Deeptech startups face a unique management challenge: how do you build a business around a technology that can take years to develop, when market trends and customer needs may shift every few months?

Big companies like Nvidia, Google, or Meta can take on these kinds of problems with large, highly specialised engineering teams and deep reserves of capital. Even then, success isn’t guaranteed. Startups don’t have that luxury. They operate with limited resources, and any sudden change in the market can render years of painstaking work irrelevant.

Conventional startup wisdom suggests analysing the market, identifying a niche, and then building a product to fit it. But in deeptech, this model often breaks down. By the time the technology is ready, the “perfect niche” may have disappeared or evolved into something entirely different.

Why invest in an engineering challenge over a market niche

When GPU Audio launched in 2017, the team made a deliberate choice: rather than chasing an existing niche, they decided to solve a fundamental engineering problem that could survive multiple shifts in the market. Their focus was on adapting graphics processing units (GPUs), which were originally designed for parallel rendering of graphics, to process audio data in real time.

It wasn’t a quick win. Between 2012 and 2015, the team built multiple prototypes and failed more often than they succeeded. A turning point came in 2017, when a leading researcher — a former advisor to Qualcomm’s founders and a recognised authority in ray-tracing engine design — joined the project.

Even then, it took years before the first working demonstrations appeared in 2020 and 2021, nearly a decade after early experiments began. During that time, GPU architectures changed, AI went mainstream, and consumer trends shifted dramatically. But the team stayed focused on their core technical goal, resisting the temptation to pivot toward short-lived opportunities.

In this case, prioritising long-term technological resilience helped the team navigate several market cycles and uncover opportunities that weren’t on the radar at the start. For teams in similar situations, it shows that this path — while risky and demanding a long planning horizon — can sometimes open unexpected doors.

The depth and complexity of deeptech engineering challenges

The obstacles GPU Audio faced illustrate why deeptech startups must often think differently. Two problems in particular stood out.

First, GPUs were never designed for audio. For graphics, small delays are acceptable — a dropped frame may go unnoticed. But audio requires near-instant precision. Even a tiny delay of just a few milliseconds can be audible, disrupting the experience entirely.

Second, the way sound is processed is fundamentally different from graphics. GPUs excel at handling millions of identical, independent tasks in parallel — the computational equivalent of a factory full of workers all performing the same action simultaneously. Audio, on the other hand, is a chain of small, interdependent steps. Each calculation depends on the result of the one before it. Getting GPUs to handle this kind of workload required more than optimisation — it demanded a reinvention of how audio processing itself could be structured.

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Challenges of this scale affect more than just the engineering roadmap — they shape how a team operates. Long development cycles call for clear, ongoing communication with both investors and internal teams. In our case, we made a point of sharing intermediate milestones to keep everyone aligned, even when the road to a finished product stretched over years.

Creativity in engineering: Borrowing ideas from adjacent fields

Without the deep pockets of a tech giant, GPU Audio needed more than persistence; they needed creativity. The team had to rethink audio algorithms from scratch, searching for ways to break down sequential tasks into parallelisable ones.

The breakthrough came by borrowing ideas from another domain: ray tracing in 3D graphics. The company’s chief scientist had extensive experience in this field, having built one of the fastest ray-tracing engines in the world. Ray tracing calculates reflections, shadows, and interactions across countless objects at once — problems not unlike the hundreds of processes required in real-time audio.

Applying these principles, the team built a new kind of audio process manager — a scheduling system that could aggregate audio streams, distribute workloads efficiently, and maintain the responsiveness required for real-time sound. What the industry had long dismissed as technically impossible suddenly became feasible.

Shifting from consumer products to developer SDKs

Solving the core technical problem was only half the battle. Next came the question of product-market fit. Breakthrough engineering doesn’t automatically translate into customer demand — especially in markets where preferences shift quickly.

Initially, GPU Audio released consumer software for musicians and sound engineers, tools that ran on GPUs rather than CPUs. While useful, this approach wasn’t scalable. The team realised that instead of building end-user products themselves, they could multiply their reach by offering a software development kit (SDK) to other application developers.

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This shift made the technology more flexible, less tied to short-term consumer trends, and far more attractive to potential partners. It also created a pathway into industries that the founders hadn’t originally targeted. For some deeptech startups, shifting from direct-to-consumer products to an SDK model can provide more flexibility and make it easier to keep pace with changing industry needs — that was the case here.

Discovering unobvious opportunities

Moving to an SDK unlocked a surprising new vertical: the automotive industry.

Car audio systems were lagging behind broader automotive innovation. Even as electric vehicles, advanced infotainment systems, and autonomous driving became more sophisticated, most in-car audio processing still relied on outdated DSP chips. GPU Audio saw an opportunity to modernise this layer.

The company developed zoned audio technology — allowing different passengers to hear different content simultaneously without interference. A driver could take a call over the front speakers while children in the back seat enjoyed a movie, all without overlapping sound.

This innovation didn’t just improve in-car entertainment; it opened the door to entirely new use cases, from personalised multimedia systems to interactive voice-based services. It also showed how a deeptech startup could scale by partnering with established industries, repurposing its core technology to meet needs far beyond the original vision.

The takeaway here is to stay open to markets beyond the original target segment. Often, meaningful scale comes from industries that haven’t yet gone through a full wave of innovation.

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