0:00 90% of AI tools will fail in the next 2 years. Not because technology is bad, but because they are all making the same fatal mistakes. Picture this. You raise hundreds of millions of dollars. You hire the best engineers. You launch to massive hype. Millions of people start using your product. And then everything falls apart because viral doesn't mean profitable. And that's not even the biggest problem. In the past 2 years, this has happened over and over again. Billions in funding followed by massive hype followed by total collapse. Right now, there's a big graveyard of AI startups that had massive funding, viral hype, and convinced millions of people that they were the future. All of them made the same mistakes. And 90% of the AI scene will repeat the same cycle. We're living through the biggest AI gold rush in history. Every minute, a new
0:46 tool launches. Every month, another startup raises millions. And every quarter, this hype cycle repeats. But here's what nobody's talking about. the fact that statistically speaking, most of these tools will not exist in 2 years. Not because AI is not powerful and not because the technology has failed, but because the people building them are making predictable mistakes. And these mistakes go into four big categories. Number one, they are solving problems that do not exist. Number two, their economics do not work. Number three, they're caught up in the hype machine. And number four, they misunderstand how people actually adopt new tools. In this video, I'm going to break down all four. And by the end of this video, I'm going to give you a framework that will help you separate real AI value from simple hype. Because when you can spot these patterns, you will save yourself a lot of time, money,
1:32 and career mistakes. Let's start with the biggest problem. The core issue here is building solution without actual demand. You see, most AI tools do not start with a real problem. Yes, that is shocking to hear. They start with technology looking for a use case. Here's how it happens. A founder sees Chad GPT or Claude and they think to themselves, hm, this is amazing. What can we build with it? So instead of observing real workflows and finding genuine pain points, they just reverse engineer a problem just to fit the technology. This is backwards and this is the reason why most AI tools just feel like solutions in search of a problem. Mistake number one, shiny object syndrome. AI tools chase novelty, not utility. They focus on what's impressive rather than what's useful. Let me give you an example. Do you
2:17 remember all of those AI powered meeting notetakers? They were crazy popular back in 2023. They all did the same thing. Transcribe meetings and generate summaries. Is it impressive? Yeah, sure. Is it useful though? Barely. Because here's the thing. Most people do not have a meeting note problem. They have a too many useless meetings problem. And AI note-takers do not solve that. They just automate something that was not the real bottleneck. Mistake number two, forced use cases. Startups like to slam LLMs onto things that do not need them. They add AI to products just so they can say that they have AI. This creates a what I like to call AI theater, which means that these tools have AI features that look smart but add no real value. Let me give you some examples. AI powered email subject line optimizer, AI
3:02 generated social media captions, AI resume builders. They are not solving urgent problems. They are nice to haves at best. And these nice to haves will never survive when budget titans or free alternatives emerge. Mistake number three, built by technical people for technical people. Most AI tools are designed by engineers who think like engineers. The UI is complex, the setup requires technical knowledge, and the value proposition assumes that the user actually cares about the underlying technology. But most people do not want to use AI. They just want to solve a problem. And if your tool requires them to understand prompting, tokens or model selection, you've already lost them. Look at the AI tools that actually succeeded. Grammarly, Canva, Mjourney. None of them ask the users to think
3:49 about the AI that they are using. They just work. Period. Mistake number four. They are solving hypothetical problems. This is the most common failure. A big majority of AI tools nowadays are built on wouldn't it be cool if we would do this instead of I'm desperate to find a solution for this. And here's the difference. The hypothetical problem is what if AI could help you brainstorm blog ideas. The real problem is I need to respond to 285 customer support tickets by tomorrow and I'm drowning. The first one is interesting. The second one is urgent and urgency is what drives adoption. So if your tool solves a hypothetical problem, users will try it once. They will say, "Wow, that's cool." and never come back again. But if you solve a real painful problem, they will
4:35 gladly pay for it and also tell others. The difference is everything. Even if an AI tool solves a real problem, most of them will still fail because the economics do not work. And here's why. First of all, open source cannibalizes everything. Unless you are OpenAI, Anthropic or Google, you are in a race to the bottom because open source models are getting better, faster and cheaper. Six months ago, proprietary models had a massive advantage. Today, we have open- source models like Lama, Mistrol, DeepSeek, they are nearly as good, and they are totally free. So, if your product or the product that you're using is just a wrapper around Chad GPT, you're in trouble because sometime in the near future, someone will build the exact same thing with an open- source model and undercut you on the price. or even worse than that, users will just use the models themselves. This is
5:21 already happening as we speak. Look at all of the AI writing assistants that were really popular in 2022 and 2023. Most of them are dead or dying. Why is that, you might be asking? Because users realize that they can just use chat GPD for free instead. Prompt plus UI products will get cloned instantly. If your entire product idea is a prompt template and a simple interface, you don't have a defensible business. You just have a feature and features get copied immediately. Do you remember 2023 there was an explosion of AI logo generators. They were all the same. A form, a few inputs, and a call to Odally or Midjourney API. Within months, Canva added the same feature for free. That's it. Game over. That is why without a unique data set, proprietary technology, or a very strong distribution, you're
6:08 just waiting to be cloned by a company with way more resources. After that, I want to talk to you about how switching costs are real. Even if your AI tool is better, people will not switch to your tool if the improvements are only marginal. Because switching has a lot of hidden costs. Learning a new interface, migrating existing data, changing establishment workflows, retraining team members. These costs are real, even if your tool is free. So, for someone to consider switching to your tool, you need to be 10 times better, not 10% better. Most AI tools are only incrementally better than what people already use. An incremental improvement does not justify the friction of switching. Next up, supply economics don't work in the real world. AI makes execution abundant. And when something becomes abundant, its value collapses.
6:53 This is basic supply and demand. If an AI can generate a thousand blog posts in an hour, the value of any single blog post approaches zero. If an AI can design a 100 logos in 5 minutes, the value of a logo design collapses. So if your AI tool is just making it easier for me to produce something that's already abundant, you're not creating value. You're just accelerating commoditization. That is why the winners in AI will not be the ones who can create more output. They will be the tools that will help people make better decisions, manage complexity, or even own accountability because those are the only things that do not scale with AI. Let's be honest, a lot of the AI tools out there do not fail because they have a bad product. They fail because the demand was never there in the first place. It was manufactured. And here's how this hype machine works. First of
7:38 all, creating a demand that does not exist. Most AI tools do not start with user demand. They start with a product and then they try to convince people that they need it. It's a solution looking for a problem pattern, but in the marketing level. You see this everywhere. Revolutionize your workflow with AI. 10x your productivity. The future of work is here. Big promises, vague value. And when users actually try the tool, they just realize that this doesn't solve anything. I actually struggle with. So they churn and then the company burns through its funding trying to manufacture more demand and they usually do that through paid ads and influencer marketing campaigns. But artificial demand doesn't last. Real demand is organic. It spreads because people genuinely need that product. After they use it, they will tell
8:24 others. Artificial demand dies the moment you stop paying for it. Next up, we have the AI influencer paradox. There's an entire ecosystem of AI influencers that need to promote something new every week in order to stay relevant. They get paid to hype the tools. Then they get affiliate commissions. They get early access and free credits. And because of the fact that AI moves so fast, they cannot afford to do a deep, honest review of every single tool. So what do they do? They just take the lowhanging fruit. They hype whatever just launched regardless if it's actually good or not. This right here creates a feedback loop. First of all, the tool launches, then the influencer hypes it, people try it, people churn, and then the tool dies. The influencer just moves on to the next launch. And the same thing will happen over again. The incentives are broken.
9:10 The influencer gets paid either way, but the users waste time and money on tools that were never going to last. Next up, trends on X are like gasoline fire. If a tool trends on X, it explodes very fast. Everyone talks about it, everyone tries it, it feels like the next big thing. But gasoline fires burn hot and fast and then they're gone because virality on axe doesn't mean real adoption. And most people who try a viral tool that they found on Axe will never come back. They were curious, not committed. And once the trend dies, so does the tool. Real tools are built on sustained usage, not viral spikes. And if your growth strategy depends on becoming viral on X every single week, you don't have a business, you just have a lottery ticket. Next up, here's something that most people don't realize. Everything
9:56 out there is a paid promotion one way or another. Almost every AI tool launch you see out there is a paid promotion in some form. Sponsored tweets, affiliate links, paid campaigns, influencer partnerships, even many reviews are just sponsored content. And because everything moves so fast in AI, most reviewers do not have the time to actually use the tool long term. They just try it for an hour or two, make a video about it, collect a check, and then move on. This creates a mirage of legitimacy. A tool can look like it's everywhere when in reality it's just being artificially amplified by paid campaigns. And once the money runs out, the visibility disappears and so does the tool. Okay, so we covered why 90% of AI tools fail. But how do you identify the 10% that actually matters? Here's the framework. Before you use any AI tool or even if you want to build one
10:42 yourself, ask yourself these questions. First of all, does this solve a problem that I already have? Not a problem you might have, not a problem that could emerge, a problem you're experiencing right now in this moment. The answer is no. Just walk away. Question number two, is this 10 times better than what I'm already using? Remember this, switching costs are real, even if the tools are free. If the new tool is only slightly better, you will not actually switch. You'll just say, "Wow, that's nice." and go back to your old workflow. Number three, can I explain the value of this tool to someone in one single sentence? For example, there's an AI for that. I can go out on the street and I can clearly explain to anyone I meet on the street. There's an app for that is the biggest website for AI tools in the world. You can find an AI tool for anything that you can imagine. That's it. They will instantly know what this
11:29 is about. If you cannot explain why a tool is valuable in a single sentence, then the tool is probably not valuable. Some other examples would be Grammarly catches grammar mistakes in real time. Notion organizes all of my notes and projects in one single place. Midjourney generates highquality images from text. Next up, does this tool have a unique data set, distribution, or defensibility? This question is used to decide whether the tool will survive long-term. If the tool is just a UI on top of Chad GPT, then that's not defensible. If it has a unique data set, like how Perplexity has a real-time search integration, then it's harder to replace. If it has distribution, like Grammarly is embedded in all of the browsers, then it has a lot of staying power. If it has neither, then it's probably going to get cloned or killed
12:14 by a bigger player. Next question is, are real people actually using this tool or just influencers hyping it? Look for organic testimonials. Look for case studies. Look for people who've been using the tool for months, not days. If everything you see online is just influencers with affiliate links, that is a big red flag. Real tools have actual users who aren't getting paid to promote them. And final question is, does this tool make me faster or does it help me make better decisions? AI tools that just help you produce faster outputs are in a race to the bottom because output is becoming infinite. But AI tools that can help you make better decisions and manage complexity, they are valuable. So here's what we learned. 90% of the AI tools out there fail because they solve problems that do not exist. They have unsustainable economics and they are built on manufactured hype
13:01 rather than real value. The graveyard is full of tools that had funding, press, and influencers backing them up, but they didn't had real demand. Without real demand, nothing else matters. So, if you want to build with AI, start with the problem, not with the technology. And if you're just adopting AI tools, use the simple six question framework in order to separate real from hype. The next wave of AI tools is already being built. Most of them will follow the same exact path. Launch, hype, collapse. The ones that will survive will be the ones that will actually matter. the ones that will reshape the world that we live in. And now you know how to spot them. And the more people know how to spot them, the more the AI landscape will shift in a better way. If you found this video valuable, let me know in the comment section which tool you think will survive and which ones you think are
13:47 already dead. Make sure to check out there's an air for that link in the description. The biggest website for AI tools in the world. And while you're at it, make sure to check out our newsletter as well. It's also the biggest AI newsletter in the world with more than 2.5 million readers every single day. Thank you for watching. Until next time.
