Meta’s shocking purchase of 49% of Scale AI at a ~$30B valuation shows that money is of no concern for the $100B annual cashflow ad machine. Despite seemingly unlimited resources, Meta has been falling behind foundation labs in model performance.

The real wake-up call came when Meta lost its lead in open-weight models to DeepSeek. That stirred the sleeping giant. Now in full Founder Mode, Mark Zuckerberg is personally leading Meta’s charge, identifying Meta’s two core shortcomings: Talent and Compute. As one of the last founders still running a tech behemoth, Mark doesn’t need SemiAnalysis to tell him to slow down stock buybacks to fund the future!

In addition to throwing money at the problem, he’s fundamentally rethinking Meta’s approach to GenAI. He’s starting a new “Superintelligence” team from scratch and personally poaching top AI talent with pay that makes top athlete pay look like chump change. The typical offer for the folks being poached for this team is $200 million over 4 years. That is 100x that of their peers. Furthermore, there have been some billion dollar offers that were not accepted by researcher/engineering leadership at OpenAI. While these offers aren’t all successful, Zuck is crushing the competitors by drastically increasing their cost per employee.

Perhaps even more iconic, Zuck threw his entire Datacenter playbook into the trash and is now building multi-billion-dollar GPU clusters in “Tents”!

As this report details, nothing is off the table. We unpack Meta’s unprecedented reinvention from Compute to Talent in the pursuit of Superintelligence as well as the story of how we got here. From Llama 3.0 open-sourced dominance to the epic fail of Llama 4 Behemoth, this Titan of AI is down but not out. In fact, we believe Meta’s ramp in training FLOPS will rival even that of OAI. The company is going from GPU-poor to GPU-filthy-rich on a per researcher basis.

Meta GenAI 1.0: AI Incrementalism

Compared to pure-play AI labs like OpenAI, companies like Meta and Google have followed an “AI Incrementalism” strategy by enhancing existing products with better recommendation systems and GenAI to improve ad targeting, content tagging, and internal tools. This has paid off handsomely in financial results, allowing Meta to shrug off Apple’s attempts at stopping them from tracking users with the release of their App Tracking Transparency (ATT) feature in iOS 14.5 (late 2021, early 2022).

While Meta is arguably more insulated from GenAI disruption than Google, LLM efforts at both companies have been somewhat disappointing. One reason is the allocation of capital primarily for the core business rather than pursuing superintelligence.

“Our CapEx growth this year is going toward both generative AI and core business needs with the majority of overall CapEx supporting the core.”

Source: Meta Q1 2025 earnings call, emphasis SemiAnalysis

GenAI acts as an extension to these tech giants’ businesses. They don’t have the same existential need to dominate new use cases like OpenAI in chatbots and Anthropic in coding APIs. This is readily apparent in the compute vs human capital allocation of a leading AI foundation lab like OpenAI compared to Meta. Zuck’s impact on competing offers and inflation of salary will hurt badly.

As a result, when measuring GenAI consumer app traction, Meta and Google meaningfully lag ChatGPT in its reach and engagement.

But that’s changing. Leveraging our proprietary Accelerator Industry Model and Datacenter Industry Model, we forecast a meaningful step up in GenAI investment from Meta in the coming years.

Meta GenAI 2.0 – Part 1, Re-Inventing the Datacenter Strategy (Again)

From Buildings to Tents

Just a year ago, Meta scrapped its decade-old “H”-shaped datacenter blueprint for a new AI-optimized design.

Now in 2025, Zuck decided to re-invent the strategy again. Inspired by xAI’s unprecedented time-to-market, Meta is embracing a datacenter design that prioritizes speed above all else. They’re already building more of them!

Meta’s shocking purchase of 49% of Scale AI at a ~$30B valuation shows that money is of no concern for the $100B annual cashflow ad machine. Despite seemingly unlimited resources, Meta has been falling behind foundation labs in model performance.

The real wake-up call came when Meta lost its lead in open-weight models to DeepSeek. That stirred the sleeping giant. Now in full Founder Mode, Mark Zuckerberg is personally leading Meta’s charge, identifying Meta’s two core shortcomings: Talent and Compute. As one of the last founders still running a tech behemoth, Mark doesn’t need SemiAnalysis to tell him to slow down stock buybacks to fund the future!

In addition to throwing money at the problem, he’s fundamentally rethinking Meta’s approach to GenAI. He’s starting a new “Superintelligence” team from scratch and personally poaching top AI talent with pay that makes top athlete pay look like chump change. The typical offer for the folks being poached for this team is $200 million over 4 years. That is 100x that of their peers. Furthermore, there have been some billion dollar offers that were not accepted by researcher/engineering leadership at OpenAI. While these offers aren’t all successful, Zuck is crushing the competitors by drastically increasing their cost per employee.

Perhaps even more iconic, Zuck threw his entire Datacenter playbook into the trash and is now building multi-billion-dollar GPU clusters in “Tents”!

As this report details, nothing is off the table. We unpack Meta’s unprecedented reinvention from Compute to Talent in the pursuit of Superintelligence as well as the story of how we got here. From Llama 3.0 open-sourced dominance to the epic fail of Llama 4 Behemoth, this Titan of AI is down but not out. In fact, we believe Meta’s ramp in training FLOPS will rival even that of OAI. The company is going from GPU-poor to GPU-filthy-rich on a per researcher basis.

Meta GenAI 1.0: AI Incrementalism

Compared to pure-play AI labs like OpenAI, companies like Meta and Google have followed an “AI Incrementalism” strategy by enhancing existing products with better recommendation systems and GenAI to improve ad targeting, content tagging, and internal tools. This has paid off handsomely in financial results, allowing Meta to shrug off Apple’s attempts at stopping them from tracking users with the release of their App Tracking Transparency (ATT) feature in iOS 14.5 (late 2021, early 2022).

While Meta is arguably more insulated from GenAI disruption than Google, LLM efforts at both companies have been somewhat disappointing. One reason is the allocation of capital primarily for the core business rather than pursuing superintelligence.

“Our CapEx growth this year is going toward both generative AI and core business needs with the majority of overall CapEx supporting the core.”

Source: Meta Q1 2025 earnings call, emphasis SemiAnalysis

GenAI acts as an extension to these tech giants’ businesses. They don’t have the same existential need to dominate new use cases like OpenAI in chatbots and Anthropic in coding APIs. This is readily apparent in the compute vs human capital allocation of a leading AI foundation lab like OpenAI compared to Meta. Zuck’s impact on competing offers and inflation of salary will hurt badly.

As a result, when measuring GenAI consumer app traction, Meta and Google meaningfully lag ChatGPT in its reach and engagement.

But that’s changing. Leveraging our proprietary Accelerator Industry Model and Datacenter Industry Model, we forecast a meaningful step up in GenAI investment from Meta in the coming years.

Meta GenAI 2.0 – Part 1, Re-Inventing the Datacenter Strategy (Again)

From Buildings to Tents

Just a year ago, Meta scrapped its decade-old “H”-shaped datacenter blueprint for a new AI-optimized design.

Now in 2025, Zuck decided to re-invent the strategy again. Inspired by xAI’s unprecedented time-to-market, Meta is embracing a datacenter design that prioritizes speed above all else. They’re already building more of them!