Oct 1, 2026 · 26 min · 8 segments
In this episode of "Full Tech Ahead," host Amanda Razani interviews Stefaan Vervaet, co-founder and CEO of Akave. They discuss the major shifts in AI infrastructure, specifically the rise of "Neo…
Well, knowing this, what can business leaders do to sort of stay ahead of this and be more prepared?
I mean, first of all, I think it's important to understand what you're trying to focus on.
What is the type of AI project that you're driving? Are you looking at using your existing datasets to monetize those datasets and differentiate yourself from your competitors by fine-tuning, let's say, LLMs based on your private datasets? or are you in are you inferencing are you actually providing more of a better customer experience through let's say running llms at the edge or in your applications that the customer engaged with today so the two different uh main workloads inferencing is very hot lots of interesting things happening to improve uh customer experience let's say you know you're going to a support service or you're calling support instead of a human person, you have a bot or an LLM that you're engaging with.
That's where we're talking about larger clusters that are typically dedicated to your specific workloads, whether you're using video content, images, or text, or maybe it's audio, depending on your business.
So what you need to do today or what we're seeing customers look at is the business model as a whole.
Can we make the case to own the equipment ourselves and make a long-term commitment where we believe that we will find more use cases to justify the purchase or the leasing of that GPU equipment? Or do we go forward with a rental where We want to still have the flexibility to test and run proof of concepts and validate our pipelines that we're testing, but at a lower commit.
So that's first, trying to figure out what is your long-term strategy and how much of a commit are you willing to take as a whole, as a company? Because if you're an AI startup, you want to move really fast, but you may not have the budget to make this long-term commitment.
Meaning if you really want like a three-month contract today, or H100 or that's no longer possible.
And if you look at this ecosystem, you have neocloud partners that really focus on being that broker between the capital provider.
So they will find your capital provider and they will map you and connect your capital.
and you then can get the full end-to-end experience where they will come with the software to load your AI pipelines.
Some of them have built-in computer orchestration stack, and it will package that as part of the service.
others will force you to rent the equipment from them and so then you're a little bit more at their uh how would i say uh you know you're sort of like dependent on that infrastructure provider because you know last last week for example there are customers that were complaining that only had a one-year contract their one-year contract was up they thought they could renegotiate But those interrupt providers, those new cloud providers, they couldn't extend their contracts because they were replacing that old equipment with brand new equipment that was yielding high returns, was able to serve a higher level of customer or customer that was willing to pay more money.
So even if you have a contract in place, it doesn't necessarily mean that you can extend that contract.
know you're sort of like back in the queue if you want to purchase the hardware there as well you have to really understand that there's a huge uh shortage in gpu so you have to be aligning yourself with those neo clouds that really have the allocation and can provide you a commitment on getting an allocation because there as well, I've heard cases with customers last week that said that they had been waiting for months to get allocation of these GPUs and in the end didn't get the allocation and were back in the queue.
Well, knowing this, what can business leaders do to sort of stay ahead of this and be more prepared?
I mean, first of all, I think it's important to understand what you're trying to focus on.
What is the type of AI project that you're driving? Are you looking at using your existing datasets to monetize those datasets and differentiate yourself from your competitors by fine-tuning, let's say, LLMs based on your private datasets? or are you in are you inferencing are you actually providing more of a better customer experience through let's say running llms at the edge or in your applications that the customer engaged with today so the two different uh main workloads inferencing is very hot lots of interesting things happening to improve uh customer experience let's say you know you're going to a support service or you're calling support instead of a human person, you have a bot or an LLM that you're engaging with.
That's where we're talking about larger clusters that are typically dedicated to your specific workloads, whether you're using video content, images, or text, or maybe it's audio, depending on your business.
So what you need to do today or what we're seeing customers look at is the business model as a whole.
Can we make the case to own the equipment ourselves and make a long-term commitment where we believe that we will find more use cases to justify the purchase or the leasing of that GPU equipment? Or do we go forward with a rental where We want to still have the flexibility to test and run proof of concepts and validate our pipelines that we're testing, but at a lower commit.
So that's first, trying to figure out what is your long-term strategy and how much of a commit are you willing to take as a whole, as a company? Because if you're an AI startup, you want to move really fast, but you may not have the budget to make this long-term commitment.
Meaning if you really want like a three-month contract today, or H100 or that's no longer possible.
And if you look at this ecosystem, you have neocloud partners that really focus on being that broker between the capital provider.
So they will find your capital provider and they will map you and connect your capital.
and you then can get the full end-to-end experience where they will come with the software to load your AI pipelines.
Some of them have built-in computer orchestration stack, and it will package that as part of the service.
others will force you to rent the equipment from them and so then you're a little bit more at their uh how would i say uh you know you're sort of like dependent on that infrastructure provider because you know last last week for example there are customers that were complaining that only had a one-year contract their one-year contract was up they thought they could renegotiate But those interrupt providers, those new cloud providers, they couldn't extend their contracts because they were replacing that old equipment with brand new equipment that was yielding high returns, was able to serve a higher level of customer or customer that was willing to pay more money.
So even if you have a contract in place, it doesn't necessarily mean that you can extend that contract.
know you're sort of like back in the queue if you want to purchase the hardware there as well you have to really understand that there's a huge uh shortage in gpu so you have to be aligning yourself with those neo clouds that really have the allocation and can provide you a commitment on getting an allocation because there as well, I've heard cases with customers last week that said that they had been waiting for months to get allocation of these GPUs and in the end didn't get the allocation and were back in the queue.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
