AI Bubble Bursting? Investors Reconsider Their Bets on AI Startups

A good number of businesses around the world embraced artificial intelligence (AI) last year — and we saw many AI startups spring up with enticing AI suffixes to their names alongside bold press releases.

In fact, some commentators in the first quarter of 2024 suggested AI may well be the missing link between businesses and maximum profitability. This painted a picture of an all-booming AI industry where every Tom, Dick, and Harry have found ways to make good returns from their AI investments.

However, even sectors with a proven value to the world can be susceptible to a bubble, and with the AI craze at a crescendo, perhaps there’s good reason to ask questions.

Table of Contents Table of Contents

1. Key Takeaways
2. Big Money Bet On AI Startups Amid Few Returns
3. Why AI Startups Are Struggling So Bad
4. It Goes Beyond Funding, AI Startups Are Also to Blame
5. What Investors Should Look Out for Before Investing in AI Startups
6. The Bottom Line
7. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. Big Money Bet On AI Startups Amid Few Returns
3. Why AI Startups Are Struggling So Bad
Show Full Guide
4. It Goes Beyond Funding, AI Startups Are Also to Blame
5. What Investors Should Look Out for Before Investing in AI Startups
6. The Bottom Line
7. References

Big Money Bet On AI Startups Amid Few Returns

The AI sector is experiencing a reality check as high-profile startups grapple with financial pressures. Despite investors pouring $330 billion into over 25,000 AI startups over the past three years, many of these startups are struggling to bridge the gap between their high expenses and modest revenues.

This financial strain is gradually causing changes in the industry, with companies like Stability AI laying off 10% of its employees and parting ways with its CEO.

Or consider the case of Inflection AI, the startup that raised over $1.3 billion in June 2023. The startup prided itself on being the first to develop a chatbot that offers emotional support to its users. Today, Inflection AI founders have been snapped up by Microsoft and no one seems to be interested in the company’s personalized AI assistant (Pi) which was hyped as the next big thing after ChatGPT.

AI startup Olive, once valued at $4 billion, announced its shutdown in November 2023. The unicorn decided to sell its core businesses to Waystar and Humata Health while winding down the rest of its operations.

With these uncertainties mounting, some AI stocks are beginning to slide into a bear run, after they enjoyed bullish stints. In June, voice AI and speech recognition company SoundHound AI, experienced a 22% drop in its stock price. This decline occurred despite the absence of negative news from the company last year.

The data, provided by S&P Global Market Intelligence, highlights a disconnect between the company’s actual performance and market sentiment during this period.

Why AI Startups Are Struggling So Bad

The story of OpenAI is one that deflects attention when you zoom in on the struggles of AI startups in recent years.

The ChatGPT maker which is arguably the most successful AI startup in the last five years has seen its rank grow from strength to strength. With its arm tied with Microsoft and more recently, Apple, we can say that the company is operating from a safe place.

But what about other AI startups without the heavy backing of big tech? How do they go about offsetting the enormous costs associated with developing and maintaining generative AI systems?

This question arises as generative AI models, unlike previous advancements built on existing components like smartphones, require billions to develop and maintain, posing a financial challenge for startups without big tech backing.

According to recent estimates published in Time, the cost of training advanced AI models is doubling approximately every nine months. By the end of this decade, hardware and electricity costs alone for developing state-of-the-art AI systems could reach billions of dollars.

In addition to the cost of training, the specialized chips needed for these systems are expensive and in short supply. With Nvidia almost turning the AI chip market into a one-man show, the price of AI processors might not drop anytime soon.

This financial reality has left several AI startups handicapped on several levels as they grapple with the daunting prospect of competing against industry giants like Google, Microsoft, and Meta.

It Goes Beyond Funding, AI Startups Are Also to Blame

Apart from the cost of training, developing and maintaining AI models, Swell VC‘s Co-founder and General Partner, Rusty Ralston, argues that many AI startups are to blame for their current funding woes and lack of profitability.

Speaking to Techopedia, Ralston argues that many AI startups are chasing trends and failing to address real customer needs.

“The major reason why some AI startups are struggling is that they might not truly be solving a real customer problem.”

The VC partner cautions against forcing AI into scenarios where it’s not necessary, advising startups to focus on identifying and solving “burning problems” regardless of the technology used.

He also warns against overreliance on large language models (LLMs). “Anyone who says LLMs solve everything is wrong” he asserts, pointing out “massive limitations”.

He suggests that combining LLMs with other AI technologies could be more effective for problem-solving than using LLMs in isolation.

Ralston also expresses concern about founders who “constantly jump from hot trend to hot trend,” citing examples of those moving from cryptocurrencies to Web3 to AI.

He contrasts this with high-caliber founders who have “spent the last several years in their industry, creating value, solving real problems and have incredible track records.”

What Investors Should Look Out for Before Investing in AI Startups

The AI funding spree which happened at the peak of the bubble last year seems to be running dry. Venture capitalists, once eager to fund any promising AI founders are now taking a more cautious approach, leaving the funding in the hands of tech giants, whose funds are often spread just among a few.

The challenge seems to lie more in identifying promising AI founders to back, rather than a lack of available investment.

Techopedia spoke with industry experts who suggested three things that investors should look out for.

Extraordinary Founders

For Ralston, extraordinary founders are the backbone of any successful startup and bring vision, resilience, and the ability to inspire and lead their teams through challenging times.

“Investors should look for commercially driven founders with a history of creating value from scratch, overcoming significant obstacles, and achieving remarkable results.”

Lean, Scalable Startups

Co-founder and CAIO of Nord Comms, Philip Gj?rup, emphasizes the importance of scalability without massive funding or resources. Gj?rup suggests focusing on projects that embrace decentralized building methods. This approach allows startups to grow more efficiently and sustainably.

The Next AI Breakthrough

In a chat with Techopedia, Founder and CEO at NLP Cloud Julien Salinas notes that as far as VC-funded AI startups are concerned, they have to be profitable within a year or die. Unfortunately, in the current competitive landscape, few of them can be profitable so quickly.

From an investor’s perspective, Salinas advises a “wait and see” approach.

“If I were an investor today, I would wait and see. Especially I would wait for the next AI architecture breakthroughs as generative AI seems to hit a limit, or maybe new hardware breakthroughs like quantum computing.”

The Bottom Line

Amid the growing cases of AI startups packing their bags due to a lack of profits, there is a need for new founders to look for a huge market with strong tailwinds. Ralston suggests targeting a large and expanding market as it’s crucial for a startup’s potential to scale and succeed. Companies that operate in vast markets can achieve substantial revenue and have numerous opportunities for growth and dominance.

Getting teamed up with more established companies could also be the answer to the issues of funding. Some of the most competitive AI startups, such as Anthropic and Open AI, are choosing to align themselves with the very tech giants they might have previously hoped to surpass.

This approach has offered them access to financial resources and expertise, allowing them to stay in the game. Other AI startups with great ideas can copy that rather than run their endeavor aground.