為什麼 hard tech startups，在 AI 泡沫疑慮升溫時反而更值錢？
- 前半段為文章的英文版本 (The first half is the English version)
- 後半段為中文版本 (The second half is the Mandarin version)
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Why Hard Tech Startups Gain Value Amid Growing AI Bubble Concerns
Over the past two years, simply attaching an “AI” label to a startup has often been enough to attract attention, funding, and aggressive valuations. Generative AI, agents, copilots, and countless “X for AI” products now dominate pitch decks and headlines.
At the same time, a more nuanced debate is emerging. It is not about whether AI is transformative—most agree that it is—but about whether parts of the AI startup ecosystem may be overheating.
Amid these AI bubble concerns, long-term investors are increasingly re-evaluating where durable value lies. One category stands out in this discussion: hard tech startups.
What Defines Hard Tech Startups—and Why They’re Often Misunderstood
Hard tech startups are frequently misunderstood as simply “hardware companies.” In reality, hard tech startups usually share a deeper and more complex set of characteristics:
- Deep engineering foundations (mechanical, materials, control systems, firmware)
- Heavy reliance on real-world deployment and physical validation
- Long development cycles and significant upfront capital requirements
- High customer adoption friction—but strong lock-in once deployed
Historically, these traits caused hard tech startups to be viewed as difficult to fund, slow to scale, and challenging to pitch. However, as concerns around AI defensibility grow, these same traits are now being reassessed as structural advantages.
AI Bubble Concerns Are Changing How Startups Are Evaluated
AI bubble concerns do not suggest that AI innovation is slowing down or that a collapse is inevitable. Instead, they reflect a shift in investor questions—especially when evaluating early-stage companies.
Common concerns include:
- Rapid commoditization of AI models, reducing long-term differentiation
- Thin product moats, often limited to prompts, UX, or integrations
- Low switching costs, making customer retention fragile
- Valuations advancing faster than proven revenue models
As these questions surface, investors increasingly ask:
If AI capabilities become widely accessible, how defensible is this startup?
This is where hard tech startups often appear fundamentally different.
Why Hard Tech Startups Look More Defensible in This Environment
1️⃣ Hard tech startups build domain expertise that AI cannot compress
The competitive edge of hard tech startups rarely comes from a single algorithm. Instead, it is accumulated through:
- Years of trial and error in harsh, real-world environments
- Operational knowledge developed directly with industrial customers
- Deep familiarity with edge cases, failure modes, and physical constraints
These advantages cannot be replicated quickly—even with better AI models. For hard tech startups, experience itself becomes a moat.
2️⃣ High adoption friction creates long-term lock-in for hard tech startups
Unlike many software tools, hard tech startups sell into environments where “trying something new” is costly. Adoption often requires:
- Redesigning standard operating procedures (SOPs)
- Passing safety, compliance, and regulatory reviews
- Accepting long-term operational and liability implications
As a result, customers approach hard tech startups with a different mindset:
Choose carefully once, because switching later is expensive.
This high switching cost is one of the strongest defensive characteristics a startup can have.
3️⃣ Hard tech startups develop revenue discipline earlier
In periods of abundant capital, markets reward growth narratives and future potential. When uncertainty rises, focus shifts toward fundamentals:
- Real customers
- Measurable cost reduction or risk mitigation
- Predictable and repeatable revenue
Hard tech startups are often forced to confront these realities earlier than software-first companies—and those that survive emerge more resilient.
A Case Study That Illustrates the Strength of Hard Tech Startups: Droxotech
Droxotech is not positioned as an “AI-first” startup. It is a representative example of how hard tech startups create value through engineering depth and domain trust.
Its core product is a wall-climbing robotic vehicle designed for inspecting, cleaning, and maintaining large-scale industrial structures.
Rather than offering marginal efficiency gains, Droxotech focuses on structural risk reduction:
- Replacing dangerous, high-altitude manual labor
- Enabling inspections without shutting down operations
- Lowering costs while significantly improving industrial safety
This approach allowed the company to raise NT$200 million (approximately USD 6M+) at the Pre-A stage, enter the semiconductor supply chain, and expand from petrochemical facilities into higher-standard manufacturing environments.
These outcomes are characteristic of successful hard tech startups: progress driven by execution, not hype.
How Hard Tech Startups Actually Use AI
Hard tech startups are not anti-AI. Instead, they adopt AI on a different timeline.
A common pattern among hard tech startups looks like this:
- Make something physically possible
- Make it operationally reliable
- Then apply AI to enhance, optimize, and scale performance
In this structure, AI is not the core story—it is a force multiplier layered on top of hard-earned capabilities.
Conclusion: Why Hard Tech Startups Gain Value as AI Concerns Grow
As AI bubble concerns continue to be debated, investors increasingly prioritize:
- Technical and engineering depth
- Real-world integration and validation
- Businesses that remain defensible even as AI tools become more accessible
Hard tech startups may not dominate headlines, but they often become the companies investors trust when narratives are questioned.
In periods of uncertainty, it is frequently these slower-moving, engineering-driven hard tech startups that quietly gain long-term value.
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Further Reading
- DroxoTech Completes Series Pre-A Funding in 2025 to Accelerate Intelligent Robotics and Mass Production
- 🏢 FunRaise and the Rise of Data-Driven PropTech: Why Investors Now Bet on Data, Not Demand
- 🌐 Findit: Taiwan’s Official Startup Database — The Local Version of Crunchbase and Dealroom
- 🦄 Taiwan’s Newest Software Unicorn: Owlting Debuts on Nasdaq with a $4.9B Market Cap
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為什麼 hard tech startups，在 AI 泡沫疑慮升溫時反而更值錢？
過去兩年，只要在簡報或產品描述中加上「AI」，往往就能快速吸引資金、注意力與高估值。
生成式 AI、Agent、Copilot、各種「X for AI」，幾乎成為新創圈的共同語言。
但與此同時，一個更成熟、也更理性的討論正在浮現——
不是否定 AI 的長期價值，而是市場開始對「AI 是否在某些層面過熱」產生疑慮。
在這樣的 AI 泡沫疑慮（AI Bubble Concerns） 氛圍下，投資人開始重新思考一個更根本的問題：
哪些公司，具備真正長期、難以被取代的價值？
越來越多人的答案，指向 hard tech startups。
什麼是 hard tech startups？為什麼長期被誤解？
hard tech startups 並不是單純「做硬體」的新創公司。典型的 hard tech startups，通常具備以下特徵：
- 深度工程能力（機構、材料、控制系統、韌體）
- 高度依賴真實場域驗證，而非單純模擬或 Demo
- 研發週期長、前期資本投入高
- 客戶導入門檻高，但一旦導入就極難被取代
也因此，長期以來 hard tech startups 常被視為：
募資困難、成長緩慢、故事不好講。
然而，當 AI 商業模式與估值開始被反覆檢視時，這些特質反而逐漸被重新理解為 結構性的競爭優勢。
AI 泡沫疑慮，正在改變投資人評估新創的方式
強調 AI 泡沫疑慮，並不等於認定 AI 泡沫已經存在，而是反映投資人心態的轉變，常見的關注點包括：
- AI 能力快速商品化
技術差距可能比預期縮小得更快 - 產品差異層過薄
許多產品只差在 Prompt、UX 或整合方式 - 客戶切換成本偏低
長期留存與黏著度存在不確定性 - 估值跑在營收前面太遠
風險結構開始被更嚴格地檢視
於是，一個問題再次被放到檯面上：
當 AI 能力逐漸普及，這家新創還剩下什麼不可取代？
這正是 hard tech startups 開始重新受到重視的背景。
為什麼 hard tech startups 在此時更顯價值？
1️⃣ hard tech startups 的場域 know-how，AI 無法壓縮
hard tech startups 的競爭力，幾乎從來不是來自單一演算法，而是來自：
- 在惡劣、不可控環境中反覆失敗與修正
- 與高要求客戶共同磨出來的流程與標準
- 對極端情境、例外狀況與物理限制的深刻理解
這些能力，無法靠換模型或增加算力快速複製。
對 hard tech startups 而言，時間與經驗本身就是護城河。
2️⃣ 高導入門檻，反而替 hard tech startups 建立長期鎖定
對工業客戶而言，導入 hard tech startups 的解決方案，往往意味著：
- SOP 重寫
- 工安、法規與合規審查
- 長期維運與責任歸屬
因此客戶的決策邏輯通常不是「先試試看」，而是：
選對一次，之後就不想再換。
在許多 AI 軟體強調「快、輕、可替換」的時代，這種高切換成本反而成為 hard tech startups 最強的防禦力。
3️⃣ hard tech startups 更早面對營收與現金流現實
當市場資金充沛時，成長敘事往往優先於基本面；
但當不確定性升高，投資人開始重新聚焦在：
- 是否真的有客戶？
- 是否能量化地幫客戶降成本、降風險？
- 營收是否可預期、可重複？
hard tech startups 通常更早被迫回答這些問題，
能活下來的，也更具長期韌性。
一個代表性案例：佐翼科技
佐翼科技並不是一間「AI-first」的新創，而是一個典型的 hard tech startups 案例。
它的核心產品是 貼壁式攀附智能車（俗稱爬牆機器人），用於大型工業設備的檢測、清洗與維護。
它帶來的不是小幅效率提升，而是「風險結構的改變」
- 取代高空、重裝備、危險的人工作業
- 讓設備在 不停機 的情況下完成檢測
- 同時降低成本並大幅改善工安風險
這樣的價值主張，讓佐翼科技在 Pre-A 階段募得 新台幣 2 億元，成功切入 半導體供應鏈，並從石化產業擴展到更高標準的製造場域。
這正是 hard tech startups 典型的成長方式：
靠工程深度與場域信任，而非市場敘事。
hard tech startups 與 AI：不是對立，而是不同時間軸
多數 hard tech startups 並非排斥 AI，而是採取不同的導入順序：
- 先把「不可能」變成可行
- 再把「可行」變成穩定可靠
- 最後，才用 AI 來加速與放大價值
在這樣的結構中，AI 不是主角，而是 乘數效應。
結語：當 AI 敘事被反覆檢視，hard tech startups 正在悄悄升值
隨著 AI 泡沫疑慮 持續被討論，市場開始更重視：
- 工程與技術深度
- 真實世界的落地能力
- 即使 AI 普及，也依然難以被取代的商業模式
hard tech startups 也許不會佔據最多頭條，
但在不確定性升高的環境中，它們往往成為資本最願意長期持有的標的。
在這樣的週期裡，真正慢、真正深的 hard tech startups，反而正在默默變得更值錢。
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