MEMO FROM THE FUTURE: xAI

CEO Edition

BOARD STRATEGY SESSION June 2030

TO: xAI Board of Directors

FROM: Elon Musk, CEO

DATE: June 2030

SUBJECT: Grok Dominance and the Compute Race with OpenAI and Anthropic

OPENING

xAI's Grok is the only LLM integrated with real-time information (from X/Twitter). This is a genuine advantage. As the AI race escalates, the primary constraint is compute: whoever has the most compute, trained with the most data, wins.

This memo proposes: (1) Maximize Grok's advantage through X integration, (2) Secure compute resources needed to compete with OpenAI and Anthropic, (3) Build Grok into the world's best real-time AI.

THE REALITY

Current state: - xAI founded 2023 - Grok launched late 2024 (real-time LLM via X integration) - Estimated valuation: $24 billion - Employees: 200+ - Compute: 100,000 GPUs (growing)

Competitive position: - Unique advantage: Real-time integration with X/Twitter (150M+ daily users) - Disadvantage: Late to market (OpenAI, Anthropic, Google ahead) - Advantage: Elon's capital and willingness to invest heavily in compute

Strategic reality: - This is a compute race - Whoever has the most compute + best data wins - xAI has access to capital and compute; most competitors don't

WHERE WE ARE

Today: - Grok is competitive with GPT-4, better with real-time information - Growing user base through X integration - Limited enterprise adoption (compared to OpenAI, Anthropic) - Compute infrastructure: 100,000 GPUs, targeting 1M GPUs by 2030

The challenge: - Enterprise adoption is slow (companies prefer "neutral" AI, not "X-integrated") - OpenAI/Anthropic have stronger enterprise relationships - Google has unlimited compute resources

THE OPPORTUNITY

Opportunity 1: Grok Enterprise Integration

The play: Make Grok indispensable for enterprises by integrating real-time X data into business intelligence.

How: - Sell Grok to enterprises for customer insights (Twitter sentiment, trends, competitive intelligence) - Integrate Grok into enterprise workflows (marketing, customer service, analytics) - Offer premium pricing for real-time Twitter data integration - Build enterprise-specific Grok models (industry verticals)

Estimated impact: - Enterprise customers: 5,000+ by 2035 - ACV: $500K-$5M (high enough to offset openness concerns) - Revenue: $3-5 billion by 2035 - NRR: 130%+ (customers expand as they see value)

Timeline: 18-24 months to significant enterprise adoption

Opportunity 2: Compute Race Victory

The play: Invest aggressively in compute to achieve 10x advantage over competitors in training and inference capacity.

How: - Deploy 500K-1M GPUs by 2032 - Train bigger, better models than competitors - Achieve inference cost advantage through superior hardware utilization - Create moat through sheer computational advantage

Estimated impact: - Models trained on 10x more data than competitors - Inference cost advantage: 30-50% better than competitors - Ability to fine-tune models quickly (faster iteration) - Winner-take-most dynamics in LLM market

Timeline: Ongoing; key deliverables 2031-2032

Opportunity 3: Grok API and Ecosystem

The play: Make Grok the standard LLM for developers by building better API, ecosystem, and developer experience.

How: - Best-in-class API (faster, cheaper, better than OpenAI) - Developer ecosystem (models, tools, libraries) - Fine-tuning and custom model support - Real-time data access (Twitter data for context)

Estimated impact: - Developer adoption: Become top-3 choice after OpenAI/Anthropic - API revenue: $2-4 billion by 2035 - Ecosystem effects (lock-in from applications built on Grok) - Platform advantage

Timeline: 12-18 months to competitive API; 3+ years to ecosystem

MY RECOMMENDATION

Pursue all three. Enterprise integration gives near-term revenue. Compute race is long-term winner-take-most. Developer API/ecosystem is defensive/offensive play.

This is about winning the LLM market. Winner is determined by compute, data, and talent. We have access to all three.

EXECUTION PLAN

Phase 1: Enterprise Grok (2030-2032)

• Build enterprise sales team
• Create industry-specific Grok models
• Land 1,000+ enterprise customers
• Target: £1-2 billion revenue by end of phase

Phase 2: Compute Scale (2031-2034)

• Deploy 500K-1M GPUs
• Train superior models
• Achieve inference cost advantage
• Maintain technology lead

Phase 3: Ecosystem and Platform (2032-2035)

• Scale developer adoption
• Build ecosystem of applications
• Establish as top-3 LLM platform
• Revenue reaches £5-8 billion

FINANCIAL IMPLICATIONS

By 2035:

• Grok Enterprise revenue: £3-5 billion
• API and developer revenue: £2-4 billion
• Data and analysis services: £1-2 billion
• Total revenue: £6-10 billion
• Gross margins: 70-80% (software/API model)
• Operating margins: 30-40% (at scale)

Valuation: £100-150 billion by 2035 (vs. current £24B estimate), assuming Grok achieves competitive parity with OpenAI/Anthropic in market share.

Elon

Confidential — Board of Directors Only