The AI Daily Brief: Artificial Intelligence News and Analysis - Ambient Agents and 6 Other Big Ideas Coming Out of AI

Starting point is 00:00:00 Today on the AI Daily Brief, seven big ideas coming out of AI. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, back with another long reads episode. But before we get into that, a couple of quick announcements. First of all, thank you to today's sponsors, KPMG, Blitzy, Vanta, and Superintelligent. To get an ad-free version of the show, go to patreon.com. And for sponsorship opportunities, email me directly at nLW at breakdown.network. But with that, let's dive into these ideas, starting with Amtriol.

Starting point is 00:00:36 be an agents. As usual for the weekend, we are doing a big think episode. And this week, instead of focusing on just one idea or one concept, I was noticing that there are a bunch of really interesting themes that I've seen starting to emerge more and more, that at some point we'll probably all get their own episodes of the AI Daily Brief if they haven't already. So what we're going to do today is go through seven big ideas coming out of AI, talk about what they mean for AI, but also for the world at large. And we're kicking off with a concept that is certainly not new, but is coming to the fore right now, which is background or ambient agents. Now, why I think this is interesting is that it's a reframe and a reimagining of the way that agents will interact with how we do work.

Starting point is 00:01:18 It is a cleaner break from the co-pilot or assistant model than some of the agents that people use and deploy on their behalf today. What's more, based on some new features from Cursor over the last month, it's something that's more in production now than it ever has been. This is a concept that people have been talking about for a while now. Langchains Harrison Chase back in August of last year, 2024, wrote, one of the UX patterns I'm most interested in is the idea of ambient agents working in the background for us. This can be super powerful but needs some UX tricks to get right. What are some considerations? Human in the loop? We mostly talk about human in the loop, but that doesn't work for ambient agents. Still, having observability and

Starting point is 00:01:55 potentially control over what the agent does is important. So rather than have human in the loop, what if they were on the loop, you can observe what is happening but after the fact. Next UX pattern is asking for help. Oftentimes, agents can't be fully autonomous. They may need input from a human on a particular point. For example, I have an email agent which often needs input from me on how to fix this bug, whether I want to schedule this meeting, etc. This means that agents need a way to reach out to humans and ask for help. What might this look like? I think something like a customer support dashboard is a reasonable U.X. So this was back before the reasoning models had even launched. Then in January of this year, Langchane took it a step farther. They wrote,

Starting point is 00:02:30 Today we're releasing an open source email assistant agent that we've been using internally for the past six months. This is the first agent in a new UX paradigm we're calling ambient agents. They continue, while the dominant UX for LLM application so far as chat, we imagine this changing. Ambient agents are agents that are always on, listening to an event feed and responding to or flagging those events as necessary. This will let us scale ourselves far more than chat. So you're seeing here that this is not just about a different type of agent, it's about a different type of interaction with agents. In June, Lance Martin from Langchain dropped a free course on GitHub about building ambient agents. Again, he writes, this is one of the most interesting Agent Ux patterns allowing the agent to do work in the background and interact with the user via Human in the Loop for Select Actions and Approvals.

Starting point is 00:03:14 In this clip, Langchains Harrison Chase explains why this could be so significant for professionals. There's a few reasons why we're excited about ambient agents. The ambient agents would be defined as agents that are triggered by events run in the background, but they're not completely aton. this. And so I want to break down those things. So if they're triggered by advice, that means that I can scale myself as a professional a lot more. Because I'm no longer kind of like kicking off an agent, but there's not a one-to-one kind of like interaction. It's now, it's now triggered by a bunch. So there could be thousands, hundreds, thousands, millions, whatever, but even running in the background at the same time. So again, the idea here is that these are agents that don't just make us

Starting point is 00:03:53 more productive, but actually fundamentally change and scale the amount of work we can do. Sean Wang, aka Swix from Leighton Space and the AI Engineering Summit, writes, Ambient agents are going to completely dominate the rest of 2025. He points out that, one, human deep work and focus requires at least one to two hours on interrupted, and two, by end of year, all next-gen models will pass the one to two-hour autonomy barrier. Basically, they will be used in completely different ways than the current one to 15-minute autonomy frontier. So what Sean is arguing here isn't just that this is a hot trend that people are watching, but that from a capabilities perspective, we are right on the cusp of agents being able to do

Starting point is 00:04:29 more extended and more complex work in a way where we can let them off and do their thing. You'll see that SWIX is a mainstay of this list, and so if you are looking for new follows on Twitter slash X and you're somehow not already following SWIX, highly recommend it. Fizi Jessica Liao points out the obvious but important fact. The greatest ROI for AI comes when AI is working in the background, handling a workflow rather than requiring excessive prompting. And I think there's a strong chance that once we are fully in the ambient agent paradigm, it's going to make the primarily prompting paradigm feel like the Stone Age.

Starting point is 00:05:01 Google DeepMinds Philip Schmidt writes, chat agents are successful but limited by their request response human initiated nature. The next iteration of agents will operate proactively in the background or in ambient. Ambient or background agents will not wait for direct human command, instead will operate proactively with trigger initiated by events. Now, Philip points out that one of the key updates will also be expanded long-term memory, as that's going to be required for ambient agents to accomplish their goals. Now, as I mentioned, one of the reasons that this is interesting now

Starting point is 00:05:27 is that in a recent update from Cursor at the end of June, they basically launched background agents that you could initiate from your phone. We want Cursor to be the best place to write code with an AI agent. And currently, that means working with the agent side by side in your editor or having its start tasks in the background for you. And today, we're bringing Cursor agents to the web. Just type in a task and an agent will spin up and get to work for you. making changes to your code base, answering contextual questions, and opening up PRs on your behalf.

Starting point is 00:05:55 When it's done, you can carry on the agent's work directly in cursor or add follow-up instructions with additional context and even make inline edits. And if the agent's work looks complete, you can create and merge a pull request directly from the app. So what does this look like in practice? Entrepreneur and podcaster Ryan Carson writes, I've been testing the brand new cursor mobile background agent for almost a week and I've been shipping a lot more code. I want to do a quick review of the new cursor background agent on mobile. So here I have it installed right here on my home screen. It's fired up.

Starting point is 00:06:30 And this is beautiful. So from the couch, from bed, wherever you are, you can go ahead and ask cursor do stuff like this. So I previously said fix a critical bug and then fired that off. And you can choose your model here if you want. So CloudForce on it. is my go-to and Macs. You can also upload a photo if you'd like, and then you fire that off. And this is what you get once an agent has been working. So you can see it found a bug related

Starting point is 00:07:03 to the middleware. So what's important is not exactly what it's doing, but the fact that this is initiated from a mobile app all happening in the background. It's a new mode of interaction. And another important thing is that this is not just coding. With Chatchibt's recently released agent, we're starting to get this experience as well. One of the things that OpenAI did in their announcement was basically say this is something that's meant to run in the background while you go to other things. Yes, you can watch the chain of thought and see how it's interacting with the computer and even intercept it and take over its virtual machine if it's helpful, but mostly the idea is that you tell it to do something and then it does it. Boxes Aaron Levy

Starting point is 00:07:37 writes, here's the new chat GPT agent combing through market and data strategy stored in box to produce a PowerPoint presentation. The ability for agents to connect to data from anywhere, use a computer, and automate work in the background is going to be wild. This is a obviously a paradigm we are going to be talking about a lot more, but it's definitely hit an inflection point recently, where it's worth considering on its own terms and starting to think about what it's going to mean for your own work to spin up and deploy ambient agents that run in the background. Today's episode is brought to you by KPMG. In today's fiercely competitive market, unlocking AI's potential could help give you a competitive edge, foster growth, and drive new value.

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Starting point is 00:10:39 That's VANTA.com slash NLW for $1,000 off. Next up in our big ideas is one that we actually have done a full primer show on context engineering. For a bit of a definition, we once again turn to Langchains Harrison Chase, who writes, Context engineering has been an increasingly popular term used to describe a lot of the system building that AI engineers do. But what is it exactly? The definition I like, context engineering is building dynamic systems to provide the right information and tools in the right format, such that the LLM can plausibly accomplish the task. Now, as he points out, this is not a new concept per se, but it is changing the way that we think

Starting point is 00:11:15 about what our job is vis-a-vis LLMs. Whereas before many of us were just focused on how do we prompt it in the right way, now we're increasingly thinking about how do we give it access to the right data and the right information or the right tool set to go do what we need. Shopify's Toby Lookke, he writes, I really like the term context engineering. It describes the core skill better, the art of providing all the context for the task to be plausibly solvable by the LLM. the things that I pointed out in my primer episode is that context engineering is itself a banner term,

Starting point is 00:11:45 which includes both a dimension for non-technical people, for whom it's useful to think about context engineering, even in how they prompt new set of general consumer agents like chat GPT agent, but it's also increasingly an AI engineering discipline. There's this excellent public repo on GitHub called awesome context engineering that has tons and tons of information with background, implementation and challenges, components, techniques, and architectures, and a ton of additional materials. Also, the more that people dig into this, the more interesting challenges and opportunities they're discovering. For example, in this presentation from a recent context engineering meetup, they discuss context poisoning, such as Gemini playing Pokemon, hallucinated an item and then

Starting point is 00:12:24 tried to reuse it, context distraction, Gemini favored repeated actions over new plans, context confusion, example models perform worse with more tools, especially if the tools are similar, and context clash, models perform worse if back-to-back tool calls contradict each other. Now, going in deep on all of these is beyond the scope of this show, but the point is now that we are thinking about context engineering as a thing, in quotes, it's helping us better plan out and understand how to do it well and how to avoid common traps. Hey, Nita 101 sums it up. Prompt engineering is for hobby projects, context engineering is for production.

Starting point is 00:12:57 Prompts are what you write in a chat box. Context engineering is what powers real AI systems. In serious LLM apps, it's not just about giving an instruction. You have to build the entire world around the model. Task setup, examples, documents, history, tool outputs, state, memory, compression, multimodal context, too little and it underperforms. Too much and it breaks, slows or costs too much. Getting this just right is not a prompt.

Starting point is 00:13:18 It's a system design challenge. Next big theme, which relates to ambient agents and just in general increase agentic capacity, is tiny team's big salaries or big cash compensation for startups. Now, it is basically gospel in Silicon Valley that when you are, building a startup, you want to find missionaries, not mercenaries, and that means people who want the upside of your equity, not high cash comp. Right, you're never going to compete with other bigger, more mature companies on cash. You have to give them bigger chunks of equity because they're all in on the dream. However, that truth is increasingly being questioned.

Starting point is 00:13:52 Controversial founder Roy Lee from Cluelly writes a startup truth I disagree with, don't pay high cash comp. We now pay 250K to 350K base for designers and 300k to 1 million base for software engineers. In a thread explaining why, first, Roy takes on the concern that this doesn't scale. You can't pay 100 people 600K a year. But Roy says, you don't need 100 people anymore. Companies grow faster than ever with less people than ever. You can hit 10 million ARR with less than 20 people in one year if you hire right and prioritize well. Speed over scale today. Consideration two, they should want to work here for our mission. Be for real, bro. You're a random B2B SaaS with zero traction and your seven pivots deep into your mission. I don't even believe early Airbnb employees were loyal to the mission,

Starting point is 00:14:33 but they might have been loyal to the culture. Roy continues, risk reward has to make sense if you want 110% performance. Founders will earn 10 to 100x what founding engineers do if the company succeeds. You need to de-risk early employees. The worst cope is if you pay too much, people will be lazy because they need time to go spend the money. The best companies are built by the best people. He concludes, there are very few real winners in startups and winning companies

Starting point is 00:14:55 are always a direct function of how strong the early team is. If you want to win, you just have to be the best at everything, including comp. A ton of people jumped in to basically say, yeah, I agree with this. Swix pointed out that this is a part of the tiny team's playbook. Others pointed out that there are fundamental problems with the way that stock compensation works at startups. And as if to demonstrate this point, we recently had the whole hullabaloo around windsurf, where it seemed like a huge portion of the people who had helped build a company, weren't going to get anything in the aqua hire by Google.

Starting point is 00:15:25 Now, it would be one thing if that was just a windsurf issue, but we've seen this over and over again. There is a new pattern emerging where big tech firms and acquirers are willing to pay an extraordinary premium for top founders and a very small handful of researchers and engineers, but then basically leave everyone else behind. What people have pointed out is that the problem with this isn't just the single one-off time that people get screwed. It's that it's going to fundamentally change how people think about working at startups in general. Legendary VC Vinod Kostla writes, Winsurf and others are really bad examples of founders leaving their teams behind and not even sharing the proceeds with their team. I definitely would not work with their founders

Starting point is 00:15:59 next time. The problem is that it's not about those founders, it's about every other founder in the future, where people get more and more skeptical. And so the point is, when you're thinking about this big idea, it's clearly not just being driven by AI, although the patterns of acquisition around AI companies are exacerbating it. There's something bigger going on here. The smaller and more concentrated teams can be to achieve huge results, the more that we'll think differently about how they get compensated in the beginning. Next big idea is pay per crawl. Now, this comes directly from Cloudflare. At the beginning of July, they wrote, publishers, we see you. Cloudflare just launched paper crawl to put control over your content back where it belongs. Now, crawling is more

Starting point is 00:16:36 transparent and controlled by default, creating a better web ecosystem for creators like you. This is about real content independence. And the idea here is very simple. The new program allows content owners, i.e. the people who own websites, to charge AI crawlers for access. In their announcement post, they wrote, many publishers, content creators, and website owners currently feel like they have a binary choice. Either leave the front door wide open for AI to consume everything they create or create their own walled garden. But what if there was another way? After hundreds of conversations, we heard a consistent desire for a third path. They'd like to allow AI crawlers to access their content, but they'd like to get compensated. And thus was born in the paper

Starting point is 00:17:11 crawl system. Greg Eisenberg wrote, Cloudflare just broke the internet's business model. They launched paper crawl websites that can now charge AI crawlers for scraping content. Instead of block all bots or let them steal everything, there's option three, pay me. Why this is a big deal? Every SaaS has valuable data rotting in help docs, case studies, feature pages. AI companies have been training on this for free while building your competitors. Now you can charge them 10 cents per page. Your dental SaaS help docs? Well, that's years of practice wisdom.

Starting point is 00:17:37 Charge crawlers for access and make 3K a month from companies building dentist AI. Tons of opportunities for founders building businesses. Now, Greg is viewing the very optimistic side of this. He writes, what valuable data do you have sitting around? Internal company docs, customer conversations, industry insights, process knowledge. All of it can now generate revenue from AI companies who desperately need training. training data. The winners will be content businesses that move fast. Imagine owning a cooking blog with 10 years of recipes. That's training data gold for AI food companies. Early adopters will

Starting point is 00:18:04 set market rates before competition drives price down. SEO consultant Bill Hartzer said, not so fast. He writes, Cloudflare is rolling out paper crawl, a new feature that allows websites to charge for crawlers like search engine and AIs to index their content. At first glance, it sounds like a win for publishers. But for the overwhelming majority of websites, it's a traffic trap disguised as a revenue stream. Cloudflare powers about 20% of the web. So any new feature they introduce carries weight. But realistically, this one will only matter to the top 1,000 or so sites in the majestic million, those elite destinations with thousands of referring subnets.

Starting point is 00:18:35 If you're not in that top 0.1%, paper crawl probably won't make you a dime. In fact, it could do real damage to your site's visibility. Now, again, beyond the scope of this show to go into a full debate, but this big idea is, I think emblematic of an even bigger change, which is that the fundamental agreements and social contracts of the web, based around the search paradigm, are totally up for grabs right now. now. What the future of generative engine optimization or paper crawl actually looks like, I don't think anybody really knows. All we know for sure is that the web of tomorrow will not be exactly like the web of today. Now, for the next idea, we're turning once again back to Greg Eisenberg

Starting point is 00:19:10 to discuss the transition from user experience to agent experience, or UX to AX. The idea here is pretty simple. We've designed software so far for human users. Increasingly, we're going to have to design it for AI agent users. Greg writes, there's a quiet shift happening in how we design software. We're moving from UX to AX. Traditional UX is screen-centric. You tab a button, product reacts, job done. Every session starts from zero. Designers pre-plan every path with hard-coded flows. Users fill out forms and drop-downs because the product remembers nothing about you. Success equals fewer clicks and faster flows. Trust equals interface looks clean so it must work. Agentic experience is relationship-centric. The agent keeps track of ongoing goals, nudges next steps, improves over time.

Starting point is 00:19:53 You're never starting over. The system plans its own path. It senses, infers, chooses, chooses actions the designer didn't script. Context is learned, not asked. Preference, patterns, even team norms are remembered. Success equals earned trust and compounding value. Metrics shift to retention, satisfaction with decisions how much autonomy you hand over. Greg argues most apps will eventually work this way. Your email client will learn your writing style and priorities. Your design tool will remember your brand guidelines and suggest layouts. Your CRM will track relationship patterns and recommend next moves. Now, Greg is talking about a specific version of this, but the way that people are thinking about this is much broader. A great example is e-commerce. At the most recent Amazon Prime

Starting point is 00:20:30 Day, Gen AI traffic was up 3,300 percent. And while that was still a tiny fraction of overall traffic, it shows just how much growth there is in new ways that people actually get to their shopping experiences. Pretty soon as agents come online, it's not just going to be that people are going to e-commerce sites from different places like chatbots, but instead that agents are actually doing the shopping for them. Those will require totally new interactive paradigms that are going to need to be designed. Two more quick ones before we get out of here. The first is the financialization of compute. This one comes out of the White House AI Action Plan once again highlighted by SWIX. Sean writes, buried in the AI Action Plan is an endorsement that the U.S. compute market will financialize with

Starting point is 00:21:09 spot and forward contracts. One of the most consistent themes with the latent space podcast GPU infrastructure in neocloud market coverage is that the status quo of three-year lock-in long-term contracts with hyperscalers is causing unsustainable market volatility and inefficiency, not just in GPU prices and the rise and fall of startup fortunes, but also inefficiency in ideas and resources for open AI and research. Now the U.S. government is fully behind this movement, and most importantly, they demonstrate that they get it. Now, for a little bit more background, in April, late in space did a deep dive into this, featuring Evan Conrad of SF Compute. They write, it should not be normal for the prices of one of the world's most important

Starting point is 00:21:44 resources right now to swing from $8 to $1 per hour based on drastically inelastic demand and supply curves, from three-year lock-in contracts to stupendously competitive over-ordering dynamics for invidia allocations, especially with increasing baseline compute needed for even the simplest academic ML research and for new AI startups getting off the ground. The ultimate end state of where all this is going is GPUs that trade like other perishable staple commodities of the world. Oil, soybeans, milk. Because the contracts and markets are so well established, the price swings are also not nearly as drastic, and people can also start hedging and managing the risk of one of the biggest cost of their business, just like we have risk-managed commodities risks of all other sorts for

Starting point is 00:22:21 centuries. So basically the idea here is that the financialization of commodities allows for better price discovery and more efficient markets, and that needs to come to compute as well. So what did they actually say in the AI Action Plan? Under the section encouraging open source and open weight AI, one of the recommended policy actions was this. Ensure access to large-scale computing power for startups and academics by improving the financial market for for compute. Currently, a company seeking to use large-scale compute must often sign long-term contracts with hyperscalers, far beyond the budgetary reach of most academics in many startups. America has solved this problem before with other goods through financial markets,

Starting point is 00:22:55 such as spot and forward markets for commodities. Through collaboration with research, the federal government can accelerate the maturation of a healthy financial market for compute. What the government's role in this is a whole different conversation, but this is, I guarantee, one of those things that is going to seem so unbelievably obvious in retrospect and incredibly inefficient that it doesn't exist right now. Last big idea, and this is one that I know for sure is going to turn into a full podcast one of these days very soon, is using AI coding tools for non-coding use cases. Once again, Swix leading the charge on this one. On July 7th, he tweeted, by far the most interesting new pattern we're seeing is that people are using Claude Codod

Starting point is 00:23:32 and Klein for non-coding tasks, and this is becoming surprisingly effective for things like sales and business intelligence automation and G Suite. I then noticed that Tariq Anthropic wrote, When I first joined Anthropic, I was surprised to learn that lots of the team use Claude Code as a general agent, not just for code. I've since become a convert. I use Claude Code to help me with almost all the work I do now. He explains a little bit. And for those of you who are trying to wrap your head around this, he said in ClaudeCode, everything is a file, and it knows how to use your computer like you do. Name your files well and Claudecode will be able to search them like you would. This lets you make custom setups for memory, to do's, journal, screenshots, and more. Now recently, this conversation has expanded dramatically.

Starting point is 00:24:07 Peter Yang got almost a half million views and about two and a half thousand bookmarks from a simple question two days ago, does anyone use code for non-coding use cases? If so, what do you use it for? And the short answer is, yep. Greg Kesewrelli writes, so many things. Meta-prompting to create world environments with V-O-3 video creation, writing a ton blogs, posts, etc., designing animations, prompting to generate prompts for images. Darren writes, full-on personal assistant, build a little cognitive loop for me to brain dump in the

Starting point is 00:24:35 morning and it organizes tasks across markdown canband boards by project. Gives back priorities focus for the day according to values and urgency. ADHD miracle. Alex Albert from Othropic is also interested in this. Tweeting a day later, I'm making a list of all the non-coding things people are doing with Claude Code. That one has around a quarter million views and one and a half thousand bookmarks. Like I said, this is very clearly an emergent theme that is deserving of a full exploration. For now, I'll wrap it there on that intriguing teaser, and we will come back to it in the future. Let me know which of these ideas you think is most significant.

Starting point is 00:25:08 Is it ambient agents, context engineering, tiny teams, big salaries, paper crawl, agent experience, the financialization of compute or coding tools for non-coding use cases. Whatever the case, I hope that this has been a fun weekend episode. Appreciate you listening as always. And until next time, peace.

The AI Daily Brief: Artificial Intelligence News and Analysis - Ambient Agents and 6 Other Big Ideas Coming Out of AI

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