As artificial intelligence and large language models continue to gain traction, tech companies are investing heavily in this technology. To discuss the outlook for the sector, Goldman Sachs Global Head of Asset Management Client Portfolio Luke Barrs joins Catalysts.
Barrs highlights the profound impact of AI, saying it is “transformational” and permanently alters productivity and dynamics across industries. He notes that some tech companies are pouring significant resources into AI development, investing “every dollar they can find” to maintain their competitive edge and “stay ahead of the pack.” However, as more companies unveil their AI capabilities and cost-effective solutions, Barr expects competition to increase and market dynamics to change.
“I think the very short answer is that we're still in the early stages of this transformation, especially in the public market context,” Barrs told Yahoo Finance.
For more expert insights and the latest market action, click here to watch this full episode of Catalysts.
This post was written by Angel Smith
Video transcript
Video is driving the market.
Rally companies try to reallocate their spent capital through A. I training.
They are not all created equal.
Yahoo Finance spoke with Alex Karp.
He is PCEO about the challenges facing executives regarding I costs.
Here's what he had to say.
I think what everybody watching this is aware of is that you have a huge high cycle around big language models, and then when you try to use them in your enterprise, you finds that this is similar to self-flagellation and is costly with no output.
So how can investors figure out I costs and not run into that self-flagellation quote that Alex Corp just talked about?
For more on this, we have Luke Bars.
He is my Global Head of Portfolio Management for Primary Equity at Goldman Sachs Asset Management.
Luke, great to have you here.
I heard that voice you just heard.
But from your perspective, what's the right framework for investors to use to break out capex and what's, you know, really going to drive growth on the R and D side versus something to say on an earnings call? Cowardly.
Yeah, so, first of all, Shawna, thank you so much for having me. I would first say that we need to consider the fact that I am a convert.
It's changing how we work.
And that is rapidly changing the dynamics in some of the technology hardware businesses that we as investors evaluate and value.
Now, what I would say is just thinking about the timing and the timing of it, we're at a point where your hyperscaler is investing every incremental dollar they get into staying ahead of the pack. can do as much as they possibly can. LLM Development Site.
And that means if you're a semiconductor business involved in server and hypercar capacity expansion, you're in a very sweet spot right now.
Now I think as we go forward, we should expect competition.
We should see some change in the dynamic in this industry.
As we see more companies come to market with capabilities in this space and or as we see a shift from class-leading edge nano-capable semiconductors to something slightly more cost-effective in the ASIC space, Actually changing it up a bit.
But right now we are very fast on space.
We have to think about pricing, but we see a lot of opportunity.
Luke, by the way, you recently had the opportunity to join a small group of investors invited to spend some time with Microsoft's senior management team.
I want to know what you take away from these conversations just in terms of where we are in this AI cycle and how much opportunity there still seems to be.
Yeah, and really the only interesting thing is, our lead investment team on the global and US side has spent time on the West Coast meeting with about 20 companies, public, private and actually on the VC side as well.
So I think we've got some real-time intel on what's going on in that space, and I think the very short answer is that we're still in the early stages of that transformation, especially In the public market context.
We are still at the cutting edge of how you can use LLM and big data analytics tools in the I framework, and then how you can apply it to real-world solutions?
And so I think right now we're seeing a clear period of rapid earnings in enabling technologies.
We are not seeing evidence of the software business going through the way we would expect in the medium to long term.
And so as we see long-term adoption of AI techniques in these productivity-enhancing solutions, I think that's another ball case for some of these software businesses, especially the larger ones. Names we know are at the forefront of this development.