Be Careful About Investing In AI
Welcome to the Real Estate Espresso podcast, your morning shot on what’s new in the world of real estate investing. I’m your host, Victor Menasce. On today’s show, we’re looking back in history for some of the narratives that surrounded the adoption of new technology. I’m going to arm you with three important questions.
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Now today’s show we’re looking back in history for some of the narratives that surrounded the adoption of new technology. The year was 1999. At the time it seemed like the Internet was the answer what’s the question? Companies were spending hundreds of millions being optical fiber anywhere they could. After all, the internet would need lots of fiber to carry all that traffic. There was tons of investment in the core of the network to carry all of this traffic.
I personally was an executive in the tech industry. I left Nortel in 1997 just before the peak. The next company I joined was ~~Tunder~~📝Thunder Semiconductor which we took public in 1999. We were designing microprocessor core logic chips that were used in all kinds of different applications. One of our customers was Motorola who was shipping a quarter million cellular base stations a year at the time. These would eventually be upgraded from the GSM base stations to edge and eventually 3G. Back in those days the emphasis was on building out the network.
You might be wondering how the core of the network is going to need all of this capacity if nobody is making any major moves at the edge of the network. Well that’s a great question. The limiting factor was the amount of data that you could carry on a copper twisted pair phone line which was coming into your house at the time. Coaxial cable to carry cable TV was designed as a broadcast network. It would only take signal to your house but not the other way. It would take a long time to figure out how to change out that infrastructure to make it work in both directions. It seemed like nobody did the math to figure out that the emphasis on the capacity of the core of the network was somewhat misplaced when the business case for the demand in the broader population was being largely ignored.
Now I see a lot of parallels today. There’s tons of money going into artificial intelligence data centers. That’s the core of the network. But what about the actual use cases? For AI to truly proliferate, it needs to exist in millions of autonomous robotic devices in the real world. Will they have a connection to the data center? Well maybe but I’m not convinced. So the true rollout of AI capability on a global scale goes far beyond ChatGPT or Gemini. We need to be cautious as investors when looking at promising solutions.
Or later my career, I took progressively more senior positions in the tech industry. By 2004 I was vice president of engineering at AMCC that was headquartered in San Diego. I was also president of AMCC Canada, which is where I live. My company had raised about a billion dollars in the public markets at the height of the dot-com frenzy. As a result we had all kinds of startup companies parading through our boardroom with the hopes of getting acquired by a company with a ton of cash.
Now I learned to ask three very simple questions of every startup company. First question is, what problem are you solving? In today’s environment, I’m pretty confident that no venture capitalist would fund a startup without a solid answer to that question. But so often founders will come into the boardroom and talk about how cool their technology was. We often didn’t get good answers to that question. What problem are you solving? Naturally they walked out of our boardroom empty-handed.
Now if you can’t answer the first question, there’s no point asking the second or the third. The next question is, is the problem acute enough the customer is willing to spend money to have that problem solved? Now of course that second question cascades into a whole series of questions that unpack the value proposition. What is the solution worth? How will the customer value the solution? How will they pay for it? Do they want to pay for it in a lump sum up front or as a recurring cost? There’s all kinds of business model questions related to establishing the value proposition.
And then the third question, assuming the value proposition is well established, is are their willing to buy the solution from you? Now if your solution has a low barrier to entry and your company only solves this one problem, then will they buy that solution from you. If the customer already has a commercial relationship, say with Amazon or Microsoft, and one of those companies easily can add that capability to their offering versus requiring a separate commercial relationship with your startup company that no one’s heard of, they may choose to go with the established player like Amazon or Microsoft.
Now as private investors you’re going to be approached by all kinds of startup companies to invest in all kinds of AI initiatives. You might even be approached to invest in data centers. This is where you need to perform proper due diligence and ask yourself these questions and more. It’s that third question that often trips people up. In a world that has not consolidated from thousands or maybe hundreds of companies to a few dominant players, remember consolidation will happen. It happens in every industry. It happened in automotive. It’s happened in television. It happens in every industry. When a startup company is using an open source model to create their solution, the barrier to someone else copying that solution is extremely low. Just look at how quickly Deepseaking China was able to come up with a competitive AI solution in a very short period of time.
These companies will deliver a working product but bridging the gap between a working product and something that’s going to work commercially is a huge leap. So, be extremely cautious when you’re approached to invest in AI. As you think about that, have an awesome rest of your day. They’ll make some great things happen. We’ll talk again tomorrow.
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