Alex Kruglov is co-founder and CEO of pop.in..
As artificial intelligence (AI) transforms one industry after another, we will soon witness not only the automation of tasks but also a proliferation of good mediocrity: a wave of “good enough” AI-driven output flooding the market with astonishing speed. This relentless pace of disruptive imitation will do more than just increase productivity; it will create small industries where constant self-disruption by copycats is the norm, and traditional venture capital models will struggle for relevance.
In this brave new world, any innovative idea will be instantly adopted by a large following, blurring the line between breakthrough ideas and baselines and reshaping the startup ecosystem as we know it.
Rapid improvement in AI capabilities
In less than a decade, AI has gone from struggling with complex tasks to mastering them with greater ease. AI Index Report The Stanford study provides a startling snapshot of the explosive progress in AI capabilities over the past decade. Not only are AI capabilities gradually improving, but they are also approaching and exceeding human performance levels in a range of tasks. These models are approaching human standards at an incredibly fast rate, highlighting the exponential evolution of AI technology.
Consider the change in AI-generated imagery. As recently as 2022, AI prompt-driven renderings were vague approximations that were unimpressive at best. Less than two years later, images are not only recognizable but stunningly detailed, exceeding the artistic flair of many human illustrators. The output is astonishing. Even more astonishing is how fast and easy it is to get it. This new art mode has instantly reset the bar for what is expected.
And this is just the beginning: leading companies are investing billions of dollars in learning models that go beyond just enhancing output capabilities, turning entire business models of their competitors into mere functions.
More importantly, these organizations are pioneering a key development called “AI agents.” These agents enable AI to not only manage individual complex tasks, but also coordinate multiple parallel and sequential tasks simultaneously. As Mustafa Suleiman put it in his groundbreaking book: Incoming Waves“An AI that can respond well to instructions like, ‘Make $1 million on Amazon within a few months with just $100,000 investment.'”
Consider the implications of this development. Suleyman describes tasks that typically take teams months, or even years, to accomplish, with low chances of success. We’re not there yet, but Suleyman predicts it’s imminent. When what he calls “the modern Turing test” passes, Marc Cuban’s response to every Shark Tank pitch will probably be something like, “Can’t I just get an AI agent to handle that while I take a nap? For that reason, I quit!”
The traditional venture capital model is becoming irrelevant
For decades, venture capital has been the lifeblood of innovation, fueling startups aiming to disrupt markets and redefine industries. Investors poured money into early-stage companies in the hopes of guiding them along the treacherous path from idea to product-market fit. The journey from startup to scale-up was full of risk, but the rewards could be astronomical.
This model relied heavily on finding human ingenuity, raising funds, and scaling it, a costly and often inefficient process. Startups spent money to develop products and capture markets, always looming over the shadow of failure. The long-awaited payoff, represented by the sharp rise after the initial dip of the J-curve, was a venture capitalist’s dream: a startup that finally turned around and generated revenue that would last.
If the modern-day Turing test is passed, the emergence of AI agents will profoundly change the entrepreneurial landscape. Solo entrepreneurs equipped with advanced AI capabilities will be able to execute and scale their business ideas at unprecedented pace and with significantly lower overhead. This will change the traditional role of venture capital and call into question the very need for it.
Moreover, if an AI-enabled venture proves successful, its strategy will be quickly analyzed and copied by competitors equipped with advanced AI capabilities. This can create a highly competitive market environment with diminished first-mover advantages and significantly shortened business lifecycles. The path to becoming cash-flow positive will be shorter, but margins will likely disappear within months, if not weeks.
Forget J-curves, cash flow looks like a “rib curve” – each “rib” is a new project with its own J-curve, and the sole proprietor milks that project until it no longer generates cash flow.
If you’re a VC investing in vertical SaaS, consumer packaged goods, content farms, B2B sales, or marketing tech, you need to pivot or let your limited partners know you’re closing down. If entrepreneurs aren’t capital constrained, why would they take your money and dilute themselves? More importantly, if each idea they start has a short lifespan, they can easily close down and start a new venture without you.
The market will be flooded with very good, but mediocre products, and if you have a decent idea, a mini-industry will quickly emerge producing an equal or better copycat.
So who will benefit most from this dramatic shift? Today, it’s NVIDIA. NVIDIA’s Graphics Processing Units (GPUs) are in limited supply and are used to create learning models. Tomorrow, after tens of billions of dollars in spending, it will be these learning models that will underpin the bulk of the agent-driven accomplishments of solopreneurs. Just as Apple will make a killing by charging per app that generates revenue in the App Store, so will learning models, where agents do all the work to build these companies. Today’s leaders have the potential to make huge profits if they play it right.
What about individuals? Ten or twenty years ago, we were told to learn to code. Tomorrow’s most successful solopreneurs may be the ones who know how to ask the most interesting and creative questions. It’s quite possible that we’ll see a massive resurgence of the liberal arts. It might pay off to read everything Atwood, Nabokov, and Marx have written.
What about venture capitalists? Is venture capital dead? Not yet, but it needs to adapt. Venture capitalists need to take bigger risks by targeting potentially transformative technologies that are beyond the scope of current AI capabilities, such as cold fusion or room-temperature semiconductors.
With the constant evolution of AI, the landscape is changing rapidly. As we stand on this brink, the challenge for investors and entrepreneurs today is not just to keep up, but to predict and shape the future. As we enter an era of “rising mediocrity,” it becomes even more important to ask what truly makes something good in this new world.
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