"It's the same AI word, but a different world."

When this author began discussing the rationale behind an AI-era innovation fund nearly 300 meetings before, it was relatively easy to find an agreement that post-2022 "AI" represented a seismic shift worthy of material investment.. Of course, some hype-believers disagreed. Another group reduced everything to holding nVidia and Microsoft forever, which has worked beautifully and spared them deeper analysis.

Yet, many traditional allocators, while believing in an unprecedented change, would ask to see the "track record" in the very next breath. The author would counter:

• AI and tech are different. Traditional tech investing requires analysis of hardware cycles, enterprise software trends, or consumer apps, for instance. Investing in understanding-based AI domains is as different from these as perhaps investing in financials.
• Copper and cotton are both commodities. Would one give copper allocation to a trader with a cotton history? Or, the pool reserved for India to a manager with an excellent record running China?
• An irrelevant record is perhaps more dangerous than having no track record. "AI" and post-"AI" investing, like any other investment field, can benefit from those with knowledge of the field but also with long experience, maturity, and sincerity given various ethical, moral, and innovation process risks.

Over the last few weeks, these arguments have been met with far less polite nods or eye rolls than before. As always, the tech industry - despite an overabundance of "tech" staff - is the first to recognize that the field requires a different skill set and, hence, different types of people. These professionals notice the critical differences between those with experiences in other technologies, including even earlier ML/AI fields, versus what is needed now.

Outside tech, the trend is no different (https://bit.ly/3GkGx7M), at least in the technology staff hiring. Most tech departments do not permit mere repurposing of the existing staff where possible. Those carrying the technologist tags cannot claim to be experts of LLMs and foundation model details on previous credentials only. In fact, an increasing number is focusing on the finer lines between those with previous AI/ML experiences and the skills needed to work on the new models and methods.

The list of companies where tech departments are paying a premium

for the new AI skills includes finance companies and those doing investment allocations. 

In the investment world, similar discernment is needed to ensure investments are made in the right areas when everyone proclaims, "We do AI" and "We use LLMs." More domain knowledge and diligence are required because of the unforeseen pace of technological change and continuously shifting regulatory sands. If there was ever an exciting, growth, multi-year field that needed its own experts - not just in real life activities but also in the investing domains - here is one.

But, then, would this author have concluded anything else?!