You’re Not Behind (Yet): How to Learn AI in 29 Minutes

0:00 AI is becoming more powerful and more deeply  woven into everything we do. Some people try   to ignore it, but it's not going away. If you're  watching this, you already knew that. You're not   asking if you should learn AI. You're asking how.  Whether you want to work smarter, spark new ideas,  

0:16 automate parts of your business, or just buy back  your time, this video will give you the full road   map. You'll learn the key concepts, the right  tools, and a clear step-by-step action plan.   It's simpler than you think, and by the end,  you'll be ahead of 99% of people trying to  

0:32 figure this out. But I also know the AI landscape  can feel overwhelming. So, before we dive in,   let's break down the biggest barriers that keep  most people stuck. I'm not technical. That's   totally fine. Most modern AI tools are built for  non-technical users. If you're even a little tech  

0:49 curious and willing to learn and experiment, which  you probably are if you clicked this video, that's   all you need. And just to be clear, there will be  zero coding involved here. It's changing too fast.   Every week there's a new model, a new update,  a shiny new benchmark. One day it's ChatGPT in  

1:08 the lead, then it's Claude, then Gemini. But the  truth is, most of that is just noise. If you stuck   with one solid model instead of chasing every new  release, you'd be way better off. They all catch   up to each other within a month anyway. What  actually matters is the fundamentals, the core  

1:23 skills, and those don't change. I'll walk through  all of them soon. There are too many tools. Yep,   there are thousands, but you don't need most of  them. In fact, you can do 90% of what you need   with just three to five solid tools. The rest are  either repetitive or super niche. I'll help you  

1:41 narrow down that list later in this video, too.  I can't keep up with all the AI news. Honestly,   don't. Unless you're creating AI content like I  do, there's no reason to follow every headline or   test every new tool. You're better off focusing  on the bigger picture, the underlying trends,  

1:58 and stay aware of the updates that actually  matter. The easiest way to do that is by   subscribing to a couple good newsletters, people  whose job it is to sift through everything,   test what's worth testing, and summarize  the highlights. There are plenty out there,   including ones tailored to your industry. We run  one at Futurepedia. I'm obviously biased, but I  

2:16 think it's the best. That's not the point of this  video, though. There's no one-size fits-all here,   but most people fall into one of three paths.  Path one is the everyday explorer. You're not   trying to build anything complex. You just  want to make life easier. Summarize documents,  

2:32 write clearer emails, prep presentations,  organize your learning. You're here for more time,   less stress. like maybe a teacher using Chat  GPT to draft lesson plans and tailor them to   different grade levels or a student using Notebook  LM to organize notes and prep for exams. Path two  

2:49 is the power user. You want to do more faster.  Whether that's content creation, brainstorming,   or solving problems. Maybe you're a creator using  Perplexity for research, ChatGPT to write scripts,   MidJourney for thumbnails, runway for B-roll, Suno  for music, Descript for editing, and n to automate  

3:06 your posting workflow. Stacking tools can become  extremely powerful. Path three is the builder.   You want to go deeper. Automate tasks, build  custom tools, or scale parts of your business.   Tools like NADN, Manis, and Cursor. They let you  connect apps, automate complex tasks, and build  

3:24 powerful systems all without writing code. Maybe  you create an agent to handle support tickets or   automate your lead genen or build an internal  tool that saves your team hours every week.   And just to be clear, in this video, I'm focusing  on no code builders. Everything I'm talking about  

3:39 here is totally accessible. And the cool part is  moving from one path to the next is easier than   you think. You might start as an explorer and  end up building real tools a few weeks later,   hopefully with the help of this video. Let's  break down a few core concepts before we jump  

3:53 into the tools. Artificial intelligence is the  broad umbrella software designed to simulate   human intelligence like learning, reasoning, or  problem solving. Within that you have machine   learning which is how AI systems actually learn  by finding patterns in data and improving over  

4:07 time without being explicitly programmed. Then  there's deep learning, a sub field of machine   learning that uses neural networks. And these  days when most people talk about AI they're   usually referring to generative AI tools that can  create new content, text, images, videos, music,  

4:23 and more. That's what we'll be focusing on in this  video. I just mentioned the others to give a bit   of context. And there will be some new terms that  show up and I'll explain them in context. Now,   let's talk about tools. One of the most  important parts of this video, but also the  

4:38 one that can feel the most overwhelming. There  are literally thousands of AI tools out there,   but I'll break this down into five main  categories. LLMs, research, image, video,   and audio. Then there's one more category I'll  cover that probably 80% of the AI tools you'll   come across will fall into. These are specialized  wrappers that use a foundation model and build a  

5:00 nice UI and additional features on top. There's  more to it that I'll cover in that section,   but understanding this makes the entire AI tool  landscape feel less overwhelming. You don't need   to spend hours researching every tool. Instead,  start by identifying the problem you want to  

5:15 solve, the task that's eating up your time or  energy, and look for the best tool to help with   that. In a huge number of cases, the solution  will be a large language model or LLM. The LLM   is the most important tool in most people's AI  toolkit. There are a ton of options and honestly,  

5:31 it doesn't matter that much which one you use.  Maybe you go with ChatGPT because you're used   to it, Gemini because you use Google products, or  Claude because you like their philosophy, or Grok   because you're an Elon fan, or Meta because you're  into open source. They all have slightly different  

5:45 strengths and vibes, but the core functionality  is very similar, and the underlying concepts,   especially prompt engineering, are the same across  the board. For this video, I'll be using Chat GPT   in most of the examples since it's the most widely  used, but everything I show here applies no matter  

6:00 which model you choose. These tools are all  powered by what's called a large language   model or LLM, a type of neural network trained  on massive amounts of text data to understand,   generate, and manipulate human language. They're  incredibly versatile and powerful. People use them  

6:16 for everything from content creation and research  to coding, translation, customer support, and   more. This is where most people start and for good  reason. Almost everyone can find high impact use   cases for an LLM in their work or day-to-day life.  Many of these models including Chat GPT, Claude,  

6:32 Gemini, and Grock are also multimodal, meaning  they can work with more than just text. They   can analyze images, describe visuals, and in some  cases process video or audio. Gemini, for example,   is currently one of the best at understanding  video input. But here are a few terms you'll see  

6:48 around LLMs that are helpful to understand. So,  a prompt is the instruction or input you give the   model. A token is a small chunk of text, usually  just a few characters or part of a word. LLMs   process input and output in tokens, not words.  Understanding tokens is useful when you're dealing  

7:04 with length limits or pricing, since most models  charge by the number of tokens used. Hallucination   is when the model makes something up, usually  with confidence. This happens frequently, so   never assume the answer is 100% accurate. Always  double check important outputs. Rag or retrieval  

7:21 augmented generation. This is a setup where the  model retrieves real data or documents to ground   its answer instead of relying only on its training  like searching the internet and using that   information. Neural networks are the underlying  architecture powering LLMs. They're inspired by  

7:36 how the human brain processes information and are  designed to recognize patterns and relationships   in data. You don't need to memorize these. They'll  make more sense as we keep going and you see them   in context. Here are a few simple use cases  using ChatGPT. Paste in a URL and get a summary  

7:51 of an article. Upload a rough script and ask it to  tighten the writing while keeping your voice. Drop   in a massive PDF and get a digestible breakdown.  Solve complex math problems. Brainstorm ideas,   automate writing, simplify tasks, the list goes on  and on. If you have a problem you want to solve,  

8:07 start here. If you want a full deep dive into  everything ChatGBT can do, I've made a separate   video on that. Another fast way to level up with  ChatGPT is with this free ChatGPT resource bundle   provided by HubSpot. There's a total of five PDFs  that go in-depth on how you can utilize ChatGPT  

8:24 in your career to get ahead, solve problems or  save time. My favorite is called supercharge your   workday with ChatGPT. It covers specific examples  of how ChatGPT can be used in various industries   sales and marketing, project management,  enhanced decision-m and problem solving,  

8:40 time management and organization. It walks through  step by step with different tips and even has   a section titled 100 ways to try chatbt today  with 100 sample prompts you can use and modify   no matter what career you have. There's sure to be  a bunch in there that apply. And that's just one  

8:56 of the resources in the bundle. Use the link in  the description to go download that. Thank you to   HubSpot for sponsoring this video and providing  free resources to the people that watch this   channel. This next category is technically built  on top of LLMs, but it's so useful and distinct  

9:12 in how it helps you think that it deserves its  own category. At the core, these tools combine   language models with real-time information and  or your personal data sources to help you search,   summarize, and synthesize fast. Perplexity is one  of the biggest players here. It's an AI powered  

9:28 search engine that uses rag, retrieval augmented  generation, to give you answers grounded in real   sources. Tools like ChatGPT and Claude can search  the internet, but Perplexity is built from the   ground up to specialize in research and is so  good at it, it's worth checking out. Another  

9:43 standout tool is Notebook LM. This might be the  most powerful second brain I've used so far. You   upload your own materials, notes, PDFs, articles,  YouTube videos, and it helps you query, summarize,   and connect them in genuinely useful ways. It's  like having an AI research assistant that knows  

9:59 your personal knowledge base inside and out. It  can find and locate sources directly within any   of your documents and show you where it got it  from. But whether you're a student, strategist,   researcher, or just trying to think more clearly,  these types of tools can seriously upgrade how you  

10:14 process and apply information. The image category  has exploded, and the quality of what these tools   can create is honestly incredible. Now, we're  talking hyperrealistic scenes, branded graphics,   stylized illustrations, and even clean editable  text, all from a single prompt. Most image models  

10:31 today are based on something called diffusion.  They start with a field of random noise and   gradually remove that noise to reveal a final  image that matches your prompt. Different tools   have different strengths. The midjourney is  still my favorite for realism and aesthetic  

10:44 quality. Chat GPT's image generator is amazing for  interactive creation. You can generate an image,   ask it to change small details, remove  the background, or add new elements,   all using natural language. Ideogram is especially  strong when it comes to graphic design and text  

10:59 within images like posters, logos, or UI mock-ups.  And to be clear, all of these tools can do a bit   of everything pretty good. But depending on your  goal, one may serve you better than the others,   and there are far more than what I listed.  Video is one of the fastest moving areas in AI,  

11:15 and new updates are constantly reshaping what's  possible. Just recently, V3 from Google dropped   a huge update that's gone super viral that you've  probably seen. It can generate full scenes with   synchronized video, dialogue, sound effects, and  like emotions all from a single prompt. We can  

11:30 talk. No more silence. Yes, we can talk. [Music]  That used to take a whole production pipeline. Now   it happens in minutes, and Hyo 2 has pushed things  even further with insane physics. You can create   scenes with complex motions that felt impossible  just months ago. The list of other amazing video  

11:50 tools is continually growing. There are two main  ways to generate AI video. There's text to video.   You just write a prompt and it generates the  full scene. Then there's image to video. You   provide a start frame, an end frame, or both, and  the model animates from that. This gives you more  

12:05 control and lets you control the aesthetic while  guiding the action through prompting. There are   additional tools that let you animate characters  using real motion. Runways Act 2 lets you upload   a video of yourself or someone else and drive a  character or scene with that motion. Mo is really  

12:21 good with restyling footage into any style you  can imagine. Topaz can creatively upscale videos,   enhancing the quality while reimagining  the details. There's a ton of fun stuff   to play with here, and it's evolving fast. Many  people are using it to go viral on social media,  

12:36 but also to create full music videos or even  advertisements for major companies. There are   a few main areas in AI audio. Text to speech has  come a long way, and 11 Labs is still the leader   here. You can generate hyperrealistic voiceovers,  clone your own voice, or create custom voices  

12:52 with different accents and tones. Write a script,  pick a voice, and generate a polished narration in   seconds. These voices can sound very natural and  conversational. It's amazing. Music generation is   a category that's kind of mind-blowing. There's  a few key players here, mostly suno and yo, that  

13:08 let you create fulllength multi-instrument songs  with singing just from a text prompt. [Music] champagne and cyanide. Or you can also guide the generations by uploading  a reference track. [Music] Then there's voice  

13:41 input like what you can do in chat GPT. You can  talk to it in real time and it responds with a   natural conversational voice. It's surprisingly  fluid, like having a back and forth conversation   with a super helpful assistant. Isn't that right?  Exactly. It's pretty cool how natural it can feel,  

13:59 right? It's almost like chatting with a friend who  just happens to know a ton of stuff. It definitely   makes things super convenient, especially  when you're on the go or multitasking.   And then pushing things even further, tools like  Google AI Studio can listen to your voice and  

14:14 watch your screen at the same time, giving you  real-time guidance or instructions as you work.   I've used this before as an assistant to help  me learn new softwares. Yeah. What's next? The   background is still there. Okay. Now, go to  the effect controls panel at the top left of  

14:28 the screen. There you should see the options  for the ultra key effect. Click the eyropper   icon next to the key color option and then click  on the blue background in the program monitor.   There's one additional category I want to cover.  Let's call it specialized rappers for now. You'll  

14:45 see thousands of tools online that look brand  new, but under the hood, most of them are just   custom interfaces built on top of foundational  models like chatbt, Claude, or Gemini. They're   designed for very specific use cases, things like  writing emails, fixing resumes, reviewing PDFs,  

15:01 or generating marketing copy. and they usually  add a clean UI, some guard rails, and pre-loaded   prompt engineering to make those models easier  to use for that one task. And that's not a bad   thing. These tools can be genuinely useful. But  it's important to understand what you're actually  

15:15 looking at. Just ask yourself, is this a new  capability or just a polished wrapper? If it's the   latter, you might be able to recreate it yourself  inside Chacht with a well-crafted prompt and a few   examples. From there, it's a choice. Do you want  to pay for the convenience and user experience,  

15:30 or would you rather build it yourself? that might  take more time but could be more cost-effective   and customizable. That said, some platforms go far  beyond basic rappers. They combine multiple tools   into full endto-end workflows. For example, a  marketing platform that writes ad copy, generates  

15:45 branded visuals and videos, runs Facebook ad  campaigns, and then AB tests the results all   automatically. And those can be game changers  for the right use case. And could you recreate   something like that with LLMs, automations, and  custom agents? Absolutely. And I'll show you how  

16:00 later when we get to those sections. That's where  we're changing paths from the power user to the   builder. It involves a lot more setup, testing,  and trial and error. For many people, paying an   extra $20 or $50 a month is worth avoiding that  hassle. My goal here isn't to tell you which  

16:16 path to take, just to help you clearly see what  these tools are, why they exist, and how to decide   what's worth your time and money. Those are the  main categories. And to cut the learning curve on   some of these tools, we do have an entire learning  platform on Futurepedia. There's over 20 full deep  

16:31 dive courses into all aspects of AI, including  courses on most of the leading tools like chatbt,   notebook, LM, midjourney, and others. Then many  of the skills and other aspects I'll cover like   prompt engineering or building a chatbot for  your site. There is a whole library if you want  

16:47 to take the next step there. Of course, there's  other resources across the internet, but we have   tried to make this the most userfriendly and  comprehensive platform for learning AI. But   moving on, let's zoom out for a second. The  tools will change. The features will evolve,  

17:01 but these four core skills will stay useful no  matter what. Prompting is the most essential   skill. Learning how to clearly communicate with  AI will get you better, more useful responses.   You don't need advanced prompt engineering for  most tasks, but a few simple best practices can  

17:18 dramatically improve your results. Just start by  being specific. If you use vague prompt, catch PT   has to guess what you really want and fill in all  the gaps. One of the easiest ways to improve those   prompts is to follow a simple structure. Aim,  context, rules. Aim is what do you want the AI  

17:34 to do? Write a product description. Explain  this concept. Brainstorm five ideas. Number   two is context. This is critical. Give the model  relevant background and information. Who is this   for? What's it about? Like for a Gen Z audience  based on this resume from these bullet points. Or  

17:49 a powerful form of context is examples, especially  in writing. If you want a specific tone or format,   include a sample. Then number three is rules. Add  any limits, formatting, or style preferences. Use   bullet points. Keep it under a 100 words. Use  simple language. Respond with JSON. Make it  

18:05 sound like a friendly expert. Include a table  or flowchart. Let's do a quick example. So,   here's a vague prompt. Write a blog post about  productivity. After I send that, you can already   see what it had to guess. Who was the audience?  What kind of tone do you want? How long should  

18:19 it be? What kind of productivity are we talking  about? That's a vague term. Now, compare it to   this. I'm a business productivity coach. Write  a 500word blog post for busy entrepreneurs about   how to plan a productive Monday. Make it casual  and include three actionable tips. End with a  

18:34 motivational quote. This is much more useful. It  doesn't matter if you follow the aim context rule   structure in the exact order. What matters is that  you cover those elements. Like in this example,   aim is write a 500word blog post. Context  is I am a business productivity coach for  

18:49 busy entrepreneurs about planning Mondays.  Rules was 500 words, casual tone, three tips,   end with a quote. And in the case of a blog post,  you'll typically have previous blog posts that   you can upload to ask for it to write in your  style. You can just add to the end, here's an  

19:04 example blog post. Write in this style. Easy.  Roll prompting is another powerful technique.   It's like a shortcut that instantly shifts the  tone, perspective, and depth of the response just   by telling the model who it is. Here's a quick  example. You are a travel vlogger. Describe the  

19:20 experience of visiting Tokyo for the first time  versus you are a business travel consultant.   Describe the experience of visiting Tokyo for  the first time. This is a simplified example, but   notice how much of the context and tone is shaped  just by assigning a role. even before adding the  

19:36 additional details you normally would. The first  response will tend to focus on food, culture,   street scenes, and sensory details. The second  will highlight airport efficiency, transportation,   meeting spaces, and business etiquette. It's the  same city, same question, completely different  

19:50 output. Now, over time, you'll start thinking  this way naturally. You won't always follow   a strict order like aim, context, rules. It will  all be included, but mixed in together naturally.   The key is just to think clearly about what you  want, who it's for, and how it should sound. That  

20:04 mindset will help no matter what you're trying to  create. The more you practice, the more powerful   it becomes. For a deeper dive, I'd recommend this  resource that has a bunch of additional tips and   techniques you can use. You don't need to know  every AI tool, just the landscape. Understand  

20:18 the main categories and what's possible.  That way, when you run into a problem,   you'll recognize that it's solvable, and you'll  know where to start looking. Workflow thinking   is the ability to break big tasks into smaller  steps that AI can help with. If you try to throw a  

20:32 huge multi-step request at an LLM all at once, it  usually falls apart. But if you break it up into   clear steps and use the right tools for each one,  you'll get way better results. Sometimes it might   seem like a task can't be done with AI, but maybe  80% of it can. That's still a massive timesaver.  

20:48 Creative remixing is the skill of combining tools  in unexpected ways. Not always to follow a plan,   but to explore what's possible. Sometimes you  start with a clear goal. Other times you try   something, get an interesting result, and decide  to follow that direction instead. This happens a  

21:03 lot with AI, especially the creative tools. The  results aren't always predictable, but sometimes   leaning into what the AI is good at produces  better results than sticking rigidly to your   original plan. Now, it's time to level up. Once  you understand how individual tools work and start  

21:18 linking them together, you can begin automating  tasks. That means building workflows that complete   steps for you without manual input. Platforms like  Zapier and Make have been around for years to do   this, but Naden has become especially popular  lately. Part of that verality is because it  

21:34 lets users sell workflow templates, and that has  led to some grifting. You know, make $1,000 a day   on autopilot if you buy my $50 template, that  kind of thing. So, if you're watching YouTube   videos about it, just know what to look out for.  That said, the platform itself is incredibly  

21:48 powerful. And one big reason for its rise is the  introduction of the AI agent node. That's one of   the most intuitive ways to build agents. So, it's  a great entry point into one of the most hyped and   genuinely useful concepts in AI. And there's an  important distinction here between automations  

22:04 and agents. Automations are fixed. They follow  a step-by-step sequence A to B to C. Even if   they get complex with branching logic, they still  follow a predetermined path. Agents are dynamic.   They can reason, make decisions, and choose which  actions to take based on context. To function,  

22:20 an agent needs three things. A brain, usually a  large language model, memory to retain context   or past interactions, and tools, actions it can  take, like sending messages, updating documents,   triggering workflows, or calling APIs. A great way  to practice is by slowly building an AI personal  

22:38 assistant. You start simple and add tools and  functionality as you go. So, maybe you start just   with an agent that reads your calendar and gives  you a quick summary of your day, prioritizing   what matters most. Then you add the ability  to reschedule events or time block. And after  

22:51 all that, maybe it starts reading and summarizing  your emails and eventually even sending replies on   your behalf. Then you could give it access to your  SOPs or notion docs for added context and connect   everything through a simple chat interface. And  that could just be in Telegram or WhatsApp. Over  

23:06 time, you'll be able to just send a quick message  like something came up, rearrange my schedule   for tomorrow, and it will be able to execute  that. Or it could be summarize anything urgent   for me today or write me hooks for a video on AI  agents inspired by my hook database in notion or  

23:19 summarize the comments on my latest YouTube video.  You can build in all sorts of things that apply to   you. And I recommend starting with something like  this because you'll catch every error and it's a   safe way to experiment, debug, and iterate before  building agents that run inside your business. I  

23:33 do have a full video on how to build this kind of  workflow if you want to go deeper. It is probably   the most straightforward agent guide out there.  And I'll mention you may already be using agents.   Chat GPT's deep research mode or the similar  feature in perplexity in Gemini. It's a simple  

23:47 but powerful agent. So you give it a research  task and then it plans the best way to approach   it. It searches multiple sources all over the  internet, identifies gaps, pivots its strategy,   and then compiles everything into a clean report.  It is incredibly useful. But learning how to build  

24:02 your own agents that give you that same kind  of reasoning and execution power tailored to   whatever task you choose is the next level. Vibe  coding is a new approach to building software and   tools that's emerged from some of the later  AI updates. But here's the basic idea of how  

24:17 vibe coding usually works. You describe what you  want in plain language using voice or text. The   AI generates the code or a basic app structure.  You test it, see what works and what doesn't. You   describe your changes. Then the AI updates the  app. And you just repeat that until it's working  

24:33 the way you want. You're just going with the flow  of what the AI gives you, vibing, until you get   something functional with no coding required.  Now, this isn't at the point where you'll get   full-scale productionready software through vibe  coding, unless you're Jack Dorsey, I guess, but  

24:47 you can get a proof of concept prototype or an MVP  you can test. I mean, there are cases of people   fully vibe coding apps and publishing them to the  app store. But an amazing way a lot of people are   using this right now is building personal  use tools or internal apps that streamline  

25:02 their own productivity. Like for example, you  might build a lightweight CRM just for your   sales workflow or a content creation app with  your voice and hook templates and storytelling   formats built in. A few tools that support this  kind of workflow. Windsurf lets you build simple,  

25:16 usable apps with a polished interface. No code  required. It's best for MVPs or internal tools.   Lovable is designed for solo creators and small  teams. It helps you design and build AI powered   products quickly with a focus on user experience.  Replet lets you build and test full apps with a  

25:32 clean UI all in your browser. It's good for rapid  prototyping, especially with some light technical   knowledge. Cursor is the most powerful. It's a  desktop coding environment powered by AI. This is   ideal if you already know a bit of code and want  hands-on control. You can use it if you don't know  

25:48 how to code, but it will look more intimidating  when you first start. But why this all matters is   it makes software creation more accessible than  it's ever been. If you're building for yourself   or just testing an idea, it's often faster and  more enjoyable than traditional coding. And as  

26:03 the tools get better, more people will be able  to replace subscription-based SAS tools with   personalized versions just by prompting for them.  Now, I don't have a deep dive video on this yet. I   haven't gotten to the level of expertise I'd want  before making one, but if you want to go further,  

26:16 there are already a lot of good resources out  there to explore. To make this actionable, I've   broken it down into a simple plan. First, identify  the biggest pain points in your life, work,   or business, like what causes the most stress or  procrastination, and what takes the most time.  

26:31 Next, write out what a potential solution could  look like, even if it feels rough or incomplete.   Then, research tools could help solve it, and  ask ChatGPT to help. In many cases, it will be   a large language model like ChatGPT, but based on  the categories I covered earlier, you should have  

26:46 a pretty good idea where to look if it's not. From  there, iterate. You may need to break it up into   subtasks or use a bit of the prompt engineering  we covered. You don't need to get it perfect right   away, but just make adjustments, iterate until you  can solve that task. Just dedicate whatever time  

26:59 you can to this. You don't have to go all in.  Even 15 minutes a couple times a week can lead   to serious time savings later on. Now, in parallel  with this, just try exploring new tools. If you're   already using ChatGPT, try doing something  new inside of it, like creating a project,  

27:13 generating an image, making a mind map, or  analyzing a document or data set. It has way more   built-in capabilities than most people realize.  I've got videos that cover all of them. I'd also   recommend experimenting with tools like Perplexity  and Notebook LM. They're both incredibly useful  

27:28 and their free versions give you a lot to work  with. And once you've explored individual tools,   start combining them. Just build a simple  workflow that connects two or more. Then   take the next step and automate something. Pick a  basic repetitive task and set up a simple workflow  

27:42 that does it automatically. Once you get over  the hurdle of building your first automation,   you'll start seeing opportunities everywhere.  So to sum all that up, start with a pain point,   find the right tool, iterate, combine, then  automate. That's the full road map. Don't just  

27:56 use AI because it's cool. Use it to actually solve  problems. Start with one friction point in your   life or work and see how far you can get with the  tools and concepts I covered today. Most of this   will come a lot easier than you first expect, and  you don't need to keep up with every new release.  

28:09 The tools will keep changing. The core skills and  principles won't. Even if you only apply a small   part of what we covered here, you're already ahead  of 99% of people. And if you do want to go deeper,   we've built a full course platform at Futurepedia.  It has over 500 lessons across over 20 AI courses.  

28:25 You'll find full learning paths on chat GPT,  prompt engineering, automation, custom GPTs, video   generation, coding with AI, and more. All included  in one subscription. So whether you're just   getting started, you're building internal systems,  or applying AI in your business, there's probably  

28:40 a course that fits exactly where you're at. You  can get a 7-day free trial using the link in   the description. Or if courses aren't your thing,  the newsletter will keep you in the loop with the   most important updates. But bottom line, you don't  need to master everything today, but the next step  

28:54 is to just keep going. If you're ready for that,  this video is the one I'd recommend watching next.