Generative AI, Vibe Coding, and automation tools are spreading fast, and more founders are chasing what some call “AI Maxing” — buying large amounts of tokens, deploying AI agents, and building apps quickly in an effort to get more done with fewer people.
Alex Hormozi, a U.S. entrepreneur and investor, has pushed back on that mindset. In a recent video titled “Why AI Won t make you rich in 2026,” he said many people now use AI heavily every day, while their token bills keep rising and their revenue does not rise with them.
More output is not the same as more value
Hormozi’s argument is not that AI is inefficient. His point is that many companies mistake increased output for value creation. Once AI gives a business the ability to do more work, that business does not always focus resources on the most important constraint. Instead, it may start moving faster on lower-priority tasks that were never worth doing in the first place.
That can make a company busier without making it more profitable.
AI has not erased other forms of leverage
Hormozi defines leverage as the gap between input and output. A small input that produces a large return is high leverage. A large input that leads to limited results is low leverage.
By that definition, AI is clearly a high-leverage tool. One person can now use generative AI to write copy, analyze data, create images, and build software — work that once required several people.
Still, he said AI has not canceled out other forms of leverage. Capital can still amplify investment returns. Media can influence thousands or even millions of people at once. Teams let a business push forward on more fronts at the same time. When capital, media, talent, brand, and AI are combined, the result can be multiplicative rather than additive.
He also pointed to large AI labs. Even companies with the world’s most advanced AI models still employ thousands of people. At a minimum, he said, that shows human labor, organizational management, and professional judgment still matter at this stage.
For many businesses, speed is not the main problem
Hormozi said many operators bring in AI, see their own capacity rise along with their teams’ capacity, and then start doing the work they never had time to do before. On the surface, productivity improves. But the work that sat on the shelf often stayed there for a reason: it was less important.
So companies end up using AI to produce more content, build more features, and create more automation, without first answering the central question: will any of that raise revenue?
He argued that the absence of AI can sometimes force clearer prioritization. When people and capital are limited, managers have to direct those resources toward the work most likely to change operating results. Once AI makes resources feel more abundant, companies may lower the bar for what gets done and allow more low-value work into the workflow.
AI can improve efficiency without fixing the real bottleneck
Hormozi did not reject AI outright. He said it is already producing practical results in some business settings.
- Ad teams can use AI to create more ad assets.
- Sales teams can deploy AI sales representatives for initial outreach, qualification, and information responses.
- Businesses can use automation to reduce some administrative and delivery costs.
But those tasks are not always the main constraint on growth. A company may really be missing market demand, brand trust, a compelling product, an effective sales process, or the ability to deliver consistent, high-quality service.
If the core product is not competitive, fully automating the back end will not necessarily create meaningful growth. Hormozi said that if AI could already solve most business bottlenecks perfectly, then companies that adopted it at scale should, in theory, be seeing clear revenue gains. That is not what he sees in reality. The reason, he said, is that many companies are applying AI in places that are not actually limiting revenue growth.
There are ways to get 10x leverage without AI
Hormozi said businesses still have many ways to increase leverage without using AI at all.
One example is turning a one-to-one service into a one-to-many group model. If a consultant could serve only one client at a time before, then serving 10 people at once could, in theory, increase the revenue and impact generated by that same block of effort by 10x.
A business can also change scheduled synchronous services into asynchronous delivery, cutting down the time staff must be present at the same moment. It can redesign the sales process as well, educating prospects earlier so that the customers who reach a live sales call already understand the product and have stronger purchase intent.
Under that setup, a company may no longer need 10 salespeople having large numbers of low-efficiency conversations. It may need only 2 sales staff serving screened, high-intent customers while closing the same number of deals or more.
Hormozi said process design like this does not necessarily require AI, yet it may improve unit economics more directly than simply adding a chatbot.
The highest leverage move is often the decision itself
In Hormozi’s view, the highest leverage action in running a business is not always using AI. It is making the right resource-allocation decision.
He gave a simple example. If a team is about to invest a large amount of time into a project, and a manager can correctly decide that the project is no longer important and cancel it outright, that decision immediately frees up the whole team’s time. If the manager only automates that project with AI instead, the company still ends up producing something that does not matter.
His core line was this: deciding that something is no longer a priority is more efficient than automating something unimportant.
A summary of the program described the same idea in similar terms: when AI is used on low-priority tasks, it only makes a company more efficient at work that has no real impact.
Higher token bills do not mean higher revenue
Hormozi said many people now treat heavy token usage, more agents, and more apps as proof that they are becoming more competitive. For a business, though, the relevant test is not how much AI it uses. The relevant test is whether AI adoption actually increases revenue, improves gross margin, reduces delivery costs, or removes the company’s main bottleneck.
He said operators should ask themselves directly: after bringing AI into my company, am I actually making more money?
If the answer is no, the problem is usually not that AI has no value. It is that the company may be applying AI in the wrong place. In his view, the sensible use of AI is to focus it on the key problems that constrain growth, not to create new work simply because the tools make it possible.
AI is a tool, not a business strategy
Hormozi compared today’s AI to suddenly giving a company a large team of virtual assistants. Those assistants can research information quickly, organize content, generate drafts, handle administrative work, and even help build software. But even in the past, the ability to hire low-cost assistants never guaranteed a strong brand or a good product. AI does not remove market competition either.
Companies still compete on product quality, brand trust, sales capability, service experience, and positioning. Better products still compete against weaker ones. Stronger brands still close more easily than brands that customers do not trust. If a company’s offer is not compelling, AI may help it produce more ads, write more copy, and build a more complete automated funnel, but that gain in speed does not necessarily change the basic fact that customers do not want to buy.
Hormozi’s conclusion was that AI has increased the amount of leverage available to businesses, but it has not replaced capital, talent, media, brand, process design, or management judgment. For most companies, the forces that actually drive growth remain the same: creating more market demand, improving demand conversion, and delivering high-quality products and services consistently.
Once AI sharply lowers execution costs, deciding what to do may matter even more than deciding how to do it faster.