Inside the global investment pivot that is pushing hardware-AI hybrids into the center of the innovation universe — and how ICTGC helps founders build the systems that matter.

A New Gold Rush Forged in Silicon, Not Software

For most of the last decade, investors across Silicon Valley and beyond repeated a familiar mantra: software scales; hardware fails. The capital flowed toward SaaS, marketplaces, fintech, and cloud-based AI tools. Software seemed infinite; hardware seemed slow, expensive, and perilous.

That worldview has now flipped.

As the industry moves into an era where AI is no longer an abstract cloud utility but a concrete capability deployed in homes, factories, hospitals, warehouses, vehicles, and devices, a new truth is emerging: the future of AI depends on hardware. Not just chips themselves, but the entire ecosystem surrounding them — custom accelerators, edge compute platforms, sensing stacks, robotics systems, embedded AI modules, and the deeply integrated hardware-software architectures that allow large models to actually run at scale.

A recent Nasdaq analysis captured the moment with unusual clarity, calling the current surge “a hardware renaissance that marks a dramatic shift in how venture capital is deployed.” This renaissance is not a short-lived trend. It is quickly becoming the defining feature of the AI investment landscape.

2026 is on track to become the most consequential year yet for founders building chip-adjacent companies — those who operate in the critical space between silicon design and real-world AI deployment. They are increasingly the most fundable startups in the industry.

Why the Investor Mindset Is Shifting Away from Pure Software

For the first time since the mobile boom, software alone is no longer seen as a uniquely defensible advantage. Many VCs now describe AI software as “highly leveraged but easily replicated,” a polite way of saying that differentiation is becoming harder as models and tooling converge.

The logic behind the investment shift is straightforward: when models become commoditized, and access to large-scale compute is consolidated among a few hyperscalers, the most durable and defensible value shifts downward into hardware.

The commoditization of AI software is happening faster than most anticipated. Open-source models are catching up to proprietary systems at an astonishing rate. Foundation models are becoming interchangeable utilities. Fine-tuning, once a competitive moat, is now mainstream. As Aileen Lee of Cowboy Ventures put it in a recent interview, “It’s a funky time. Series A investors aren’t just seeking revenue growth—there’s a new algorithm with new coefficients.” In other words, software traction alone no longer guarantees a premium valuation.

The pressure comes from both directions. On the top end, training gargantuan frontier models is dominated by resource-rich labs. On the bottom end, smaller models are democratizing. What remains scarce — and therefore valuable — is the ability to deploy AI efficiently and affordably in the real world.

This is where hardware earns its crown.

Inference, not training, is now the industry’s real bottleneck. Enterprises adopting AI at scale are discovering that cloud-based inference bills can quickly surpass the costs of building the model itself. The shift is driving unprecedented demand for specialized inference chips, edge compute modules, robotics AI platforms, and embedded systems that can run models closer to where the data originates.

Jonathan Ross, CEO of Groq, succinctly summarized this shift when he told Reuters, “Inference is defining this era of AI.” The data bears him out: the global AI hardware market — valued at over $59 billion in 2024 — is projected to swell to nearly $300 billion by 2034. The AI chip market, a subset of that, is growing even more aggressively, from roughly $29.6 billion in 2024 to well over $160 billion by 2029. These growth rates dwarf most software verticals.

VCs, always sensitive to market momentum, are now allocating capital accordingly.

The Chip-Adjacent Frontier: Expanding the Startup Landscape

The most exciting part of this hardware resurgence is that it is not limited to traditional chip design. A wide constellation of startups operating around the silicon core — in inference acceleration, transformer specialization, embedded AI, sparse compute, sensor fusion, and robotics — are attracting major attention.

Groq: Building the Rails for an Inference-Driven Economy

Groq has emerged as one of the clearest indicators of the shift. The company has raised more than a billion dollars across its last two rounds, and its valuation soared to $6.9 billion in 2025. Groq’s unique LPU (Language Processing Unit) architecture is designed to perform inference at extraordinary speeds and predictable latency — a key demand for industries deploying real-time AI.

In practical terms, Groq is trying to build the “rails” for AI deployment, much like AWS built the rails for cloud computing. Their design philosophy goes beyond raw performance; it emphasizes determinism, power efficiency, and parallel scalability. Investors are betting that the company will become a pillar of the global AI infrastructure. As Ross put it: “We’re building the American infrastructure for fast, low-cost AI execution.”

Etched.ai: A Bold, Singular Bet on Transformers

While Groq offers versatility, Etched.ai focuses on radical specialization. Founded by young Stanford dropouts, Etched raised $120 million in its Series A round to build Sohu, an ASIC designed specifically for transformer models. Their thesis is provocative: the fastest, most efficient AI hardware will come from chips that do only one thing — but do it orders of magnitude better than general-purpose accelerators.

Etched’s benchmark claims are eye-popping. An 8-chip Sohu server can generate more than 500,000 tokens per second on Llama-70B, compared to 23,000 tokens per second from an 8×H100 configuration. In an industry obsessed with throughput and energy efficiency, that kind of performance is irresistible to VCs.

CEO Gavin Uberti encapsulated their audacious approach with characteristic bluntness: “If transformers go away, our company dies. If they stick around, we become one of the biggest companies of all time.” It is exactly the sort of conviction — backed by extraordinary execution — that investors flock to.

Tenstorrent: The Architecture Rebel

Tenstorrent is an entirely different kind of player, straddling multiple architectures including RISC-V, custom inference hardware, and automotive-grade compute. It is a rare startup attempting to compete with Nvidia not just in the datacenter, but across autonomous vehicles, robotics, and edge deployments. With backing from Samsung, Hyundai, and Fidelity, the company’s valuation approaches $3.2 billion.

Tenstorrent’s strength lies in its hybrid strategy: combining flexible architecture with scalable hardware blocks. In an era where enterprises want independence from any single chip vendor, Tenstorrent’s “modular” approach speaks directly to market appetite.

femtoAI: Intelligence That Fits in the Palm of Your Hand

While companies like Groq and Etched dominate the datacenter narrative, femtoAI represents a different revolution: the rise of on-device AI. Formerly known as Femtosense, the startup is pioneering sparse compute architectures that allow sophisticated AI models to run on power budgets tiny enough for wearables, consumer gadgets, robotics sensors, and industrial devices.

Their approach addresses a problem many overlook: as AI becomes ubiquitous, not every device can afford to offload inference to the cloud. The future depends on small, efficient, embedded AI — a market projected to reach tens of billions of devices by the end of the decade.

Investors recognize that femtoAI is not simply building a chip but enabling a paradigm shift: intelligence that resides directly on the device, not merely at the edge or in the cloud. Its long-term potential rivals many of the names dominating current headlines.

A Growing Constellation of Chip-Adjacent Innovators

Beyond the marquee names, a vibrant ecosystem is emerging: startups designing neuromorphic accelerators, companies building low-latency sensor fusion hardware for drones and robotics, firms specializing in power-efficient inference for automotive systems, and new players in chiplet architectures, advanced packaging, and thermal management.

Together, these companies represent a new frontier of AI innovation — one that extends far beyond the walls of datacenters.

2026: The Year of AI Deployment

Unlike the previous three years, which focused overwhelmingly on training frontier models, 2026 is shaping up to be the year AI truly enters the physical world. Enterprises are now shifting from experimentation to deployment. Governments are investing in AI infrastructure as a national priority. Consumer electronics firms are embedding AI into every product line. Robotics companies are racing to commercialize intelligent machines. And the demand for low-cost, low-latency inference is outstripping the capabilities of existing hardware.

Analysts project that inference workloads may represent as much as 80 percent of global AI compute demand by 2026. This is a foundational realignment of the value chain — and it places enormous importance on chip-adjacent innovation.

Generative AI will become cheaper, faster, more personalized, and more ubiquitous. But that future cannot be built on the cloud alone. It requires a new generation of hardware innovation delivered by startup teams bold enough to tackle systems-level challenges.

In such an environment, venture capital’s pivot toward hardware is not a trend. It is a structural correction.

The Founder Reality: Hardware Is Hard — and That’s Why It Wins

Founders who build AI hardware or chip-adjacent products must confront a unique set of challenges. Unlike software founders, who can iterate daily and deploy globally with a click, hardware teams deal with long lead times, complex supply chains, manufacturing risks, and multidimensional engineering challenges.

Creating a successful AI hardware product requires mastery of areas most early-stage teams have never fully navigated: thermal management, mechanical design, reliability engineering, firmware integration, manufacturability optimization, logistics planning, and rigorous testing. The jump from prototype to production is where many promising hardware startups struggle — or fail.

Yet this difficulty creates opportunity. Because the barrier to entry is so high, the companies that succeed often build defensibility that software startups envy. They can command premium pricing, build enduring customer moats, and shape entire industries.

But they cannot do it alone.

Why ICTGC Is Becoming the Strategic Launchpad for AI-Hardware Founders

The IC Taiwan Grand Challenge (ICTGC) is emerging as one of the most important global programs for founders who are building the future of AI hardware. It offers something that no accelerator in Silicon Valley can match: deep, direct access to Taiwan’s world-class manufacturing ecosystem, paired with the cross-border support needed to scale.

Taiwan is the heartbeat of the world’s hardware industry. It is home to the densest concentration of PCB manufacturers, PCBA lines, thermal specialists, mechatronics experts, component suppliers, inspection labs, advanced packaging giants, and fabrication partners anywhere on Earth. The country’s ecosystem is tailored to support rapid iteration and high-precision production — a perfect match for the needs of AI hardware startups.

ICTGC gives founders a guided path into this ecosystem. It helps them turn prototypes into production-ready designs, match with the right suppliers, reduce BOM costs, perform DFM checks, optimize yields, and prepare for pilot manufacturing runs. It provides access to mentors who have built iconic hardware companies and shipped millions of units globally.

Perhaps most importantly, ICTGC gives startups credibility in the eyes of investors. Being part of the program signals that a founder understands manufacturing realities and has the network to execute. As VCs grow more skeptical of AI software pitches, they are increasingly seeking teams who can articulate — and derisk — the complete hardware lifecycle. An ICTGC-backed team stands out immediately.

The Future of AI Belongs to the Builders of Systems, Not Just Software

The old rules of the tech industry no longer apply. The next decade of innovation will not be defined solely by cloud-based AI models or clever software abstractions. It will be shaped by the companies that master the full stack — silicon, systems, sensing, and real-world deployment.

Groq, Etched.ai, Tenstorrent, and femtoAI offer early clues about what the next generation of AI giants will look like. They are not merely writing algorithms; they are building the physical infrastructure of the intelligent world.

Founders who understand this shift — and who can execute in the unforgiving world of hardware — stand to build the most valuable companies of the AI era.

For those founders, ICTGC is not just an opportunity. It is a strategic advantage. A bridge to the world’s best hardware ecosystem. A partner in reducing technical and manufacturing risk. And, increasingly, a launchpad for the most fundable startups of 2026 and beyond.

To learn more about how ICTGC can support your hardware-AI journey, join us at the upcoming event, Bridging Silicon Valley and Taiwan: Semiconductor & AI Synergies, on January 13, 2026, in Palo Alto. This gathering brings together founders, researchers, investors, and Taiwan’s world-class hardware ecosystem for an unprecedented look at the resources available to help early-stage teams turn breakthrough ideas into global-scale products.

Whether you are building the next great chip-adjacent platform or exploring how Taiwan can accelerate your path from prototype to production, this event is your gateway to a new era of innovation.