI actual software engineers are talking about right now since early 2023. Doing this gives you about a six month head start on everyone else when it comes to everything from the tools people are using to how they're thinking about the relationship between models and larger ecosystems, to their relationship fundamentally with work in a new AI mediated way. And one of the best sources for keeping up on what AI engineers are thinking about is of course the AI Engineering Summit and the AI Engineering World's Fair hosted by Sean Wang, AKA SWIX and a group of other collaborators. The World's Fair happens annually and the summits usually happen in the spring and the fall and are always just chock full of alpha. Earlier this month the annual AI Engineering World's Fair happened in San Francisco and unfortunately this time I was not able to be there. Luckily, Richard McManus who works with Latent Space, which is also a SWIX joint, wrote up a post about the five trends that defined the event. So what we're going to do today is look at those trends and try to understand what the implications might be for non engineers down the line. Now, this year's event actually happened on almost the three year anniversary of SWIX coining the term AI engineer. Back then, people were still referring to the intersection of AI and software development vaguely as prompt engineering, although prompt engineering would go on to be a non technical discipline as well. The point was it was not nearly as clear back then as it is today that AI writing software was going to be not only the key early professional use case for AI, but also the thing that would unlock everything else. Back on June 30 of 23, Swix wrote, Emerging capabilities are creating an emerging title. To wield them, we'll have to go beyond the prompt engineer and write software and AI that writes software. Importantly, and this was not consensus at the time, Swix wrote, I think it is a full time job. I think software engineering will spawn a new sub discipline specializing in applications of AI and wielding the emerging stack effectively, just as site reliability engineer, DevOps engineer, data engineer and Analytics Engineer emerged now. If anything, in retrospect, SWIX ended up underselling how significant it would become as effectively all engineers are AI engineers to greater or lesser extent, but directionally he was dead on. Now Richard writes that three years on, the field of AI engineering has changed dramatically and in these five trends we see quite a bit about the state of AI building. Now, I think taking a step back, the most fascinating through line across all of these trends is a recalibration of our relationship with autonomy. So keep that in mind as we go through these. The first trend that Richard calls out is that the quote focus shifts from agents to the systems around them. Now this is something we've obviously been talking about a lot on this show throughout the year. The idea that alongside agents coming to full production, we're realizing that agentic capacity is not just a question of model, and it's not even just a question of context that you feed the model. It's about the harness and broader system that the agent operates in that includes yes, access to context and data, but also access to skills, ability to interact with tools, and increasingly ability to route between different models based on the task at hand. Now Richard argues that one good way to compare how AI engineering has evolved is to compare two essays three years apart from Lillian Wang, formerly an OpenAI researcher and now co founder of Thinking Machines Lab. Back in 2023 she wrote a post called LLM Powered Autonomous Agents which described the anatomy of an LLM agent in terms of things like planning, memory and tool use. The examples she pointed to were things like Auto, GPT Baby AGI and GPT Engineer, which were, I remember for the first couple months of this show back in April and May And June of 2023, the topics that always received the most interest and the most downloads and the most views on YouTube. Richard points out that her new essay Harness Engineering for Self Improvement focuses not on the agent itself, but on that system surrounding the agent. The harness, he writes, that manages workflows, context, permissions, evaluations, persistent state and continuous improvement. In other words, he writes, AI engineering has moved beyond prompting models towards engineering reliable systems around them. Now this is the first place that we see that trend that I mentioned of the recalibration of our relationship with autonomy. Richard writes that at the event this year, agents were largely positioned as augmenting the AI engineer rather than replacing them. This was a big part of the substance of the Day 2 keynote with OpenAI's Romaine Hewitt, who said software ate the world and then AI ate software, but now what we're here to say is that the AI engineers are eating the world. He argued that the goal of tools like Codex is to make it easier for engineers to collaborate with agents. Now this dovetails especially with the early comparisons between Fable 5 and 5.6 Sol, which have tended to find Sol being the preferred model for when you have a big job that you want the AI to go off and do and only come back to you when it's done, versus 5.6Sol, which is a better collaborator. As an aside, it also seems like the launch of 5. 6 has done a lot when it comes to codecs adoption, given that just in the last couple of weeks it's jumped from 5 million to 7 million active users. Trend number two, according to Richard, will also probably not surprise regular listeners. He identified the trend as loop engineering is the new control layer. Indeed, Richard said that if there was a single word that was the buzzword du jour of the event, it was loops. OpenClock creator Peter Steinberger prominently shared a slide that said the future isn't 20 terminals. It's better loops. But interestingly, especially with the lens of trying to understand what AI engineers are talking about that might be relevant for other types of knowledge workers. Richard writes that the loop discourse is actually separating into two loops that interact an inner loop and an outer loop. The inner loop is the largely autonomous work being done by agents, and the outer loop is the job of leading engineers to oversee that work. The improve it with things like better skills. Roland Gavrilescu, the co founder and CEO of Introspection, said, you can think of the system having an inner loop and the outer loop. The inner loop is the primary system interacting with users and performing the work. Roland then introduces the concept of auto research, which is concerned with the outer loop and is another system that studies and maintains the primary system. The outer loop can include feedback signals, evals and human input. So even if it is still largely autonomous, it's a method of oversight for the primary agent loop. Former Google engineer Addy Osmani said agents can run much more of the inner execution loop, but that outer loop is still engineering. Indeed, Richard writes that the term loop engineering came up multiple times, suggesting in his estimation that it is the human AI engineer's responsibility to build these loop systems. Summing up again, Peter Steinberger said, the agent runs the inner execution loop. I set the direction and make decisions in the outer loop. Now again, taking a step back, what the loop discourse is actually about is more than anything else is about figuring out repeatable interactions that allow us to get the most from agents who are now doing an increasingly large portion of our work. In some ways this separation of inner loop and outer loop is part of this reclamation where the AI engineers are asserting their agency relative to their agents. And I think what's important because it can sound so buzzwordy is a lowercase l, not an uppercase, by which I mean loops is a word for that doesn't necessarily mean exactly one thing, but stands in for the emerging set of interaction patterns that allow agents to improve their own work over time and that allow us as humans leading the agents to improve their ability to work over time. Third trend from the AI Engineering World's Fair was AI engineering enters the enterprise. Now this was in some ways the forward deployed engineering track. And given that pretty much every AI company has now determined that to get their products to actually work inside their intended customers, they have to support their customers with a much more hands on type type of implementation force. It's probably not all that surprising to see an FTE track show up at the event now one of the interesting questions that seems to come up is what the end result of an FTE type of engagement is supposed to actually look like. Cursor's Pauline Brunet said, when we walk away at the end of the engagements in our case we have deployed cloud agents, long running agents, automations, and we've built applications on top of our Cursor SDK and indeed a more general term for this set of enterprise infrastructure that apparently was being used a lot at the event was Software Factory. And yet that, if anything, is perhaps even less defined than something like loops. Zach Lloyd is the CEO of Warp and was a speaker at the event and the company views itself as a software factory platform. In an interview with Latent Space CEO Zach Lloyd explained where he first came across the term software factory. Zach said, we started with one off automation Run an agent in the cloud. A lot of platforms began there. Then it became run an agent in the cloud on a timer. The next question was what was the most valuable loop to automate? The answer is basically the main loop of software engineering triage, specification, implementation, review, verification, shipping and monitoring. Now, when it comes to the relationship between an enterprise and a software factory, Zach argued that one of the big pieces of work for enterprises is figuring out which parts of their work lifecycles need to be automated and where humans should be brought into the loop. And interestingly, in a follow up blog post on X, Zach argued that the reason the factory framework has become more important is not only that agents are now more capable of doing big chunks of the coding lifecycle, but that also quote that extensive interactive agent use has created a number of problems, from cost controls to governance to security. The root problem, Zach says, is that interactive agents have human operators and those humans all use them in very different ways, which don't always maximize business value and can create risk. An example he gives is a human always using the most expensive model, even when they don't need to for a given task. The idea behind a factory approach, he says, is to set up a system that minimizes human variability and maximizes output with controls that ensure security and compliance. Now this is very much a software conversation, but I think that a lot of the problems that these software factories are trying to solve for are going to find themselves repeated as agents find their way into other areas of knowledge work. That example of always using the most expensive model is certainly not limited to software development or his other example, a human inadvertently creating a security issue by installing MCPs that have too much access. As everyone gets familiar with managing context to support the agents that they're using, whether it's in software engineering or product development or marketing or sales, this is going to be an issue for them as well. The fourth trend at the event was coding agents replacing IDEs as the developer interface, and I think that this is one that's already indicative of a larger interaction pattern trend that we're seeing shift. An episode from a couple of weeks ago was all about the new Claude tag, with each of those instances of CLAUDE not being connected to the individual user who tagged it in, but instead connected to a specific set of permissions in context within the channel that they were engaging. In other words, there might be a single Claude tag instance with a specific set of context and a specific set of tool access and a specific set of permissions that everyone in your organization's marketing channel is interacting with. That's different than the permissions and tools and context of the CLAUDE that the folks over in the sales chat are interacting with. One of the things that was super notable about the Claude tag announcement was that Boris, one of the creators of Claude code, said that something like 65 I think percent of their new code was now being initiated in Claude tag chats. A major, major shift in the interface and interaction pattern with which we manage this intelligence. Now, one of the things that's interesting about these types of interaction patterns is that this is one area where the big labs are not waiting for non engineers to adapt to engineer type practices. They are dragging functionality over from the engineering first experiences into the experiences that they're offering for all consumers. The best example of this is probably ChatGPT work, which effectively takes Codex and plops it into the main ChatGPT app for everyone to use. The fifth trend, according to Richard, was every agent platform building around skills. Richard referenced Adi Osmani's definition. Writing skills encode the workflows, quality gates and best practices that senior engineers use when building software. They are packaged so AI agents follow them consistently across every phase of development at the event. Vercel's Andrew Q said that skills were useful as portable on demand knowledge and introspection. Co founder Roland Gavrilescu argued that AI engineering had shifted from agent tools to agent skills. Google DeepMind's Philip Schmidt argued that skills reduced the need for orchestration code, allowing developers to use agents without code. One speaker, Paul Bacchus, even discussed his project around agent skills called Impeccable, an open source design skill system for improving the interfaces that coding agents create now more interesting even than that project, Paul argued that skill engineering is going to be a discipline in its own right. And as we wonder which of the conversations happening with AI engineers right now are going to be happening with AI knowledge workers in the future, skills, I think, are incredibly high on the list. Think about this. It is highly likely that if you are very good at your job, you frequently run across things where even the best models, including Fable 5 and GPT 5.6 Soul, don't do as well as you want them to. Now, that's not everything some things are going to be great at, but especially when you're really, really good at something, you tend to see the inherent limitations of AI even faster. The folks who are advocating skill engineering would argue that rather than just waiting for the models to improve with Babel 6 or what have you, starting to figure out how to use skills, how to encode knowledge and rules, and, dare I say it, taste in skills is potentially a more productive path. In his closing keynote, Y Combinator president Gary Tan argued that using skills effectively, including in business functions including sales, support and finance, was fundamentally integral to being an actual AI native organization. And yet skills themselves can be their own sort of autonomy trap. AI Engineer World's Fair attendee Tyler Brown wrote that one of his lessons from the event was to revisit and re implement your skills each time there's a new model release, he wrote. It's as if you have a kid that grows from middle school to high school. You have to change the curriculum for them to get the benefits of the new model. Yet it was not that, but Tyler's broader observation that I think runs throughout the trends that Richard identified, and also a lot of the chatter that I've seen around the event. Tyler wrote something about this year's AI Engineer World's Fair just hit different Last year was the year of let the agents rip. This year was the year of realizing that autonomy without structure creates as much slop as leverage. In short, while it's not universal, broadly speaking, one of the standout sentiments from the AI engineering folks is the reclamation or re emergence, or however you want to put it, of the human at the center of these agentic systems. Back in March I tweeted, call me crazy, but I think the companies that give everyone on their team a team of agents are going to kick the slats out of the companies that replace their teams with a team of agents. And I think, and I hope that we're starting to see that sentiment move structurally into the mainstream. So guys, those are the five trends that defined AI engineering at the AI Engineer World's Fair this month and their implications for other people who aren't just AI engineers. Big thanks to Richard McManus for capturing all of this and of course SWIX and the team for putting on the event. If you want to follow my advice and stay closer to the pulse of what AI engineers and developers are talking about, the single best source for that is going to be latent space. Latent space. And might I particularly recommend their AI News Email Weekday roundups. They can be really dense, but they aggregate a ton of discourse and are very much worth your time. For now, however, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching as always and until next time, peace.