Will AI widen the gap between small and large companies — or shrink it? | AI Insights | Lumman.ai

What if the thing that was supposed to level the playing field ends up turning it into a cliff?

That’s the anxiety circling AI right now: for every headline promising productivity gains and creativity unlocked, there's a shadow question underneath—

Will this new wave of tech actually help small companies compete, or will it just hand big companies even bigger advantages?

It’s a fair question—because we’ve seen this movie before. Software was supposed to empower everyone. Instead, the companies with the deepest pockets bought the best tools, hired armies of consultants to implement them, and built walled gardens others could barely peek into.

So now that AI is here, is it a reset—or just another round of the same game?

Let’s dig in.

AI’s promise: Infinite leverage. For everyone?

On paper, AI is the ultimate equalizer.

A small team can suddenly feel like an army. One founder with ChatGPT can write code, summarize research, write a sales email, and draft a pitch deck—all before lunch. No procurement process, no IT team, no training module required.

That kind of leverage used to be the exclusive domain of large companies with big budgets. Today, it costs $20 a month.

It’s why bootstrapped startups are doing things in 2024 that would have required entire teams in 2019.

But before we assume this is some utopian redistribution of power, let’s look a little closer.

Because AI is increasingly becoming two things at once: easier to access, but harder to dominate without serious resources.

And that might be the twist in the story.

The open tools are great. But they’re not the whole game.

Let’s start with the good news.

Open AI tools really do give small teams unprecedented capability. The internet has no shortage of examples:

A solo indie dev using GPT-4 to build and launch a Chrome extension in a weekend
A one-person marketing agency pumping out landing pages and content calendars in a few hours
A Shopify entrepreneur automating 80% of customer service with a fine-tuned language model

These aren’t hypotheticals. They’re happening.

AI lets you move faster, try more, and do it all with fewer people. For early-stage founders and small businesses, that’s rocket fuel—especially when you’re scraping for margin every month.

But here’s the catch:

Access is universal. Advantage is not.

As the AI stack matures, we’re already seeing the emergence of two very different playing fields: one for companies using AI tools, and another for companies building serious, strategic AI capabilities into their core business.

Guess who’s winning at the second one?

AI as a commodity vs. AI as infrastructure

Most small companies are still using off-the-shelf AI tools: copywriters, chatbot assistants, maybe a code copilot or analytics summarizer.

These tools make you faster—but not fundamentally different.

Meanwhile, large companies are embedding AI much deeper. Not just using it for tasks, but rewiring how decisions are made, how products are personalized, how supply chains flex in real-time.

This doesn’t just improve efficiency—it creates new defensibility.

Take Amazon. Of course they’re using AI to write product descriptions and automate customer service. But they’re also using it for dynamic pricing models, inventory prediction, fraud detection, and machine learning models that improve search ranking and ad targeting across billions of data points.

Or look at Netflix: AI isn’t just recommending content—it’s influencing which shows get made.

This kind of AI isn’t open-source. It takes proprietary data, research talent, integration horsepower, and years of iteration. None of that is cheap.

And that’s where the divide begins.

Talent is still a moat

It’s easy to get excited about the idea that “anyone can prompt.” But prompting isn’t strategy.

The best use cases often require people who understand both the business and the model—what’s technically possible and what’s commercially useful.

Right now, those people are getting hired by the companies that can pay them top dollar. And the talent gap is widening faster than many realize.

If you’re a startup, sure, you can tap into open models and stand on the shoulders of giants.

But if your competitor has a custom-trained model fed with unique datasets and a dozen machine learning engineers refining it every week—you’re not playing the same game.

They’re building the engine.

You’re renting the car.

Speed vs. scale: who’s really ahead?

There’s a moment where fast beats big.

AI lowers the cost of experimentation dramatically. That means tiny teams can out-innovate lumbering giants—at least in the early innings.

No committee meetings. No procurement gates. Just idea ➝ prototype ➝ deploy.

Startups like Harvey and Perplexity have jumped into crowded spaces (legal tools, search) and sprinted past incumbents, in part because they’re moving at GPT speed while others are locked in PowerPoint hell.

But over time, speed meets scale.

And if the large players are investing that time and capital into technical depth, proprietary data flywheels, and structural transformation—not just point solutions—they will turn that scale back into dominance.

It’s not about tech tricks. It’s about building AI into the bones of the business.

And that’s something very few small companies are doing yet.

What does all this actually mean?

It’s not all doom.

The short term is still very much up for grabs. The AI gold rush is opening plenty of doors, especially for those who move fast, think creatively, and stay close to customers.

If you’re scrappy and plugged in, you can use AI to squeeze more out of every minute and dollar than ever before.

But we shouldn’t kid ourselves:

Long-term advantage will go to companies that treat AI not as a tool, but as infrastructure
Proprietary data, deep integration, and technical talent will form the new moats—not just speed or clever prompts
The gap between casual users and serious builders will widen unless small companies deliberately upskill and rethink their AI strategies

The good news? The playbook isn’t locked.

Yes, VC-backed giants are racing to build vertical AI platforms.

But so are small companies who are picking one niche and going deep: capturing unique data, building models around customer behavior, partnering with domain experts to build something that’s not just fast—but differentiated.

The question every business should be asking right now isn’t “are we using AI?”

It’s “what are we doing with AI that others can’t easily copy?”

Because soon, everyone will have access.

But not everyone will have advantage.