Everyday AI Podcast – An AI and ChatGPT Podcast - 5 Simple AI Strategies to Supercharge Your Workflow with Google

Episode Date: November 20, 2025

Richard Seroter is a Chief Evangelist at Google. 📢So it’s LITERARLLY his job to help people use Google’s AI products. So with him joining the Everyday AI show, you KNOW he’s gonna be dropping some time-saving and business building strategies. And a bit of future of work knowledge along the way. This is one you DO NOT wanna miss. 5 Simple AI Strategies to Supercharge Your Workflow with Google -- An Everyday AI Chat with Jordan Wilson and Google Cloud's Richard SeroterNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion:Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:Five Simple Google AI Workflow StrategiesGemini Deep Research for Fast AnalysisNotebookLM Grounded AI Learning ExplorationGemini CLI and Code Assist for BuildersGoogle Jewels Autonomous AI Coding AgentsAI-Powered Context Integration in Google WorkspaceAI Tools for Change Management and ProductivityAI Interface Revolution Across Google ProductsTimestamps:00:00 "Simple AI Strategies for Workflows"06:51 "AI First, Thoughtful Application"09:21 "Deep Research for Change Management"11:54 Context Engineering with LLMs16:30 Simplify Complex Launches Effectively19:41 "Learning the Hard Way"22:36 "Empowering Everyone to Build"24:16 "Builders Winning with AI"29:38 "AI: The Smarter Interface"30:44 "AI Strategies for Future Work"Keywords:Gemini, Google Cloud, AI strategies, workflow automation, generative AI, Gemini Deep Research, deep research analysis, NotebookLM, grounded AI answers, personal context in AI, Google AI mode, AI-powered search, AI code assistant, Gemini CLI, coding with AI, agentic workflow, autonomous AI coding agent, Google jewels, background AI teammate, demos over memos, change management in AI adoption, cultural change in tech, context engineering, AI in Chrome, onboarding with AI, synthesizing information,Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. I think sometimes when we think about implementing AI into our day-to-day workflows, we overcomplicate things, right?

We think sometimes we have to be very technical or it's a big endeavor to get started. And that's definitely not the case. And today on Everyday AI, I'm excited for today's show because we're going to be going over five simple AI strategies to supercharge your work. with Google. So I'm excited. I hope you are too. Let's get into it. Welcome to Everyday AI. If you're new here, Everyday AI, it's an unedited, unscripted, live stream podcast, helping everyday business leaders like you and me, not just keep up with everything that's happening in the world of AI because it is hard to do. But the show helps us make sense of it and grab the information that we actually need to grow our companies and our careers. If that's what

you're trying to do, awesome. Starts here. But if you want to to take it to the next level. Make sure you go to our website, Your Everyday AI.com. Sign up for the free daily newsletter. We're going to be recapping the five simple strategies we're going to be going over today, as well as keeping you up to date with everything else happening in the world of AI. So make sure you do that. But without further, chit chat from me. Let's bring on an expert from Google to help walk us through this. So I'm excited. And live stream, audience, if you could, please help me welcome to the show. We have Richard Sirruder, who is the senior director and chief evangelist at Google Cloud.

Richard, thank you so much for joining the Everyday AI show. Yeah, I'm really happy to be here, Jordan. All right. So tell us, like, what the heck do you do at Google Cloud? Because it sounds like you do a lot. But walk us through your day-to-day. Yeah, I definitely can't even explain it to my parents. Right now, it's a problem.

But look, I lead teams like developer relations, our technical docs team, our open-source program office, just anybody who's about how do we inspire and activate builders on Google Cloud? How do you give people the confidence, right? There's a lot of information out there, but how do you give them the confidence they can do it too? So they can use cool open source stuff, cloud services, AI stuff. I spend most of my day talking to customers, working with my team, going hands on. I still code a decent amount. I'm not good at it, but enough to use the products. I don't think we should be talking about products we don't know how to use. And, you know, I'm curious. So throughout your years at Google, right,

And obviously on the AI side, Google's been there for a very long time before the large language model surge from a couple of years ago. But I'm curious, the people that you're talking to specifically about AI, is it more? Yes, the technical dev type people. Are you talking to the everyday business leader? And is it changing as AI, especially generative AI, becomes more and more accessible? Yeah, I mean, I've been in this field now too long. I don't guess my age. But this is the, I mean, I think this, we're at internet level in terms of people who care about this outside of IT.

No one outside of IT cared about Kubernetes, serverless, arguably cloud computing, maybe mobile. But we're back to like internet level conversation of like the people I talk to are sometimes in marketing, sometimes in HR, sometimes in CIO roles. It's not just builders. Of course, for tech folks, it's awesome. But the difference is this isn't just about how do I improve my day-to-day work with tech stuff. Some of it's like, how do I change my business mix and products they offer? I change how my team works. I don't think this is just about what you can do.

I think it's about how you do it. And we haven't had a change like that industry-wide in a long time. Yeah, that's a good point. And I think people, when making comparisons about generative AI and what can accomplish, they're not always going back to, you know, cloud or mobile. They're saying like electricity, right? Like so much bigger than that. You know, I'm curious even for you before we get into our five strategies, like,

how big or how much of an impact has Gemini and just Google AI in general had on how you work personally? Yeah. I mean, look, on one hand, I don't want to be one of these wacky AI influencer types who says everything's unbelievable. Everything changes with AI. Like, look, it's still hopefully good people using good tools. You still need human thought. You still need human creativity. Some of these things don't work as they advertise. Some things are better. Some things are worse. It's all great. These are tools. These are ways we do better work. Now, they're transformative tools for some teams. And so for myself, and we'll talk through some of these strategy things,

how I research, how I learn, how I build, how I do some of my day-to-day things. Absolutely. And look, there's other areas where I am purposely staying low-tech. I write a daily newsletter and I write every word. I don't want AI to do it for me. I learn by writing. I learned by doing that work. And so I think all of us want to make sure we hold closely to those things we actually love doing

and make sure that we're building depth, not just sort of, shallow knowledge because we've outsourced all our thinking to the AI. So use this as a tool to augment yourself, not replace yourself. You know, like what Richard just said there, like, take, take that away. That's so important. Don't just, you know, kick everything over to AI. You still have to practice those skills, the human side, if you really want to augment to the level that you can, right? So real quick, I'm going to give everyone the five different strategies and then we'll unpack them, have Richard unpack them one by one. So number one, Gemini deep research for analysis.

Number two, notebook L.M for exploration. Number three, Gemini, CLA and Codacist to build. Number four, jewels for background work. And five, AI, just kind of rolling out everywhere. So let's get into them. Let's start at the top, a tool that I love and use all the time, Gemini deep research. Richard, can you explain it for us and how can people,

what's a good strategy to put this into the two, your daily workflow. Yeah. Yeah. I mean, with all of these and the ones you called out there,

these aren't just about using new tools to me. These are about forming new habits. And that's the hard part. That's the change management piece,

right? Like you could one off use any of these and then never use it again. So what we're all trying to do is almost reprogram ourselves and be like, when I get a hard question,

what do I do first? To me, that's what AI first means. AI first does not mean I use AI for everything. It means that when a situation comes out, I ask myself,

if you can do here? No, fine. Do your thing. yes, do it. So Gemini Deep Research is part of the Gemini app. And we've added a ton of stuff to that over this year, whether that's helping you vibe code an app or build a storybook for your kids, which is crazy.

All sorts of cool things. But deep research is awesome. And there's other deep researchy things out there, but I'll focus on this one. The idea, and I just used it last week, I had a complex problem. I was trying to actually bias it and say, look, at Google, we're only focused on this part of the application delivery right now.

Is the rest of it kind of boring? Should I ignore it? And so what deep research did, it went to, because it's connected to Google Search, which is awesome. It went and looked at, I think I count, it was over 150 sites, synthesized it all, gave me a report that repudiated me constantly, saying I was missing the boat, which was amazing, tables, charts, all this stuff in about six minutes. So this was work legitimately that would have taken me two to three days. I would have gone to a Google search. I would have typed in words. I would have clicked blue links.

Keep doing that. We need that ad money. Like, it is got to keep the lights on. But if I'm doing a research project, why in the world would I do that today? Instead, I'm going to deep research. I'm getting a really good synthesis. And now you can upload your own files. You can redirect that research.

The results of that research, I can turn into different forms and export to a doc. And so I think we change how we do research. No one should say I need weeks and weeks for normal projects that we might be doing personally, planning a vacation, understanding competitors, doing analysis of a market, figuring out a business scheme, figuring out of technical architecture, instead of just kind of going in and doing all that yourself, how about you kickstart it? To me, AI is the best thing for blank pages. It's the easiest way to now go from, I don't know where to start, to hear something I can start with. And throw it away, keep 5% of it, but none of us just want to stare at a blank page going,

what do I do next? So deep research is an amazing way to go, I have a question. It might be, involve a lot of different angles. Can you give me a look at that and get it back. in minutes and go, that's not it at all. Or shoot, I'm even asking the wrong question. I don't want to find that out three days later, a month later after my giant research project. I want to know now. And so the ability to learn faster might be the only remaining professional competitive advantage out there. And so how do we all just learn faster? That's how you stand out. So so much to unpack there, Richard. We might have to just cancel the other four because I want to talk to you just about that for multiple hours, but I won't. But, you know, a couple things that I

heard there that I really want to zoom in on is this really talking about like change management, right? So even for me personally, this is how I use like deep research. If I wake up and I'm like, oh, I'm going to go grab a coffee and sit down. I sit down first, have Google deep research start on something, get my coffee and come back. So this is like making those little changes, but something else you said intentionally have it like not challenge your thinking, but sometimes go against a preconceived notion, right, but doing it with Google deep research. What's maybe a strategy that you can leave people with on how they can use deep research for maybe either challenging their thinking or when you are just having to take on a big project

instead of staring at a blank page? What's maybe a piece of advice you have to people? Yeah, I would say stop thinking of AI as a great way to get answers. Think of it as a way to get great questions. And we don't use it that way. But if you go to deep research and even, or frankly, Google AI mode, go to Google.com slash AI, go to any of our AI tools and say, I have this issue. What question should I be asking? What should I be thinking about? I've done this with, I use some tools sometimes if I get a really technical doc from a team. I know they're just trying to show me up. I can pass that doc and go, what are really smart questions to ask about this doc in my review. Now, do I take them all? I don't know, but you might have sparked something with me. Go, that's a good angle. I should think about that. So generative AI is pretty good. at that. And so sometimes you just don't know what to ask. And so it's great to sometimes give adversarial questions to a deep research thing going, this is what I think, but tell me why this is wrong, or tell me what I'm missing, or look for counter views. Because honestly, I think we do our best strategic work when we take these 360 views of an issue and don't just get myopic about the

preconceived solution we thought. And, you know, I don't think we'll have time to go feature by feature and update by update. But one thing that I think is important for our audience to know, a new update in Gemini deep research that I am loving is now the ability for it to, you know, go through your calendar, to go through, you know, if you choose to connect it, right, if you enable that, to go through your email. Because that's one thing I struggle with so much. And then to combine it with normal kind of deep research across the web, you know, help us understand what that can unlock for people.

Because even for me personally, that unlocks so much. I mean, that's the contextualization of LLMs are amazing, and we'll all keep shipping amazing things, and that's awesome. But this world of more agent stuff, and agent stuff really just, how do you give, kind of overlay these models with things like tools, which access other real-time data or your personal data,

how do you have long-running conversations, not just stateful one-offs within LLM? And so things like deep research are agents, and be able to pass in data that might be your inbox, or your whole set of style guides that you want to feed in and ask you for, ideas on a look and feel. The model doesn't know that by itself. And so being able to give this thing context, that's why you hear this term context engineering. I just don't want to write a

clever prompt. That's cool and that's a skill, but it's insufficient when I want to give it a bunch of things like, hey, you know, here's a bunch of SOP documents. Help me figure out new standards for my team. I can't figure it out by itself. But once you give it that context, you get something pretty awesome back. So thinking about your context, what do I need to tell this thing so it can properly give me what I need and not just assume it's a magic robot who knows all this stuff. Give it a little help. This is again where you stay in control. I think that's what we've learned even the last 12 months. There was a lot of fear of this thing's just going to do all our work. These things don't know as much as we do, regardless of what some influencer types say. Like they need context. They need

certain things. You have that. You're the orchestrated. You're the engineer. Every individual is now becoming a manager because you are managing the work of these things. You've got to change your mindset to think about that. Yeah, it's it's a great call out and even just the concept of providing more and more context is going to make kind of the agentic work from Gemini deep research much more fruitful in the long run, the more context you share, right? Speaking of sharing context, I mean, notebook L.M for exploration. Like I can't in anyone that's listened to the show, I've talked for literally countless hours about how much now I just rely on notebook LN. and how it's completely changed, not just how I work, but how I think and how I organize myself, right?

Richard, maybe for those who haven't heard me talk for hours about No Book LM, explain a little bit what it is, what it does, and then let's maybe dive in a little bit deeper after that. Yeah, I didn't understand what it was when we first announced the Google I.O. whenever it was, like, that's neat. What the heck is the use case for that thing? And I finally kind of had some light bulb moments, but how do you have a bunch of data that you collect and your terms could be your own data, could be links, could be YouTube videos, could be now it connects to drive and pull in your own stuff. And then how do you then turn this into a form that you can learn however you want?

That's the big takeaway is at this point, 2025, you can learn what you want, how you want to. And I don't think, I don't think you and I are Gen Z. Let's pretend we're not. You and I learned from teachers teaching us in class one way to 30 kids. We read books, maybe had some CDs, watched maybe an online training. that wasn't personalized. That was whatever the heck would be delivered to the masses. And if you fell behind, you fell behind.

If you had a dumb question, you'd either ask or keep it to yourself. This is the first time where you and I can learn how we want to. I can use notebook LM to turn piles of information into a 15-minute podcast. Listen to it on the way to work while I'm walking the dog. I can turn that into a video podcast. Maybe I'm a visual learner. Flash cards, because I'm about to get tested on it, sounds good. Have a chat with the data going, I don't understand this or make sense of this.

there's no company that should have the same onboarding process they have today in two years because they're all terrible. Like all the onboarding is just here's a pile of information, study it and get to work. Why are we doing that? You should be giving every new hire a link to your notebook L.M instance going, here's our business. What do you want to know about it? Oh, you want to understand vacation? We'll give you all the details. It's chat with it.

You want to figure out, you know, how the org is set up. It'll figure out the org chart for you. Tell you how, like all of a sudden learn it on your terms. And so it's free. we've made it available to students. It's an amazing student tool, personal tool. But again, we talked first about changing how you research. This is changing how you learn.

And again, it's a due habit. It's a new muscle. But all of a sudden, we're all learning the hard way until we use things like this. Once maybe, you know, even going back to your initial reaction when you heard about it at I.O. and you're like, okay, what's the use case, right? Now, fast forward that it's been out for, you know, a year and a half, two years. you know, maybe what's an important takeaway that you've maybe experienced or your team has experience using Notebook LM that you think would be helpful for our audience.

Yeah, look, I saw, I think it's a couple of the events Google ran over the summer where the result of it was a notebook LM because no one pays attention to the 200 announcements we just made or all the videos or the 15 blog posts or whatever. And so even as you're doing big complex things, guess what, no one's paying attention. It doesn't matter if it's Google's event, Amazon's, event, your crazy, awesome launch. No one cares about it as much as you do. Awesome. Give them a way to synthesize it then. So first off, for every big complex thing, even you or your business does, give an easy way for people to digest it all on their terms and learn about it. And then the other way

is I think all of these tools are amazing at taking really complex things and helping us finally understand what they mean. Whether that's AI and Chrome or whether you use notebook L.M. or Gemini app, whatever it is, give it the terms and conditions to your credit card, which no one in the history of time has ever read or your employment agreement where you're like, this may be, this is five, but this is 15 pages. Give it that to the AI and go, what's a weird thing in here that I should be aware of? Awesome. Where else can you do that? So look for those applications that either make the complex very simple or to take a very big distributed set of announcements and turn that into something that anybody can figure. Yeah, and it probably

would have been helpful for me to set the stage a little bit and just, you know, for people that aren't familiar with notebook alum, but the concept of grounding, right? Because I think sometimes people are like, well, okay, notebook LM is powered by Gemini. Why wouldn't I just use Gemini for these things? So Richard, could you just kind of explain kind of the concept of how notebook LM just grounds answers in the information that you give it? Yeah, it's like we said, look, a lot of these things are all going to be based on the same, let's even say the same Gemini model or whatever your favorite model is. It's about the experiences on top. That's where the magic's happening now. And so can I solve similar problems with different tools in different ways, maybe, but notebook LM is purpose

built to say, let me take a bunch of your information, your preferences, your links, grounded on your truth data, and turn that into a form that you can consume. And that's just what it's good at. Do I would I use that to look up the latest baseball scores? I don't think so. And maybe it could even do it, but that would be a weird use of it. That would jump to the Gemini app or whatever. So just knowing what these things are good for, and even Google Cloud customers have it all baked into Gemini Enterprise. It can be private just for you. Google doesn't train on it, all that sort of stuff. So you have enterprise versions of Notebook LM, the Gemini deep research experience, all in Gemini Enterprise for corporate customers. So a lot of this is just about what's the

interface you need to solve a given problem. And Notebook LM is amazing if you just say, I want to learn from a lot of material that I've curated. And that could be my curriculum for this class this semester. That could be about my business. But it saves it. And then it's something where I can just keep rowing it or removing it or pairing it, learning about it different ways. It's such a unique experience. And it's free to use. Everybody can use it on their phone, web app, party on.

Yeah. And it's the fact, number one, that it's free. But number two, the fact that this technology exists and is this easy is still bonkers to me, right? Like thinking back like two years ago and like before notebook LM and then seeing what we can do with it now, I'm just sometimes like, how is this so easy and how is it available for everyone, right? Absolutely. No, I mean, you're going to look back even in a year ago. We've just all been doing it the hard way. And that's okay. Sometimes you have to learn it the hard way,

back to the skills and things. And so it's good to know the hard way to do research. Like, we shouldn't just have the easy way. Maybe people don't go to libraries anymore and no kid under 30 knows what the Dewey Decimal system is. But like that was a big part of how you and I probably had to do real research. And it's good to know that because you've got to hunt and you got to figure stuff out. But let's do it easier now. All right. So we just, uh, bailed two of our first five, maybe for more non-technical people that are next to, maybe if you want to get a little technical, we're going to get there. But before we do, just a real quick break for a word from our sponsors.

All right. So let's get into it, Richard. Number three on our list, talking about Gemini, C-L-I and codicist. So explain what is, what is it? How does it work? And who should be paying attention? Because I'm even experimenting with this a little bit, even though I'm not a developer or code. We're all developers now, Jordan. I mean, I think that's the most exciting things. We're all builders. You don't have to wait for an engineer

to build the thing for you to at least see it in action. Now, would you put build that in production? No. So you, everybody's a builder now. And I can use the Gemini app to vibe code a web app and see what it looks like and all that stuff. But for these things, you know,

we talked about learning differently. We talked about researching differently. It was about building different. And say, how do I use tools that help me as a developer, write software, learn my system. So the CLI or the command line interface is something that sits in the terminal,

you know, think a loss prompt or whatever, being able to come in there and have access to the Gemini model, being able to reference a bunch of extensions so I can reach into third-party systems. And maybe I would use that to do something like, hey, I'm trying to take this really old app

and make it new. Okay. And then I think it passed that into Gemini, update it, iterate back and forth. It's really powerful or it could be as simple as because it's internet connected. hey, I'm trying to build a chart of today's stock rankings, put it into a table, though, and factor this in. And it can reach out to the internet, synthesize it all, put it back into language.

So, if it's internet connected, I can use it to build. I can use it to connect to third-party systems. But for some people, it's a really good, hardcore programming way and system administrative way to manage things without a point and click GUI. Right? Plenty of people, I just faster with the keyboard, easier to build, full power of Gemini, giant free tier that anybody can mess with, just give us an email address, and that's it, and party on. And use corporate versions if you want to, too. But then sometimes if you're a coder, use things like an integrated development environment

or IDE. I want to see my code. I want to write it. I want to do things. And things like Gemini Code Assist can help you complete a line of code or say, hey, I just need a function that reverses, you know, or adds two numbers together and I'll write the function for you. That sounds good.

Or I got this giant code from somebody that is retired. I don't understand any of this. What does this application even do and get back an answer in a second versus four days? And so how do I understand code, write code, change code? Again, you're seeing at this point, I think 90% of devs are using some of these tools at this point. But so can everybody, to some extent. I could use the Gemini CLI as a business analyst or a financial analyst and maybe, hey, can you look at this spreadsheet and make some updates to this? I might jump into Gemini Code Assist and go, you know, I have this kind of cool idea for an app.

I'm not a programmer. but here's what I want. And that's for builders nowadays. I think the biggest takeaway is we've moved away from you having to know everything to do anything, so you have to know your intent. We all know our intent. What am I trying to do? Now, again, don't take those things that you don't understand and then push it all

the way to production. You'll get hacked or something that'll be screwy. But to prove your ideas faster, there's a motto in my product area right now. I'm in a product area in engineering that focuses on all these dev tools. and one of our leaders, Ryan and Scott, they both kind of coined demos over memos, build stuff, stop writing so many freaking docks, build your idea out, prove if it makes sense.

And then when it does write the document, but stop wasting months, pixel pushing a dock and tables and perfect pros, when your idea's not right. Build it. Build demos. Prove your ideas. Everybody can do that. And then once you have a solid idea, you jump into real building and real scaling. But get that first, experiment and learn stage done fast.

before the next one, that's a culture change. Like that transforms a business from being paralyzed by these giant release stages. So like, let's all be builders. Let's all prove ideas and move the blockers. Richard, I think what you said there is really important. Just responding with like, Jordan, no, we're all builders, right? It reminds me when a story when I had Paige Bailey from Google on the show, and I do suggest people go listen to that episode 619.

She talked about now at hackathons, it's non-technical people. people that are winning AI hackathons, right? And I love what you said. They're demos over memos and just encouraging people to build it. Do you think it's going to become, whether it's using, you know, Gemini, CLI and Code Assist or, you know, other kind of vibe coding tools, is it going to become common or is it already common? And maybe I don't know because I don't live in California where everyone's just building their

own, you know, solutions, right? Like, oh, this piece of software stinks or I'm spending hours. you know, a week just for this one figure, I should just build something. Is that going to be the de facto way to work? We'll probably swing the pendulum that far. And we're all just going to be building everything. And you might build personal software. You might be like, I'm just trying to track my recipes better because they keep forgetting

what I make. And let me just build an app for myself. We're going to all do. I think we're going to see an explosion of software. That just we have an explosion of software that replaces reduction grade software. I'm not sure. Like there's going to be times where could you write your own customer relationship management system, my goodness, you could.

It's not feeling like that's going to be a competitive differentiator for you or something you want to maintain. So I think we're going to swing the pendulum too far for a while where we all just build everything because we can. And it's super easy. And then we'll find as usual that middle ground of still buy, you know, buy commodity and build differentiators. Like be careful.

Don't accidentally build everything and then realize you took your eye off the ball of your business because you were geeking out on something that actually doesn't matter to your success. All right. So let's move from the terminal to the autonomous AI coding agent Google Jewels. So explain, you know, explain the real big use case here, Richard, and specifically the concept of, you know, having a background teammate. Yeah, I mean, this is the world. Look, if you're a builder now, there are things that you do.

Think of it as a, when I talk about the CLI, that's really almost like working with a junior engineer. Oh, you're collaborating. You're hanging out at the same time. you're both working the same shift. Amazing. When you work with things like Jules, you're actually working around the sun or you're working with an outsource agent

or a partner in saying, let me write a quality spec. You'll hear the term spec-driven development. Let's write a specification that's machine readable, still natural language, but maybe organized really effectively. Let me iterate on a spec with this background agent and then hand it off.

And I might go to lunch. I might go home. Might have your cup of coffee. And when it's done, it gives me a pull request or gives me the changes going, here's what I did, check it out. You can go, like that. No, not doing that.

Let's iterate that again. But it's almost like having work that you can truly offload. Again, you're still in control. You're still the engineer. You're still the orchestrator. You're still the coordinator. But the idea of having a bunch of background work, this is the next cultural change. I think the future for a lot of builder teams is that you're going to have multiple of these going at the same time.

If you're a software developer, here's an agent, can you write the docs? I'll check back with you. You had some tests to this code. Got it. Can you add this new feature? because I'm wondering about this. And you're just coordinating responses and things. And maybe it won't be that extreme for everyone.

But this idea of multiple independent agents doing work that you've directed, which then you pull back together when it's done, that's part of the future, one way or the other. And so how do you get ready for that? Look, the most important programming language for the next few years is English or whatever, your spoken language. How do you communicate intent to these agents? Because if you write terrible specs,

you will get packed terrible responses from these agents. So how do I convey my intent effectively? How do I think about communicating guardrails? Hey, hey, don't do this. You should only be scoped here. Or, hey, don't, you know, do things that are insecure. Like, how do I know enough? This is where we still need expertise.

If I give incomplete instructions, I'm going to get incomplete results. So we got to keep building our expertise so that we can properly narrate this. And then we're just doing work all over the place at a faster speed at higher quality. But there are prerext to getting that right. So that's number four. Let's go straight to number five. So just AI rolling out everywhere. And it's like, I kid you not.

I have a working notebook L.M document of just everything Google Gemini rolls out because it's every day. Every day something's coming out. But tell us, Richard, what does this ultimately mean? Because even like Google AI mode is coming out with canvas and, in, all these other, right? It's, it's all these features that I'm using from different products are seemingly being rolled into even just Google search, but just Gemini AI everywhere. What does this look like? Part of me hopes that we stop worrying that it's AI pretty soon. And it's just we have

smarter things. I may go to Google.com slash AI and I just get a more interesting way to search. Or I can use Google Sheets and have an AI function that can help me build a table real quick. It's cool. It's awesome. Or I could use our public cloud and Google Cloud. and go to BigQuery and just explain what I want to do and have it turned my natural language into a proper SQL statement, which I forget how to write now. That's amazing.

I don't even care that it's AI personally. I just care that it's smarter. And again, I don't have to know everything to do anything. And so as you look across all these tools, I think you're just going to see these interfaces have changed. It's the new interface of technology, right? It's not just UIs or APIs or, you know, it's AI. AIs a new interface.

And so how does this just make a smarter, way where I can express my intent to some of these systems and build a pivot table, write a resume, understand a document, perform a search. And then when you get into people who build agents, how can we change customer support? How can we change onboarding and knowledge management? So I think what the future is just going to look like is a smarter way to interface with these technology systems without being locked out by our lack of knowledge about the nuance of every programming language and syntax and tool and how do I call this? I don't care. I don't care. I just want to go on vacation, block it in that system.

I don't even care what system it is. And so I hope we just keep returning more autonomy to the human by offloading a lot of this different mucking around between systems to the agents and the AI that can do it. All right. So, Richard, we've covered a ridiculous amount of great information on today's show. So everything from talking about cultural change to shifting our mindset of being, we're all builders, demos over memos.

I mean, I think we're literally going to put up like 50 of your quotes in today's newsletter. But, you know, as we wrap up, you know, after going over these five simple AI strategies, maybe what's the one most important takeaway? Because, you know, you and your team are really helping build the future of work. So maybe what is your one strategy or one most important takeaway for people to be able to take advantage of all these things that we've talked about today? Yeah, I don't. I mean, I think to some extent, either AI is going to happen to you or you're going to happen to AI. And I think you have to decide if you're going to lean in or not because this is all coming in some way, shape, or form.

If you want to be ahead of it and then be in control of it, be smart about it, understand the best ways to use it, make it work for you. I don't want to work for AI. I want it to work for me. But that requires me to lean into it then and understand how to use it well and stay up to date and listen to podcasts like this and lean in because you know what? Plenty of people won't. And I think you want to be on the side that's shaping how this industry is going to look and how work is going to look, not just be subjected to what's going to happen to you. So take some command of the of the scenario by being smart, going hands on. I think everything we talked about today has free tiers of service. Dry stuff. You know, to me, the most important two traits every single human should have right now and professional world is curiosity and humility. Be endlessly curious. Keep learning. Don't ever calcify your knowledge because it's changing every week. And yeah, be super humble because all these

opinions you have today are probably wrong next week. And it's okay. So if you have those two things, you are set up for success. My gosh, what inspiring and invigorating 31 minutes from Richard. Richard, thank you so much for taking time out of your busy day to join the Everyday AI show. We really appreciate it. Hey, thank you so much for having me. All right, y'all. And if you miss anything, don't worry.

We're going to be recapping it all in our newsletter. So if you haven't, go to Your Everyday AI.com. Thanks for tuning in. Hope to see you back tomorrow and Every Day for more Everyday AI. Thanks, y'all. Meet Firefly AI Assistant. Now live in Adobe Firefly. the Allman One Creative AI Studio.

Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adop.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us.

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