Blogs

Published At Last Updated At
Riddhesh-Profile
Riddhesh GanatraFounder, Code-Bauthor linkedin

Since the first release of ChatGpt 3.0, infact, even before that, the buzz around machine learning models, generative AI and its tremendous potential for business & commercial applications was quite widespread.

As with any new piece of the technology or innovation, investors have been consistently trying to identify ways to create shareholder value by identifying profitable endeavours in the Artificial intelligence space and those who can leverage the new technology to maximize business outcomes.

As we've seen in the past however, investments aren't always distributed equitably. In fact, in many cases the investment decisions seem quite questionable and in rare cases, are seemingly made based on a superficial understanding of impact the technology is creating on the business.

Historically, we've experienced this same phenomenon with the dotcom bubble during the early 2000s, where any business that was merely associated with internet could easily raise huge sums of money from unsuspecting investors with deep pockets who were keen to jump onto the "next big thing" instead of making sound investment decisions.

This phenomenon resulted in the demise of many internet startups but paved the way to several more in the future, many of which are behemoths today.

We are hearing similar stores with Artificial intelligence today, wherein companies that are associated with machine learning and artificial intelligence are likely to be valued far higher that what is justifiable from a business standpoint.

But these stories are anecdotal and not exactly representative of the actual investment landscape in tech.

So we drilled down the numbers behind investments done in 2024 so far, and how many of them were ploughed into Artificial intelligence and how many of them weren't.

Research Methodology

Our research methodology was rather straightforward and relied heavily on collating data from multiple platforms, web-scraping and a bit of SQL.

Here's the step-by-step methodology

  1. Firstly, we collected funding data from Crunchbase and Apollo.io on companies who've received Equity Funding only in 2024 so far (uptil 15th of May)
  2. We them compiled this data, deduplicated it, cleaned it and ordered it
  3. Quite a few fund raises were from investment firms themselves in order to invest in other companies subsequently. In order to ensure consistency with the data, we excluded these fundraises altogether
  4. We were then left with a dataset that only included last-mile investments made directly to the beneficiary company
  5. Lastly, we scraped the company website, associated press articles, and official LinkedIn page to identify which of these firms mentioned any of - "generative AI", "artifical Intelligence", "machine learning" and other phrases associated with AI

Key Insights

What we learnt was a mix of surprises and affirmations to what was already well known.

See the numbers for yourself below.

Number of Companies Funded

Surprisingly, only 30% of companies that were funded had an element of "AI" mentioned around their product/service.

The rest of the 70% businesses spanned both tech and non-tech business models but were devoid of any element of AI in their services or business processes.

Thus underscoring the importance of innovation without necessarily jumping on the trends.

*** INSERT GRAPH HERE**

Total Funding

While the insights from the # of companies funded is fairly straightforward, to get a more lucid picture of the funding landscape, we should be looking at the total funding numbers.

We can see clearly that the absolute funding numbers are quite skewed towards AI-first startups, indicating a high-level of confidence within the investment community when it comes to AI-led startups.

**** Insert Another Image Here ****

Which industries were the Non-AI companies from?

Funded companies that weren't in AI were from a whole range of other industries with no consistent pattern, except that they identified a potential for business innovation without necessarily relying too heavily on modern technology.

We've listed them below.

*** Insert One Graph Here**

** Insert Another Graph Here**

Are the investments wise?

Like we mentioned in the beginning of the article, it seems quite likely that most investments into generative-AI based startups are likely to not yield the expected ROI.

However, investment decisions can be rational and like in the case of the dot com bubble, it's likely that investors will continue to inflate the valuations of these startups citing future potential instead of Justifiable revenue growth.

Some companies, undoubtedly, will emerge as the very leaders and pioneers in leveraging this technology for tangible benefits and with justifiable business results.

Similarly, there certainly will be a few bad apples who can hardly justify any commercial benefit of the technology, or worse, who don't leverage any form of artificial at all.

In 2024, there have been instances where companies misrepresented their involvement with AI to attract investors, a practice often referred to as "AI washing." The SEC has taken action against such deceptive practices.

For example, the SEC charged Delphia (USA) Inc. and Global Predictions Inc. with making false and misleading statements about their use of AI.

These firms claimed to use advanced AI technologies to attract investments, but investigations revealed they did not possess the AI capabilities they touted.

Delphia falsely advertised the use of AI for investment predictions, while Global Predictions falsely claimed to be the first regulated AI financial advisor and offered AI-driven forecasts without actually using such technologies​ (SEC.gov)​.