<img decoding="async" alt="Dot com to Dot ai” width=”100%” src=”https://technicalterrence.com/wp-content/uploads/2024/04/Dot-com-to-Dot-AI-The-new-technology-bubble.png”/><img decoding="async" src="https://technicalterrence.com/wp-content/uploads/2024/04/Dot-com-to-Dot-AI-The-new-technology-bubble.png" alt="Dot com to Dot ai” width=”100%”/>
Image: ai-dot-com-bubble” target=”_blank” rel=”noopener”>Futurism
The dawn of the era of generative ai has surprised everyone: technologists and enthusiasts alike. There are several reports and guides on how to take advantage of the wave of generative ai that is being touted as the industry's “iPhone moment.”
Interestingly, it is not just limited to the facade, but has become a central topic in boardroom discussions. Executives and technologists are facing a sense of urgency to embrace this revolutionary change and accelerate the growth of their businesses.
Some consider this wow factor an inflated expectation of ai and fear revisiting the dot-com bubble.
Let's talk about Nvidia first!
Amid all this frenzy, one company has recently made headlines: chipmaker Nvidia. In particular, Nvidia is the leading supplier of GPUs (Graphics Processing Units), which are in high demand following the rise of the ai world. The availability of these GPUs is crucial for creating ai models that require high computing power.
The stellar performance of Nvidia stock is evidence of its track record of success, as also highlighted below:
<img decoding="async" alt="Dot com to Dot ai” width=”100%” src=”https://technicalterrence.com/wp-content/uploads/2024/04/1713640063_361_Dot-com-to-Dot-AI-The-new-technology-bubble.png”/><img decoding="async" src="https://technicalterrence.com/wp-content/uploads/2024/04/1713640063_361_Dot-com-to-Dot-AI-The-new-technology-bubble.png" alt="Dot com to Dot ai” width=”100%”/>
Fountain: The Motley Fool
Its growth trajectory is a function of increasing investments in ai, providing a good transition to compare today's Dot ai (.ai) world with the Dot Com (.com, which existed at the beginning of this millennium).
The beginning of the comparison
This comparison between “.ai” and “.com” is inspired by a series of events, one of which is the latest news of aai-startup-krutrim-unicorn-in-50m-funding” target=”_blank” rel=”noopener”> year-old ai startup which reportedly became the fastest company to gain unicorn status in India.
A similar sentiment floated last year when ai-vc-investment-dot-com-bubble/index.html” target=”_blank” rel=”noopener”>Mistral ai raised $118 million in what appears to be the largest seed fund in Europe.
In particular, companies building large language models require a significant amount of funding to make big strides, as companies like OpenAI, Anthropic, and others have also raised billions of dollars in this pursuit.
This news creates quite a stir in the investment community, especially when ai is a highly sought-after industry that can offer investors premium return on investment (ROI), also known as generational return.
HBR also highlights this by associating the investment thesis with the industry focus rather than the idea focus: “Venture capitalists must earn consistently superior returns on investments in inherently risky businesses. The myth is that they do it by investing in good ideas and good plans. They actually invest in good industries, that is, industries that are more forgiving of competitiveness than the market as a whole. And they structure their deals in a way that minimizes their risk and maximizes their returns.”
One thing is clear: the world seems binary in the midst of ChatGPT fever: GenAI and the rest of the world.
Bubble or not?
Now comes the big question: is this a bubble?
Consider these statistics ai-market-107837″ target=”_blank” rel=”noopener”>FortuneBusinessInsights They expect the global GenAI market to grow at a CAGR of ~40% to $967 billion by 2032.
With such potential, there are also reports comparing this “.ai” bubble to the “.com” bubble.
So let's look at the logic that makes the market think of ai as another impending bubble.
While ai is the most sought-after industry, it is necessary to keep an eye on the main indicators of an upcoming bubble. Speculative investments, lack of adequate expertise and the absence of a clear differentiator or innovation are the first signs of an attractive bubble.
Investors generally look for a robust diligence process, which includes, but is not limited to, evaluation of the business model, financial and legal complexities, market demand and analysis, which is a critical step in evaluating the investment opportunity.
Furthermore, strong governance policies, relevant product-market fit and feasibility of the proposition in terms of viability, scalability and potential to achieve higher returns are some of the key factors driving investor decisions. Additionally, revenue generating capacity, understanding of the total addressable market, barriers to entry, business moat and growth strategy also indicate a green signal.
Novel and cutting-edge offerings like ai are seen as a golden opportunity to earn substantial returns on investment.
Many investments go rogue, but why?
However, choosing the right investments is a challenging task. Let's discuss some Statistics that describe these risks:
- ~75% of companies do not even manage to balance investments
- In the context of disruptive technologies like ai, reports suggest that these startups have a higher failure rate due to the inherent risk associated with them.
ai-vc-investment-dot-com-bubble/index.html” target=”_blank” rel=”noopener”>cnn It also reports that “some investors and industry insiders are worried that the funding frenzy is turning into a bubble, with money being thrown at companies that have neither profits nor an innovative product nor the right expertise.”
Let's see what investors usually look at. It is a common perception among investors that company success depends largely on the founders' resilience, integrity and ability to turn innovative ideas into reality. Some factors consider the strength of the business concept itself and its ability to address customer pain points.
In addition to these attributes, several psychological factors, such as confidence in the founders' ability (which could be assessed based on whether they are first-time founders or whether they have been successful in the past), or the founder's receptiveness to including insights On the contrary, they also provide an additional set of indicators (although not quantitative) to incorporate.
However, human experts, in this case investors, can only consider limited factors at a time to make the most effective decision. That's where the power of computing, also known as machines, comes in to help investors make data-driven decisions.
Then versus now of the VC world
Due to the inherently high-risk, high-impact nature of the venture capital industry, ai could be used to augment the VC's hunch, something that relies more on quantitative analysis coming from historical data points. These models evaluate the viability of the proposal and predict the probability of success of an investment
Welcome to modern data-driven investing.
Quoting tech-investors-will-prioritize-data-science-and-artificial-intelligence-above-gut-feel-for-investment-decisions-by-20250″ target=”_blank” rel=”noopener”>Gartner:
“The traditional presentation experience will change significantly by 2025 and technology CEOs will have to confront investors with ai-enabled models and simulations as traditional presentations and finance will be insufficient”
Creating ai tools to evaluate attractive ai opportunities seems like an effective use of the technology among multiple attractive uses of ai. It is a fair expectation that the investment community will benefit from quantified tools that make informed investment decisions, saving the industry from another bubble.
Vidhi Chugh is an ai strategist and digital transformation leader working at the intersection of product, science, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, author and international speaker. Her mission is to democratize machine learning and break down the jargon so everyone can be a part of this transformation.
