AI ambitions could push SpaceX to tens of trillions in value by the 2030s, analyst argues

Aaron Burnett, founder and CEO of Mach33 Financial Group, made a striking call on CNBC on August 7: SpaceX's artificial intelligence ambitions could push the company's value into the tens of trillions of dollars by the 2030s, even if the business hits major delays along the way. That framing positions SpaceX not primarily as a launch provider or satellite operator, but as an AI platform play whose long-term valuation case hinges on what it builds with intelligence, not just rockets.

Burnett also told CNBC that a merger between SpaceX and Tesla carries strategic logic, a view that will land differently for enterprise operators than for retail investors. For fleet operators, infrastructure buyers, and logistics networks that are already evaluating autonomous and space-connected services, the prospect of a combined entity changes the vendor landscape materially.

Google restructures around speed, not science

The same week, a major organizational shift at one of the world's largest AI labs sent a clear signal about where the competitive frontier is moving. Demis Hassabis, co-founder of DeepMind and one of the most prominent figures in AI research, stepped aside as CEO of Google DeepMind, according to the Financial Times. Jeff Dean, the lab's chief scientist, departed simultaneously to found his own startup.

The Financial Times reported that the restructuring consolidates control with Google's Silicon Valley parent and marks a shift away from DeepMind's historically research-first culture toward faster delivery of AI products. Sergey Brin's role is expanded under the new structure, giving the Google co-founder more direct authority over AI direction.

For enterprise buyers evaluating Google's AI stack, including Vertex AI, Gemini-based APIs, and DeepMind's applied research outputs, this is a governance change worth tracking. A move toward product urgency typically accelerates release cadences but can also shift what gets prioritized. Teams betting long-term on Google's AI roadmap should watch whether the reorganization speeds up enterprise-facing tooling or concentrates resources on consumer and ad-driven products.

ByteDance bets on raw scale in the foundation model race

While Google reorganizes, ByteDance is competing on model size. The Financial Times reported that TikTok's parent company is training a foundation model roughly three times larger than Moonshot's Kimi K3, a benchmark that positions the new model in the same scale tier as Anthropic's Mythos. ByteDance is already a significant operator in global AI infrastructure, and a model at this scale would represent a substantial step up in its capability ambitions.

Scale alone does not guarantee enterprise relevance, but it does constrain who can compete. Training models of this magnitude requires sustained capital, chip access, and data infrastructure that narrows the field quickly. For enterprise procurement teams evaluating which foundation model providers to build on top of, the shrinking number of credible frontier-scale labs is itself a supply-chain consideration.

What the convergence means for enterprise operators

Taken together, these developments point to a compressing window for enterprise teams to lock in AI infrastructure choices. SpaceX's AI positioning, if Burnett's CNBC thesis holds, means that connectivity and compute infrastructure are converging faster than most procurement cycles account for. A company that controls low-Earth orbit satellite bandwidth and builds AI agents on top of that layer is a different kind of vendor than a pure cloud provider.

At the same time, the leadership upheaval at Google DeepMind and ByteDance's aggressive model-scaling push suggest that the set of credible frontier AI vendors is not expanding, it is consolidating. Operators who built their AI strategies around a particular lab's research output, Google's scientific culture at DeepMind being a clear example, may find the product roadmap they were tracking no longer exists in the same form.

ByteDance's mega-model ambitions add a geopolitical dimension to vendor evaluation that compliance and procurement teams cannot ignore. Sourcing foundation model capabilities from a ByteDance-built system carries regulatory and data-governance questions that have no clean answer yet, and enterprise legal teams in regulated industries should be mapping that risk now rather than after deployment decisions are made.

The next concrete marker to watch: whether ByteDance's new model reaches public availability before the end of 2026, and how Google's reorganized AI division responds with its own product announcements under the new structure.