In August 2024, I invested in Groq at a $2.8B valuation, with a simple thesis: as AI moves from experimentation to everyday deployment, inference (serving models in real time) becomes the true bottleneck – and one of the most valuable control points in the stack.
In December 2025, Groq and Nvidia announced a non-exclusive inference technology licensing agreement, alongside a transfer of key Groq leadership and engineering talent to Nvidia. Some media outlets described the overall package as roughly a “$20B deal,” often framed as acquisition-like. Regardless of the exact structure, the strategic signal is clear: inference is becoming decisive – commercially and competitively.
The original logic: inference would outgrow training in economic importance
In 2023–2024, much of the public narrative centered on training: ever-larger models, ever-larger clusters, ever-larger capex. But the end-state of AI adoption is not training forever. It is serving inference at scale – billions of daily tokens powering copilots, agents, enterprise workflows, voice interfaces, search, and automation.
That shift creates three hard constraints:
- Latency becomes a product feature. If an AI system is smart but slow, it fails inside real workflows (customer service, security, trading, operations).
- Unit economics become existential. At scale, cost per token and energy efficiency determine margin structure – and ultimately who wins customers.
- Predictability becomes a moat. Enterprises want consistent performance and deployability, not just peak benchmark numbers.
Groq’s positioning mapped cleanly onto those constraints: a company built around fast, low-latency inference as a first principle – rather than treating inference as “whatever the training hardware can also do.”
Why Groq: a “picks-and-shovels” bet inside AI
I generally prefer investments where you are not betting on which specific model or app wins – you are betting on the infrastructure layer that benefits from broad adoption. Groq stood out for four reasons:
A clear wedge: inference-first performance. The message was simple and enterprise-friendly: serve inference faster and more efficiently, with predictable throughput and latency – exactly what scaled AI adoption demands.
Timing: demand was outrunning supply. By mid-2024, accelerator demand and deployment timelines were becoming a strategic headache for many buyers. In that environment, credible alternatives – especially for inference – get pulled forward.
Capital formation as validation. The $640M Series D at a $2.8B valuation signaled that sophisticated capital believed the scale-up path was real, not theoretical.
Asymmetric payoff. At $2.8B, success did not require Groq to “beat Nvidia.” It required Groq to become strategically unavoidable in inference – valuable enough that a platform incumbent would prefer to partner, license, or integrate rather than compete head-on.
What changed: the strategic value of inference control points
Between 2024 and 2025, the market began to reprice inference. As AI usage broadened, buyers cared less about abstract benchmarks and more about real-world throughput, reliability, and cost. Inference moved from a technical consideration to a board-level economic variable.
The Nvidia–Groq announcement should be read through that lens. When the market leader is willing to sign a non-exclusive license and absorb key builders of a competing inference approach, it is acknowledging that the control points of inference matter – and that differentiation is worth paying for.
From $2.8B to an implied $20B: what “success” looks like
If you compare the headline numbers – acknowledging that this is not a plain-vanilla merger – the jump from $2.8B to ~$20B implies roughly a 7.1x step-up in value (20 / 2.8 ≈ 7.14). In venture, outcomes like that do not come from guessing the next app. They come from being right about where the profit pool will consolidate in an ecosystem – and investing before the market reprices that layer.
The deeper lesson: invest in inevitabilities, not narratives
Models will change. Interfaces will change. Distribution will change. But the world will demand cheap, fast, scalable inference everywhere. That is an inevitability.
When you can identify an inevitability and back a team building a control point in that future stack, you are not chasing a story – you are underwriting a structural trend.
Disclosure and precision
Any investor’s ultimate return depends on the security type, dilution, rights, and transaction-specific payout mechanics. This article reflects the investment logic and how I interpret the strategic significance of the transaction structure.