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Build by AI
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Topics covered: artificial intelligence news, large language models, generative AI, AI tools, ChatGPT, Claude, Gemini, AI regulation, machine learning research, tech industry news, AI startups, and the future of work.
Build by AI
The Agent Wars Begin I 31st May
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Microsoft just announced they're basically killing Copilot, and I think this is the best thing that could happen to everyday users.
SPEAKER_01Wait, what? Microsoft is ditching their flagship AI assistant that they've been pushing for two years, and you think that's good news? You have 30 seconds to justify that.
SPEAKER_00It's been a glorified chatbot with some integrations. What they're building with NVIDIA, actual autonomous agents, that's the real revolution. Instead of asking Copilot to help you write an email, imagine an agent that just handles your entire workflow.
SPEAKER_01Okay, but we're talking about replacing something people actually use with something completely theoretical. That sounds like a recipe for disaster.
SPEAKER_00Except it's not theoretical anymore. OpenAI just released codecs with computer use capabilities. It can literally control Windows programs autonomously. The agent wars have officially begun.
SPEAKER_01Alright, now you have my attention. Let's dive into this. You're listening to Build by AI. I'm Alex Shannon. And we are covering a day that might mark the official transition from AI assistants to AI agents.
SPEAKER_00And I'm Sam Hinton. Today we've got Microsoft and NVIDIA teaming up to replace Copilot with actual autonomous agents. OpenAI solving decades-old math problems and taking control of Windows PCs, plus a massive funding round that has everyone scratching their heads.
SPEAKER_01We've also got SoftBank making a 75 billion euro bet on French data centers and GitHub developers in full revolt over new pricing. It's May 31st, 2026.
SPEAKER_00And honestly, this feels like one of those days where we're gonna look back and say, this is when everything changed. Let's get into it.
SPEAKER_01They're planning to unveil new Windows computers from Dell and Microsoft Surface at Computex and Build. And the software is potentially based on something called OpenClaw. Sam, break this down for me. What's the fundamental difference between what we have now and what they're building?
SPEAKER_00Okay, so think about how you use Copilot today. You ask it a question, it gives you an answer, maybe it helps you write something. It's reactive, right? You're still doing all the actual work. These autonomous agents are proactive. They don't wait for you to ask. They understand your goals and execute tasks independently.
SPEAKER_01But that sounds like a massive leap in complexity. How confident are we that they can actually deliver on this? Because we've seen a lot of iPromises that turned into disappointments.
SPEAKER_00That's where the NVIDIA partnership becomes crucial. Nvidia isn't just providing chips here, they're bringing their entire infrastructure for running complex AI models locally. And here's the thing like we're already seeing early versions of this working. Look at what OpenAI just shipped with Codex.
SPEAKER_01Right, but I'm thinking about the average user here. If I'm someone who just figured out how to use Copilot effectively, and now Microsoft is saying, forget all that, here's something completely different. Isn't that going to create massive confusion?
SPEAKER_00You know what? That's a fair point. But I think Microsoft learned from the Windows 8 disaster. They're not going to just rip Copilot away overnight. This is probably going to be a gradual transition, maybe even opt-in at first.
SPEAKER_01The timing is interesting too, right? This comes at the same time that GitHub is changing Copilot's pricing model and facing major backlash from developers. It feels like Microsoft is hedging their bets across their entire AI product line.
SPEAKER_00Exactly. And that's smart strategy. Look, you know, if autonomous agents work the way they're promising, the whole concept of AI assistance becomes obsolete. You don't need an assistant if you have an agent that can actually accomplish tasks without you.
SPEAKER_01I guess the question is whether consumers are ready for that level of AI autonomy. There's a big psychological difference between asking an AI for help and letting an AI just do things on your behalf.
SPEAKER_00True. But I think we're going to see adoption happen faster than people expect, especially if these agents can deliver on productivity gains. But the early adopters will be businesses and power users who can immediately see the ROI.
SPEAKER_01Let me play devil's advocate for a second, though. What about trust and control? If an agent is making autonomous decisions about my work, my emails, my documents, what happens when it makes a mistake? With Copilot, I can review everything before it happens.
SPEAKER_00That's exactly why the OpenClaw Foundation is so interesting. From what we're hearing, it's designed to be transparent and auditable. You should be able to see what the agent is planning to do and intervene if needed.
SPEAKER_01But will regular users actually do that? Or will they just trust the agent to get things right? Because if it's the latter, we could be setting up a lot of people for some really expensive mistakes.
SPEAKER_00Look, that's always the trade-off with automation, right? The more automated it gets, the less control you have. But the productivity gains could be massive if they nail the implementation.
SPEAKER_01The Dell and Surface hardware announcements at Computex will be really telling. If they can show smooth, reliable agent operation in real-world scenarios, not just controlled demos, that could be the tipping point.
SPEAKER_00Absolutely. And honestly, if Microsoft and NVIDIA can't make this work with their combined resources and market position, it probably means the technology isn't ready for mainstream adoption yet.
SPEAKER_01Keep an eye on those computer techs and build announcements. If Microsoft and NVIDIA can actually demo this working smoothly, it's going to put enormous pressure on Apple, Google, and everyone else to catch up quickly. Speaking of OpenAI, let's talk about what might be an even bigger story. Multiple sources are reporting that OpenAI's AI model has solved a decades-old mathematics problem. And here's the key part. The proof has been verified by researchers. This isn't just AI generating something that looks right, this is mathematically sound work.
SPEAKER_00Dude, this is huge. We're talking about moving from AI that can write code and essays to AI that can actually advance human knowledge in pure mathematics. That's a fundamentally different category of capability.
SPEAKER_01But what does this actually mean in practical terms? I know this sounds impressive, but how does solving a decades-old math problem translate to real-world impact for most people?
SPEAKER_00Okay, so think about it this way. Mathematics is the foundation of everything, from encryption to engineering to physics. If AI can now solve problems that human mathematicians couldn't crack for decades, that's like giving every field that depends on math a massive upgrade.
SPEAKER_01That's a good point, but I'm also thinking about the implications for academic research and scientific discovery more broadly. If AI can solve these kinds of theoretical problems, what happens to the role of human researchers?
SPEAKER_00I think it's going to be more collaborative than competitive. Like the AI didn't just magically solve this. It had to be guided and the proof had to be verified by human researchers. But now those researchers can tackle much bigger questions because they have this incredibly powerful tool.
SPEAKER_01Right, but there's also this question of whether we can truly understand the solutions that AI generates. If an AI solves a math problem using methods that human mathematicians can't easily follow, how do we verify that it's actually correct beyond just checking the final answer?
SPEAKER_00That's exactly why the verification by researchers is so important here. It suggests that the AI didn't just brute force a solution. It generated work that human experts can examine, understand, and validate. That's actually more impressive than if it had just spit out an answer.
SPEAKER_01I'm curious about the process here. Do we know anything about how long it took the AI to solve this problem compared to how long human mathematicians have been working on it?
SPEAKER_00The reports don't give specific timelines, but we're talking about a problem that's been unsolved for decades being cracked by an AI system. Even if it took the AI weeks or months, that's still an incredible acceleration of discovery.
SPEAKER_01And this ties back to what we were talking about with the Microsoft NVIDIA story, right? We're seeing AI move from being assistive to being genuinely productive and creative in ways we hadn't seen before.
SPEAKER_00Exactly. Whether it's autonomous agents handling your workflow or AI solving mathematical proofs, we're hitting this inflection point where AI isn't just responding to human prompts, it's actually advancing human capabilities in meaningful ways.
SPEAKER_01But here's what worries me. If AI can solve decades-old math problems, what does that do to funding priorities in academic research? Why would you fund a team of human mathematicians to work on a problem when an AI might solve it in a fraction of the time?
SPEAKER_00That's a legitimate concern. But I think the human element becomes even more important for defining which problems are worth solving and interpreting what the solutions actually mean in broader context.
SPEAKER_01The question is whether academic institutions and research organizations are prepared for this shift. If AI can solve decades-old problems, how do we restructure research priorities and funding?
SPEAKER_00I think we're going to see a complete rethinking of how research gets done. Instead of spending years on problems that AI might solve in days, researchers can focus on defining the right problems and interpreting the solutions.
SPEAKER_01There's also the question of reproducibility. If an AI solves a math problem, can another AI system independently verify that solution? Or do we still need human mathematicians as the ultimate arbiters of correctness?
SPEAKER_00Great question. I suspect we'll need both. AI for speed and scale, humans for intuition and judgment. The combination could be incredibly powerful.
SPEAKER_01This is definitely something to watch closely. If OpenAI can replicate this success across other mathematical domains, we could be looking at an acceleration of scientific discovery unlike anything we've seen before.
SPEAKER_00And not just in pure math. This could revolutionize fields like cryptography, material science, drug discovery, anywhere complex mathematical modeling is the bottleneck to progress.
SPEAKER_01And here's where OpenAI's advances get really practical. Reports from the Decoder and Google News indicate that OpenAI's Codex application now runs on Windows 11. With something called computer use capability. This allows the AI to autonomously control programs, test applications, and identify bugs. But here's what caught my attention. Users can also remotely start and monitor tasks from the ChatGPT mobile app. So you could literally be on your phone, tell your AI to run tests on your desktop and monitor the progress remotely.
SPEAKER_00Okay. This is exactly what I was talking about with the agent revolution. This isn't an AI helping you write code. This is an AI actually using your computer like a human would. It can click buttons, open programs, run tests, find bugs. That's autonomous operation.
SPEAKER_01But I have to ask, are we comfortable with AI having that level of control over our computers? There's something that feels both exciting and terrifying about letting an AI loose on my desktop.
SPEAKER_00Yeah, that's a legitimate concern. But think about who this is targeting first. Developers and testers who are already running automated scripts and tools, for them, this is just a much more sophisticated version of automation they're already using.
SPEAKER_01That makes sense, but what about security implications? If an AI can control programs and access files autonomously, what happens if something goes wrong? Or if the AI gets confused about what it's supposed to be doing?
SPEAKER_00That's where the remote monitoring through Chat GPT becomes crucial. You're not just setting it loose and walking away. You can watch what it's doing in real time and intervene if needed. It's autonomous but supervised.
SPEAKER_01I'm also thinking about the productivity implications here. If AI can handle testing and bug hunting autonomously, that could free up developers to focus on more creative and strategic work. But it could also make certain testing roles obsolete.
SPEAKER_00Absolutely. But but I think we've seen this pattern before with other forms of automation. The roles don't disappear, they evolve. Instead of manually testing applications, you might be designing test strategies and interpreting AI-generated test results.
SPEAKER_01Let me ask you this though.
SPEAKER_00Fair point. But that's exactly why the supervised approach is so important. Traditional automation follows rigid scripts. This is supposed to be intelligent enough to adapt when things don't go as planned.
SPEAKER_01Supposed to be is the key phrase there. I'm curious about error handling. When this AI agent encounters an unexpected dialogue box or system error, does it know to stop and ask for help? Or does it try to power through and potentially cause more problems?
SPEAKER_00That's going to be the make or break factor for widespread adoption. And if it can gracefully handle edge cases and know its limitations, this could be game-changing. If it can't, it'll be relegated to very controlled use cases.
SPEAKER_01The interesting thing is how this connects to the Microsoft NVIDIA story. If Microsoft is moving toward autonomous agents, and OpenAI is already shipping autonomous computer control, we're seeing the ecosystem align around this agent-based approach.
SPEAKER_00Right. And that's not a coincidence. Microsoft has a significant relationship with OpenAI, so they're probably getting early access to these capabilities and building their agent strategy around them.
SPEAKER_01So it also raises questions about data security. If an AI agent is autonomously controlling your computer, what data is it accessing? What's being logged? Where is that information going?
SPEAKER_00Uh those are crucial questions that I hope OpenAI is addressing transparently. For enterprise adoption, companies are going to need ironclad guarantees about data handling and privacy.
SPEAKER_01And what about liability? If an AI agent autonomously deletes important files or corrupts data while hunting for bugs, who's responsible? The user who initiated the task. OpenE, the software vendor, where?
SPEAKER_00Those legal frameworks are definitely going to need to evolve quickly. We're moving into uncharted territory where AI isn't just generating content, it's taking actions with real consequences.
SPEAKER_01For developers listening, this might be worth experimenting with now, especially if you're doing repetitive testing tasks. The learning curve is probably going to be steep, but early adopters could see significant productivity gains.
SPEAKER_00And honestly, if you're not at least exploring this kind of automation, you might find yourself at a competitive disadvantage pretty quickly.
SPEAKER_01Just make sure you're doing it in a safe environment first. Maybe set up a virtual machine for the AI to play in before letting it loose on your main development setup. Now let's talk about something that's got developers pretty fired up. Both TechCrunch and the Decoder are reporting that GitHub Copilot's new token-based billing model has generated significant criticism among developers. The feedback has been harsh. We're seeing reactions like what a joke from the developer community. This marks the end of what people are calling the free or low cost period for the service. Sam, help me understand the anger here. Is this just about price increases or is there something deeper going on?
SPEAKER_00It's definitely deeper than just pricing. Developers got used to Copilot being this relatively affordable flat rate tool that they could use freely. Moving to token-based billing makes the cost unpredictable and potentially much higher for heavy users.
SPEAKER_01But isn't token-based billing pretty standard for AI services at this point? Open AI, anthropic. Most of the major players use some version of usage-based pricing.
SPEAKER_00True. But those are API services for businesses. GitHub Copilot positioned itself as a developer productivity tool and more like an IDE plugin than an AI service. Imagine if your code editor suddenly started charging you per keystroke. That's how this feels to developers.
SPEAKER_01That's a great analogy. And the timing is interesting too, right? This happens just as Microsoft is announcing they're moving away from Copilot toward autonomous agents. It feels like they're squeezing revenue out of the old model while transitioning to something new.
SPEAKER_00Exactly. And I think that's what's really frustrating developers. They feel like they're being asked to pay premium prices for a product that Microsoft is basically admitting is obsolete.
SPEAKER_01But playing devil's advocate here, if Microsoft is investing heavily in autonomous agents and next generation AI capabilities, don't they need to fund that development somehow? Maybe this pricing change is necessary to support the innovation.
SPEAKER_00Look, I get the business logic, but the execution is terrible. Instead of just jacking up prices, they could have offered a migration path to the new agent-based tools or grandfathered existing users. This feels like they're prioritizing short-term revenue over developer relationships.
SPEAKER_01And developer relationships are crucial for Microsoft, especially given their competition with Google, Amazon, and others in the cloud space. Alienating the developer community over pricing seems like a risky move.
SPEAKER_00Right. And here's the thing: developers are already exploring alternatives. There are open source coding assistants, other commercial options. Microsoft built up this huge user base, and now they're giving people a reason to look elsewhere.
SPEAKER_01What's particularly frustrating for developers is the unpredictability. With flat rate pricing, you know exactly what your monthly bill will be. With token-based billing, your costs could vary wildly depending on how much you use the service.
SPEAKER_00If you can't predict your copilot costs, it becomes much harder to factor into project pricing and budgets.
SPEAKER_01It also raises questions about the broader sustainability of AI-powered developer tools. If the underlying AI models are getting more expensive to run, are we going to see price increases across all these services?
SPEAKER_00Probably, yeah. The free lunch period for AI tools is definitely ending. But the companies that handle this transition well by providing clear value and reasonable pricing are going to win in the long run.
SPEAKER_01I wonder if this is also about Microsoft trying to segment their market. Maybe they want to push casual users toward their new autonomous agents while keeping Copilot as a premium service for power users.
SPEAKER_00That's possible, but if that's the strategy, they're not communicating it well. Developers just see a price increase without a clear explanation of what they're getting in return.
SPEAKER_01The timing with the agent announcements is really unfortunate. If Microsoft had launched autonomous agents first and then said, here's our legacy copilot pricing, developers might have been more understanding.
SPEAKER_00Exactly. Instead, it feels like they're charging more for an inferior product while promising something better in the future. That's not a great value proposition.
SPEAKER_01For developers who are unhappy with this change, now might be a good time to evaluate alternatives and diversify the tools they're using. Don't get too dependent on any single AI coding assistant.
SPEAKER_00Absolutely. And honestly, this might be a wake-up call for the entire ecosystem. That one provider can suddenly change their pricing model. You want to have backup options ready to go.
SPEAKER_01The silver lining is that this controversy might accelerate development of open source alternatives. Nothing motivates open source development like proprietary software getting expensive. Alright, let's hit some rapid fire stories. First up, early reports suggest that SoftBank announced plans to invest up to 75 billion euros in building French data centers, aiming to develop up to five gigawatts of additional capacity.
SPEAKER_00Softbank is betting huge on European AI demand.
SPEAKER_01EU AI regulations, having data centers in Europe is going to be crucial for compliance. Softbank might be positioning themselves as the infrastructure backbone for European AI companies.
SPEAKER_00Exactly, and five gigawatts is enough to run some seriously large AI training operations. This could be what enables European companies to compete with US and Chinese AI development at scale.
SPEAKER_01I'm also wondering about the timeline here. 75 billion euros doesn't get spent overnight. This is probably a multi-year build-out. But the fact that they're committing this much capital suggests they see massive demand coming.
SPEAKER_00And SoftBank has a pretty good track record of spotting tech trends early, even if they don't always nail the timing. This feels like them positioning for the AI infrastructure boom before it really hits Europe.
SPEAKER_01The energy requirements alone are staggering. Five gigawatts of data center capacity is going to need serious power infrastructure. This could drive renewable energy development in France too.
SPEAKER_00True. And that might be part of the appeal for the French government. This isn't just about data centers, it's about positioning France as a major player in the global AI economy.
SPEAKER_01Next, early reports from Hacker News suggest that OpenRouter has raised $113 million in Series B funding, though details are still limited.
SPEAKER_00Open Router is that API platform that lets developers access multiple AI models through a single interface, right? A 113 million Series B suggests investors are betting big on the multi-model approach rather than being locked into a single provider.
SPEAKER_01That makes sense given what we're seeing with pricing changes at individual providers. Having a platform that can switch between different AI models could become really valuable as the market matures.
SPEAKER_00Yeah, and with all these autonomous agents we've been talking about, you probably want access to specialized models for different tasks rather than trying to use one model for everything.
SPEAKER_01The timing is perfect too. As companies get burned by unexpected pricing changes from major providers, having a platform that offers choice and flexibility becomes more attractive.
SPEAKER_00Exactly. Open router essentially provides insurance against vendor lock-in. If one AI provider jacks up their prices or changes their terms, you can switch to another model without rewriting your entire application.
SPEAKER_01113 million is also enough to build serious enterprise features. Things like guaranteed uptime, dedicated support, custom model fine-tuning. That could help them compete with the big players.
SPEAKER_00And they're probably going to need those enterprise features as more businesses look for alternatives to direct relationships with OpenAI, Anthropic, and the other major providers.
SPEAKER_01Speaking of agents, early reports suggest that Google's agentic AI tool, Gemini Spark, is now available to users, marking Google's expansion into agent-based AI applications.
SPEAKER_00Okay, so now we've got Microsoft with autonomous agents, OpenAI with computer use, and Google with Gemini Spark. The agent wars are definitely heating up. Everyone's trying to move beyond chatbots to actual task execution.
SPEAKER_01Google's been playing catch-up in the AI space, so launching an agent platform makes sense. But they'll need to differentiate somehow. Just being another AI agent isn't going to cut it.
SPEAKER_00Google's advantage could be integration with their entire ecosystem.
SPEAKER_01That's true, especially for companies that are already invested in Google Workspace. If Gemini Spark can actually automate workflows across Gmail, docs, and sheets, that could be a major productivity boost.
SPEAKER_00The question is whether Google can execute on that vision. They have all the pieces in place, but they need to make them work together seamlessly. That's harder than it sounds.
SPEAKER_01And the competitive pressure is intense. Microsoft is already working with Nvidia on hardware accelerated agents. OpenAI has computer control working. Google can't afford to be late to this party.
SPEAKER_00Right. And Google has struggled with AI product launches before. They need Gemini Spark to work flawlessly from day one, or they risk falling even further behind in the agent race.
SPEAKER_01And finally, early reports suggest that Anthropic has raised $65 billion in funding, achieving a $965 billion valuation. The question being raised is whether Claude's enterprise growth can justify that massive valuation.
SPEAKER_00Wait, $965 billion? That would make Anthropic worth almost as much as Apple. I'm gonna need to see some serious revenue numbers to justify that kind of valuation. That seems aggressive.
SPEAKER_01Yeah, that valuation definitely raises eyebrows. Even with Claude's enterprise success, we're talking about a company that's still primarily competing on being a better chatbot. The fundamentals would have to be incredible.
SPEAKER_00Unless they've got some major technological breakthrough we haven't heard about yet, this feels like we might be hitting peak AI hype in terms of valuations. Those numbers are gonna need to be backed up by real performance.
SPEAKER_01The enterprise angle is interesting though. If Anthropic has cracked the code on enterprise AI deployment at scale, that could justify premium valuations. Enterprise customers pay a lot for AI that actually works reliably.
SPEAKER_00True. But nearly a trillion dollar valuation? That suggests they'd need to capture a huge chunk of the entire enterprise software market, not just AI. The expectations are incredibly high.
SPEAKER_01This also puts enormous pressure on them to deliver. When you're valued like the world's most valuable company, anything less than extraordinary results is going to disappoint investors massively.
SPEAKER_00Exactly. And it makes me wonder about the sustainability of these AI valuations across the board. If Anthropic is worth nearly a trillion, what does that imply about OpenAI, Google, Microsoft? The numbers start getting pretty wild.
SPEAKER_01All right, if you zoom out and look at everything we covered today, there's a really clear theme emerging. We're seeing this massive shift from AI assistants that help you do things to AI agents that actually do things for you.
SPEAKER_00Right. And it's happening across the board. Microsoft is ditching copilot for autonomous agents. OpenAI is shipping computer control. Google is launching agent tools. Even the infrastructure investments and funding rounds are focused on supporting this agent-based future.
SPEAKER_01But I think the GitHub copilot pricing backlash is a warning sign. As companies transition from the current generation of AI tools to agents, how they handle that transition is going to make or break user adoption.
SPEAKER_00Absolutely. And the winners are going to be the companies that can deliver genuine autonomous capability, not just rebranded chatbots with better marketing. The bar for what counts as AI agent is about to get much higher.
SPEAKER_01The infrastructure story is fascinating too. Softbank dropping 75 billion euros on French data centers isn't just about capacity. It's about positioning Europe to compete in this agent-based future. That level of investment suggests they see massive demand coming.
SPEAKER_00And that that connects to the open router funding round too. As we move toward more complex agent workflows, you're going to need platforms that can orchestrate multiple I models and services, the single provider approach might not be sufficient anymore.
SPEAKER_01What's interesting is how the mathematical breakthrough fits into this pattern. OpenAI solving decades-old math problems shows that AI is moving beyond just automating existing human tasks. It's actually advancing human knowledge in fundamental ways.
SPEAKER_00Exactly. And when you combine that level of problem-solving capability with computer control and autonomous operation, you get agents that aren't just following scripts. They're genuinely intelligent workers that can adapt and discover.
SPEAKER_01But there's also this tension between the promise and the current reality. Anthropic's trillion dollar valuation suggests massive investor confidence, but the GitHub pricing backlash shows that users aren't necessarily ready to pay premium prices for current generation AI tools.
SPEAKER_00That's exactly the gap that needs to be bridged. The technology is advancing incredibly quickly, but user adoption and willingness to pay is going to depend on delivering real measurable value, not just impressive demos. Right. And that's both exciting and risky. If autonomous agents deliver on their promise, Microsoft looks prescient. If they don't, they've alienated their user base and handed market share to competitors.
SPEAKER_01What should people be watching for over the next few months? What signals will tell us whether this agent revolution is real or just hype?
SPEAKER_00Um look for real demonstrations of complex task completion. Not demos where an AI writes an email, but where it actually completes multi-step workflows autonomously. And watch the enterprise adoption numbers. Businesses will be the first to pay premium prices for genuine productivity gains.
SPEAKER_01I think we're also going to see a shakeout in terms of which companies can actually deliver on these agent promises versus which ones are just riding the hypewave. The valuations we're seeing today won't survive if the technology doesn't deliver.
SPEAKER_00And pay attention to the infrastructure build out. If companies are really preparing for agent scale computing demand, we should see massive investments in data centers, specialized chips, and networking capacity. Softbank's French investment is just the beginning.
SPEAKER_01The regulatory piece is going to be crucial too. As AI agents become more autonomous and capable, governments are going to need to figure out liability, safety, and oversight frameworks. That could accelerate or derail adoption depending on how it's handled.
SPEAKER_00True, and we're already seeing that with the EU's AI regulations. Companies that can navigate those regulatory requirements while delivering genuine agent capabilities are going to have a huge competitive advantage. This has been Build by AI. If you're as fascinated by this agent revolution as we are, make sure you subscribe so you don't miss how this plays out.
SPEAKER_01Tomorrow we'll be covering whatever new developments emerge from this rapidly evolving space. The pace of change is honestly incredible right now.
SPEAKER_00Thanks for listening, and we'll see you tomorrow.