Like I said 3 days ago. Before we talk about full AI ROI we need to learn the basics.✍️ (must read for the people that need to bridge all these tech post on models, GPUs, etc to human psychology and adoption)
Yesterday at AI Infra Summit, I listened to a panel called “Operationalizing AI: Turning Data, GPUs, and Inference into Enterprise ROI.”
It was moderated by Radhika Malik of Dell Technologies Capital, with Kaushik Shirhatti of NVIDIA, Boris Lukashev of WhiteFiber, Ruben Bryon of Verda and Alex Saroyan of Netris on the panel.
A lot of things where so inline with my own thinking that i feel the industry is moving in the right direction.
I was very clear on something a while back: you need to give people time to adopt AI and get used to the basic ways of using the tools. Otherwise, it becomes way too fast, and that essentially stops adoption. Thats not rocket science.
Kaushik compared generative AI to a vending machine. You press a button and get an answer. An agent, on the other hand, is more like a new employee.
It needs to be onboarded. It needs the most current information, access to the right tools and an introduction to the systems. It also needs boundaries, security, supervision and all of that.
Kaushik also compared the complete team to a relay team so the compute, network and software may all be super good individually, but if you have four sprinters on your team, you need to practise the handoff. Its as simple as that.
That is where a lot of performance, and therefore ROI, is won or lost for the enterprise and, to keep it even simpler, for people using AI.
I’m going to show you a few angles here.
Ruben focused on speed and adaptability. He compared a neocloud to a fighter jet and a conventional cloud more to an aircraft carrier.
This is also so spot on because I’ve been talking about the agility that, for example, Nebius $NBIS provides and WhiteFiber $WYFI also.
This panel showed again that you need to adapt fast, with physical deployment, software and operations at the same time, even when billions in capital have just been spent.
A lot of people misinterpret the agility that is needed for this build-out.
Ruben’s advice was to get people using the tools quickly, including in areas where the return is not yet completely clear. The cost of producing intelligence continues to fall while the models keep improving.
I couldn’t agree more with that.
This session made so much sense to me, based on how I was already thinking, that I had to share it with you.
Boris, for example, said:
“Preemptive optimization is the work of the devil.”
You need to reach the required level of accuracy first and then optimize for performancenot earlier.
So how much proof of ROI should we demand before allowing people to experiment? We can measure GPU utilization, inference speed, token volume, everything but those numbers don’t tell you anything about whether AI improves a decision imo.
@tengyanAI talked about it this week as well.
I personally see that it is a really fine line that you need to walk. If you ask too early, you are killing promising applications before people really understand what the technology can do.
I think we are now at the point where we should start that.And if you leave experimentation unfocused for too long, you burn through a lot of resources without actually learning anything from feedback.
The panel agreed, and I was already fully aligned with this. The right approach is to let people explore what becomes useful and then concentrate investments on three or four workflows that materially change businesses.
I found this such a good panel discussion. Everybody should read it if it comes outor when it comes out. I’m not sure.👌👌
$IREN $DELL