The ChatGPT Moment SEMRON Saw Coming: How to Position Your Hardware Before Market Timing Hits

Being right about the future doesn’t matter if you’re building hardware and the future arrives before your chips do. The market moved. You predicted it perfectly. And you’re still two years from shipping.

In a recent episode of Category Visionaries, Aron Kirschen, CEO of SEMRON, an AI chip maker that’s raised $7.3 million, described what it felt like when ChatGPT exploded onto the scene in late 2022. “So actually in November, to be honest, it became a big thing in December for us. But I also have to say we had a great advisor in Berkeley and he’s really into AI and he was one year ago before that happens, he was like, okay guys, you have to have a look on transformer based large language models.”

They were prepared. They’d been watching transformer models for a year. They knew something big was coming. “So when it happened it was like, great for us. So we are kind of prepared.”

But here’s the challenge: being prepared when you’re building hardware doesn’t mean you can capitalize immediately. It means you avoid being caught completely flat-footed. For hardware founders, market timing isn’t about riding waves. It’s about building the surfboard before the wave arrives.

The Lag Between Prediction and Product

Software founders can pivot quickly. See a trend emerging? Sprint for two weeks and ship something. ChatGPT launches in November, and by December there are dozens of wrapper products in market.

Hardware operates on a different clock. SEMRON is four years into development and “close to process freeze”—the moment when they finalize their semiconductor device and can start building demonstrators. Not shipping to customers. Building demonstrators.

The timeline for semiconductor startups is brutal: “You don’t have the timeline or the roadmap usually of 1 year for go to market to really have to think in three, four, even five years,” Aron explains.

This creates a fundamental market timing challenge: you need to predict what will matter three to five years from now, commit to that prediction with millions in development costs, and hope you’re right when your product finally ships.

The penalty for being wrong is existential. The reward for being right is just staying in the game.

The Advisor Network That Provided Early Signal

What gave SEMRON their early warning? Not market research or trend analysis. A trusted advisor who understood AI deeply enough to spot transformer models as significant a year before mainstream adoption.

This reveals something critical about market timing for hardware: you can’t rely on obvious signals because obvious signals arrive too late. By the time ChatGPT is mainstream, if you’re just starting hardware development, you’re already three years behind.

You need access to people who can spot inflection points before they’re obvious. “He didn’t use these words. I remember we didn’t have the wording, but were keeping an eye on it and tried to make sure that our hardware is fit for that,” Aron notes.

This is how hardware founders need to think about positioning: you’re not positioning for the current market. You’re positioning for the market as it will exist when your product ships. And you’re doing it with incomplete information and terminology that doesn’t even exist yet.

The Market Moment They’re Still Waiting For

Here’s where SEMRON’s story gets interesting. ChatGPT created a watershed moment for AI in data centers. It changed how people think about AI capabilities. It created massive demand for compute.

But Aron sees an incomplete revolution: “When it comes to edge devices, we still miss this kind of chatgpt moment.” ChatGPT transformed cloud AI and productivity tools, but “it’s not really a game changer I would say in for smartphones or wearables in general.”

The edge AI moment hasn’t happened yet. “I think we are still missing the color app for these kind of devices.”

This is the bet SEMRON is making: the transformer model revolution that hit data centers will eventually need to work on edge devices. “Even though we have a lot of AI functionality in smartphones today. I think a lot of new things will come and we believe, and of course we might be a little bit biased here. We believe the reason why we don’t have the killer app is because you don’t have the hardware.”

They’re positioning for a market moment that hasn’t arrived yet. The infrastructure needs to exist before the killer apps can be built.

Why Current Technology Won’t Get There

Aron’s conviction about market timing comes from a technical argument: “We won’t have it with the current approach in five years from now because there’s no way current technology can do that.”

The use case he gives is specific: “To run for example a large language model for live translation like continuously in a discussion there’s nothing you can do, right? Or nothing without battery is down in like some minutes.”

This is the gap between the ChatGPT moment in cloud and the edge AI moment that hasn’t happened: power and cost constraints. Running transformer models continuously on battery power with current technology is impossible. The physics doesn’t work.

SEMRON’s bet is that new hardware architecture—their 3D AI chips with fundamentally different power characteristics—enables capabilities that are currently impossible. “If you have this functionality, this could be a paradigm shift and could create a completely new devices similar to what happened when we got smartphones and not just phones.”

They’re not positioning for incremental improvement. They’re positioning for the moment when entirely new device categories become viable.

The Cost Economics That Define Market Timing

But technical capability isn’t enough. Market timing for hardware also depends on economics. “I know that’s an important thing, but you also have to talk about cost, right? Cost per compute,” Aron emphasizes.

Even if you could run transformer models on edge devices with current technology (which you can’t), “we would not be able to make a product out of it because nobody would pay for like $5,000 more for something like that or even $200 more.”

The target is precise: “We really have to be like let’s say 20, $30.”

This cost constraint defines when the market moment can actually happen. Revolutionary capability that costs $5,000 per device is a science project. Revolutionary capability that costs $30 per device is a category-defining product.

SEMRON’s market timing thesis isn’t just “edge AI will be big.” It’s “edge AI will be big when hardware exists that can run transformer models at under $30 per chip with acceptable power consumption.”

They’re building toward that economic threshold, betting it arrives within their product timeline.

The Risk of Being Too Early

There’s an obvious risk in SEMRON’s approach: what if the edge AI moment doesn’t arrive before their runway ends? What if the killer app they’re enabling takes longer to emerge than their funding lasts?

This is the hardware founder’s nightmare: you predicted the trend correctly, built the right technology, hit your technical milestones, and ran out of money before the market materialized.

Software founders can pivot. Hardware founders can’t. Once you’re three years into semiconductor development, you’re committed. The sunk costs are too high. The technical dependencies are too deep. You’re all-in on your market timing thesis.

The only defense is building relationships with the customers who will deploy your technology when it’s ready. For SEMRON, that means engaging with “very big, well known companies that are market leaders in their application or in that vertical” right now, even though full commercialization is still ahead.

These early customer relationships serve dual purposes: they validate that your market timing thesis makes sense to the people who will actually buy your product, and they create commercial momentum before the broader market moment arrives.

The Framework for Hardware Market Timing

SEMRON’s approach suggests a framework for hardware founders trying to position before market timing hits:

Identify trusted advisors who can spot inflection points early. You need signal before the market makes trends obvious. By the time everyone sees it, you’re too late if you’re building hardware.

Separate hype from capability constraints. ChatGPT created hype around edge AI, but the capability constraints (power, cost) haven’t been solved yet. Understanding what’s technically impossible today helps you position for what becomes possible tomorrow.

Define the economic threshold for market adoption. It’s not enough to enable new capability. You need to enable it at a price point where products become viable.

Build relationships with deployment partners before the market moment. You can’t wait until your product ships to find customers. They need to be part of your development process.

Accept that you can’t pivot. Once you commit to hardware development, you’re betting on your market timing thesis. Make sure it’s a thesis you’re willing to bet years of your life on.

Structure funding around market timing uncertainty. Your runway needs to account for the possibility that the market moment arrives later than you expect.

For SEMRON, the ChatGPT moment wasn’t their moment. It was validation that the transformer model revolution was real. Their moment—the edge AI revolution—is still coming.

The question for hardware founders isn’t whether you can predict the future. It’s whether you can predict it accurately enough, with enough lead time, to ship hardware that arrives exactly when the market is ready. Sometimes the hardest part of perfect timing is staying alive long enough to be proven right.