324 | Breaking Analysis | From Tokenmaxxing to Sovereign Alpha: Who Controls Your AI Economics?

This is breaking analysis with Dave Volante. The AI industry wants enterprises to measure progress in tokens or things like model calls and usage, but those are largely vendor revenue metrics. They're not enterprise of value metrics. You know, this month's Canva example explains why. On August 6th, the information reported that Canva cut its 2026 revenue growth forecast from 30% down to 20%. because its AI features cost more to run than it expected. Let that sink in for a minute. A company that was generating almost a billion dollars in revenue a quarter and growing 25% had to lower its outlook because of an input cost. Canvas said it had relied too heavily on ex expensive thirdparty frontier models like OpenAI or Anthropic and the fix was not a negotiated vendor discount through its procurement department. It rebuilt the stack. It had to rebuilt the stack inhouse with open-source models and its Leonardo AI acquisition and task level routing reportedly cutting the cost of an AI task by roughly 90%. Its video and image models were reportedly 17 and 30 times cheaper than frontier alternatives. Your CFO is not buying tokens. The enterprise wants outcomes. So who captures the economic benefit after the model and the the cloud vendors after you do all the integration, after you figure out your governance, you got to pay the the power bills. Who captures the economic value after that? That is what we call financial sovereignty. It's not just about governments. It's not just about data residency or even self-hosting. It's the ability to control and change, if necessary, the economic terms under which AI operates. A sovereign enterprise controls its data. It controls its evaluations. It controls its own policies, its routing, its costs, the the telemetry of that cost, and and of course exit strategies if necessary. It decides what to own, what to rent, and which frontier model capabilities create advantage for that company. Capabilities, they can be rented, they can be outsourced, but control has to be architected. Alex Karp, he attacks what we call tok token maxing, optimizing the vendor's meter rather than the value that the enterprise retains. The alternative is what we're calling sovereign alpha. Retaining more of the value created from your data, your workflows, and your domain expertise because you control the cost curve and preserve the ability to move. No vendor can confer that sovereignty onto you. Vendors, they get you components. Only the enterprise can define and enforce its own sovereignty boundaries. Welcome to this week's breaking analysis. We've titled it from token maxing to sovereign alpha who controls your AI economics and joining me is Amit Aal Goffin CEO of agency labs and a principal analyst for the cube research emit amit great to see you thanks so much for coming on amazing setup this week with your Canva post thank you for being here >> thank you for uh having me here and David I can't wait for this one I'm I'm chomping at the bits for I bet you are okay and I are going to examine what enterprises have to control, what they can safely outsource, where the real unit economics are created, and why the financial sovereignty may determine whether organizations capture the real value of AI or simply fund someone else's alpha. So, let's start with the evidence that this is not an ideological debate. So, these companies that we're showing here are, you know, they're not rejecting AI, they're leaning in. And in each case, the capability appears to have delivered real value. The problem was control of the economics. You got the example of Uber reportedly consumed an entire year's AI budget in one quarter. And they didn't stop using AI. They just reset the defaults and and routed workloads, you know, toward lower cost models. Microsoft is building more of its own model capability while openly say saying it wants to reduce and ultimately maybe even eliminate the cost of paying anthropic. Lindy offers the clearest supplier switch example. Anthropic had become the largest expense bigger than its payroll. So, it had to move traffic to another model provider. And it says it reduced cost while improving performance on its core use cases. You know, this is really, really important. The product can work great and still fail your financial sovereignty test. Nobody is doing this because of ideology. They're they're doing it because they got hit with an expense that they didn't expect. So am I mean I want you to add some color to these examples. You've come up with these and and tell me this. When an organization doesn't control the model path or the pricing of of its models or frankly the ability to switch if it needs to to exit, what is it giving up? Please help us understand this. >> So thank you for providing this valuable context statement. Uh it cannot be overstated that financial sovereignty is not a pass or fail exercise. And and I'm going to say this a few times throughout today's conversation. There's a spectrum of acceptable risk that every organization must decide where to place those boundary conditions and what it can live with. So let's take the Microsoft example. Now Microsoft has a platform called Microsoft Foundry. Um it's AI foundry actually that offers a broad spectrum of AI solutions including Microsoft models, Microsoft uh partner models and also c uh customer control deployments. Its use of enthropic can be understood as a developer focused decision. Simply put, engineers may prefer clot for certain tasks over open AI and Microsoft own models, which it fair, but once you per once that preference breaches a company's uh pain tolerance for margin degradation, it creates a financial trip wire that imposes controls and routes work elsewhere uh for lower cost models and open weight models. Now, you can argue that this is as much a technology sovereignty issue as much as anything else, but the takeaway here is that the trip was in fact a financial pillar trigger. Now, let's go over to the Uber example because unlike Microsoft, there's an element of coopetition uh with anthropic and and Microsoft. Here with Uber, that's not the case at all. It's viewed as a pure partnership, as a straightforward one. use state-of-the-art models for highest and best use uh for its engineering talent. Now, that strategy worked well up until it ran into a brick wall 200 miles or that. Now, this happened because when you burn through an entire year's budget in roughly four a month, there's no way to confidently budget two, three years out as a CFO would require, let alone budgets out here. So of course the CFO had to pull the plug in this particular case. Now with Lendy and Canva uh and much like Uber as well, they discovered that owning their financial alpha was more strategic than claiming state-of-the-art capabilities at the time of the um I mean really at the end of the day we're all running a business here and we we can inside of that. If your financial livelihood relies on a vendor whose economics can compress your margin as as easy as that happened here with KA that example of losing 10 billion in market cap overnight because of these UN economics I mean the saying goes Houston we have a problem that's truly the case here now with Switzerland I'll give you a bit of a contra example but still the same concepts here Switzerland gave us a more nuance example because There was a Swiss national supercomputing center that developed what it called Aparitus or Aperitis, a fully open-source training model that Alps uh that was trained in the Alps on a supercomputer across a thousand languages. Now, this is truly an impressive engineering feat and was really meant to the sovereign asset that the Swiss government could tout. But here's catch. Um even though owning the model was a big factor here, the training and inference and everything else happened on top of that. the downstream effect was the deployment sovereignty and what had happened is the Swiss uh uh government had organized and uh and kind of rallied around this and recognized that operationalizing this and making this a multicluster multi- deployment scalable uh across all the different region it would require to do so was much more than they could bite off of and they didn't have confidence that they could do so in the in the in the right way. So, so even though the purist in me said this is in fact the right approach to take it as a sovereign uh deployment, they did go ahead and wire 140 million Frank uh check to Microsoft to take on the ownership of uh both the operational and the technology stack that they eventually had to go into forfeit as a result of a pure financial consideration. So again, we could argue whether this was the right approach or not from a legal pillar standpoint. Uh they did expose themselves to what is called the US cloud act that could uh subpoena them uh for data residency and go ahead and take that piece of it away. That is not a perfect uh that is not a perfect solution. But if we had to go and put a price tag in sovereignty, uh the financial pillar is what triggered and essentially took all of those away. So no fault of their own. Uh sometimes it's intentionally done so and deliberately by virtue of making those trade-offs and sometimes there are trade-offs to do. But from our perspective, the reality is that financial sovereignty was in fact much more important to them than giving up all the other pillars combined. Hopefully that makes sense. >> Yeah, thank you Amit. Alex, bring up that last slide again if you would. I I just want to comment that these four examples that Amit to just took us through and the fifth of course is Canva. Amit is one of the foremost experts on this topic and it's a relatively new topic not in terms of sovereignty that's always been around but in terms of how it applies in this era of AI and he has done deep research on on each of these and and and has taken them through the the model the five pillar model that you saw you know behind him. Thank you Alex. And so this information is available. It's publicly available on the the Cube Research uh website. And if you need more information, we're happy to, you know, get into it with you. Um let's move on. Alex Karp's argument is that the enterprise should control their compute, should control their models, their data stack, and the alpha. That was his big point on that CNBC interview. Their alpha created from their proprietary knowledge. We have long argued that Jaime Diamond is Sam Alman's biggest competitor because he's never gonna not gonna give up, you know, all of his alpha, if you will. George Gilbert and I published that, you know, a couple years ago. But anyway, Karp attacks this usagebased frontier pricing as a kind of a wealth tax on the enterprise. And you know our sort of short version of that for that behavior is he's he's simply crit criticizing the the tok to token maxing trying to you know optimize the vendor's media meter versus you know your own alpha more tokens more calls more consumption rather than optimizing the economic value that the enterprise retains but this slide extends Karp's argument there are really two forms of alpha at risk one is the differentiated capability created from your data and your knowledge and your workflows and the other is the financial you know increment the surplus that you retain you know or give up frankly depending on who controls the c cost curve. So I mean I wonder if you could explain those two alphas why you see them as sort of the same you know surrender just in in in different currencies and and Alex if you wouldn't mind as as as Amit you know speaks just bring up the the slide so we can sort of see some of the key points. Go ahead. >> Yeah, absolutely. So the alpha described by Alex falls under a technology pillar and it directly relates to intelligence. His thesis by the way which is mostly correct is that renting the intelligence can mean giving up your alpha. The provider can learn and shape uh the size of your market, aggregate demand and your product signals and make eventually what would be a competitive offering or adjacent workflows. Just ask companies such as cursor how they feel about Frontier providers moving into their space. His solution is to take openweight models, host them on Nvidia and GPUs in a customercont controlled data center and use this proprietary operating layer to own everything outright. Well, right advice, wrong approach because every effectively what he just told you is go to the pawn shop and swap financial capture for technology capture. If you really wanted to execute on his advice in a sovereign way, you would use an inspectable forkable fully open-source operating system that allows you to own your crown jewels. And to be precise about open-source licensing has a fit uh and deployment distribution model. So permissive licenses such as uh Apache 2 and MIT are often simpler for enterprises. AGPL is a copy left license um that can also be used but only when the organization understands and complies with those obligations which in my experience is much harder to to do and to enforce. So take for example the Chinese auto manufacturer MG who is currently in the litigation in Germany courts concerning their alleged failure to provide notices and source code requirements by GPL family licenses and that used in their car chips. So they're currently facing a potential embargo for selling in the European markets because of that license violation. Again, something to be watchful for, but there's plenty of open- source options around Apache to MIT permissive licenses that are much more easy to work with. >> Yeah, your point about those licenses is key. Of course we we we many of us remember when Facebook or Meta rather introduced Llama they had these restrictive uh you know licenses and of course the newer open source models I believe are Apache too. Um so that's really important consideration but the but the right conclusion basically emit from what you just said is that is not that Palanteer delivers sovereignty in a box. That's not vendors don't it's not a skew as you've written um the sovereignty that is it it's that palunteer can make an organization more sovereign while leaving important dependencies intact and that brings us to sort of the definition of what exactly is financial sovereignty if it is not a purity test or simply a synonym for lower cost. So at this point some people may be asking whether financial sovereignty is equal to sort of is it just like TCO with a whole new label and we don't think it is. TCO looks at what a system costs under a given set of assumptions over a period of time. Financial sovereignty asks a different question. It asks who controls those assumptions and whether the enterprise can change them without ripping and replacing the core system. And we should highlight the you know the the truth straight away. The upfront invoice for self-hosting oftentimes is not cheaper. The objective is not no external spending. It's to make the cost curve predictable, keep dependencies bounded, and preserve a tested exit. And as you know the line on this slide says, cheap is a price. Sovereign is a position. It's a posture. So am I mean define that distinction for us? What makes financial sovereignty a control posture rather than just another cost calculation? how how should enterprises think about the sovereign alpha equation, you know, at at the bottom of that slide. >> Thanks, David. That that's actually a very important distinction that cannot be overstated as a major enterprise doing financial modeling three years out. What is a scarier scenario in your opinion? Budgeting $500 million towards reserved infrastructure in a self-hosted model that gives your organization a predictable intelligence baseline for three years. as expensive as they may sound or signing a three-year 500 million commitment with a Frontier Lab where you may find yourself burning through those same $500 million uh three-year commitment up front in an 18-month period. So, what you're describing is the same paradox enterprises actually faced back in the early of the cloud days. I was early on in those days as well in the PHOPS domain and I remember these conversations circa 2010 to 2015 very very vividly. Now guess what was the solution back then and probably still is today? Keep the data center for steady state and burst into the cloud or for the cloud purists out there reserve capacity and burst into ondemand and spa instances. What does this history lesson really teach us? the alpha state with the companies because it retained the ability to choose where the workloads ran. No different from using frontier models today where many way there are many ways by the way uh to architect these systems of intelligence so that frontier intelligence is used only for orchestration and smaller models do the work. uh you could do this with hyperscalers and to a lesser degree even with some of these frontier labs offering multiple different models out there. All of that is true, but the hard lesson here is that it's easier to price a financial sovereignty even when the baseline is expensive versus to live with the unknown risk that you can materially damage your UN economics. So true sovereignty is not a purity test. You do not necessarily need to be sovereign to stay in business. But financial sovereignty is ultimately a measure of control and you'll be out of business very fast if you don't if you end up losing the ala around that. Yeah, thank you for that. I mean, so the basic takeaway there for the audience is is you know, you don't have to self-host. You don't have to be the token generator for every request, but you do need to own the base architecture. you want to be able to burst, you know, purposefully, deliberately, when it makes sense. And you you you have to be in control of that intelligence flow. And so the the practical answer is not repatriate every AI workload or run every request, you know, on on your own on prem infrastructure. The strategy is hybrid as we're showing here. uh but but you persistent sensitive and predictable workloads put them in a controlled sort of baseline and then use o openw weight models that have been vetted and and serving that infrastructure that you govern. You want to burst to frontier models when they're you know frontier models are amazing. They have better capabilities in in many cases and they're certainly justified for the right workload. that premium is justified for the for the right right application. You know, deep reasoning. Um, if you're doing model evals or, you know, specialized tasks that you you you want to get most precise, that makes sense. And then you got to own the ability to to control that, the dial, if you will, the the gateway that that decides which requests and routes the the the request to the specific model. Um, and it goes based on whether it's quality or cost or your policy or your your regional jurisdiction, you know, the the doicile or or maybe it's availability or the risk profile. You know, the frontier API is definitely a useful tool, but don't let it become the enterprise control plane without understanding what you're giving up. So Amit, walk us through these three zones and explain why owning owning the routing decision is foundational as it pertains to financial sovereignty and double click in the economics if you would because cloud providers you know they can spread demand you know with with with multi-tenency across thousands you know hundreds of thousands of customers. Um when does you know owning a baseline capacity when does the math actually work? You know what utilization and steady workloads and operating burden sort of justify that floor versus you know renting? You're showing some things here. We showed some things in the earlier slide. Um please take it from there. >> Thanks Dave. I I'll actually start with the last question you asked around this. Uh when does it make sense? So to be fair, this is very case-pecific uh question because buy versus build discussion assumes several factors including your ability to access GPUs and compute capacity and in-house operational knowhow to run multicluster multi-reion GPU even super node capable of hosting a large openweight model with high availability and strong SLAs for a multinational user base. So depending on your requirements is truly the the the answer here because this came uh if if you were to make such an investment of what I just described, this could easily be an investment in hundreds or uh millions or even billions of dollars over several years. So it is not trivial for many companies. Often however just modeling these things out up front um and understanding your needs and your versus the requirements versus what is practical uh is where these type of conversations of buy versus build uh die on the fine. Uh that being said there are mitigating cost controls as well and what I call PHOPS practices uh where really my early days in software began. um as the industry knows them, uh there's really uh what what we today know as LLM gateways, okay? And that's the most obvious place to start. Uh this is a reason, by the way, Stripe announced this week its intent to acquire Open Rider for reportedly $7 billion figure. The ability to route intelligence to the best value model for each task um allows workload optimization at the edge. And given that capabilities of smaller models especially for repeatable work, uh these are many times lower cost alternatives to frontier models doing all of the work al together and having that mixed strategy, right? Very much similar to that phob strategy of mixing spot instances with reserved instances with on-prem workloads. That's very much the same kind of concept here. >> Yeah, that stripe announcement was really fascinating. they're basically taking control, you know, of the meter. Um, so this is, as we said, this is this is hybrid. It's not, you know, running from the cloud. It's not putting everything on prem. The key is sovereign, you know, decisions uh are not either or, it's likely both. And it's deciding which workloads belong on on prem on the floor that you control and which capability, you know, makes more sense to outsource and and rent. So the gateway controls the routing, but it's it's it's only one dimension. The economics are you got to be engineered across the entire inference stack. And so that gets us into the sort of makeorb breakak economics. And most AI dashboards that we've seen lately, they they emphasize tokens that are being consumed and what you're spending on tokens and how many requests can be completed. You know, what's the cost per token? You know, those are inputs. Uh but they're not outcomes. The enterprise gets value only after you deal with security and governance. You got to do all the integration. The vendors never tell you about the integration work that you have to do. That's your problem. You know the human review, all the rework and you know your audit edex edicts. So the KPI at the top of this slide is the really the one that matters. It's cost per accepted governed business outcome not cost per token is you know like say that's an input. Now, that might mean less paper cuts, like less support tickets or or or or tickets that don't get opened. Maybe it means an SLA is met at a lower cost. Or it means, you know, higher acceptance rates at at first pass, you know, less work or an audit that takes days instead of of weeks and lowers your risk further. You know I mean you have taught us that tokens and completed workflows as I said are are vendor input metrics. It's the vendor out view. The buyer has to measure what the organization cares about what they actually uh attain. So walk us through where that number gets engineered and rather than you know covering the six items here on this slide. You know take it maybe you could take us deep on two and then give us quick feedback on on the other. So how caching and context management eliminate repeated work and can improve quality. That seems like a really important one. and how the LLM gateway turns, you know, to the Stripe example, turns routing and budget controls and failover and and and you know, provider, you know, op optionality and competition, turns that into a financial control mechanism, not just about cutting cost and then give us the quick read on things like, you know, queuing and inference run times and and and energy if you would. >> And thanks, Dave. This this topic is near and dear to my heart. I know you could geek out for hours on this thing. So I could talk about it for hours, but let's let's begin with the politics management as it has a direct correlation to token consumption because an agent, if you were to to begin with this example, will attempt to run through a brick wall trying to fulfill a user request until it succeeds or hits a limit. Now if you send for example um an agent to scan a repo to run a QA test or a fix a bug right um but if it does not if you don't specify which repo uh or ARM it will um it will um which repo or skills it will require aring with the right skills or access for example uh it will send it into a loop even uh burning through millions of tokens while scanning through a codebase and writing scripts to gain access, giving the security team many heart attacks along the way and eventually timing out. Or it could bloat the model context window, degradating the relevance and increasing the risk for errors and hallucinations. So either way, this or that, you're still uh operating at a loss. either you're burning through too much tokens and degradating the the the overall uh output or it's going to time out and you're at a total loss in this case. So what what if uh I was going to intend well let me take one one step back let me oversimplify things just a bit and offer a solution. So what if we for example took a knowledge graph and injected just in time context to that same agent allowing the agent to traverse a graph or it at any given state. Now this would support a more deterministic and repeatable action using a fraction of context um and the tokens not to mention faster SLAs's and fewer hallucinations. And now let me go back to that second example you wanted me to bring in. Maybe double click. Um bringing back the AI gateway the routing strategy. Um there's added benefits beyond just the cost controls. This is around governance and and and and security. You can set daily, weekly or monthly budget limits uh for workloads. Um and then the users and teams on a on that level could also be enforced. So just imagine if a user were to go into go into a agent loop back uh a crash crash loop back off on an agent that burns through tickets inadvertently or maybe it's a man in the middle of attack and somebody's using their identity to go into transfer more tokens. You could go ahead and have those same uh uh same type of behaviors monitored within the AI gateway and then have that being enforced. So there's a lot of different uh I'd say use cases around AI gateway uh especially when you want to go and route a human in a loop for those approvals. So I mentioned a few things we mentioned flagging security and production uh anomalies. Uh the API uh the AI gateway acts very much in similar way that API gateways and casbies of previous generations did only with more of a PHOPS twist. So imagine there's governance, there's security controls and just as importantly FinOps uh specific um use cases that you could go and model all around the AI gateway. It's no surprise that uh Stripe made that play obviously with open router but there's others out there building in this space and from my perspective owning this layer is non-compromisable right it's it's not tied to any infrastructure it's not tied to any model um it's not tied to any technology provider you have to own this in order to own those crown choice >> yeah thank you so for the audience the value is not just the gateway finds you know less expensive model. It's that the enterprise owns the policy that decides which model gets the work under which budget with what fallback and the owner can change that decision without having to rebuild the application like we saw with Canva. So those components you know these com these components come from many many different vendors but the sovereignty the the the sovereignty model the sovereignty architecture the boundaries cannot belong to any one single vendor and this is where we apply the same sovereignty test that you saw behind Amit's screen behind him we can apply that test to every vendor it's not just the frontier labs palanteer They're they bring orchestration, they've got ontology, they have governance, and they've obviously got deployment. They've been incredibly successful. But the, you know, the inconvenient truth is its operating layer is proprietary. Microsoft and data bricks, they got cloud, they got data, they got governance, enterprise integration, you tremendous, but they introduce platform and jurisdiction and roadmap dependencies. Of course, OpenAI and Enthropic, you're in the news every day. They provide exceptional frontier model capabilities. But you know the buyer inherits inherits that meter. You know the policy. We saw that again with the Canva example example, the Uber example. So many others. They the the buyer inherits the uh the the risk um and the provider's depreciation schedule. Then there's Nvidia of course the the king of AI. They provide you know clearly the leading compute platform and the software ecosystem. And that creates you know a concentration around silicon and supply chain and and of course [snorts] their moat is your moat except it's your moat in reverse. It's it's a it's the moat that you can't cross. So none of those dependencies automatically make the vendor a bad choice. We we really want to stress that. What we're saying is the mistake that we see companies making is allow one of their vendors that are selling components to define their sovereignty strategy. That's the failure mode. No vendor can confer sovereignty because the sovereignty is ultimately determined by the enterprise and it's it's the risk that you are taking and it's your decision. So am a meet what's the minimum control plane that an enterprise has to be able to retain so it can use vendors these vendors and other vendors without surrendering control of the entire system. How does that get architected? >> So so this builds on my previous response. Uh we see that an agentic operating system as a minimal viable technology stack that moves you toward a soaring posture. what this consists of in high level right uh we we don't have to go into the bits and bites but in high level this is a self-hosted full stack agentic system that includes everything from the context engine to multi- aent orchestration uh harness profile skills AI gateway policy and governance and infrastructure as code so the full stack ideally all of this should be an open-source framework that is fully extensible inspectable and forkable uh these are all crown jewels that you must absolutely own or you give up that alpha. There's no other way around it. This is the same technology capture that Alex Karp wanted you to deposit with him. You're now removing that away. Okay. So, not coincidentally, of course, my team at agency labs. We work very closely every day with customers in the media and entertainment space to manufacture and govern it. They consistently raise this exact concern and our advice is very consistent. own your agentic operating system, which is your alpha outright, then you can rent around the edges. I'd give the same advice if you're a startup, a publicly traded company, or a government looking to go into this field. >> Thank you. So, the point is a vendor can sell you the piece parts that support your sovereign stack. It can't sell the enterprise sovereignty as a finished product. So, you know what? If a company comes in and says, "Hey, buy our product. We are sovereign by default." That's you know the red flag should go up alarm bells. So that leaves a short set of questions that that every architecture and and every vendor should be able to survive. These are questions like can you substitute the model without having to rewrite the application like Canva had to. Uh if the if the vendor disappears or refuses service or gets acquired uh if you get you know Broadcom VMware can you still operate at the intended levels? Are your agent definitions and prompts and evals and and memory in a format that you own that you can take and is is it portable or is it stuck inside of a vendor's lockbox? So, we've covered a lot of architecture. Let's turn our thesis and our premise into something that an enterprise can can take make it a actionable take into a a vendor meeting next week. So, let's let's look at these six questions. Let's turn them into three sets, if you will. First, let's look at the economics. Can we forecast the cost per accepted governed outcome over the next 24 or 36 months? And if the provider doubles its price, what happens to our margins? I use the Broadcom VMware example. It may not matter. It may just be dimminimous, but it may matter to you depending on who you are. What would it actually cost you to switch? Second is ownership. Who owns the weights? Who owns the data? Who owns the eval? Who owns the policies and the routing logic that make this system so valuable? Do you own it or do your vendors own it? And third, what's the exit strategy? Can the frontier dependency be metered, substituted, or shut off if necessary? Is control enforced in the architecture or is it contractual? I mean, if you're only doing this through contracts, you you you could be a problem. Does my question to you is does anyone actually clear all five pillars or is every organization trading at least one of them away your your Switzerland example? It it it's a fair question and the honest answer to this is that no organization clears every buyer entirely let alone all five bars together. The reason is there's many many dependencies that go far beyond what any one uh company or nation or organization can account for. I'll give you an example. Uh how many organizations can claim that they own their own semiconductor fab um and can replace Nvidia GPUs tomorrow if they had to or what uh how many could even say they control their own grid power grid. So even if you did, do you own the mines in a refining capacity needed to remove dependencies on foreign critical minerals? So let's be honest, if that's no company or country can credibly claim perfectly air gap sovereignty in this respect. Luckily though, sovereignty is not binary. It's a scale against which you can measure yourself, define acceptable risks, assess uh when you fall outside of those boundaries. Most importantly, you have to be aware of the risks and exposures that you're willing to accept. If you're a Fortune50 company comfortable with developing on top of entropic models and hitching yourself to their price list, so be it. That's your choice. But make sure that your critical infrastructure and vendors do not quietly compound those same dependencies uh if they're themselves hitched to those models because that makes your cost and risk much harder to predict uh if it's compounded across your entire vend. Now I'm also going to use another illustrative example of how a procurement department should be think about these issues. What if you're a German company um um comfortable with using AWS for over a Frankfurt region? Uh by all means, by the way, do so right there. That may be a perfectly sound decisions but don't assume that regionally hosting uh in Frankfurt alone will eliminate the legal sovereignty exposure you have because um in this case AWS is a US company under the US cloud act can reach uh can reach for your data within your position and custody and control because as a US provider they're obligated to do so of course subject to any applicable legal protections and challenges and so forth But just imagine you could be a German company hosting in Frankfurt and still be under US jurisdiction. That's a legal pillar that you have to be aware of. So again, procurement department especially, there's a list of questions that you could be asking to tremendously reduce not necessarily the risks, but knowing what type of risks are acceptable to you. be aware of them and more importantly be aware of all the risks that you're willing to accept so that there's no surprises along the way. You know that Frankfurt example is interesting. So you better be better understand that risk and make sure that the data that you're going to put in that cloud is of of you're willing to absorb that risk i.e. it's acceptable. So I think the practical conclusion here is sovereignty again as you said it's not binary and almost no enterprise is going to maximize every pillar across every workload nor should they. The more sensible approach is to sort of name that dependency make it clear and make it one of of of three decisions. either you accept it and you understand it, you know, deeply or you accept it, but you've got some kind of compensating control that if something goes wrong, you can you can you can you can pivot or flat out reject it and say no, you know, you go back to the drawing board. So, every exposure that you accept should have a named owner. It should have a defined set of boundaries. It should have an expiration date that you review this stuff in a in a trigger for that sort of review. The failure mode is not not it's not accepting the risk. That's every company has risk. The failure is not knowing which risks you've you've accepted and what that exposure is. So you know where does this leave us? Cheap that's a price. Sovereign is a posture. It's a position. It's strategic. A vendor cannot sell you sovereign components. It cannot sell you sovereignty. that control and that alpha that it protects is yours and it has to remain yours. Amit Amit, I'll give you uh the final word. Please bring us home. Thanks, David. And I truly feel like I could talk about this all day. We don't have all day clearly. Uh but there's a ton of topics that we can double click on in future episodes. Uh but I'll repeat my advice. I've said a few times here. It is perfectly legitimate and acceptable not to be fully sovereign across all five pillars. Perfectly okay in the absence of legal or regulatory requirements. By the way, a full sovereignty posture may not even make sense at all for you as a business. However, if there is one thing I'd caution against is you have to be honest with yourself. Don't sovereign wash your own actual sovereign posture uh because that could lead to an unintended consequences and it can come back to you in multiple ways. If however uh you want to be as as tactical as possible, there is one area that you must exert full ownership and control over which is your financial alpha because if you do not own it, someone else does. >> Yeah, that is the most important pillar. Um, Amit, thank you so much guys. Amit is, you know, our newest analyst. He's been publishing weekly, sometimes multiple posts per week on the cuber research.com. So check that out. If you want to go deeper, you know how to get in touch with us. Amit, great having you on. Fantastic work. Thank you for the collaboration. >> Thank you, David. >> And thank you for watching this breaking analysis. This is Dave Volante. We'll see you next time.