The Winners and Losers from OpenAI's Developer Day

Analysing OpenAI's developer day and its impact on startups

Anyone who works in technology can't help but notice the unstoppable flow of artificial intelligence (AI) startups over the last year. It feels as if a floodgate has opened, and AI applications and startups are popping up everywhere. Let's step back and see what happened over the last year and what impact OpenAI’s developer day will have on the future of the AI startups.

A Glimpse Into the Last Year

Starting about a year ago, AI and Large Language Models (LLMs) held the promise of a revival for the startup ecosystem. The potential of this technology was promising in a hardening climate for startups, with high interest rates and lower valuations. It seemed poised to rejuvenate an industry that had faced its fair share of trials. Companies like Jasper were at the lead of this technological resurgence, leading the charge.

But in tech, nothing remains the same for long. That horizon has evolved clearly now, casting a dark shadow on the earlier optimism. With OpenAI’s developer day introducing more trouble. Jasper, once the poster child of AI startups, is now slowing down. It’s reminding us that in the tech world, even the best companies can stumble.

I think that after a year the AI startup ‘realm’ can be divided into three categories: the (I) losers, (II) the winners, (III) and the bold ones.

1. The Losers

Let’s start with what I call “The Losers”. In this category, we find the likes of Jasper - and the venture capitalists who bet big on them. And with OpenAI introducing their Assistant API even ‘moat' companies such as Pinecone are in trouble. Jasper's quite lofty valuation, built upon a tech foundation that is essentially a wrapper around OpenAI’s API is now facing stiff competition.

Yes - their user experience is solid, their brand is reputable, but these components can only carry a company so far. Competition from companies crafting specialised, high-value niche products is putting immense pressure on Jasper's growth. The uncertainty is high, and I think that VCs may be in for a rough ride.

Another set of losers includes VC-backed teams that joined the chatbot goldrush, raising sums ranging from $250,000 to $25 million from ~ December to March. Their dream was to cater to later-stage and enterprise companies. These startups, while more focused than Jasper, also lacked the "tech moat" that sets them apart. Their products, though promising, often lack the unique elements necessary for survival in this competitive landscape.

I think that the underlying issue here is the misjudgment of the startup community. They believed that enterprise executives, excited about AI, would be promising first customers.

Yet, what happened is that executives and enterprise customers, when given the choice between developing their AI solutions using open-source tools or buying from unproven startups, they chose the former.

And sadly for many startups, this choice has been extremely common for executives amongst different skill levels. It's a clear testament to how large companies are building their own AI success stories, stopping startups from achieving the growth metrics that they need to raise their next round.

This shift in the way technology is adopted is part of a larger trend that deserves more exploration, but that's something I will focus on in another post.

2. The Winners

But amidst the unfolding drama, there are winners who emerge in two distinct groups:

The first group comprises established companies and market incumbents. These companies, often accused of being slow to adapt, have, to much surprise, smoothly integrated AI into their products. These are the common use-cases, where they have build internal applications like "chat-your-docs”. I think that the reasons for their success are two-fold.

Firstly, these companies recognised AI adoption as a life-or-death proposition. Failure here would mean a slow decline, which they can not afford. They'd rather lead projects internally to ensure everything goes as intended.

Secondly, there's a breath of fresh air going through the C-Suite because of a harder environment, which has created the potential for ambitious projects. This change in attitude owes a debt of gratitude to people like Elon Musk, who have shown what's possible when a small group of highly motivated individuals set their minds to it. Reduce red tape, increase personal responsibility, and watch the magic unfold. Lean teams are in.

The second group of winners are: indie developers and solopreneurs. These small, often one-person enterprises don't raise outside capital or build extensive teams. Their advantage lies in their speed and ability to adapt quickly with low overhead. They create niche products for niche markets and often dominate them. Their goal is to develop a SaaS product generating approximately $10,000 per month in relatively passive income, a strategy sometimes known as "micro-SaaS."

They operate in the intersection of software development and content marketing, answering only to the market and their own intuition. They don't need to aim for billion-dollar exits or $100 million in annual recurring revenue (ARR). They iterate rapidly, ruthlessly shutting down underperforming products.

The synergy between large language models and text-to-image models, such as Stable Diffusion, has breathed life into these entrepreneurs. The success stories are common, with a lot of apps launched within the last six months. The lifestyle and freedom these companies afford to those that perform well are alluring, and thus attracting more talented builders to this category.

This group of winners is poised to grow, creating real value with this transformative technology. As we move forward, their impact is likely to be profound. We will see much more products being built by small - yet powerful teams.

3. The Crazy Ones

The final group to consider are ‘The Crazy Ones’. These companies are reimagining entire industries from the ground up. They are venture-backed, creating products that could redefine how highly skilled individuals interact with and are assisted by technology. While it's too early to determine their success, early prototypes have been intriguing. This is the most exciting segment to watch, and it holds immense potential for the future.

In this category, we find ventures like Cursor, an AI-first code editor that could revolutionise software development, Harvey.ai, an AI-powered solution for legal practices, and RunwayML, an AI-powered video editor, among others. The potential for these companies to disrupt their respective industries is immense, making this space a captivating one to observe.

Getting Ready for Round 2

As we think about the next phase of AI startups, I think that we should acknowledge that this landscape is in its infancy. It will continue to evolve as more foundational models emerge and toolchains improve. Those will be adapted for niche use-cases. Open source models also are recapturing ground quickly on OpenAI and Anthropic. And yes - counter examples will certainly emerge, challenging this narrative.

As I wrap this up, it's clear that the AI landscape will be extremely dynamic for at least a few more years. The Winners, Losers, and the Crazy Ones are navigating an unpredictable landscape, with the promise of tomorrow's possibilities being even crazier.