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Companies are about to waste billions on artificial intelligence — here’s how to avoid becoming one

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“This is venture capital, not venture capital.” This was the loving response a dear friend got from a venture capitalist when he pitched an idea. But when we’re in the hype cycle of new technology, that caution is thrown out the window. After all, VCs have to deploy all the money they raise, and the cost of missing out on something big is higher than the negative impact of swinging and missing out, especially when everyone else is taking the same swing.

A similar dynamic exists within most companies – the current technology is artificial intelligence and anything related to it. Large Language Models (LLM): They are artificial intelligence. Machine Learning (ML): This is Artificial Intelligence. You are told that project is unfunded every year – call it artificial intelligence and try again.

Billions of dollars will be wasted on artificial intelligence over the next decade. If this sounds like the opposite, it’s not. Every major technology wave creates excitement—even before we know how real and transformative it will be. Search, social and mobile have all had widespread and lasting impacts, but virtual reality (VR) and cryptocurrencies have had a much more limited impact.

You wouldn’t know that from the headlines five years ago, though. Now, everyone is running to show how much they are spending on AI and how it will change everything. This shotgun approach to investing inevitably leads to some huge successes and many failures. For venture capital, the same dynamic drives company leaders to approve investments in the name of artificial intelligence that are optimistic at best, but more often a sign of missed hope and risk-taking.

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This does not negate the fact that the LLM is a game-changing technology. Just look at how quickly ChatGPT reached 100 million users relative to other transformative companies:

Almost every enterprise company has some kind of job utilizing LLM and AI. So, how should you decide where to place your bets and where to stand a chance of winning?

With a clear understanding of these three things, you will reduce wasteful spending by 80%:

  1. Understand total costs over time;
  2. Ask why others can’t do it;
  3. Make some bets that you’re willing to stick to.

1: Understand total costs over time

As you consider saying “yes” to your next AI project, consider the current and future costs of the resources required to sustain the project. A data science team’s 10 hours of work typically consumes 5 times the engineering, DevOps, QA, product, and system operations time. The company is littered with scraps of projects that once were a good idea but lack the sustained investment to sustain them. It’s hard to say “no” to AI initiatives these days, but saying “say” too often often comes at the expense of fully funding the few things worth supporting tomorrow.

Another dimension of cost is the increasing marginal costs driven by AI. These large models are expensive to train, run, and maintain. Overusing AI without a corresponding increase in downstream value can eat into your profits. Worse, withdrawing a released or promised feature can lead to customer dissatisfaction and negative market perception, especially during a hype cycle. Just look at how quickly Google’s reputation as an AI leader was tarnished by a few missteps, not to mention the early days of IBM Watson.

2: Ask why others can’t do this?

Lessons learned from textbooks are easy to forget. We’ve all read about commoditization. The same lessons you learn from getting knocked down in real life stick with you. When I was a chip designer at Micron, our core product was a near-perfect commodity—memory chips. No one cares about the brand of the memory chip in a laptop, only how much it costs. In that world, scale and cost are the only sustainable advantages over time.

The tech industry can be bimodal. There are monopolies and commodities. When you say yes to your next AI initiative, ask yourself: “Why us?” It’s not fun to work on commoditization over time, especially when you don’t have the scale/cost advantage. Take it from me. The only ones that will definitely benefit are Nvidia and AWS/Azure. The only way to solve this problem is to focus on things that have a defensive moat. Prioritize access to data, proprietary insights around use cases, or applications with powerful network effects to keep you ahead of the curve.

3: Make bets you’re willing to see through

The simplest bets are those that will improve the business you’re already in. I’m reminded of that old BASF ad: “We don’t make what you buy, we make what you buy better.” If the application of artificial intelligence powers the products you already make, then the bet is The easiest to place bets and scale. The second easiest bets are those that allow you to move up and down the value chain or expand laterally into other industries.

The most challenging but important bet requires you to cannibalize your current business with new technology—and if you don’t, someone else will. Double down on the few that pass both tests and be prepared to let those bets pass. Venture capital firms and startups will do the rest.

So while the hype around AI is real and justified, if there’s one lesson we’ve learned over the years, it’s that these cycles bring with them not only legitimate investments, but also a lot of waste. By following some of the tips outlined above, you can ensure that your investments have the best chance of bearing some algorithmic fruit.

Mehul Nagrani is Managing Director of InMoment North America.

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