The Ultimate AI Catch-Up Guide

0:00If you have been feeling behind on AI, today's episode is for you. This is the ultimate AI catch-up guide. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.

0:17All right, friends, quick announcements before we dive in. Today's episode is brought to you by KPMG. Robots and pencils, blitzy and super intelligent. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe to Apple Podcasts. Ad free starts at just$3 a month. And if you are interested in sponsoring the show, send us a note at sponsors at aidailybrief.ai. Now today we are doing something that I have wanted to do for a little while now. The average listener of this show is a fairly advanced AI user. For example, in our February AI usage pulse survey, 97 % of the respondents were using AI every day, and more than 60 % of them were using advanced agentic or automation use cases.

0:59And this year, to support that audience, part of what I wanted to do is a lot more resources of all types. So we've had a couple of different free self-directed training programs. The AIDB New Year's program was a 10-project-based program that was meant to help people up their skills for the new year. And then, of course, we launched Claw Camp, which was a way to learn how to use OpenClaw and other agentic systems to build agent teams. But what that's left out is resources that are really focused on the actual beginner. And what's clear to me is that 2026 so far has been quite a realization moment for a lot of folks.

1:33In a four-week span alone between February and March, this show grew 50 % in terms of listeners and downloads. And as much as I'd love to attribute that to our wonderful content, what I actually think it reflects is the byproduct of all of this discourse in mainstream media and major news outlets about how significant AI's impact on the world is already becoming. And so with that in mind, for today's episode, we are doing the Ultimate AI Catch-Up Guide. This might not be the most useful for our average listener, but when you're thinking about the show that you want to send to your friends or your loved ones or your neighbors or whoever who is asking you how can they get up to speed on AI, this is the episode that's designed for them.

2:12And if you are that person, I could not be more excited for you to be here, and hopefully you feel after this episode that you have your head much more wrapped around this than you did before. So let's kick off with some fundamentals. When we talk about AI, what are we referring to? In short, in terms of how you'll experience it, AI is software that takes inputs and creates things. It can do research, it can write documents, it can fill in and interact with spreadsheets, it can create pictures, it can create movies. Sometimes we use it like an assistant, where we tell it precisely what we want and it does that thing for us.

2:45Think drafting an email or a memo or an essay, or doing some research. Sometimes we treat it more like an employee, where we give it a goal we have, and it figures out how to go and do that. This is what people are talking about when they say the word agents. The big difference between using AI as an assistant and interacting with an agent is that with agents, you're kind of letting the AI figure out how to accomplish whatever goal you're giving it. A key term that you're going to hear a lot is model, which is short for large language model. It's not a perfect analogy, but you can kind of think about it as the version of the software that you choose.

3:18Models are trained on a combination of external data, basically corpuses of human creation, writing, images, etc., with a big dose of human feedback as an addition. Different models have different approaches to training, different approaches to that human feedback process, different amounts of data they're trained on, different types of data they're trained on. And because of that, different models have different strengths and weaknesses. One of the biggest mistakes that stops people from getting a lot out of AI, especially at the beginning is that they accidentally use a model that's ill-suited to their task because it's the default model in a free version of a chatbot tool like ChatGPT.

3:53Because models cost a lot to serve and are pretty data-intensive, the average company like Anthropic who makes Claude or OpenAI who makes ChatGPT is not going to be to put their best models front and center. A lot of the default free-tier models are a step behind the state-of-the-art. This mistake of using the wrong model then, especially for beginners, is not your fault. It's not even really the model company's fault exactly, it's just a UX problem. The fix, which we see with power users, is to use different models for different jobs. Going back once again to our monthly AI usage pulse surveys that we do here at AIDB, the users who respond to those surveys use on average about three and a half different models.

4:33They might use one model for their Excel tasks and a different model for their writing tasks and a different model yet again for their image generation tasks. Now that we have some of that terminology out of the way, let's talk about some of the common impressions that people have of AI and things that you might have heard about AI. Now, one note here is for the sake of this show, I'm not going to focus on things like societal impact, energy consumption, policy debates. Today, we're focused on practical impact. I want this to help people who want to get up to speed and actually start using these tools do that a little bit better.

5:07So those are the common impressions that I'm going to focus on. The first common but wrong impression is something like, well, I heard AI actually isn't all that good. This is a pretty common reason people cite for not trying AI. And it's usually a byproduct of either A, that being a weird strand of criticism from people who don't like AI that tends to have outsized mindshare and media share, or even more prominently, it's just the byproduct of a stale experience. For example, if someone tried a model a year ago, and maybe because of the problem we discussed just a minute ago, it wasn't even the best model then, and it didn't do a great job of whatever their task was, maybe they then wrote off the entire space.

5:46Another version of this you might hear is around some specific type of output like AI photos that have six fingers. The reality is that AI is really good at a lot of things right now. A meaningful portion of the tasks that comprise the day-to-day of pretty much any knowledge worker at this point are things that AI can do quite well or be, frankly, exceedingly helpful for. And even if you can find something where capabilities aren't up to stuff for what you need, right now, capabilities are doubling roughly every four months, meaning that even if it doesn't do great on your task at the moment, it probably will be for too long.

6:23Next common misconception, isn't it really easy to tell that AI content is AI content. Isn't it just all slop? Slop is, of course, the AI critic's favorite word. In fact, I think it was Merriam-Webster's word of the year last year. I think you can tell a lot about the state of the AI discourse that the word of the year last year was slop rather than something like vibe coding, which was the actual transformative capability that might have, through its impact on markets or something else, led you to be here today. In any case, what is absolutely true is that AI allows for the creation of a huge amount of content, of all types, writing, analysis, images, etc.

7:00And not all of that content is going to be good. In fact, it is absolutely true that in many advanced AI using organizations, a new challenge that they are experiencing is people cranking out so much content with AI that it's hard for them to sift through what is actually good. When people outsource their thinking and judgment to AI, it can absolutely be problematic. But the idea that all AI content is just slop, that all AI writing is going to fall into common AI writing traps, that all AI images just look like AI images. These things just aren't true anymore. Evidence of this comes from a recent New York Times study where they allowed people on the internet to effectively take a test where they read two different passages on the same topic and chose the one they liked more.

7:44More than 50 % of the time, AI actually beat human writing. Yeah, but doesn't AI hallucinate a lot? This is another misconception which I think very reasonably, if you thought this was the case, might lead you to stay away. Between 2021 and 2025, state-of-the-art models went from 21.8 % hallucination to just about 0.7 % hallucination, a 96 % reduction in four years. What's more, that was even before the current crop of state-of-the-art models. Now it is true that when you get into domain-specific questions like legal questions, these numbers tend to go up. and so it is an important part of using AI to have systems for verification.

8:26But functionally, for a lot of the types of day-to-day ways that you would use AI, hallucination is effectively either a solved problem or certainly at least not enough of an issue to justify holding back from using the tools. Yeah, but okay, even if AI doesn't hallucinate a lot and it's not all just slop, don't you need to, like, be a prompting expert or something to use AI well? This misconception is a legacy of all of those 2024-era prompt engineering courses. While there are definitely ways to use it well or not so well and to communicate with it in a better or worse fashion, you absolutely do not need to know some complicated set of tricks to get a lot out of these models.

9:03In fact, kind of the whole idea is that you just talk to them in English and they'll figure it out. And if they don't figure it out, you talk to them some more, you refine it, and you go again. And then when that doesn't work, you can talk to them again, etc., etc., and so on. In fact, it is increasingly the case that many of these models will take whatever it is that you said and turn it in the backend into a better prompt. And they do this all in the background without even telling you. An example of this is Ideogram, which I use for the thumbnails for this show. For my Why AI Won't Take Your Job episode, my prompt that I gave Ideogram was, Huge text, light on dark teal, quote, Why AI Won't Take Your Job, end quote,

9:50Ungrammatical, smashed-together elements, that's what I gave the machine. The magic prompt that it automatically turned this into on my behalf was this. A 1950s retrofuturism-style illustration featuring huge glowing text that reads Why AI Won't Take Your Job, in bright white and yellow lettering against a dark teal background. Below the text, an optimistic scene shows a smiling person in vintage clothing working alongside a friendly chrome-plated robot with rounded features and glowing blue accents. The human and AI are collaborating at a sleek atomic age workstation, blah, blah, blah. You get the point.

10:19It's actually twice as long as that. And so the TLDR is that you absolutely just do not need to be a prompting expert to get value out of these tools. Now, with those misconceptions out of the way, one of the things that is important with AI is to start thinking differently in a couple key ways. Our next conversation then is about the mindset shifts required to get the most out of AI, which I referenced in the prompting misconception, is that AI is fundamentally an iterative tool. By virtue of using natural language to prompt it, you can go back and forth. Rather than spending all of your time getting the prompt perfect and hoping the output is perfect on the first go, view things as an iterative cycle with extremely short cycle times.

11:00Think about the way that you would interact with an employee. If you gave an employee an assignment and it came back with something that wasn't up to snuff in the first try, you wouldn't just wipe your hands and say, well, better luck next time. You'd give them feedback, send them off to do it again, and then see what they brought back the second time. And then if you needed to a third time and a fourth time and so on and so forth. That's exactly how you should use AI. It's just that the iterative cycles get to be extremely, extremely quick. Next up, in terms of how you think about AI, the people who get the most out of it do not treat it like a tool.

11:31They treat it more like a partner. It's not something you pick up and put down. It's something that knows your goals and helps you get there. This has implications for the way you use AI. One really common theme you'll hear throughout this episode, and honestly, in all of the educational and tips and tricks type shows that I do, the best way to get value out of AI is to get AI's help on getting value out of AI. Use AI as a coach. This is Jerry Maguire, man. Help it help you. Now, speaking of the idea that AI is something that knows your goals, another important truism is that the more that AI knows about you, the better it gets.

12:07And here we have our next important term, context. Context is all the information that surrounds any goal that AI is trying to achieve or any prompt that you've given it that allows it to do its job better. We basically are all in a never-ending battle to increase the context available to AI. In fact, on the other end of the builder spectrum this week, I shared a personal context builder agent for advanced users. For your starting point, where context is going to come up is in things like background documents that help the AI understand more about your work before you ask it work questions. If you are in marketing and you're asking AI to write some marketing copy for you, it stands to reason that it's going to do a better job if it has your brand guidelines or examples of successful past campaigns that you've run.

12:53Now extend that across any goal that you give AI and you'll see why context becomes so important. Another mindset shift, which can be really hard because it's so fundamentally different than pretty much all the other tools we've ever had to use, is that you can't get too wedded to any one behavior pattern when it comes to using AI. The tips that I would have given you to get the most out of AI two years ago, while not totally dissimilar to what you're hearing now, have evolved and changed because AI itself is constantly evolving. You can't have a system whose capability is doubling every four months and not have that happen.

13:28And because of that, you're going to have to evolve in how you work with it, which is, of course, another great reason to keep that iterative approach close at hand so that when the thing that used to work stops working, you can figure out something that does again. Ultimately, to reinforce, AI is ultimately not a technology topic. The more that you can view it like a new operating layer through which you do all sorts of different things, the closer you're going to get, I think, to unlocking its full value. So now that we've got some key terms, some common misconceptions out of the way, and a few important mindset shifts, let's talk about the AI landscape.

14:04When people talk about AI, they're going to talk about everything from chatbots to agents to automation tools. So how does that all fit together? The front door and most common interface for most people using AI at this point is still chatbots. Examples of chatbots are Anthropics' Claude, OpenAI's ChatGBT, Google's Gemini, and XAI's Grok. These are tools where you type into a chat window, and the AI talks back to you. Now, these interfaces themselves have gotten more complex from where they started a couple years ago. All of these tools can now produce documents, working code, website samples, markdown files, and pretty much any other type of computer format that you might need.

14:41But the core interface experience is you talking to a chatbot that talks back. Another category of AI that you'll probably come across if you haven't already is AI that gets embedded in your existing tools. Pretty much every software company in the world is racing to figure out how AI can actually be useful inside of their systems. And while it's tempting sometimes to view this as a cynical grab to capture headlines, I think it's actually more about the fact that we're still so new with this that we just don't know exactly what the right ways for AI to interact with the other things that we do are without trying them.

15:13So some examples of this are going to be Notion, where you have AI deeply integrated into your writing and document storage, Zoom, where AI meeting transcription is now just built in, Salesforce's entire agent Enforced Suite, and so on and so forth. And pretty much every other software that you use, if it hasn't introduced some set of AI tools already, will at some time in the near future.

15:37Alright folks, quick pause. Here's the uncomfortable truth. If your enterprise AI strategy is, we bought some tools, you don't actually have a strategy. KPMG took the harder route and became their own client zero. They embedded AI and agents across the enterprise. how work gets done, how teams collaborate, how decisions move, not as a tech initiative but as a total operating model shift. And here's the real unlock. That shift raised the ceiling on what people could do. Humans stayed firmly at the center while AI reduced friction, surfaced insight, and accelerated momentum. The outcome was a more capable, more empowered workforce.

16:10If you want to understand what that actually looks like in the real world, go to www.kpmg.us.ai. That's www.kpmg.us slash AI.

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18:26Again, that's besuper.ai.

18:32Now, one thing I didn't mention about chatbots is that they are extremely general purpose. One person can use them for writing memos, another person can use them for writing sonnets, while another person can use them for research and another person can use them for clerical or accounting work. Sometimes, though, people build specialized AI applications that are purpose-built for one specific type of generative output. Some of the apps that you might have heard of include Runway, which is focused on video, Midjourney, which is focused on images, Gamma, which is focused on slides and deck presentations, Eleven Labs, which is focused on voice, or Suno, which is focused on music.

19:08Sometimes these companies build their own models. Sometimes they do refinements of other companies' models. The common thread is just that they are specialized on a particular type of output and try to use that specialization to improve the results. Now, one thing that is worth noting is that there is a fairly open debate around what the balance between these specialized AI apps and the more general model companies will ultimately be. Even though Midjourney's images right now show incredible taste and are extremely visually compelling, can they keep up ultimately with the incredible amount of raw visual data that a company like Google has access to?

19:41That is an unresolved question, but when it comes to the practical day-to-day for you, these tools just give you more options to get exactly what you need out of AI. Another category of tool that you might run across are automation tools. Basically, no-code tools that allow you to automate entire workflows end-to-end. These take discrete defined goals that have a specific set of steps to achieve them and wires together an automation that connects each of those steps so that this can happen mostly hands-off. This type of automation comes up a lot in enterprise settings, where a lot of the work is very consistent in repeated patternistic workflows.

20:17The building tools or vibe coding tools are software that lets you build other software without necessarily being a developer. With these tools, you don't need to know how to code to use code. Companies like Lovable, Replit, and Base44 all allow you to articulate a goal of a software that you'd like developed. Think a personal fitness tracking application that's perfectly customized to your specific wants and needs. And these tools will build it end to end in a way that you can actually launch it, deploy it, add a custom URL, put it on your phone, whatever it is that you want. These tools are some of the most popular and fastest growing ever and are very quickly reshaping how people think about their capabilities when it comes to using AI.

20:57From there, we move into agents. Whereas automations have a discrete set of steps that the user articulates and gets AI to help them automate, agents are slightly different. The key idea of agents is increased autonomy. Instead of telling them what to do, you give them a goal and they figure out how to achieve it. Now, right now, people are building agents for absolutely everything. But for beginners, the type of agents that you might run across most commonly are some generalist agent tools like Manus or GenSpark, which have a broad set of different things that you can do from within a single interface.

21:33That is different from vertical agents, which are agents that are built for a specific industry or domain. The legal industry, healthcare, finance, sales, HR, pretty much all industries at this point have some set of highly specific vertical agents who are purpose-built for the types of things that go on in that industry. Now, once again, it's an open question of the extent to which we'll use vertical agents versus more general horizontal agents in the future. But the common thread is, once again, a higher level of autonomy, where you can give them a goal and they figure out how to go achieve that goal.

22:05Now, one reality to keep in mind, which I think actually should be fairly liberating for you, is that we're in this weird moment right now where every AI product is basically turning into every other AI product. You might have heard of Claude Code or OpenAI's Codex or Perplexity. all of those tools are seeing a real convergence of features. Lovable and Replit recently, despite their Vibe coding origins, recently released updated versions that allow you to use them for design or for building slide presentations. And so why I say this should feel a little bit liberating is that it's not like you need to have clear coverage into all of these different types of applications and tools and interfaces.

22:41As they kind of converge on one another, you can pick a couple that are really useful, and they're likely to give you a broad-based set of capabilities. Which gets us to how to get started. And one thing that's really important with this is that as you get started with AI, you are not going to do it with case studies and sample work. You are going to use these tools for only your real work to see what value they can bring you. Now, my suggestion is to start with a handful of very common use cases across a lot of different types of work. The five that I would suggest, if you're just looking for a quick template, are research, analysis, strategy, writing, and images.