“What if Google builds this in-house?” As an AI startup founder or investor, you’ve probably heard (or asked) that question. In today’s AI boom, it’s not just paranoia, it’s a reality check. Big tech companies (and their powerhouse research labs) are doubling down on vertical integration: controlling everything from silicon chips to the user interface and this is creating stormy weather for AI startups trying to find their footing. In this conversational deep-dive, let’s unpack why the giants’ full-stack power plays could spell trouble for the little guys, and what founders and VCs can do about it. 🗺️💡
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🚀 What Is “Vertical Integration” in AI?
Vertical integration is a fancy term for “going full-stack.” In plain English, it means a company controls multiple layers of its product’s supply chain or technology stack, instead of relying on outsiders . In the AI world, this might look like a tech giant that designs its own AI chips, runs its own cloud data centers, develops advanced AI models in-house, and then delivers the final AI-powered apps or services directly to users. The big players – think Google, Amazon, Meta, Microsoft, and labs like OpenAI or DeepMind are all racing to do exactly this. Why? Because owning more of the stack reduces dependencies and costs (no need to pay a supplier if you are the supplier), boosts efficiency, and lets them tightly control how technology is delivered to customers . In short, integration = power and profit.
For example, Google builds its own Tensor Processing Units (TPU chips) instead of just buying from NVIDIA, and Amazon designs custom chips (Inferentia, Trainium) for AWS . These chips are optimized for the giants’ data centers and AI workloads, giving them a leg up on cost and performance. Analysts note this vertical move isn’t just tech vanity - it’s strategic. By using proprietary chips in their cloud, Big Tech can lock customers into their ecosystem and cut reliance on third-parties . One report even warns: such “big tech chips” tie customers to closed ecosystems and could stifle innovation compared to open hardware platforms . In other words, the house (the vertically integrated platform) always wins .
Vertical integration doesn’t stop at hardware. Take OpenA: once a scrappy research lab, now deeply entwined with Microsoft’s vertically integrated empire. Microsoft supplies the cloud muscle (Azure computing power) and in return gets exclusive access to OpenAI’s best models to embed in Windows, Office, and more . It’s a symbiotic lock-in: OpenAI needed money and scale; Microsoft wanted top AI talent and tech under its wing. The result is a quasi-vertically integrated partnership dominating large language models. As Max von Thun of the Open Markets Institute put it recently, a handful of tech monopolies are tightening their grip on AI, aiming for a future where they control AI’s infrastructure and platforms .
So, vertical integration in AI means Big Tech building an AI empire that owns every laye: from chips and cloud to algorithms and apps. It’s a power move that makes a lot of business sense for them. But for startups trying to play in the same sandbox, it can be pretty scary. 😰
😬 Why Vertical Integration Spells Trouble for AI Startups
From an AI startup’s perspective, the giants’ full-stack strategy can feel like a vise grip on the market. Here are the key reasons vertical integration by Big Tech and big labs is risky for startups (delivered candid and straight-up):
🔒 “Choke Point” Control (Compute & Data Lock-Out): AI isn’t magic; it runs on massive computing power and data. Guess who owns most of that? The cloud titans (Amazon, Google, Microsoft) and a few research labs with huge budgets. Startups often must rely on these incumbents for GPU/cloud resources to train and deploy models . This dependency is a double-edged sword: the big provider can raise prices, prioritize their own needs, or even restrict access. A 2024 EU policy brief noted that soaring AI training costs force startups to cooperate with big firms for infrastructure and users, even as those firms are direct competitors . It’s the classic platform dilemma, your supplier can also be your rival. And if push comes to shove, the supplier wins. (After all, a startup can’t exactly spin up a $10B data center overnight!)
💸 Thin Margins & “Taxation”: Vertical integration lets giants run massive scale at lower cost, and even subsidize services to undercut others. Startups, on the other hand, often pay retail prices for critical tools (e.g. cloud, APIs). If your AI app calls OpenAI’s API for every user query, a chunk of your revenue is essentially going straight to OpenAI. That’s a permanent margin “tax” on your business. And if the API provider changes pricing or terms? You’re stuck. As one analysis put it, many “AI-powered” SaaS tools today are “prompt pipelines stapled to a UI”, with no moat or proprietary tech . They burn cash paying API fees, hope to make it up in volume, but often end up with shaky economics. The reality: if you’re renting intelligence from a vertically integrated lab, your profit is at their mercy. (OpenAI, Google, etc. will always take their cut, and it might grow over time.)
🤖 Big Labs Moving Up the Stack: What about startups building on the “gaps” left by big models? Say you use GPT-4 but add your own interface or fine-tuning for a niche. Beware: those gaps are closing fast. It’s widely expected that foundation model providers will move up the value chain, from just providing an API to offering full-blown applications . A prominent VC, Sarah Tavel, highlighted that it’s inevitable, to justify their huge investments, the model makers (OpenAI, Anthropic, Google) will chase end-user use cases themselves . We’re already seeing it: OpenAI rolled out ChatGPT plugins, OpenAI-powered coding assistants, and multi-modal features that encroach on many startup ideas. In late 2023, OpenAI added a feature to let ChatGPT read PDFs, a seemingly simple upgrade that knocked out a bunch of small startups whose entire product was parsing PDFs with GPT . Those “wrapper” companies thrived only until the platform decided to bake in the same capability, natively. Ouch.
📦 Bundling & Distribution Advantage: Vertical integration means the giants can bundle AI features into their existing products and platforms, leveraging distribution that startups can only dream of. Microsoft can integrate an AI Copilot into Office and Windows, essentially preloading it to billions of users at no extra cost. Google can build generative AI into Search or Android. This bundling is a killer: why would a user pay for a standalone startup product if a “good enough” version comes free with something they already use? We’ve seen this movie before outside AI too. Think about how Microsoft Teams (free with 365) squeezed Slack, or how Internet Explorer bundled with Windows killed off Netscape. An anecdote from Sarah Tavel’s newsletter puts it perfectly: it’s “exactly Amazon’s game — when a product takes off, they produce it themselves and push it to the top, killing the competition.” In AI, the same pattern looms: if an AI startup’s feature looks promising enough, a big tech firm can replicate and mass-distribute it faster than you can raise your next round of funding.
🧲 Talent & IP Vacuum: Big Tech’s research labs are hoovering up top AI talent and IP through acquisitions and “quasi-mergers.” Instead of outright buying a startup (which might trigger antitrust alarms), giants increasingly opt for strategic partnerships that give them the startup’s brains and tech, without fully taking over the company . For example, between March and August 2024, Google, Microsoft, and Amazon each struck deals with hot AI startups (Character AI, Inflection AI, and Adept, respectively) that followed a pattern . The big company licenses the startup’s tech, hires most of its employees, and in exchange the startup gets some cash and is refocused on a narrower niche . After such deals, those startups stopped developing their own cutting-edge models, they essentially became front-end feature companies, while the giants turbocharged their own model development with the newly acquired talent . It’s a win-win for everyone except, arguably, the startup’s original mission (and any other startups left out in the cold). Regulators have noticed this trend, the U.S. DOJ and European authorities launched probes in 2024, worrying that big firms use partnerships and talent-grabs to preempt future competition and end-run merger laws . From a founder’s view, it means your “strategic partnership” with a giant could be the beginning of being subsumed into their machine. And if you don’t partner or sell, you might find yourself competing with a former teammate now backed by infinite resources.
Whew! That’s a lot of ways things can go sideways for the upstarts. To sum it up, vertical integration arms the incumbents with multiple unfair advantages: they own the picks and shovels (chips, cloud), the gold mine (data, models), and the market (user base, distribution). Startups risk getting squeezed from all sides on costs, on capabilities, and on customers. As one Mozilla/Open Markets report warned, a few “gatekeepers” controlling AI’s key inputs and channels can quickly stifle the next generation of innovators . It’s not that every big company move is evil by design; it’s that the game is inherently rigged in favor of those who control the playing field. And right now, that’s the vertically integrated titans.
Alright, by now you might be thinking: “So is it game over for startups? Should we all pack up and go home?” Not so fast! Yes, the challenges are real, but startups can survive and thrive – if they play it smart. We’ll talk about that next week!
Signing off and signing zero checks,
SWEdonym
HR is lonely. But it doesn’t have to be.
The best HR advice comes from those in the trenches. That’s what this is: real-world HR insights delivered in a newsletter from Hebba Youssef, a Chief People Officer who’s been there. Practical, real strategies with a dash of humor. Because HR shouldn’t be thankless—and you shouldn’t be alone in it.