Efficiency, speed and costs are today’s moat for tech startups.
Groq’s $750M raise - Stop selling magic and start solving friction
TL;DR: The AI game is shifting. VCs used to bet on what apps do. Now they’re betting on how efficiently they do it. Groq’s $750M raise at a $6.9B valuation proves performance—especially inference speed and cost—is the real moat. If you're a founder, your pitch should highlight friction solved, not glitter features.
Are you pitching like it’s 2022?
What do most AI startups lead with? Impressive demos. Novel features. Big "what-if" ideas.
But investors are shifting focus. They are already impressed with what AI can do - now they’re looking at how efficiently it can do it.
Groq, a chip startup, just raised $750 million and was valued at $6.9 billion—more than doubling its valuation in a year. What’s their angle? Making inference fast, cheap, and scalable so that AI isn’t just impressive—it’s usable.
Performance is the new output.
Here’s a simple way to think about it: inference is the battery life of AI. If it drains fast, people stop using it—even if the features are cool. Groq has been successful in fundraising because they are selling chips optimized for inference.
Big players (NVIDIA, AMD) are chasing this, too. The era of “train everything, figure out deployment later” is ending.
Speed, latency, cost per inference, edge vs cloud, energy constraints—these are no longer nerdy footnotes. They’re front-page differentiators - so make sure you talk about them directly in your pitch.
(Ask your Pitch Building Mentor for advice on when and how to highlight them).
Why you should pitch infrastructure even if you build software.
If you sell a SaaS or AI app, don’t just tout what it does—talk about how well it works: speed, cost, scale, reliability. These are pain points users feel, and often complain about.
When you position around infrastructure:
You differentiate (everyone builds features; few optimize inference)
You reduce risk in the eyes of investors (lower operational cost + better UX = stickier product)
You align with what the big dollars are chasing right now
Your customers pain points often relate to how well the tech works, not just what it does.
Contrast: features vs friction.
Most founders lead with features: “We do X, Y, Z with AI.” Great. But what about the friction behind X, Y, Z? High latency? GPU cost? Unreliable scaling? Data pipeline bottlenecks?
A sharper pitch:
“Our AI product doesn’t just generate responses — it does so in under 50ms, costs 1/10th per query, and scales to millions of users without blowing the cloud bill.”
See the difference? That’s selling friction solved vs. ‘what we do’.
Example: Groq’s move
Raised $750M, valuation $6.9B, led by firms like Disruptive, with participation from BlackRock, Samsung, etc.
Known for producing inference chips tuned for pre-trained models. Emphasis not on training at scale, but inference.
To quote their CEO:
— Jonathan Ross, Groq Founder and CEO
What this means for founders now
When you build, think: lowest latency / cheapest inference cost / energy efficiency / edge vs cloud trade-offs from day one.
In your pitch decks, include metrics around inference cost per query, throughput, latency—not just “we can do X” but “we can do X fast and cheap.”
If possible, show savings/resources used—real numbers beat hypothetical features.
Pitch Solutions and Friction Solved
Founders: are you pitching features — or friction solved?
Investors aren’t just listening to “what your model can do” anymore. They want to know how fast, how scalable, and how cost-efficient it runs. That’s where the real battles will be won.
✅ If you're still leading with features in your pitch, you're missing what investors are actually buying. Download our Investor Q&A Prep Guide to start answering the real questions that close funding rounds.