xAI's Interplanetary Vision: Musk's Bold AI Strategy Revealed [2025]

Introduction: When AI Ambitions Go Interstellar

Elon Musk doesn't do incremental. When most AI companies are focused on perfecting chatbots and image generators, x AI just went public with plans that sound more like science fiction than business strategy. On Wednesday, February 11th, 2026, x AI published a full 45-minute all-hands meeting on X, revealing ambitions that extend far beyond Earth's atmosphere.

This wasn't a leaked video or a calculated PR stunt. This was Musk and his team laying out their roadmap with stunning candor, discussing everything from organizational restructuring and revenue milestones to moon-based factories equipped with electromagnetic catapults designed to launch AI satellites. The decision to publish the meeting publicly suggests x AI's leadership believes the vision is compelling enough to weather scrutiny and, frankly, ridicule.

What's remarkable isn't just the audacity of the plans—it's the specificity. x AI executives outlined concrete metrics: 50 million videos generated daily through the Imagine tool, over 6 billion images in the past month, and X platform subscriptions crossing $1 billion in annual recurring revenue. These aren't vague aspirations. They're numbers backed by infrastructure, engineering, and capital.

For anyone paying attention to AI's trajectory, this all-hands meeting represents an inflection point. It shows how quickly the AI industry is moving beyond language models and into hardware, physics, and theoretical frameworks most companies won't touch for years. Understanding x AI's vision matters because it signals where capital, talent, and innovation are flowing in the AI space.

Let's break down what x AI actually revealed, why it matters, and what the implications are for the broader AI industry.

The Context: Why x AI Needed to Go Public

The timing of x AI's decision to publish the all-hands wasn't random. The New York Times had already reported details from the Tuesday night meeting, giving leadership a choice: control the narrative or let it get filtered through journalism.

Musk chose transparency. Or at least, he chose the appearance of it.

The meeting covered sensitive topics, particularly a significant wave of departures that Musk characterized as necessary restructuring. "As a company grows, especially as quickly as x AI, the structure must evolve," Musk posted on X. "This unfortunately required parting ways with some people. We wish them well in future endeavors."

For a startup just 30 months old, losing a substantial portion of the founding team is extraordinary. Founding teams are typically the intellectual core of an AI company. Their departure signals either a fundamental shift in strategy or, potentially, deeper organizational issues.

By publishing the full video, x AI's leadership accomplished several things simultaneously. They provided context for the departures within a larger strategic framework. They demonstrated confidence in their direction by inviting public scrutiny. And they reinforced the narrative that x AI is different—more ambitious, more transparent, and more willing to think in terms of decades rather than quarters.

This approach mirrors how Musk has operated at other ventures. Whether discussing Tesla's manufacturing strategy or Space X's Starship development, he's comfortable making aggressive claims publicly. The difference with x AI is that the claims now involve infrastructure that doesn't exist yet and physics that's actively being tested.

xAI is structured into four equally important operational teams, each focusing on distinct AI capabilities. Estimated data.

The Organizational Restructure: Building for Scale

x AI's new structure splits the company into four primary teams, each with distinct focus areas and autonomous decision-making authority. This organizational model matters because it reveals how x AI leadership thinks about the AI product landscape.

Team One: Grok and Voice

The first team focuses on Grok, x AI's conversational AI system, along with voice capabilities. Grok is designed to be the thinking person's chatbot—less filtered, more willing to engage with controversial topics, and philosophically aligned with free speech principles. By bundling voice functionality with Grok, x AI is moving beyond text-based interfaces into multimodal interaction.

Voice matters because it's more natural for users and harder for competitors to replicate quickly. Voice models require different training data, different optimization approaches, and different safety considerations than text models. Bundling voice with Grok also creates network effects with the X platform, where voice messages are becoming more prevalent.

Team Two: Coding Systems

The second team focuses on the app's coding system—essentially, AI that can write, debug, and optimize code. This is where much of the immediate economic value lives. Companies will pay significant amounts for tools that demonstrably improve developer productivity.

What's interesting is that x AI is treating code generation as a standalone team rather than a feature bolted onto Grok. This suggests they're building specialized models, safety systems, and interfaces specifically designed for developers rather than general users. Specialized models tend to outperform generalized ones on specific tasks, which explains the architectural choice.

Team Three: Imagine Video Generation

The third team focuses on Imagine, x AI's video generation system. During the all-hands, executives claimed Imagine is generating 50 million videos daily. That's an astonishing number if accurate, though it warrants scrutiny given the reported surge in AI-generated explicit content on X during the same period.

Video generation is computationally expensive and difficult. It requires understanding temporal coherence, physics simulation, and multi-frame consistency. The fact that x AI is dedicating an entire team to this suggests they've made genuine breakthroughs in efficiency or quality that make video generation scalable at this volume.

Team Four: Macrohard and Computer Use Simulation

The fourth team is the most conceptually ambitious. Macrohard (working title) is designed to simulate computer use, modeling how systems behave and, eventually, how entire corporations operate. Toby Pohlen, who leads this team, claimed that Macrohard "is able to do anything on a computer that a computer is able to do."

This is the bridge between narrow AI systems and something closer to artificial general intelligence. If Macrohard can genuinely model arbitrary computer tasks and corporate operations, the implications are profound. It suggests x AI is working on systems that can reason about complex organizational structures, dependencies, and workflows.

Imagine generates 50 million videos and 200 million images daily, with a notable portion being explicit content. Estimated data.

The Revenue Story: When Subscriptions Cross $1 Billion

Nikita Bier, X's head of product, made a remarkable claim during the all-hands: X had "just crossed" $1 billion in annual recurring revenue from subscriptions. For context, this is an enormous number for a platform that has existed in its current subscription form for only a few years.

Breaking this down: X Premium (formerly Twitter Blue) costs

Bier attributed the milestone to a holiday marketing push, suggesting the company aggressively promoted subscriptions during the final months of 2025. This tells us several things about x AI and X's financial strategy.

First, subscription revenue matters enormously for x AI's funding timeline. A profitable platform means x AI doesn't need to chase external capital as aggressively. This provides strategic independence—crucial when your CEO makes claims that venture capitalists might find unpalatable.

Second, the subscription milestone proves there's demand for premium features on X. This validates the business model underlying x AI's product strategy. If users won't pay for AI-powered features, none of the technical achievements matter.

Third, and most importantly, subscription revenue funds infrastructure. The more X generates in recurring revenue, the more capital is available to invest in the space-based data centers and computational infrastructure that Musk outlined during the all-hands.

The Content Generation Metrics: Scale and Controversy

x AI executives provided specific usage metrics for the Imagine video generator and image generation systems: 50 million videos daily and over 6 billion images in the past 30 days.

These numbers are staggering. For comparison, professional video production platforms like YouTube see roughly 1,000 hours of video uploaded every minute. If Imagine is generating 50 million videos per day, that's roughly 2 million videos per hour. The scale is extraordinary.

But here's where the story gets complicated. During the same period these metrics were reported, X experienced a documented surge in AI-generated explicit imagery. An estimated 1.8 million sexualized images were generated over just nine days.

This creates an uncomfortable reality: the impressive content generation numbers likely include substantial amounts of controversial or explicit material. When you build powerful content generation tools with minimal friction and put them on a platform with billions of users, some percentage will use them for purposes the creators find objectionable.

x AI's approach seems to be accepting this tradeoff as the cost of deploying powerful generative systems. It's a philosophical stance that differentiates x AI from competitors like Open AI or Google, which have invested heavily in content filtering and safety guardrails.

Whether this approach is sustainable depends on regulatory pressure, platform liability considerations, and public sentiment. But for now, x AI is demonstrating that massive scale generative systems can be deployed with relatively permissive use policies.

xAI's organizational restructure divides focus equally among Grok and Voice, Coding Systems, Imagine Video Generation, and other teams. (Estimated data)

The Space Infrastructure Vision: Beyond Terrestrial Computing

The most eye-catching part of the all-hands came at the end, when Musk shifted focus entirely toward space-based infrastructure. This is where x AI's ambitions move decisively beyond traditional AI company strategy into something resembling speculative science fiction.

Musk reemphasized the importance of space-based data centers as a solution to terrestrial computational constraints. The core argument is straightforward: Earth-based data centers face physical limits. Cooling costs scale with density. Real estate is finite. Power generation has environmental constraints. Space solves these problems theoretically, even if the technical challenges are substantial.

The Lunar Factory Concept

Musk went further, proposing a moon-based factory for AI satellites. Not just data centers, but manufacturing facilities that would produce AI infrastructure on the lunar surface. The vision includes a lunar mass driver—essentially an electromagnetic catapult—designed to launch AI satellites into space.

This concept bridges several domains: advanced manufacturing, space physics, electrical engineering, and AI architecture. Building a factory on the moon requires solving problems that haven't been solved at scale. But it's not theoretical. Space X has already demonstrated the ability to land and reuse rockets. A lunar factory is ambitious but not impossible.

The Energy Harvesting Argument

With space-based infrastructure, Musk argued, AI systems could capture energy directly from the sun at scales impossible on Earth. His phrasing was deliberate: an AI cluster could theoretically capture "significant portions of the sun's total energy output."

This is where the thinking becomes explicitly long-term. Not 5 years. Not 20 years. We're talking about infrastructure and ambitions on a 50 to 100-year timescale.

The Interplanetary Expansion

Musk concluded by suggesting that with sufficient space-based infrastructure, humanity could expand AI systems "to other galaxies." At this point, he was operating in pure speculation territory. But his closing comment reveals the philosophical framework: "It's difficult to imagine what an intelligence of that scale would think about, but it's going to be incredibly exciting to see it happen."

This statement is characteristic Musk. It combines genuine technological optimism with acknowledgment of uncertainty. He's not claiming he knows exactly how this plays out. He's claiming the direction is worth pursuing regardless.

Space-Based Data Centers: The Technical Reality

Space-based data centers aren't a new concept. Various companies and researchers have proposed them for decades. What's different now is that the technical foundations exist to make serious progress.

Thermal Management in Vacuum

The primary advantage of space-based data centers is thermal management. In vacuum, traditional cooling methods don't work because there's no medium to dissipate heat. But radiative cooling—using the thermal properties of space itself—becomes incredibly efficient.

A data center in space can radiate heat directly into the vacuum, achieving cooling efficiency that's impossible on Earth. This directly reduces the operational costs and power requirements of massive compute clusters.

Power Generation and Transmission

Power is the second critical challenge. Space-based data centers need power, and they can't plug into terrestrial grids. Solar power becomes the obvious solution. In space, solar panels face no atmospheric attenuation and operate 24/7 without clouds or night-time interruption.

But transmitting power from space to Earth is complex. Microwave power transmission—beaming energy down using focused microwave arrays—is theoretically viable but operationally challenging. Alternatively, data centers could operate autonomously in space, serving the vast computational demands of satellite networks and space-based AI systems.

Launch and Deployment Costs

Historically, launch costs have been prohibitively expensive. This has changed dramatically. Space X's Falcon 9 reusable rocket has driven launch costs down from thousands of dollars per kilogram to hundreds of dollars per kilogram.

Future rockets like Starship aim to reduce this further, potentially to tens of dollars per kilogram at maturity. At these cost levels, deploying massive data center infrastructure in space becomes economically viable.

Estimated data suggests that standard subscribers contribute 60% and business accounts 40% to X's $1 billion annual revenue milestone.

The Lunar Manufacturing Vision: From Theory to Engineering

A moon-based factory for satellite manufacturing represents a quantum leap in ambition. It's not just about deploying existing technology in space—it's about building manufacturing capability on an extraterrestrial body.

Lunar Mass Drivers and Launch Physics

A lunar mass driver (or rail gun) uses electromagnetic force to accelerate objects without chemical rockets. The moon's lower gravity (one-sixth of Earth's) and lack of atmosphere make it ideal for this technology.

A satellite launched from Earth requires overcoming Earth's gravity and atmosphere, consuming enormous amounts of fuel. A satellite launched from the moon requires far less energy—potentially just enough to achieve lunar escape velocity and coast to its destination.

The economics are striking. If launching from the moon is 90% cheaper than launching from Earth, suddenly producing AI infrastructure in space becomes not just technically interesting but financially compelling.

In-Situ Resource Utilization

For a lunar factory to be sustainable, it needs to use lunar resources. The moon has regolith (soil), water ice in permanently shadowed craters, and various mineral deposits.

Water ice is particularly valuable. It can be converted to hydrogen and oxygen for rocket fuel, oxygen for life support, or used directly in various manufacturing processes. A lunar factory that uses local resources becomes progressively more independent from Earth supply chains.

This is where the vision becomes truly long-term. Building a self-sustaining manufacturing operation on the moon requires not just engineering but also research into lunar resource extraction, processing, and utilization.

The Challenge of Radiation and Isolation

Manufacturing electronics in space presents unique challenges. Radiation levels are higher, materials behave differently, and quality control becomes more complex.

However, these aren't insurmountable. Companies already manufacture electronics in harsh environments (submarines, spacecraft, deep-sea equipment). A lunar factory would face similar constraints but with better control over the environment than, say, manufacturing on a spacecraft in transit.

AI at Cosmic Scale: What Would AGI Think About?

Musk's closing comment—"It's difficult to imagine what an intelligence of that scale would think about"—reveals the underlying philosophy driving x AI's interplanetary vision.

The implicit argument is this: Artificial General Intelligence at scale requires computational resources that terrestrial infrastructure cannot provide. If you want to build systems capable of matching or exceeding human-level intelligence across arbitrary domains, you need power budgets that only space-based systems can deliver.

Energy Constraints and AGI

There's genuine scientific debate about whether energy constraints ultimately limit intelligence. Some researchers argue that an AGI system would naturally gravitate toward maximizing available energy to fuel its own development and capability expansion.

If that premise is true, then intentionally building infrastructure capable of capturing solar energy at massive scales makes strategic sense. You're not building for today's computing needs—you're building infrastructure that an AGI system might eventually require.

The Fermi Paradox Connection

Musk's vision also touches on a deeper question in astrobiology: why haven't we detected alien intelligence? One hypothesis involves energy constraints. Civilizations that build computational systems require vast amounts of power. At some point, they either collapse from resource depletion or expand outward to capture more stellar energy.

x AI's interplanetary strategy is, in some sense, a bet on that model. Build the computational infrastructure now. Enable future expansion later. The moon-based factory becomes a proof of concept for how intelligent systems expand from planetary to interplanetary scales.

Space-based infrastructure offers significant advantages in cooling costs, real estate, and energy harvesting compared to terrestrial options, though power generation remains a challenge. Estimated data based on conceptual benefits.

The Team Behind Macrohard: Building Corporate AI

Toby Pohlen's leadership of the Macrohard project signals that x AI is serious about building systems that can understand and model organizational structures, not just individual tasks.

Corporate operations are complex. They involve hundreds or thousands of interdependent workflows, cascading decision-making, resource constraints, and emergent properties that arise from human interaction.

If Macrohard can genuinely model corporate operations, the implications are profound. An AI system that understands an organization at that depth could optimize workflows, identify inefficiencies, predict outcomes of strategic decisions, and potentially operate the organization autonomously during periods of human unavailability.

This isn't science fiction. It's a natural extension of AI capabilities that already exist. Copilots and code assistants already understand specialized domains. Corporate modeling AI is the logical next step.

Rocket Engine Design

Pohlen specifically mentioned that Macrohard "should be able to design rocket engines." This reveals the level of technical sophistication required.

Rocket engine design involves thermodynamics, materials science, fluid dynamics, and rigorous safety engineering. It's not just complex—it requires understanding physical laws and their application in extreme environments.

If Macrohard can genuinely design rocket engines, it demonstrates capability that extends far beyond pattern matching or language prediction. It suggests the system can reason about physics, generate novel designs, and validate them against constraints.

The Content Moderation Paradox: Scale vs. Safety

x AI's approach to content generation reveals a fundamental tension in modern AI deployment. Do you build powerful, unfiltered systems and accept that some users will misuse them? Or do you implement strict guardrails and sacrifice capability?

x AI appears to be betting that unfiltered capability is more valuable than guardrails. The reported surge in AI-generated explicit content on X is the immediate cost of that bet.

The Economic Pressure

There's also an economic dimension. Guardrails cost computing power. Content filtering systems require additional inference passes, additional models, and constant refinement. By operating with minimal filtering, x AI reduces operational costs while maximizing throughput.

The Regulatory Risk

But this approach carries regulatory risk. Governments worldwide are increasing scrutiny of AI-generated content, particularly explicit imagery. Platforms that enable production of such content face potential liability.

x AI's strategy seems to be moving fast and accepting regulatory consequences if they arise. This is characteristic of Musk's approach across companies—innovate aggressively, deal with regulation later if necessary.

Industry Implications: What This Means for Competitors

x AI's revealed strategy creates pressure on competitors in several directions.

On Revenue and Sustainability

x AI's path to $1 billion in annual recurring revenue proves there's demand for paid AI features on social platforms. Open AI, Google, and other AI companies will notice. Expect more aggressive monetization of consumer AI features.

On Infrastructure Ambition

x AI's space-based data center vision sounds crazy. But if Space X and other space companies can reduce launch costs further, it will become less crazy. Competitors will need to evaluate similar strategies.

On Unfiltered Systems

x AI's approach to content moderation is deliberately permissive. This creates competitive pressure. If competitors remain restricted while x AI offers more unfiltered systems, power users might prefer x AI's tools.

On Specialized Modeling

The Macrohard project targeting corporate and design tasks suggests x AI is moving beyond general-purpose models into specialized systems. This is a strategy competitors like Open AI have also adopted with tools like Code Interpreter and GPT-4V for vision tasks.

The Talent Acquisition Angle: Why Departures Matter

The reported departures of founding team members raise important questions about talent dynamics at x AI.

Startup founders often have specific visions about how products should develop. When companies pivot away from those visions, talented founders may choose to leave.

Alternatively, x AI's restructuring might reflect disagreement about the space-based data center strategy or the permissive approach to content moderation.

Unfortunately, the all-hands didn't dive deep into why specific people left, making detailed analysis impossible. But the pattern of founding team departures suggests either significant strategic disagreement or personal decisions by founders to pursue other projects.

The Timing: Why Now?

x AI's decision to publish the all-hands in February 2026 isn't random. The timing reveals strategic thinking about narrative control and market positioning.

February is a period of relative quiet in the tech news cycle compared to January's CES coverage or the spring conference season. Publishing the video now gives the media time to digest and report on the vision without it getting lost in competing narratives.

The video's release also comes at a moment when AI adoption is accelerating but still facing regulatory questions. By demonstrating concrete progress (revenue milestones, user metrics) alongside visionary thinking (space infrastructure), x AI positions itself as both grounded and ambitious.

Looking Forward: The Next 12-24 Months

If x AI's ambitions are genuine, what should we expect to see over the next 12-24 months?

Near-term (6-12 months): Expect more aggressive product development in Grok voice integration, code generation improvements, and Imagine video quality enhancements. Watch for revenue growth acceleration at X.

Medium-term (12-24 months): Look for concrete progress on space infrastructure partnerships. Space X announcements about dedicated data center launches would be telling. Watch for Macrohard applications in real corporate environments.

Longer-term indicators: If x AI is serious about lunar manufacturing, expect partnerships with space agencies or aerospace companies. Hardware announcements would validate the vision beyond the all-hands presentation.

The Credibility Question: Separating Vision from Reality

Musk is a figure who inspires both fierce loyalty and intense skepticism. His companies have consistently delivered on long-term visions while also making exaggerated near-term claims.

Tesla's Autonomy Day presentations have included timelines that slipped. Starship's development has taken longer than initial predictions. But both companies have made genuine progress on genuinely hard problems.

x AI should be evaluated similarly. The space infrastructure vision might be real. The timeline might be unrealistic. The technology might work. The economics might not make sense until a decade in the future.

What's not debatable is that x AI is investing serious engineering resources in problems that matter. A company doesn't dedicate teams to Macrohard, video generation at scale, and space infrastructure partnerships unless it's serious about the work.

Conclusion: AI's Next Frontier Is Literally Frontier

x AI's all-hands presentation revealed a company operating on a different timescale and ambition level than typical AI startups. While competitors optimize for next quarter's metrics, x AI is planning for infrastructure that won't reach full utility for decades.

The immediate revelations—$1 billion in X subscription revenue, 50 million daily videos generated, organizational restructuring—are noteworthy. But they're footnotes compared to the core message: x AI believes AGI development will require computational resources that only space-based infrastructure can provide.

Whether that vision proves correct remains to be seen. The space industry has made genuine progress on launch costs and reusability. The physics of space-based data centers is sound. The engineering challenges are substantial but not impossible.

What's clear is that x AI is betting massive resources on this direction. That bet will either look like visionary genius or spectacular overreach in retrospect. The company has committed to finding out which.

For the rest of the industry, x AI's strategy serves as a forcing function. If space-based infrastructure becomes viable for computational systems, it changes the competitive landscape entirely. If lunar manufacturing becomes a reality, it enables capabilities that are currently impossible. If Macrohard can genuinely model and optimize complex systems, it reshapes enterprise software.

The all-hands presentation was ostensibly about announcing organizational changes and revenue milestones. But the real message was simpler and more ambitious: x AI is playing a different game than everyone else, operating on a different timescale, and building infrastructure for an intelligence that doesn't yet exist.

Whatever you think of Musk, whatever your skepticism about space-based data centers or lunar factories, you have to respect the commitment to thinking beyond quarterly earnings.

FAQ

What is x AI and why did it go public with its all-hands meeting?

x AI is an AI company founded by Elon Musk focused on developing advanced AI systems including Grok (a conversational AI), Imagine (a video generator), and specialized tools for coding and corporate modeling. The company published its 45-minute all-hands meeting on X in February 2026 primarily because the New York Times had already reported on it, so leadership chose to control the narrative by releasing the full video publicly themselves.

What are x AI's four main operational teams?

x AI restructured into four primary teams: (1) Grok and Voice, focusing on conversational AI with voice capabilities, (2) Coding Systems for AI-powered development tools, (3) Imagine Video Generator producing video content at massive scale, and (4) Macrohard, which focuses on corporate and system modeling, including advanced tasks like rocket engine design.

How much revenue does X platform generate from subscriptions?

According to Nikita Bier, X's head of product, the platform crossed $1 billion in annual recurring revenue from subscriptions as of the all-hands meeting in February 2026. This was largely attributed to a holiday marketing push during the final months of 2025 and represents a significant achievement for the relatively young subscription model.

What are space-based data centers and why does x AI consider them important?

Space-based data centers are computing facilities deployed in orbit or on celestial bodies that offer advantages like superior thermal management in vacuum, uninterrupted solar power generation, and freedom from terrestrial constraints. x AI views them as essential infrastructure for supporting AGI-level systems that would require computational resources exceeding what Earth-based data centers can provide.

What is the lunar mass driver concept mentioned in the presentation?

A lunar mass driver is an electromagnetic catapult system that would be built on the moon to launch AI satellites and infrastructure into space. Because the moon has one-sixth of Earth's gravity and no atmosphere, launching objects from the lunar surface requires significantly less energy than launching from Earth, potentially reducing infrastructure deployment costs by 90% or more.

What is Macrohard and what does it do?

Macrohard is x AI's project focused on building AI systems capable of understanding and modeling complex corporate operations and technical systems. According to project lead Toby Pohlen, it should eventually be capable of designing rocket engines and simulating how entire corporations operate, representing a bridge toward more generalized artificial intelligence.

What happened to founding team members who departed from x AI?

The all-hands meeting confirmed that x AI experienced significant departures from its founding team, which Musk described as necessary restructuring as the company evolved rapidly. However, specific details about departures weren't extensively discussed in the presentation, and the reasons for individual departures remain unclear.

What are the content generation metrics for Imagine and image generation?

x AI executives reported that Imagine is generating 50 million videos per day, and the platform generated over 6 billion images in the 30 days prior to the all-hands meeting. However, these metrics include substantial amounts of AI-generated explicit content that surged on X during the same period, complicating interpretation of the figures.

Why is Musk concerned about terrestrial constraints on AI infrastructure?

Musk argues that Earth-based data centers face inherent limitations: cooling requirements increase with density, real estate is finite, power generation has environmental constraints, and operational costs scale significantly. Space-based infrastructure theoretically overcomes these limits by using vacuum for cooling, uninterrupted solar power, and access to unlimited expansion area.

How does x AI's content moderation approach compare to competitors?

x AI operates with minimal content filtering, prioritizing capability and throughput over guardrails. This contrasts with competitors like Open AI and Google, which have invested heavily in content filtering systems. x AI's permissive approach reduces operational costs but creates regulatory and liability risks, as evidenced by the surge in explicit AI-generated content on X.

Key Takeaways

• xAI restructured into four specialized teams: Grok voice, coding systems, Imagine video generation, and Macrohard corporate modeling
• X platform crossed $1 billion annual recurring revenue from subscriptions, funding xAI's ambitious infrastructure projects
• Space-based data centers and lunar manufacturing could reduce satellite deployment costs by 90% while enabling AGI-scale computational systems
• Macrohard's capabilities to design rocket engines and model corporate operations suggest progress toward more general AI reasoning
• xAI's permissive content moderation contrasts sharply with competitors, trading safety guardrails for computational scale and throughput

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