The $85 Billion AI Signal and Star Trek Computing

Alex Shannon: Genuine question — if you found out that Alphabet just raised eighty-five billion dollars specifically for AI development, and I told you that was a record-breaking amount, would your first reaction be excitement or terror?

Sam Hinton: Honestly? Both. Like, that’s more money than the GDP of most countries being thrown at artificial intelligence. My brain immediately goes to ‘holy crap, they must see something massive coming’ and also ‘what exactly are they planning to build with all that cash?’

Alex Shannon: Right? Because eighty-five billion isn’t ‘let’s see what happens’ money. That’s ‘we know exactly where this is going and we’re betting everything on it’ money.

Sam Hinton: And when you combine that with what Nvidia’s planning — which we’ll get to — it feels like we’re watching the foundation being laid for something completely different than what we have today.

Alex Shannon: Something that might look a lot like science fiction becoming reality.

Sam Hinton: But here’s what gets me — if Google feels they need that much capital to stay competitive, what does that say about how fast this space is moving? Like, are they responding to something we don’t know about yet?

Alex Shannon: That’s exactly what I was thinking. This feels less like opportunistic fundraising and more like emergency preparation for whatever’s coming next in AI.

Alex Shannon: You’re listening to Build By AI, I’m Alex Shannon, and that record-breaking funding round is just one of several major stories we’re diving into today.

Sam Hinton: And I’m Sam Hinton. We’ve got Nvidia literally planning Star Trek computers, AI companies joining forces to prevent bioweapons, and what might be the biggest AI IPO filing we’ve seen. Plus Trump finally signed that executive order everyone’s been waiting for.

Alex Shannon: It’s June 4th, 2026, and honestly, it feels like every single story today is about setting up the next phase of AI development.

Sam Hinton: Yeah, like we’re watching the chess pieces being moved into position for whatever comes next. Alright, let’s start with that massive Alphabet raise and figure out what the hell is actually happening here.

Alphabet’s record-breaking $85B raise for Google’s AI business is a helluva good signal

Alex Shannon: So according to early reports from TechCrunch, Alphabet just completed what they’re calling a record-breaking eighty-five billion dollar stock sale. And this isn’t just any fundraising — this is specifically being framed as a signal of massive investor confidence in AI-related investments and business opportunities.

Sam Hinton: Dude, let me put this in perspective. Eighty-five billion dollars is more than the annual revenue of most Fortune 500 companies. This isn’t venture capital or even a typical public offering. This is Alphabet saying ‘we need war chest money for AI’ and the market saying ‘here, take all of it.’

Alex Shannon: Right, and the timing is interesting too. Why now? What are they seeing in their internal AI development that made them go ‘we need this much capital immediately’?

Sam Hinton: That’s exactly what I’m thinking about. Because Google already has massive cash reserves, right? They’re not hurting for money. So this feels less like ‘we need funding’ and more like ‘we need to move faster than anyone else can possibly move.’ It’s like they’re trying to create an insurmountable advantage.

Alex Shannon: But here’s what I’m wondering — is this actually good for innovation, or does this kind of massive capital concentration mean that smaller AI companies are basically done? Like, how do you compete against eighty-five billion dollars?

Sam Hinton: Oh, that’s a great point. We might be watching the AI equivalent of the railroad boom, where whoever has the most capital to lay the most track wins everything. But I think there’s still room for specialized players. The question is whether they’ll stay independent or just become acquisition targets for Google.

Alex Shannon: And for regular people and businesses, what does this mean? Because that money isn’t going to sit in a bank account — it’s going to get deployed into AI products and services pretty quickly.

Sam Hinton: Exactly. I think we’re about to see a massive acceleration in AI capabilities across Google’s entire ecosystem. Gmail, Search, Cloud, Android — everything is about to get a serious AI upgrade. But also, this signals to every other tech giant that if you’re not raising billions for AI, you’re already behind.

Alex Shannon: So keep an eye on whether Microsoft, Amazon, and others start announcing similar massive funding rounds. Because if they don’t, Google might just run away with this whole thing.

Sam Hinton: You know what’s wild though? The fact that the market had this much appetite for an AI-focused raise. Like, investors are literally betting that AI is going to generate returns that justify eighty-five billion dollars in new investment. That’s not just confidence — that’s conviction.

Alex Shannon: Which brings up another angle — what happens if Google doesn’t deliver on the implicit promises of this raise? Because when you take that much money for AI development, expectations are going to be astronomical.

Sam Hinton: True. But honestly, given what we’re seeing in terms of AI capabilities right now, I think the bigger risk might be that they deliver too well, too fast. Like, what if this money actually does help them build something that completely disrupts every other tech company?

Alex Shannon: That’s probably the scenario that keeps Microsoft’s executives up at night. Because Google isn’t just raising money — they’re signaling that they think the AI race is about to enter a completely different phase, and they want to make sure they win it decisively.

Sam Hinton: And if you’re a developer or a business owner listening to this, the takeaway might be to start thinking about how Google’s massively upgraded AI capabilities are going to affect your products and services. Because this isn’t abstract — this is going to impact real businesses in the next year or two.

Alex Shannon: Absolutely. Whether it’s through better AI assistants, more sophisticated search, or completely new AI-powered tools we haven’t seen yet, eighty-five billion dollars of development is going to show up in products you actually use.

Nvidia is already planning N2X and N3X chips — the goal is the Star Trek computer

Alex Shannon: Alright, speaking of massive investments in AI’s future, let’s talk about what Nvidia’s planning. According to The Verge, they’re already developing next-generation N2X and N3X chips, and here’s the kicker — their stated goal is to create advanced AI computing capabilities comparable to Star Trek’s computer systems.

Sam Hinton: Wait, hold on. They literally said Star Trek computer? Like, Jensen Huang stood up and said ‘we’re building the Enterprise computer’? Because that’s either the most brilliant marketing I’ve ever heard or they’re genuinely serious about creating something that ambitious.

Alex Shannon: Right? And they mentioned something called RTX Spark as an intermediate development step. So it sounds like they have a actual roadmap from where we are now to, apparently, science fiction level computing.

Sam Hinton: OK but let’s think about what a Star Trek computer actually does. It understands natural language perfectly, it can analyze massive amounts of data instantly, it can run complex simulations in real-time, and it basically serves as an intelligent interface to all of human knowledge. That’s not just better chips — that’s a completely different paradigm.

Alex Shannon: But here’s what I’m skeptical about — is this actually achievable with better hardware, or are they overpromising? Because we’ve seen plenty of companies promise revolutionary breakthroughs that turn out to be incremental improvements with better marketing.

Sam Hinton: You know what though? Nvidia has actually delivered on their bold promises before. They basically created the entire modern AI boom with their GPU architecture. And if anyone has the engineering talent and resources to attempt something this ambitious, it’s them. Plus, when you combine this with Google’s eighty-five billion dollar war chest, you start to see how this could actually happen.

Alex Shannon: That’s a good point. And for developers and businesses, what does this mean practically? Because if Nvidia is planning chips that are orders of magnitude more powerful than what we have today, that could completely change what’s possible with AI applications.

Sam Hinton: Absolutely. Imagine being able to run GPT-4 level models on your laptop, or having real-time AI video generation, or actually useful AI assistants that can handle complex multi-step tasks. We might be looking at the hardware foundation for true artificial general intelligence.

Alex Shannon: Although, let’s be honest — if they actually achieve Star Trek level computing, the real question becomes whether we’re ready for the social and economic implications of that kind of technological leap.

Sam Hinton: But here’s what I find fascinating about the timeline. The fact that they’re already planning N2X and N3X suggests they think this is achievable within the next few hardware generations. That’s not twenty years out — that’s maybe five to seven years.

Alex Shannon: Which is insane when you think about it. Like, if someone in 2016 had told you we’d have ChatGPT by 2022, you would have laughed. And now Nvidia is basically saying ‘hold our beer, we’re building the Enterprise computer by 2030.’

Sam Hinton: Exactly. And the RTX Spark as an intermediate step makes me think they’re not just throwing around sci-fi references for fun. They have actual technical milestones mapped out. It’s like they’re treating the Enterprise computer as an engineering problem rather than science fiction.

Alex Shannon: From a business perspective, this is also smart positioning. By publicly stating such an ambitious goal, they’re essentially claiming leadership of the long-term AI hardware vision. Even if they only get halfway there, that’s still revolutionary.

Sam Hinton: True, but it also puts enormous pressure on them to deliver. Because if competitors start making progress toward similar goals and Nvidia falls behind on their own roadmap, this bold vision could backfire spectacularly.

Alex Shannon: Good point. And there’s the question of whether the software side can keep up with the hardware ambitions. You can build the most powerful chip in the world, but if developers can’t figure out how to use it effectively, it doesn’t matter.

Sam Hinton: Although, that’s where the ecosystem comes in. Nvidia has been really smart about building not just chips but entire development platforms. CUDA, their AI software stack, all the tools that make it easier for developers to actually use their hardware. So they’re not just betting on raw computing power.

Alex Shannon: And if this works — if they actually deliver something close to Star Trek computing — the ripple effects are going to be everywhere. Education, healthcare, scientific research, entertainment. Basically every industry that involves information processing, which is most of them.

Sam Hinton: The other wild thing is imagining what this means for personal computing. Like, if your smartphone has Star Trek computer capabilities, what does that look like? How do you even interface with something that powerful?

Alex Shannon: Right, we might need completely new user interface paradigms. Touch screens and keyboards might start to feel as outdated as punch cards. But honestly, that’s a problem I’m excited to have.

OpenAI and Anthropic Sign Letter to Prevent AI-Developed Biological Weapons

Alex Shannon: Now, shifting from the exciting possibilities to the serious responsibilities — OpenAI and Anthropic have signed a joint letter to lawmakers urging them to implement better tracking systems for synthetic DNA sequences that could be misused for creating bioweapons. This is verified across multiple sources including Wired and TechCrunch.

Sam Hinton: OK, this is huge and I’m glad these companies are taking this seriously. Because we’ve been talking about AI risks in abstract terms, but this is concrete. AI is already capable of helping design biological sequences, and that same capability that could cure diseases could also create really dangerous pathogens.

Alex Shannon: What’s interesting is that OpenAI and Anthropic are usually competitors, but they’re coordinating on biosecurity issues. That suggests this threat is serious enough that they’re willing to work together on policy responses.

Sam Hinton: Exactly. And it’s not just them — the letter mentions leading AI labs and scientists are coordinating on this. That tells me the technical experts are genuinely worried about near-term risks, not just hypothetical future scenarios. When competitors start working together on safety issues, that’s a strong signal.

Alex Shannon: But here’s what I’m wondering about the practical implementation — how do you actually track synthetic DNA sequences without creating massive privacy and research freedom issues? Like, are we talking about monitoring every biology lab in the world?

Sam Hinton: That’s the trillion dollar question, right? You need oversight that can catch bad actors without strangling legitimate research. I imagine it’s something like monitoring certain high-risk sequences or requiring reporting for specific types of synthesis. But yeah, the implementation details are going to be crucial.

Alex Shannon: And this raises bigger questions about AI governance. If AI companies are taking the initiative to self-regulate on biosecurity, what other areas might need similar proactive approaches? Are we going to see more industry-wide coordination on AI safety issues?

Sam Hinton: I think we have to. Because the alternative is waiting for governments to figure out how to regulate technologies they barely understand, and by then it might be too late. This letter feels like a template for how the AI industry could handle other risk areas — cybersecurity, misinformation, autonomous weapons.

Alex Shannon: So this might actually be one of the most important stories we’re covering today, even though it’s getting less attention than the funding rounds and chip developments.

Sam Hinton: Absolutely. Because what this shows is that AI companies are starting to think seriously about dual-use technologies. The same AI that helps scientists discover new medicines could help bad actors design biological weapons. That’s not science fiction — that’s a real capability we need to manage carefully.

Alex Shannon: And I appreciate that they’re being proactive about it rather than reactive. Too often in tech, we see companies wait until there’s a crisis before they take responsibility seriously. This feels different.

Sam Hinton: It really does. Although, I wonder if this is also strategic positioning. By taking the lead on AI safety and regulation, these companies might be trying to shape the rules in ways that work better for them than for potential competitors.

Alex Shannon: That’s possible, but honestly, even if there are strategic motivations, the outcome could still be positive. Better biosecurity frameworks benefit everyone, regardless of who proposed them.

Sam Hinton: True. And the fact that it’s a joint effort between OpenAI and Anthropic specifically is interesting. These are arguably the two most prominent AI safety-focused companies, so their collaboration on this issue carries extra weight.

Alex Shannon: What this really highlights is how quickly AI capabilities are advancing into areas with serious security implications. Biological research is just one domain — imagine similar concerns around AI-designed cyber weapons or autonomous military systems.

Sam Hinton: Yeah, and the timeframes are compressed too. Traditional biological weapons development might take months or years of specialized expertise. AI could potentially accelerate that process dramatically, which is why the tracking systems need to be in place now, not later.

Alex Shannon: For people working in biology or related fields, this is probably something to watch closely. These tracking systems could affect legitimate research workflows, so understanding what’s coming is important.

Sam Hinton: And for everyone else, it’s a reminder that AI development isn’t just about cool new products. We’re dealing with technologies that have serious implications for global security, and the companies building these systems are starting to take that responsibility seriously.

Alex Shannon: Which, honestly, gives me more confidence in the direction of AI development. When the leading companies are actively trying to prevent misuse of their technologies, that suggests we might be handling this transition better than some of the more dystopian predictions.

This Is How Trump Finally Signed the AI Executive Order

Alex Shannon: Let’s talk about the policy side for a minute. President Trump signed a new AI executive order on Monday night, and this comes after an earlier version of the order was previously shelved. Multiple sources including Wired and MIT Technology Review are reporting this represents a shift in the administration’s approach to AI regulation.

Sam Hinton: Yeah, the fact that they shelved an earlier version tells us there was some serious internal debate about how to approach this. The question is — what changed? Did they get spooked by something specific, or did they finally figure out a regulatory framework that actually makes sense?

Alex Shannon: Right, and the timing is curious too. You’ve got Google raising eighty-five billion for AI, Nvidia planning Star Trek computers, and AI companies coordinating on bioweapon prevention. It feels like the administration is responding to the pace of development rather than trying to get ahead of it.

Sam Hinton: That’s been the challenge with AI policy all along though — the technology moves faster than government can keep up. But I’m actually encouraged that they took time to revise instead of just rushing something out. Better to get it right than to create regulations that become obsolete in six months.

Alex Shannon: Although, we don’t have details yet about what’s actually in the executive order. For all we know, it could be mostly symbolic or it could be pretty substantive. The devil’s going to be in the details.

Sam Hinton: Absolutely. And honestly, given everything else happening in AI right now, this feels like one of those moments where government policy either helps accelerate beneficial AI development or accidentally slows down American competitiveness. There’s a lot riding on getting this balance right.

Alex Shannon: Especially when you consider the global competition aspect. China isn’t sitting around waiting for perfect AI regulations. They’re moving fast and breaking things.

Sam Hinton: Exactly. So the real test will be whether this executive order creates guardrails that actually work while still letting American companies innovate and compete. We’ll have to see what the implementation looks like over the next few months.

Alex Shannon: Definitely something to keep watching, especially as more details emerge about what specific requirements and frameworks are included.

Sam Hinton: But you know what’s interesting? The fact that there was an earlier version that got shelved suggests there might have been some pretty significant disagreements within the administration about how aggressive to be with AI regulation.

Alex Shannon: That makes sense, because you’ve got competing interests here. On one hand, you want to ensure AI development is safe and beneficial. On the other hand, you don’t want to kneecap American AI companies while China and other countries pull ahead.

Sam Hinton: And there’s probably also pressure from different industry groups with totally different perspectives on what good AI regulation looks like. Tech companies want one thing, healthcare companies want another, defense contractors want something else entirely.

Alex Shannon: Which might explain why it took so long to finalize. Writing AI policy that satisfies all those stakeholders while actually being effective is probably incredibly difficult.

Sam Hinton: True. And honestly, given the complexity of AI technology and its potential applications, I’d rather see thoughtful, well-considered policy than rushed regulations that sound good but don’t actually work.

Alex Shannon: The question now is whether other countries look at this as a model for their own AI policies, or whether we’re going to see completely different regulatory approaches around the world.

Sam Hinton: That’s huge, because fragmented AI regulations could create real challenges for companies trying to build global AI products. You don’t want to be in a situation where your AI system is legal in one country but not another.

Alex Shannon: And for businesses using AI, regulatory uncertainty is killer. If you don’t know what the rules are going to be, it’s hard to make long-term investments in AI capabilities.

Sam Hinton: So hopefully this executive order provides some clarity and stability, at least for the next few years. Even if it’s not perfect, predictable rules are often better than no rules or constantly changing rules.

RAPID FIRE

Alex Shannon: Alright, let’s hit some rapid fire updates. First up — early reports suggest Meta’s AI agent for WhatsApp Business is now available globally, with businesses charged based on token usage.

Sam Hinton: This is smart positioning by Meta. WhatsApp has massive global reach, especially for business communications, and an AI agent that can handle customer service automatically could be huge for small and medium businesses. The token-based pricing makes it accessible too.

Alex Shannon: And it’s interesting that Meta is going after the business market rather than just consumer features. That suggests they see real revenue potential in B2B AI applications.

Sam Hinton: Absolutely. Plus, WhatsApp Business already has built-in billing and business verification systems, so adding AI agents is a natural evolution. This could be a significant revenue driver for Meta if businesses adopt it widely.

Alex Shannon: Next, we’ve got Lovable signing a multiyear expansion deal with Google Cloud that involves a five-times increase in usage and expanded access to Anthropic’s Claude AI model.

Sam Hinton: OK, this is interesting because it shows how the cloud providers are becoming AI distribution channels. Google Cloud isn’t just selling compute anymore — they’re selling access to the best AI models. And a 5x usage increase suggests Lovable is seeing serious demand for AI-powered development tools.

Alex Shannon: That 5x expansion is massive. Either Lovable’s business is growing incredibly fast, or they’re planning something big that requires way more AI compute than they’re currently using.

Sam Hinton: And the fact that it includes expanded access to Claude specifically is notable. That’s Anthropic’s most advanced model, so Lovable must be building some pretty sophisticated AI features. This could signal where AI-powered development tools are heading.

Alex Shannon: Then we have UK regulators requiring Google to offer publishers an opt-out tool for generative AI search features, with testing in the UK before global rollout.

Sam Hinton: This feels inevitable, right? Publishers have been complaining that AI search summaries reduce traffic to their websites. An opt-out tool is probably the minimum viable solution, though I’m curious how many publishers will actually use it versus just demanding better revenue sharing.

Alex Shannon: It’s a tricky balance because users probably prefer AI summaries over clicking through to multiple websites, but publishers need traffic to survive. This opt-out tool might be the first step toward more comprehensive revenue-sharing agreements.

Sam Hinton: And testing in the UK first is smart. It’s a significant market but not so large that major mistakes would be catastrophic. If it works there, global rollout becomes much safer.

Alex Shannon: And finally, Anthropic has made a confidential SEC filing as it moves toward a potential Wall Street debut.

Sam Hinton: Whoa, that’s actually huge news buried in the rapid fire section. An Anthropic IPO could be massive, especially given their reputation for AI safety leadership and their Claude model’s capabilities. This could be the first major AI-focused public offering since the current boom started. That’s definitely worth watching closely.

Alex Shannon: The timing is interesting too. If Anthropic goes public now, they’d be capitalizing on peak AI hype while also getting access to public market capital to compete with Google’s eighty-five billion dollar war chest.

Sam Hinton: And for retail investors, this could be the first chance to directly invest in a pure-play AI company rather than just tech giants that happen to do AI. That could drive serious demand for the stock.

BIGGER PICTURE

Alex Shannon: Alright, if you zoom out and look at everything we covered today — record-breaking funding, next-generation computing hardware, proactive safety coordination, policy responses, and potential IPOs — what’s the pattern here?

Sam Hinton: It feels like we’re watching the AI industry mature in real time. Like, we’re moving from the ‘let’s see what’s possible’ phase to the ‘let’s build the infrastructure for the next decade’ phase. The amounts of money and the scale of planning are just fundamentally different than what we were seeing even a year ago.

Alex Shannon: Right, and there’s this interesting tension between moving incredibly fast — like Nvidia planning Star Trek computers — while also taking responsibility seriously enough that competitors are coordinating on safety issues.

Sam Hinton: Yeah, it’s like the industry is growing up. They’re starting to think beyond just the next product release to the long-term implications of what they’re building. Whether that’s bioweapon prevention or sustainable business models or regulatory compliance.

Alex Shannon: So the question for people listening is — are you ready for this pace of change? Because if today’s stories are any indication, the next few years are going to make the last few years look slow.

Sam Hinton: And honestly, I think the companies that figure out how to adapt quickly while maintaining some ethical guardrails are going to be the ones that succeed in this next phase. It’s not just about having the best technology anymore — it’s about having the best technology that people actually trust and want to use.

Alex Shannon: But let’s talk about what this means for different groups of people. If you’re a developer, you’re probably looking at this and thinking about how to position yourself for a world where AI capabilities are exponentially more powerful than today.

Sam Hinton: Absolutely. And if you’re running a business, you need to start thinking strategically about how these AI advances are going to affect your industry. Because Google’s eighty-five billion isn’t going to sit idle — it’s going to turn into products and services that could disrupt everything.

Alex Shannon: For policymakers, there’s this challenge of writing regulations that can adapt to the pace of AI development. Today’s executive order might be obsolete by the time Nvidia’s N2X chips actually ship.

Sam Hinton: And for regular consumers, I think the big question is whether all this AI advancement actually translates into better daily experiences, or whether it just makes technology more complex and harder to understand.

Alex Shannon: That’s a great point. Because Star Trek computers sound amazing in theory, but in practice, do people want to talk to their devices like they’re crew members on a starship? Or do they just want their current tools to work better?

Sam Hinton: I think it depends on the implementation. If AI makes technology feel more natural and intuitive, people will love it. But if it makes simple tasks more complicated because now you have to negotiate with an AI assistant, that could backfire.

Alex Shannon: And there’s the broader economic question too. All this AI investment and development is going to create winners and losers. Some jobs are going to get way better, others might disappear entirely.

Sam Hinton: Which is why I think the biosecurity coordination we talked about is so important. It shows that at least some AI companies are thinking about the broader implications of their technology, not just the immediate business opportunities.

Alex Shannon: True. And maybe that’s the most encouraging thing about today’s news. Yes, we’re seeing massive investments and ambitious technical goals, but we’re also seeing proactive responsibility and policy engagement.

Sam Hinton: Exactly. It’s not just a race to build the most powerful AI anymore. It’s becoming a more mature conversation about how to build powerful AI systems that actually benefit society.

Alex Shannon: Although, let’s be honest — eighty-five billion dollars still suggests the race element isn’t going anywhere. It’s just that now the race includes safety and responsibility as part of the competitive landscape.

Sam Hinton: Which might actually be the best outcome. If being responsible and trustworthy becomes a competitive advantage, then market forces start working in favor of beneficial AI development.

Alex Shannon: And looking ahead, I think we’re going to see this pattern continue — massive technical advances paired with increasing attention to governance and safety. The question is whether we can maintain that balance as the capabilities get even more powerful.

OUTRO

Alex Shannon: That’s all for today on Build By AI. If you’re not already subscribed, definitely hit that button because this stuff is moving way too fast to miss episodes.

Sam Hinton: Seriously, and if today’s stories are any preview, tomorrow is going to be just as wild. Thanks for listening, and we’ll see you back here tomorrow.

Alex Shannon: Until then, keep building.