The Bill Comes Due: When AI Gets Too Expensive to Ignore

Alex Shannon: There’s this moment in every industry boom where reality catches up with enthusiasm. Either companies figure out how to make the economics work, or they don’t. And if they don’t, well, we’ve seen what happens to industries that can’t control their costs.

Sam Hinton: Yeah, and right now we’re watching that exact moment play out in AI. The question isn’t whether artificial intelligence is transformative anymore – it’s whether anyone can afford to keep building it at this pace.

Alex Shannon: Because early reports suggest the entire industry conversation has shifted from ‘how fast can we grow’ to ‘how do we stop bleeding money.’ And that’s either the beginning of sustainable AI development, or the beginning of a very different kind of AI winter.

Sam Hinton: When an entire industry stops talking about growth and starts talking about guardrails, that’s not just a trend shift. That’s a reckoning.

Alex Shannon: You’re listening to Build By AI, I’m Alex Shannon, and that shift from AI optimism to AI economics? That’s just the beginning of what we’re covering today.

Sam Hinton: And I’m Sam Hinton. We’ve got government power grabs, massive infrastructure bets, and a pretty serious warning about AI systems that might start improving themselves. Plus Microsoft just found seven new ways your AI can be hacked.

Alex Shannon: It’s Thursday, June 6th, 2026, and honestly, it feels like we’re watching the AI industry grow up in real time. Not all of it’s pretty.

Sam Hinton: Let’s dive in.

The token bill comes due: Inside the industry scramble to manage AI’s runaway costs

Alex Shannon: Alright, so let’s start with this cost crisis. According to early reports from TechCrunch, the AI industry has basically done a complete 180 on how it thinks about spending. We’re talking about a shift from what they’re calling ‘tokenmaxxing’ – basically maximizing token usage and rapid expansion – to implementing serious cost controls and guardrails.

Sam Hinton: Dude, this is huge because for the past few years, the entire industry mindset was ‘spend whatever it takes, scale as fast as possible.’ Token usage was like a badge of honor. More tokens meant more capability, more growth, more everything.

Alex Shannon: Right, but now companies are apparently having very different conversations internally. What changed? Was it just that the bills got too big to ignore?

Sam Hinton: I think it’s partly that, but it’s also that we’re seeing diminishing returns. You can’t just throw infinite compute at these problems anymore and expect linear improvements. Plus, investors are starting to ask harder questions about path to profitability.

Alex Shannon: OK but hold on though – is this necessarily a bad thing? Maybe this forces the industry to get more efficient, more thoughtful about how they’re using resources?

Sam Hinton: You know what, that’s a fair point. The tokenmaxxing era gave us incredible capabilities, but it also gave us a lot of waste. Companies were running massive models for tasks that could be handled by much smaller ones. It was like using a Ferrari to deliver pizza.

Alex Shannon: And honestly, thinking about this more, there’s got to be a psychological shift happening too. When your token bill suddenly becomes one of your biggest line items, that changes how you think about product development, feature rollouts, everything.

Sam Hinton: Exactly. And I bet we’re going to see companies getting way more creative about efficiency. Like, instead of just throwing bigger models at problems, they’ll start building hybrid systems – small models for simple tasks, big models only when absolutely necessary.

Alex Shannon: That’s actually really interesting because it mirrors how other industries matured. Like, in the early days of cloud computing, people just moved everything to the cloud without thinking about costs. Then they got the bills and suddenly everyone became an expert in cost optimization.

Sam Hinton: Yeah, and those companies that figured out cloud cost optimization early had a huge competitive advantage. I think we’re about to see the same thing happen with AI. The companies that crack the efficiency puzzle first are going to dominate.

Alex Shannon: So what does this mean for regular businesses trying to integrate AI? Are costs going to come down, or are we looking at a slowdown in AI deployment?

Sam Hinton: I actually think this is good news for most businesses. When big AI companies get serious about cost optimization, those efficiencies trickle down. We should see better pricing models, more efficient APIs, smarter resource allocation. The wild west phase is ending, but that makes AI more accessible, not less.

Alex Shannon: But there’s got to be some short-term pain here, right? Like, if companies are suddenly implementing guardrails, that probably means some projects get delayed, some features get cut, some experiments get shut down.

Sam Hinton: Oh absolutely. And I think we’re going to see a lot of companies realize they were building AI solutions that didn’t actually need AI. When you’re forced to justify every token, you start asking ‘do I really need a large language model for this, or would a simple algorithm work just as well?’

Alex Shannon: That’s such a good point. The era of ‘AI for AI’s sake’ might be ending. Companies will have to prove that the AI is actually adding value, not just adding complexity and cost.

Sam Hinton: And honestly, that’s probably healthy. We’ve seen so many AI demos that look impressive but don’t solve real problems. If cost pressure forces companies to focus on actual utility, we might end up with better AI products overall.

Alex Shannon: Keep an eye on this because if the industry successfully makes this transition, we could see AI become truly mainstream. But if companies can’t figure out the economics, we might see a lot of consolidation very quickly.

Sam Hinton: Yeah, and the companies that survive this transition will be the ones building sustainable, profitable AI businesses. Everyone else gets acquired or goes out of business. Classic market maturation.

Anthropic warns AI may soon begin recursive self-improvement - Scientific American

Alex Shannon: Now let’s talk about something that should probably be getting more attention. Anthropic has issued a warning – and this is confirmed by multiple sources – that AI systems may soon enter a phase of recursive self-improvement. Essentially, AI systems improving themselves without human intervention.

Sam Hinton: OK, this is where things get really interesting and honestly a bit scary. Recursive self-improvement is one of those capabilities that AI researchers have been both working toward and worrying about for years. It’s like the ultimate double-edged sword.

Alex Shannon: Help me understand this. What exactly does recursive self-improvement look like? Is this AI rewriting its own code, or something more fundamental?

Sam Hinton: Think of it like this: right now, when we want to improve an AI system, humans analyze its performance, figure out what’s wrong, and make changes. Recursive self-improvement means the AI does that whole process itself – it identifies its own weaknesses, designs solutions, and implements them. Then it does it again, and again, potentially getting better each time.

Alex Shannon: And Anthropic is warning about this why? This sounds like it could be incredibly powerful for solving problems.

Sam Hinton: Well, yeah, but here’s the thing – once an AI system can reliably improve itself, the rate of improvement could accelerate really quickly. We call this an ‘intelligence explosion.’ The AI gets better at improving itself, so it improves faster, which makes it even better at improving itself. You see where this goes.

Alex Shannon: So we’re potentially looking at AI development that outpaces human ability to understand or control it. That’s… that’s a pretty big deal.

Sam Hinton: Exactly. And look, this isn’t necessarily doom and gloom. Recursive self-improvement could help us solve climate change, cure diseases, address major global challenges. But it also means we need much better safety frameworks and governance structures in place before we get there.

Alex Shannon: The fact that Anthropic is publicly warning about this suggests they think it’s closer than most people realize. Companies don’t usually warn about theoretical future risks – they warn about things that are actually on the horizon.

Sam Hinton: That’s what’s making me nervous about this. Anthropic is one of the most safety-focused AI companies out there. If they’re issuing public warnings, it’s probably because they’re seeing capabilities in their own models that are getting close to this threshold.

Alex Shannon: And think about the timing here. We’re talking about an industry that’s suddenly very focused on cost controls and guardrails, and now one of the leading companies is saying ‘hey, by the way, we might be approaching a point where AI can improve itself.’ Those two things feel related.

Sam Hinton: Oh, absolutely. If you’re worried about runaway costs now, imagine what happens when an AI system can modify itself to become more capable and potentially more expensive to run. The cost optimization problem becomes an order of magnitude more complex.

Alex Shannon: But here’s what I don’t understand. If Anthropic sees this coming, why not just… not build it? Why create something you’re warning people about?

Sam Hinton: That’s the classic AI safety dilemma, right? If Anthropic doesn’t build it, someone else will. And that someone else might not be as careful about safety considerations. So they’re trying to get out ahead of it, build it responsibly, and warn people about the risks.

Alex Shannon: It’s like a controlled detonation versus an accidental explosion. You’d rather have the team that’s thinking about safety do it first, even if it’s still dangerous.

Sam Hinton: Exactly. And honestly, this is why all the government intervention we’re seeing might not be such a bad thing. If recursive self-improvement is really on the horizon, we need serious oversight and coordination between companies.

Alex Shannon: What does this mean for regular businesses though? Should people be worried about implementing AI systems that might suddenly start improving themselves?

Sam Hinton: I don’t think most business AI applications are anywhere close to this level of autonomy. We’re talking about frontier models, cutting-edge research systems. Your customer service chatbot isn’t going to start rewriting itself anytime soon.

Alex Shannon: But still, if the most advanced AI systems start recursive self-improvement, that capability will eventually trickle down to business applications, right? Maybe not immediately, but eventually.

Sam Hinton: Yeah, and that’s when things get really interesting. Imagine AI systems that can automatically optimize themselves for your specific business processes, identify their own blind spots, fix their own bugs. That could be incredibly powerful or incredibly chaotic.

Alex Shannon: The more I think about this, the more I understand why Anthropic is issuing warnings. This isn’t just a technical milestone, it’s a potential inflection point for the entire relationship between humans and AI systems.

Trump Signs AI Memo Addressing Issues in Anthropic-Pentagon Feud - Bloomberg Government News

Alex Shannon: Speaking of Anthropic, there’s some major government drama happening. Multiple sources confirm that President Trump has signed an AI memo specifically addressing disputes between Anthropic and the Pentagon. This is the government directly intervening in AI company policy disputes.

Sam Hinton: Wait, this is wild. We don’t have the full details of what the feud was about, but the fact that it required presidential intervention tells you everything about how high the stakes are. This isn’t just business as usual.

Alex Shannon: Right, and this is happening alongside reports that Trump is planning to meet with AI companies about a government profit share plan. Are we seeing the government try to exert much more direct control over AI development?

Sam Hinton: I think we absolutely are. And honestly, it makes sense from a national security perspective. AI capabilities are becoming so strategically important that the government can’t just sit back and hope private companies make decisions that align with national interests.

Alex Shannon: But there’s got to be a tension here, right? AI companies have been operating with a lot of autonomy, and now you’ve got government intervention in company disputes and talk of profit sharing. How do you balance innovation with government oversight?

Sam Hinton: That’s the trillion-dollar question. Too much government control could stifle innovation and drive talent overseas. Too little, and you risk strategic AI capabilities being developed without any consideration for national security or public interest.

Alex Shannon: And the timing is interesting because we’re also hearing that the US is announcing plans to accelerate AI development for national security purposes. It feels like the government is trying to have it both ways – more control and faster development.

Sam Hinton: Yeah, and that’s probably not sustainable. You can’t micromanage companies and expect them to move at startup speed. Something’s going to have to give, and my guess is we’ll see some kind of public-private partnership model emerge.

Alex Shannon: What I find fascinating is that Anthropic seems to be at the center of all these tensions. They’re warning about recursive self-improvement, they’re feuding with the Pentagon, and presumably they’ll be part of these profit-sharing discussions. They’re kind of the poster child for this new relationship between AI companies and government.

Sam Hinton: That makes sense when you think about it. Anthropic positions itself as the safety-focused AI company, so they’re probably more willing to engage with government oversight than some of their competitors. But that also makes them a target for criticism from both sides.

Alex Shannon: Right, like the Pentagon probably wants them to be less cautious and move faster on defense applications, while safety advocates might think they’re being too cozy with the military. It’s a no-win situation.

Sam Hinton: And now Trump is personally getting involved. That suggests either the stakes are incredibly high, or the disputes are incredibly heated, or both. You don’t get presidential memos about normal business disagreements.

Alex Shannon: What worries me is that we’re seeing government intervention without clear frameworks. Like, what are the rules here? What triggers presidential involvement in AI company disputes? What are the criteria for profit sharing?

Sam Hinton: Yeah, it feels very ad hoc right now. And that uncertainty is probably making AI companies really nervous. How do you plan your business strategy when the government might intervene at any moment for reasons that aren’t entirely clear?

Alex Shannon: But from the government’s perspective, AI is moving so fast that traditional regulatory processes can’t keep up. Maybe ad hoc intervention is the only way to maintain some level of control over developments that could affect national security.

Sam Hinton: That’s fair, but it’s also really dangerous. When government intervention is unpredictable, it creates perverse incentives. Companies might start making decisions based on what they think will avoid government scrutiny rather than what’s actually best for innovation or safety.

Alex Shannon: Keep watching this space because how this Anthropic-Pentagon situation gets resolved could set the template for how the government deals with AI companies going forward. The precedent matters a lot here.

Sam Hinton: And the broader question is whether the US government can figure out a way to maintain its competitive edge in AI without strangling the innovation that got us here in the first place. Other countries are watching this very closely.

AirTrunk commits $30B to build 5GW of AI data centers in India

Alex Shannon: Let’s shift to infrastructure. Early reports suggest that AirTrunk, an Australian data center operator, is making a massive bet on AI infrastructure in India. We’re talking about a $30 billion commitment to build 5 gigawatts of AI data center capacity.

Sam Hinton: Thirty billion dollars. Let me put that in perspective – that’s more than the GDP of a lot of countries. And 5 gigawatts is enough to power a small city. This isn’t just an investment, it’s a statement about where AI development is heading geographically.

Alex Shannon: Why India though? Is this about cost, talent, market access, or something else entirely?

Sam Hinton: It’s probably all of those things, but I think the big factor is India’s combination of technical talent and growing digital economy. You’ve got millions of developers, relatively lower costs, and a huge domestic market that’s rapidly adopting AI technologies.

Alex Shannon: But 5 gigawatts of capacity – that’s assuming there’s going to be massive demand for AI compute in that region. Are we looking at India becoming a major AI hub, not just for development but for deployment?

Sam Hinton: I think that’s exactly what we’re looking at. And honestly, it makes strategic sense. Instead of building AI infrastructure in expensive Western markets and then serving global customers, why not build where costs are lower and talent is abundant?

Alex Shannon: This also fits with what we were talking about earlier – the industry getting more serious about costs. Moving infrastructure to more cost-effective locations could be part of that broader shift.

Sam Hinton: Exactly. And for India, this is huge. It’s not just about the immediate economic impact of $30 billion in investment. It’s about positioning themselves as a critical part of global AI infrastructure.

Alex Shannon: Think about the geopolitical implications too. If India becomes a major AI compute hub, that gives them significant leverage in international AI discussions. They’re not just users of AI technology, they’re enablers of it.

Sam Hinton: And it’s interesting that this is coming from an Australian company, not a US or Chinese one. It suggests that AI infrastructure development is becoming more globally distributed, which is probably healthy for the overall ecosystem.

Alex Shannon: But I have to wonder about the energy implications. 5 gigawatts is an enormous amount of power. Where’s that electricity coming from, and what does it mean for India’s grid and environmental commitments?

Sam Hinton: That’s a really good point. India’s been making big investments in renewable energy, but 5 gigawatts of additional demand is significant. This could either accelerate their clean energy transition or create new pressures on their existing grid.

Alex Shannon: And from a business perspective, AirTrunk is betting that the AI boom continues and that compute demand keeps growing. That’s a $30 billion bet on the future of AI adoption.

Sam Hinton: Right, and if they’re wrong, that’s a very expensive mistake. But if they’re right, they’ll own a huge chunk of AI infrastructure in one of the world’s fastest-growing technology markets.

Alex Shannon: What’s also interesting is the timeline. Building 5 gigawatts of data center capacity doesn’t happen overnight. This is probably a multi-year project, which means AirTrunk is betting on sustained AI demand well into the future.

Sam Hinton: And they’re probably not the only ones. I bet we’re going to see similar announcements from other infrastructure companies in other markets. The geography of AI is definitely shifting away from being concentrated in just a few locations.

Alex Shannon: If this investment pays off, we could see other major infrastructure players making similar bets in emerging markets. The geography of AI is definitely shifting.

Sam Hinton: And for developers and businesses, this could mean better access to AI compute resources at lower costs. When infrastructure is closer to where you are, latency goes down and often costs do too.

RAPID FIRE

Alex Shannon: Alright, let’s rapid fire through a few more stories. First up, early reports suggest OpenAI has agreed to provide the US government with early access to its frontier AI models. Government gets to evaluate and test advanced capabilities before public release.

Sam Hinton: This is smart politics from OpenAI. Give the government early access, build trust, potentially influence regulation. But it also means some of the most advanced AI capabilities will be in government hands first.

Alex Shannon: It’s a strategic move, but it also raises questions about transparency. If the government has early access to these models, are they testing them for capabilities that the public doesn’t know about?

Sam Hinton: Probably, yeah. And that’s not necessarily bad – you want the government to understand AI capabilities before they become widely available. But it does create an information asymmetry that could be problematic.

Alex Shannon: Plus, this sets a precedent. If OpenAI is giving the government early access, are other AI companies going to feel pressured to do the same? Is this becoming a requirement for operating in the US?

Sam Hinton: That’s exactly what I’m wondering. This could become the new normal – if you want to develop frontier AI models, you have to give the government a preview. That’s a significant shift in how the industry operates.

Alex Shannon: Microsoft has identified seven new ways that AI agents can be hacked. They’re calling out new vulnerability categories and attack methods specific to AI agent systems.

Sam Hinton: Of course they did. As AI agents become more autonomous and handle more sensitive tasks, they become bigger targets. Security is going to be absolutely critical as these systems get deployed more widely.

Alex Shannon: What’s concerning is that these are new attack vectors. It’s not just traditional cybersecurity threats applied to AI systems, it’s entirely new categories of vulnerabilities that we’re probably still learning about.

Sam Hinton: Right, and AI agents are particularly vulnerable because they’re designed to take actions automatically. If you can compromise an AI agent, you’re not just stealing data, you’re potentially controlling a system that can do things in the real world.

Alex Shannon: And the timing is interesting because as we talked about with the cost optimization stuff, companies are deploying AI agents more thoughtfully. But if there are seven new ways to hack them, that thoughtfulness needs to include security considerations.

Sam Hinton: Exactly. And this is where the cost optimization and security concerns intersect. Building secure AI agents is probably more expensive than building basic ones, but the cost of getting hacked could be much higher.

Alex Shannon: And according to reports, Trump’s planning to meet with AI companies as soon as next week to discuss this government profit share plan we mentioned earlier.

Sam Hinton: That timeline is aggressive. Either this has been in the works for a while, or the government feels urgent pressure to establish some kind of financial stake in AI development. Neither scenario is particularly comforting for AI companies.

Alex Shannon: Next week means companies have very little time to prepare their positions. That suggests either the government is trying to catch them off guard, or this is so urgent that normal consultation processes are being bypassed.

Sam Hinton: And what does ‘profit share plan’ even mean? Is this the government taking equity stakes in AI companies? Revenue sharing agreements? Some kind of special tax? The details matter a lot here.

Alex Shannon: It also creates interesting competitive dynamics. If some companies agree to profit sharing and others don’t, does that affect their access to government contracts or their ability to operate in certain sectors?

Sam Hinton: That’s a really good point. This could become a way to create preferred AI partners – companies that play ball with the government get preferential treatment. That’s a significant shift in how the market works.

Alex Shannon: Finally, the US government is announcing plans to speed up development and use of AI for national security purposes. AI is officially a strategic priority for defense and security operations.

Sam Hinton: This ties everything together, right? Government wants early access to models, wants profit sharing, wants to resolve disputes between companies and the Pentagon, and wants to accelerate military AI development. They’re not being subtle about taking a much more active role.

Alex Shannon: And ‘speed up development’ suggests they think current progress isn’t fast enough for national security needs. That could mean more government funding, but it could also mean more pressure and oversight.

Sam Hinton: The challenge is that national security AI applications often require the most advanced, frontier-level capabilities. So the government is essentially saying they need access to cutting-edge AI tech, but they also want more control over how it’s developed.

Alex Shannon: It’s like they want to be both customer and regulator at the same time. That’s a complicated relationship for AI companies to navigate, especially when the requirements might conflict with each other.

Sam Hinton: And other countries are definitely watching how this plays out. If the US government successfully asserts more control over its AI industry, that could become a model for other nations to follow.

BIGGER PICTURE

Alex Shannon: If you zoom out and look at everything we covered today, there’s a clear pattern emerging. The AI industry is transitioning from this rapid expansion phase to something that looks a lot more like a mature industry – with cost controls, government oversight, and strategic infrastructure investments.

Sam Hinton: Yeah, and I think we’re watching the end of AI exceptionalism. For a few years, AI companies could basically say ‘we’re changing the world, normal rules don’t apply.’ But now we’re seeing normal industry dynamics – cost management, government regulation, security concerns, geopolitical competition.

Alex Shannon: The question is whether this maturation helps or hurts AI development. On one hand, you get more sustainability and broader access. On the other hand, you might lose some of that innovative edge that comes from being willing to take huge risks.

Sam Hinton: I think it’s probably necessary though. The wild west phase gave us incredible capabilities, but it also created a lot of instability. If AI is going to be truly transformative, it needs to be built on sustainable economics and clear governance frameworks.

Alex Shannon: And honestly, Anthropic’s warning about recursive self-improvement makes the governance piece even more urgent. We might not have the luxury of figuring this stuff out slowly.

Sam Hinton: Exactly. The next year is going to be really telling. Either the industry successfully makes this transition to sustainable, well-governed AI development, or we see a lot more chaos as different stakeholders fight for control.

Alex Shannon: What’s fascinating to me is how all these stories connect. The cost crisis is forcing companies to be more strategic, which makes them more willing to work with government oversight. Infrastructure investments are moving to cost-effective locations, which changes the global AI landscape. Security vulnerabilities are emerging just as AI agents become more autonomous.

Sam Hinton: And the government is basically saying ‘we need to be involved in all of this.’ Early access to models, profit sharing agreements, intervention in company disputes, acceleration of military AI – they want a seat at every table.

Alex Shannon: Which makes sense from their perspective. AI is becoming too strategically important to leave entirely to private companies. But it also creates new challenges for innovation and competition.

Sam Hinton: Right, and I think we’re going to see other countries follow suit. China’s already heavily involved in AI governance, Europe’s working on comprehensive AI regulation, and now the US is getting much more hands-on. This is becoming a global trend.

Alex Shannon: The infrastructure piece is really interesting too. AirTrunk’s $30 billion bet on India suggests that AI development is becoming more globally distributed. That could reduce some of the concentration risks we’ve been worried about.

Sam Hinton: But it also creates new dependencies. If India becomes a major AI compute hub, what happens if there are geopolitical tensions or infrastructure problems? The more distributed AI becomes, the more complex these interdependencies get.

Alex Shannon: And the security concerns Microsoft identified suggest we’re still in the early stages of understanding AI vulnerabilities. As AI systems become more capable and autonomous, the attack surface is growing faster than our ability to secure it.

Sam Hinton: That’s why the cost optimization trend might actually be helpful. If companies are forced to be more thoughtful about which AI capabilities they actually need, they might also be more thoughtful about security implications.

Alex Shannon: Looking ahead, I think the next six months are going to be crucial. We’ve got Trump meeting with AI companies next week, Anthropic warning about recursive self-improvement, massive infrastructure investments, and a fundamental shift in industry economics. A lot of moving pieces.

Sam Hinton: And the companies that navigate this transition successfully are going to look very different from the ones that don’t. We might see some big players struggle if they can’t adapt to the new reality of cost controls and government oversight.

Alex Shannon: But we might also see new opportunities for companies that embrace this more mature approach to AI development. Better cost management, stronger security, closer government partnerships – these could become competitive advantages.

Sam Hinton: The key thing for everyone – whether you’re building AI, investing in AI, or just using AI – is to understand that the rules of the game are changing. The next phase of AI development is going to look very different from the last one.

OUTRO

Alex Shannon: That’s Build By AI for today. Tomorrow we’ll be back with more AI news, and honestly, at the pace things are moving, who knows what we’ll be talking about.

Sam Hinton: If you found today’s episode useful, definitely subscribe wherever you get your podcasts. And if you’ve got thoughts on government profit sharing or recursive self-improvement, we’d love to hear them.

Alex Shannon: I’m Alex Shannon.

Sam Hinton: I’m Sam Hinton. See you tomorrow.