The New M&A Playbook: What Nvidia-Groq Really Means
Last week, reports surfaced that Nvidia is in talks to acquire Groq for $20 billion.
Most headlines focused on the price tag. They missed the point entirely.
This isn't an acquisition. It's a defensive restructuring of the entire AI inference stack.
Here's what actually matters.
The Problem Everyone Gets Wrong
The conventional take: "Nvidia is buying another AI chip company to expand its empire."
But Nvidia already dominates training chips with 80%+ market share. They don't need help there.
What they need is control over inference - the part of AI that actually runs in production.
Here's the uncomfortable truth most analysts won't tell you: training a model is a one-time cost. Running that model in production? That's where 90% of compute costs live.
And Groq's LPU chips are specifically designed to make inference faster and cheaper. Much
Why This Changes Everything
In the 152+ enterprise AI projects we’ve shipped at Bles Software, I’ve seen the same pattern repeatedly:
Company builds proof-of-concept (training costs: $50K-$500K)
Model works great in testing
Production inference costs hit $2M+/year
Project gets shelved or scaled back
The training-to-inference cost ratio is completely inverted from what executives expect. They budget for training. They get killed by inference.
Groq’s architecture addresses this directly. Their chips run inference at 10x the speed of traditional GPUs at a fraction of the power consumption.
The Real Strategic Move
Here’s what I think is actually happening:
Nvidia isn’t buying Groq’s technology. They’re buying a competitive moat before someone else builds it.
Think about it:
• AMD is gaining ground on training chips
• Intel is investing heavily in AI accelerators
• Startups like Cerebras and SambaNova are attacking from new angles
If any of these players acquired Groq’s inference advantage, Nvidia’s dominance would be under serious threat. By spending $20B now, they’re preventing a $200B problem later.
This is the new M&A playbook in AI:
Buy your future competitors before they become actual competitors.
What This Means for Enterprise AI Strategy
If you’re a CTO or technical founder making AI infrastructure decisions rightcheaper.
now, here's what I'd consider:
1. Don't lock into long-term GPU contracts. The inference landscape is about to shift dramatically. 18-month flexibility beats 36-month discounts.
2. Build abstraction layers. Your AI systems should be able to swap inference providers. The companies that assume Nvidia forever will pay the premium.
3. Watch the Groq roadmap. If this deal closes, expect Nvidia to either (a) integrate LPU into their stack, or (b) slowly deprioritize it to protect margins. Neither is great for early Groq adopters.
4. Budget for 2026 inference costs at 40-60% of 2025. This deal is part of a broader price war coming to inference compute. Winners will be those who wait to lock i
The Bottom Line
The Nvidia-Groq deal isn't about chips. It's about who controls the economics of running AI in production.
Training costs will continue dropping. Inference costs determine which AI projects survive.
The company that owns inference owns the AI future. Nvidia is making sure that's them.
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This Week in AI
Agentic AI Foundation Launches Under Linux Foundation
What happened: OpenAI, Anthropic, Google, and Microsoft just donated their agent protocols to a new foundation. OpenAI contributed AGENTS.md, Anthropic donated MCP.
My take: This isn't cooperation - it's defensive positioning. Open source AI is eating Big Tech's lunch. They're standardizing now before the community does it without them. Smart chess move, not altruism.
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Andrew Ng Says AI is "Highly Limited"
What happened: The AI pioneer said publicly that AI is "amazing and also highly limited" - a notable statement from someone who's been bullish for a decade.
My take: This is the correction the industry needs. When Andrew Ng starts managing expectations, you know the hype cycle peaked. 2025 will be the year of "AI that actually works" vs. "AI that could work theoretically."
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Lovable AI Coding Hits $6.6B Valuation
What happened: The AI coding platform tripled valuation in 5 months, backed by Google, Nvidia, Salesforce, and Databricks.
My take: AI coding is the only AI category with proven, measurable ROI. Everything else is "we think it helps." Coding assistants show up directly in commits per engineer. Expect M&A here in 2025.
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Enterprise AI Spending: $37B in 2025
What happened: Enterprise AI spending up 3.2x year-over-year. 76% of AI use cases now purchased vs. built (was 47% in 2024).
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Tool of the Week: Claude Code
What it does: Anthropic's coding assistant that hit $1B ARR in just 6 months.
Why I'm recommending it: We've been using it for 3 months at Bles Software. It's the first AI coding tool that actually understands context across an entire codebase, not just the file you're editing.
Who should use it: Any team doing serious software development. The productivity gains are real - we're seeing 30-40% faster feature delivery on projects where we've fully integrated it.
The honest catch: It's expensive compared to alternatives. And when it hallucinates, it hallucinates confidently. You still need senior engineers reviewing everything.
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This Week I'm Wondering...
If inference costs drop 60% in 2025 (as the Nvidia-Groq deal suggests), which AI use cases that are currently "too expensive to run" become viable?
Hit reply and let me know your take. I read every response.
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That's all for this week.
If you found this valuable:
- Reply with your thoughts (I read every one)
- Forward to a colleague who'd benefit
See you next Tuesday,
Stanislav
P.S. Follow me on LinkedIn for daily AI insights.
