Silicon Valley has an AI Problem
Silicon Valley has an AI Problem. The rapid evolution of AI technology, coupled with an overwhelming hype cycle, has given rise to a concerning trend I have seen in many new startup founders. The collective mindset appears to be veering away from sustainable company building, clouding the judgment of startup founders and resulting in the development of suboptimal solutions.
To preface, I am bullish on AI. I have actually been extremely bullish for years now. However, I believe the pervasive hype surrounding AI has compelled many to integrate it into their solutions, whether justified or not. Amidst this fervor, it’s easy to lose sight of the fundamental principles that underpin building a lasting company. At its core, launching a startup involves identifying a genuine customer problem, comprehending its magnitude, and assessing the willingness of affected individuals to pay for a solution.
AI opens the floodgates as it allows for the creation of solutions that are exponentially better. This will undoubtedly result in the rise of massive winners that come out ahead by using the technology.
However, much of the issue I observe nowadays is that founders seem to be focused on creating companies that use AI as the solution. This means they are going out there trying to find problems that they can fix with AI, rather than using AI to solve problems they have found. It’s a very subtle difference in thinking, but one that I believe has massive implications when conducting business. Tools used to solve a problem should be picked purely in terms of what delivers the maximum amount of value to your customer. The value you are delivering to the customer should be your selling point, not the means of how you are delivering it.
This isn’t a recent phenomenon; it’s an issue we have seen for years whenever a new technology emerges. When cloud-hosted applications became prominent, many people sought to create new cloud-hosted versions of people’s favorite desktop applications. Spotify allowed people to listen to their music anywhere as it was on the cloud — great! Netflix allowed people to watch movies anywhere — awesome! But in both examples, the emergence of cloud apps was a means of creating an exponentially better solution for a massive existing problem that people were already experiencing.
What we often forget are all the cloud companies that failed because their sole selling point was being a cloud web app. I remember numerous PowerPoint clones that emerged during that time, and almost all of them faded away. Why? Because, although being hosted on the cloud had the inherent benefit of allowing presentations to be accessed from anywhere, that problem wasn’t significant enough to justify the lackluster experience those platforms offered compared to PowerPoint. Success stories finally emerged when the cloud was leveraged to address massive existing problems, such as the need for real time collaboration. The technology enabled an exponentially better solution for a substantial problem and justified its existence in the market.
Over the last couple of months, I’ve come across numerous products with AI as their foundation, yet they often fall short in justifying why the incorporation of AI at the core should make them a preferable choice over existing platforms. Additionally, I’ve also observed an abundance of similar, readily available AI products that essentially act as wrappers on top of OpenAI, competing for the attention of the same small customer base willing to tolerate a subpar experience. In many cases, this competition persists until platform leaders integrate similar functionalities natively into their platforms.
Given this, I think now more than ever, it is crucial for startup founders to revisit fundamental business questions:
1. Identify the Customer Problem:
— What is the problem faced by customers?
— Have others validated experiencing the same issue?
2. Assess the Pain Point:
— How much does the problem bother customers?
— Are they willing to pay for a solution, regardless of the experience quality?
3. Analyze the Market Gap:
— What are incumbents in the space missing?
— Is there a neglected population or a lack of technological integration?
4. Evaluate Problem Significance:
— Is it a problem worth solving?
— Avoid solutions in search of a problem.
5. Define MVP Using Available Tools:
— What does the minimum viable product look like?
— How can it justify enough value to customers?
6. Strive for Exponential Improvement:
— How can the solution be made 5–10–1000x better than existing alternatives?
This may or may not lead to AI being a part of your solution; it could be a small feature or come up later in your roadmap. Regardless, the key is to leverage tools that deliver the most value to the customer and evaluate their usefulness from the perspective of the initial problem.
So, if you are a startup founder, please don’t set out to build an AI company. Instead, identify a legitimate problem, focus on tools and technology that craft the best solution, and build from there. Whether AI becomes a central component or not, should be solely dependent on if it helps with the goal of delivering maximum value to the customer. While there will be billion-dollar businesses with AI at the core, many successful ventures won’t involve AI at all. So if today using AI isn’t in service of that, don’t let Silicon Valley, analysts or anyone dictate otherwise; stay focused and continue delivering value through any means necessary.
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Thanks for reading,
Daivik Goel
