The Open-Source AI Adoption Hypothesis: A Re-Evaluation in the Age of Edge Computing

 

Abstract

Many analysts say that current AI investment often outpaces clear, short-term revenue, leading to speculation. Recent data show sharp increases in private investment and organizational adoption, fueling both optimism and concern about valuations [1]. Our article revisits the idea that open-source AI speeds up adoption and puts pressure on proprietary strategies to adapt. We update this analysis for edge AI, meaning running AI on devices and local systems rather than only in the cloud. We argue that edge computing makes the 'AI bubble' story more complex by creating subsectors with different economics, risks, and adoption challenges. Edge AI also opens new revenue avenues, such as hardware and services, which may lower the risk of a single, market-wide crash. As a result, we expect a more fragmented, uneven correction rather than a single dramatic bubble burst.

Keywords: 

Artificial Intelligence, Economic Bubble, Open Source, Edge Computing, Technology Adoption, Innovation Diffusion, Hybrid Edge (See section 6.1 for definitions).

1. Introduction: The AI Investment Paradox

Contemporary AI markets exhibit a recurring pattern seen in prior technological waves: capital inflows can surge ahead of reliably measured fundamentals and realized cash flows. The risk is not merely “high investment,” but the combination of (a) narrative-driven expectations and (b) uncertainty about the timeline over which productivity and monetization will materialize [2, 3]. Recent indicators also point to expanding real-world adoption, with reported organizational AI usage increasing markedly in the most recent survey year covered by the Stanford AI Index 1.

This leads to a paradox. Fast adoption can justify investment, but it can also make some parts, especially software, less profitable before established companies can react. The key question is: do adoption patterns and costs cause sudden, broad changes in value, or more gradual, segmented ones?

2. The Original Hypothesis: Open Source as an Accelerant of Deflation

The 'Open-Source AI Adoption Hypothesis' can be summed up as a cause-and-effect idea:

AI’s economic value is realized through diffusion. Revenues scale when AI moves from experimentation to operational integration across industries and functions. This is consistent with diffusion theory, in which adoption spreads through a social system over time as perceived advantage becomes observable and implementable [4].

Organizations adopt new technology when they believe the benefits are real and the effort to implement it is reasonable. Models such as the technology acceptance theory explain this by focusing on how valuable and easy to use people find the technology, and whether the appropriate support is in place [5, 6].

Open-source methods can lower barriers and give users more choices. Research shows that sharing code and working as a community can change incentives, reduce redundant work, and put pressure on companies that rely on proprietary software, especially when alternatives are easy to find [7, 8].

Developer activity indicates that open development is accelerating. GitHub’s Octoverse report highlights strong growth in open and public generative AI projects, supporting the idea that a growing open ecosystem can make it cheaper and easier for others to experiment with and adopt these tools [9].

What does this mean? If open-source AI makes it cheaper and easier to switch or join at essential points, adoption speeds up, and companies relying on exclusive control may see profits shrink sooner. In terms of a bubble, faster commoditization can force companies to rethink their business models earlier, mainly if they rely on long-lasting software profits rather than on data, distribution, or specialized deployments [2, 3].

3. Rationale for Modification: The Edge Computing Variable

The original idea mostly assumed that software runs in the cloud, but this is no longer enough. Edge computing, which means processing data closer to where it is created and used, changes both the costs and practical limits of using AI [11]. Edge AI enables fast, offline, private, and reliable applications, but it also means hardware and system integration become major cost factors, and that value is delivered quickly [11].

Recent studies on 'edge intelligence' in industries show that moving AI closer to where things happen changes how well systems perform and how they are built. This shift is changing how industries like manufacturing, logistics, energy, healthcare, automotive, and smart infrastructure adopt AI [12].

So, any idea about adoption and value must consider that while open source can lower the cost of using software, the real cost of deploying AI—like hardware, engineering, certification, and ongoing operations may go up.

4. The Revised Hypothesis: Segmented Dynamics and Asymmetric Cycles

When we include edge computing, the hypothesis splits into two main paths:

Path A (Cloud/Software)

Open-source AI and tools make software less scarce, which speeds up adoption. This squeezes profits where products are easy to replace, putting pressure on companies that rely on exclusive software profits rather than on data or specialized services [7–10].

Path B (Edge/Hybrid)

Open-source deployment tools, specialized hardware, and system integration create new ways to use AI. This leads to more adoption in industries, with profits shifting toward hardware, industry-specific solutions, and ongoing services. As a result, values and market corrections vary more across different sectors [11, 12].

Another challenge is that switching platforms is hard. Even if better or cheaper options are available, companies must contend with integration costs, retraining, rule updates, and operational risks. These switching costs explain why adoption and value changes can be slower than technically possible, thereby delaying profit declines for established companies [13].

The new takeaway is that, instead of a single big bubble, the market has uneven cycles. Software-only areas may become commoditized quickly, while edge and hybrid segments can maintain their unique value and higher prices for longer, though they can also experience costly hype cycles.

5. The Effects of Edge AI on the Bubble Framework

Edge AI changes the idea of an AI bubble in three main ways.

5.1. Amplification of Subsector-Specific Boom–Bust Cycles

Edge AI usually requires significant investment and is tailored to specific use cases. This fits the 'hype cycle' pattern, in which expectations can outpace what is actually possible, especially if people underestimate the challenges of integration and slow adoption within organizations [14].

High-volatility subsectors include:

• Edge AI chips and accelerators are a high-risk area. Specialized designs and hardware cycles can lead to oversupply and a few big winners in particular niches, even if overall AI demand remains high [15].

• Industrial and vertical edge platforms show that processing data close to where it is needed changes what is possible and valuable. However, it also leads to greater fragmentation across environments, standards, and deployment, making it more likely that some projects will fail as they scale up [12].

• Autonomous and safety-critical systems are affected by both technical performance and regulations. For example, rules for automated vehicles show how investment can change quickly as progress and uncertainty about meeting requirements evolve [16].

As a result, edge AI can create 'echo bubbles,' or local areas of overheating, even if the overall market is correcting more slowly.

5.2. Reinforcement of Open Source as an Adoption Catalyst (Through Toolchains)

Adopting edge AI relies on practical tools like runtimes, converters, hardware helpers, and device-specific optimizations. Open and widely available tools make it easier to deploy models, even when resources are limited. For example:

• LiteRT (formerly TensorFlow Lite) is positioned as an on-device runtime intended to support high-performance deployment on edge platforms [17].

• ONNX Runtime explicitly targets deployment to IoT and edge devices across architectures, supporting hardware acceleration pathways [18].

• TinyML-oriented runtimes and frameworks (e.g., TensorFlow Lite Micro) aim to enable inference on highly resource-constrained embedded systems, expanding the feasible adoption boundary for “AI at the extreme edge” [19].

This means open-source and open-access tools do more than just make things cheaper—they also allow for more ways to use AI. This can speed up adoption and move profits from just providing models to actually making deployments work.

5.3. Providing More Defensible Revenue Streams

Edge and hybrid AI deployments often generate revenue through device sales, industry-specific solutions, and ongoing services for fleets and models. Research shows that value is shifting toward data-driven operations and systems that use analytics as part of ongoing processes, not just as separate software products [20].

When edge deployments are essential for things like safety, speed, privacy, or reliability, vendors can protect their revenue by offering deeper integration, certified systems, and managed services. This can lead to more predictable cash flow than relying only on usage-based cloud APIs, especially in the early stages of adoption.

6. Discussion and Conclusion

The original Open-Source AI Adoption Hypothesis is still helpful: open ecosystems can make it easier to enter the market and reduce profits in software areas where products are easy to replace, putting pressure on companies that rely on long-term control 7–10. However, edge AI changes the market structure. The AI economy is better seen as a stack of connected layers that can move at different speeds: software adoption may be quick, while edge deployment can be slower, require more investment, and be harder to replace once it is part of operations [11–13].

So, instead of one big 'AI bubble burst,' we are likely to see a series of uneven corrections. Software areas that become commodities may see sharper drops, while edge and hybrid sectors may be more stable but still face their own ups and downs.

For investors and policymakers, this means that 'AI bubble' risks should be assessed by segment—looking separately at cloud software, edge hardware and systems, hybrid lifecycle services, and regulated industry deployments. Financial reporting that breaks out these segments can help clarify risks. IFRS and U.S. rules requiring more detailed segment reporting support the idea that greater transparency improves decision-making when business models span different types of operations [21, 22].

Future research should test this updated hypothesis by tracking investment, adoption rates, and revenue across cloud-only, edge/hardware, and hybrid-service segments. Researchers should then examine whether market corrections occur together or remain separate over time.

6.1. Definitions

Economic Bubble: A bubble is a sustained deviation of valuations from fundamentals driven by expectation dynamics, narrative reinforcement, and feedback trading, often preceding a revaluation event or a regime shift in how value is assessed [2, 3].

Open Source: Open source refers to software distributed under licenses that comply with the Open Source Definition, including criteria such as free redistribution, availability of source code, and permission for derived works [10].

Edge Computing: Edge computing is a distributed paradigm that processes data near its source—closer to devices and local environments—to reduce latency and enable time-sensitive, bandwidth-efficient services [11].

Technology Adoption: Technology adoption is the process by which innovations move from awareness and evaluation to sustained use across a social system or organization; adoption rates and patterns are central objects of diffusion analysis 4. Organizational adoption is also shaped by perceived usefulness and enabling conditions that reduce implementation friction [5, 6].

Innovation Diffusion: Innovation diffusion is the process by which an innovation spreads through communication channels over time within a social system, generating characteristic adoption curves and adopter categories [4].

Hybrid Edge: Hybrid edge refers to architectures and operating models that distribute workloads across edge and cloud environments, often combining on-device inference with cloud coordination, governance, updates, and analytics across fleets of deployed systems [12, 17, 18].

References 

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16 U.S. Department of Transportation. (2020). Ensuring American leadership in automated vehicle technologies: Automated Vehicles 4.0. https://www.transportation.gov/sites/dot.gov/files/2020-02/EnsuringAmericanLeadershipAVTech4.pdf (Department of Transportation)

17 Google AI Edge. (2025, May 19). LiteRT overview. https://ai.google.dev/edge/litert (Google AI for Developers)

18 ONNX Runtime. (n.d.). Deploy ML models on IoT and edge devices. https://onnxruntime.ai/docs/tutorials/iot-edge/ (ONNX Runtime)

19 David, R., Duke, J., Jain, A., Janapa Reddi, V., Jeffries, N., Li, J., Kreeger, N., Nappier, I., Natraj, M., Regev, S., Rhodes, R., Wang, T., & Warden, P. (2021). TensorFlow Lite Micro: Embedded machine learning for TinyML systems. Proceedings of Machine Learning and Systems, 3, 800–811. (proceedings.mlsys.org)

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21 IFRS Foundation. (2022). IFRS 8 Operating Segments. https://www.ifrs.org/issued-standards/list-of-standards/ifrs-8-operating-segments/ (IFRS)

22 Financial Accounting Standards Board. (2023). Accounting Standards Update 2023-07—Segment Reporting (Topic 280): Improvements to reportable segment disclosures. https://storage.fasb.org/ASU%202023-07.pdf (storage.fasb.org)

Note:

A group of friends from “Organizational DNA Labs,” a private network of current and former team members from equity firms, entrepreneurs, Disney Research, and universities like NYU, Cornell, MIT, Eastern University, and UPR, gather to share articles and studies based on their experiences, insights, inferences and deductions, often using AI platforms to assist with research and communication flow. While we rely on high-quality sources to shape our views, this conclusion reflects our personal perspectives, not those of our employers or affiliated organizations. It is based on our current understanding, informed by ongoing research and a review of relevant literature. We welcome your insights as we continue to explore this evolving field.