Coopitition is a Pretty Old Story in Technology

“Coopitition” happens frequently in many markets, as competitors find they also cooperate with their rivals. But value chain participants also often move into other parts of the chain, meaning customers become competitors. 

Original Supplier	Customer (Later Competitor)	What the Customer Originally Bought	How/When Customer Became Competitor	Nature of Competition
Intel	Apple (M1/M2/M3 chips)	x86 CPUs for Mac computers	2020: Apple launched Apple Silicon and began replacing Intel CPUs in all Macs	Direct chip-level competitor; vertically integrated into SoC design
Qualcomm	Samsung, Huawei (HiSilicon), Apple (modems underway)	Mobile baseband chips	Samsung & Huawei developed in-house modems; Apple pursuing own modems	Reduced reliance on Qualcomm; internal components compete directly
NVIDIA	Amazon AWS, Google, Microsoft Azure	GPUs for cloud AI workloads	Clouds built custom AI chips (AWS Trainium/Inferentia, Google TPU, Microsoft MAIA/Cobalt)	Cloud providers become GPU substitutes and new chip vendors
Cisco	Amazon AWS (cloud networking), Arista, large enterprises with internal networks	Networking gear for data centers	Hyperscalers built their own switches and disaggregated network OS	Displaces traditional Cisco purchases with in-house designs
Oracle, Microsoft	Salesforce, Workday, ServiceNow	Databases and infrastructure for enterprise apps	SaaS firms built full-stack platforms competing with traditional enterprise software	Customers became full software suite competitors
Google Maps API	Uber, Lyft	Location services, navigation APIs	Ride-hailing firms built proprietary mapping to reduce dependence	Competes with mapping providers and reduces reliance on Google
Android/Google	Samsung (Tizen), Huawei (HarmonyOS)	Android mobile OS	Developed alternative smartphone OS platforms	Competing mobile ecosystems reducing Android dependency
AWS Marketplace vendors	AWS (Basics, managed services)	AWS acted as infrastructure + reseller of partner products	AWS launched services competing directly with partners (e.g., ElasticSearch/Opensearch, Datadog-like monitoring)	High-profile “customer-turned-competitor” ecosystem conflict
IBM, Dell, HP infrastructure	Major banks, retailers, healthcare systems	Enterprise servers, storage, and IT services	Internal cloud teams built private clouds replacing vendor systems	Vertical integration into infrastructure previously purchased
Facebook/Meta (mobile platforms reliance)	Meta’s VR/AR device program (Quest)	Reliance on Apple/Google mobile platforms	Meta developed its own hardware/software ecosystem	Competes with platform providers to escape dependency
Telcos buying vendor gear	AT&T, Verizon, Deutsche Telekom (open RAN initiatives)	Proprietary RAN equipment from Nokia/Ericsson	Built open-source or disaggregated RAN alternatives	Reduces dependence on traditional equipment vendors
IBM/Intel server vendors	Google, Amazon, Facebook data-center hardware	Commodity servers	Hyperscalers designed their own servers and power systems	Competing designs via OCP and private supply chain

That also can be seen in the market for neural processing units, where former customers Google and Amazon now have emerged as important suppliers of NPUs used in place of graphics processor units, even if many of the use cases are internal to those firms. 

Companies such as Google (Tensor Processing Units) and Amazon (Inferentia/Trainium chips) primarily use their NPUs internally or sell access through their cloud services, obscuring any direct "retail" market share comparison.

NPU Segment / Use Case	Dominant Architecture / Product Type	Market Share Context & Key Vendors	Key Vendor Dominance / Market Share Notes
Data Center / Cloud AI (Training,  Inference)	GPU (for Training,  General-Purpose AI),  ASIC/Custom NPUs (for specific Inference)	This segment includes hyperscalers using hardware internally (like Google's TPUs) or for cloud-based services.	NVIDIA holds a dominant share (often cited as 90%+ for high-end AI training accelerators/GPUs, which are often grouped with NPUs). Google (TPU), Amazon (Trainium/Inferentia), and AMD (Instinct) are the primary competitors in the custom/dedicated space.
Edge Devices (Retail/B2C)	Integrated NPUs (AI-SoCs) and Dedicated Edge NPUs	This segment covers chips embedded in consumer products for on-device AI (smartphones, PCs, smart home, automotive).	Qualcomm (Snapdragon), Apple (A/M series chips), and Samsung (Exynos) dominate the smartphone/tablet space, which accounts for the largest application share (e.g., 37.6% of the total NPU market application in 2024). Intel (Core Ultra) is a major player in the PC NPU market.
Edge Market Share (Application)	Smartphones & Tablets	The largest single application area, driving the growth of retail NPU units.	Estimated 35% - 40% of the NPU market application share is in retail, for smartphones and tablets.
Data Center NPU Share (Product Type)	Data Center NPUs	Market share based on the volume of processing units deployed in large-scale data centers.	Data Center NPUs maintained an estimated 51.6% of the neural processor market share in 2024, and much of that represents internal consumption by Amazon and Google.

Google designs and uses TPUs for its own services including search, translate, Gemini AI, for example. But Google does make its TPUs available to Google Cloud Platform customers as a service, though it does not sell the chips.

If the Internet Collapsed "Distance," AI Collapses Time

If the core function of the internet is connectivity and the core value is the collapse of distance, then the core function of artificial intelligence is cognition and the core value is the collapse of time. 

If the internet makes physical location less important, AI makes complexity less important, reducing the time to derive insights. 

But both the internet and AI are going to disintermediate value chains, removing distribution functions and providers. 

Dimension	Internet	AI
Core Function	Connectivity: linking people, machines, data, and services across networks.	Cognition: performing tasks that require perception, reasoning, analysis, prediction, or decision support.
Primary Value Created	Eliminating distance: collapsing geography; enabling instantaneous communication and access.	Saving time: collapsing effort; automating, accelerating, or augmenting cognitive tasks.
Economic Logic	Reduces transaction and coordination costs associated with physical separation.	Reduces cognitive labor costs and enhances productivity by automating thinking tasks.
Primary Constraint	Bandwidth, latency, physical infrastructure (fiber, spectrum).	Quality of data, model capability, alignment with goals, compute.
Main Units of Scarcity	Transport capacity (Mbps/Gbps), access points (ports, routers), spectrum.	Compute, data quality, reasoning ability, task generalization.
User Experience Shift	From location-dependent to location-independent access.	From manual decision-making to automated or assisted decision-making.
Industrial Impact	Creates global digital markets; enables remote work, cloud services, platform economies.	Automates white-collar workflows; reshapes knowledge industries; introduces agentic systems.
Business Models	Subscription access, metered usage, advertising, platform marketplaces.	API usage, per-inference billing, embedded intelligence in existing software, agentic task fees.
Strategic Advantage	Owning the pipes, connectivity footprint, spectrum, and interconnection points.	Owning the models, data, workflows, and user attention for cognitive automation.
Regulatory Focus	Universal access, net neutrality, infrastructure competition.	Bias, transparency, safety, copyright, workforce displacement.
Transformation Pattern	Disintermediation of distance-dependent middlemen (retail → e-commerce; media → streaming).	Disintermediation of cognition-dependent middlemen (analysts, coordinators, support roles via agents).

So one way of understanding AI is to view it as a new form of infrastructure, as is the internet, as was electricity or railroads. In that view, potential over-investment happens because the new infrastructure has to be created, and not because of a mania or bubble over asset values that are illusory. 

That might temper some of the concern over AI asset valuations or investment magnitude, which can appear excessive in the near term, and might well be, in some instances. Such early over-investment tends to happen when a new general-purpose technology emerges, and especially when that GPT involves infrastructure.  

Historically, transformative infrastructure projects such as railroads experienced periods of perceived "over-investment," where excess capacity was common before widespread economic and societal adoption caught up. 

The U.S. railroad boom of the late 19th century and the electricity grid’s rollout involved capital surges, initial overbuilding, and even bankruptcies. However, over time, these investments generated foundational benefits, enabling entirely new industries and reshaping nations.

So although the superficial similarity between an irrational asset bubble and an infrastructure boom can exist, they are quite different. 

While a financial bubble features a disconnect between investment and credible returns, general-purpose infrastructure has long-term value, even if some amount of capital is misallocated. 

But that’s the issue right now: some see the infrastructure for a general-purpose technology being built; others see mostly speculation. It can be hard to tell the difference in the early going. 

Criterion	Productive Infrastructure	Speculative Excess
Cost-Benefit Analysis	Thorough, data-driven	Minimal or absent
Multiplier Effect	High, measurable output/wages	Weak, limited economic return
Demand Alignment	Supported by real user/market needs	Based on future hype, not evidence
Systemic Productivity	Positive spillovers	Neutral or negative impact
Asset Price Relationship	Aligned with long-term value	Driven by short-term speculation
Evaluation Rigor	Institutional, non-partisan reviews	Ad hoc, driven by momentum