Generative AI Will Reinvent Every Customer Experience, Says Andy Jassy, Amazon CEO

It always is reasonable to ask “why do we care” about any hyped new technology, and artificial intelligence is no exception. One answer from Andy Jassy, Amazon CEO, is that “generative AI is going to reinvent virtually every customer experience we know, and enable altogether new ones about which we’ve only fantasized.”

For the moment, though, “early AI workloads being deployed focus on productivity and cost avoidance (customer service, business process orchestration, workflow, translation, etc.),” Jassy notes. “Increasingly, you’ll see AI change the norms in coding, search, shopping, personal assistants, primary care, cancer and drug research, biology, robotics, space, financial services, neighborhood networks.” 

Reinvention arguably has been a key impact of every general-purpose technology since the time of the domestication of fire. 

General-Purpose Technology	Impact on Human Life (Well-being, Health, Daily Living)	Impact on Economics (Productivity, Markets, Growth)	Impact on Work (Nature of Jobs, Skills)	Impact on Social Life (Community, Communication, Structure)
Control of Fire	Cooking (safer food, better nutrition), warmth (survival in cold climates), light (extended activity), protection (predators), tool hardening. Improved health & lifespan.	Enabled processing of new materials (food, clay, later metals). Basis for energy use beyond muscle power. Early resource management (fuel gathering).	Required skills in fire starting/maintenance, fuel gathering, cooking. Enabled early specialized tasks like toolmaking.	Hearth became a central gathering point. Fostered group cohesion, storytelling, shared defense. Extended social interaction time.
Agriculture & Domestication	Sedentary lifestyle, more reliable (though potentially less diverse) food supply, population growth, vulnerability to crop failure/disease outbreaks.	Food surpluses enabled trade, specialization of labor beyond food production, concept of property/wealth accumulation, rise of villages/towns.	Farming and animal husbandry became primary occupations. New crafts emerged (pottery, weaving, building).	Led to larger, permanent settlements. Development of social hierarchies, governance, organized religion, property disputes. Reduced nomadism.
The Wheel	Easier transport of heavy goods (less physical strain), facilitated travel (eventually). Enabled pottery production.	Revolutionized land transport for trade & resources, military applications (chariots), increased efficiency in construction and pottery.	Created roles like cart drivers, potters, specialized builders. Facilitated movement of labor/armies.	Increased interaction/trade between settlements. Enabled larger-scale projects and state administration/control.
Writing	Allowed recording & transmission of knowledge, history, laws across time/space. Facilitated complex planning and abstract thought. Foundation for formal education.	Essential for record-keeping (taxes, trade, inventory, ownership), contracts, administration of larger economic units (states, empires). Spread of technical/commercial knowledge.	Created specialized roles: scribes, administrators, scholars, librarians, teachers. Required literacy skills for certain professions.	Enabled codified laws, historical records, literature, religious texts. Facilitated long-distance communication and administration of large polities. Standardization.
Printing Press (Movable Type)	Mass dissemination of information/ideas, increased literacy rates, accelerated scientific revolution, challenged established authorities (e.g., Reformation).	Dramatically lowered cost of producing/distributing information. Spurred publishing industry, standardized texts, faster spread of technical/commercial innovations.	Created printers, typesetters, booksellers, authors, translators. Reduced demand for scribes. Increased importance of literacy in many fields.	Fueled public discourse/opinion, spread of education, standardization of languages, growth of universities, religious/political movements.
Steam Engine	Powered factories independent of water sources, enabled faster travel (trains, ships). Also led to urban pollution, crowded living conditions.	Catalyst for Industrial Revolution. Enabled mass production, factory system, expansion of mining, growth of railways/shipping, concentration of capital.	Shift from agrarian/artisanal work to factory labor. Created engineers, mechanics, factory workers, miners, railway workers. Introduced clock-based work discipline.	Rapid urbanization, emergence of new social classes (industrial working class, bourgeoisie), changes in family structures, rise of labor movements.
Electricity	Electric lighting extended day, powered home appliances (labor saving), refrigeration (food safety), enabled new medical tech (X-rays), powered communications.	Enabled factories anywhere, powered new industries (chemicals, aluminum), increased productivity, allowed 24/7 operations, basis for communications networks.	Created electricians, power plant operators, electrical engineers. Transformed manufacturing processes, enabled office automation (later).	Changed daily routines (evening leisure), new entertainment (cinema, radio), faster communication (telegraph, telephone), altered urban landscapes (streetlights).
Internal Combustion Engine (ICE)	Personal transportation (cars), faster goods transport (trucks), air travel. Increased mobility, suburban sprawl. Also pollution, accidents, noise.	Created automotive, oil/gas, aerospace industries. Spurred road construction, suburban development, global logistics networks, tourism.	Created auto workers, mechanics, truck/taxi drivers, pilots, road crews, gas station attendants. Displaced horse-related industries.	Fostered "car culture," suburban lifestyles, increased individual autonomy/travel, changed urban planning, environmental concerns.
Semiconductor / Computer	Automation of complex calculations, information storage/retrieval, digital entertainment, advanced medical imaging/diagnostics, early digital communication.	Huge productivity gains via automation & data processing. New industries (hardware, software, IT services). Facilitated globalization, financial market automation.	Created programmers, IT support, hardware engineers, data entry/processing roles. Automated many routine clerical/manufacturing tasks. Increased need for digital literacy.	Early online communities, shift in communication (email), vast information access for researchers/hobbyists, foundation for digital age.
The Internet	Instant global communication, vast information access, e-commerce convenience, social networking, online learning/entertainment, telemedicine. Issues of addiction, privacy, misinformation.	Enabled e-commerce, digital marketing, cloud computing, gig economy, data-driven business models, further globalization. Disrupted traditional media, retail, etc.	Explosion of remote work, new roles (web developers, digital marketers, data scientists, content creators). Increased demand for digital skills across all sectors. Gig work platforms emerged.	Transformed social interaction (social media), global communities, access to diverse perspectives, challenges of echo chambers, cyberbullying, digital divide.
Artificial Intelligence	Potential: Enhanced diagnostics/healthcare, personalized education, creative assistance, automation of chores. Concerns: Bias, job displacement, privacy, ethical control, autonomous weapons.	Potential: Massive productivity boosts, hyper-personalization, new business models, autonomous systems (transport, logistics), drug discovery. Concerns: Market disruption, inequality, data security.	Potential: Automation of cognitive & physical tasks, creation of new roles (AI training, ethics, maintenance). Requires: Adaptability, creativity, critical thinking, emotional intelligence. Concerns: Widespread job displacement.	Potential: New forms of interaction (AI companions), enhanced creativity tools, complex problem solving (climate, disease). Concerns: Impact on human relationships, bias amplification, misinformation generation, governance challenges.

AI Might Affect the Whole Economy, But Chip Ecosystem Not So Much

The ramifications from artificial intelligence, should it emerge as a genuine general-purpose technology, will obviously have huge potential implications for the computing industry as well, from chip design and capabilities to fabrication to the relative importance of processing functions and possible changes in the value chain related to hardware versus software and types of software. 

On the other hand, markets change all the time. It seems less clear that AI-driven changes are qualitative, at the chip end of the business, compared to the software part of the value chain. 

Taiwan’s chip fabrication dominance, largely driven by TSMC, has been tied to the Intel ecosystem for decades, for example. Intel’s x86 architecture powered the PC and server markets. 

But AI arguably is not driven by the Intel ecosystem. As computing pivots toward AI, GPUs, and accelerators like TPUs, the ecosystem arguably is liable to shift. 

Looking only at the “digital infrastructure” value chain, chips, servers, models, training and then the AI impact on software value, chip manufacturing and design likely will continue to represent 55 percent to 65 percent of value within the infra part of the value chain.

Value Chain Segment	Estimated % of Value (Revenue Share)	Key Players & Examples
AI Chip Manufacturing	35-40%	TSMC, Samsung, Intel Foundry
AI Chip Design	20-25%	NVIDIA, AMD, Google, Apple, Amazon (AWS Trainium & Inferentia)
Cloud & AI Infrastructure	15-20%	AWS, Microsoft Azure, Google Cloud, Oracle
AI Model Development & Training	5-10%	OpenAI, Anthropic, Meta, Google DeepMind
Enterprise AI Software & Applications	10-15%	Microsoft (Copilot), OpenAI (ChatGPT API), Salesforce, Adobe, ServiceNow
Edge AI & AI-Powered Devices	5-10%	Tesla (Autopilot AI), Apple (Neural Engine), Qualcomm (Snapdragon AI)

Obviously a “full” value chain would have to include the contribution to value of all markets for products used by people and businesses that include AI as part of the solution, but that ultimately will be virtually every part of an economy. 

If we might argue that the x86 ecosystem was driven by standardization, AI, so far, seems less so. AI workloads use, and perhaps can require, specialized silicon, including Nvidia graphics processor units, or Google’s Tensor Processing Units.

That doesn’t change some fundamental roles. Chip designers might still be separate from chip manufacturing. Value still will exist in intellectual property and manufacturing efficiency. Some chop run volumes might be smaller and manufacturing venues could shift away from Taiwan. 

Markets evolve over time, so this might be more a quantitative than qualitative shift. Nobody seems to believe the roles of chip design and manufacturing will fuse or that the need for chip fabs will go away as priorities shift to accelerators and parallel processing. 

Sure, the focus might shift to AI products rather than x86 processors. So the business is reframed rather than revamped. 

We probably cannot say the same about consumer and business software. In the realm of software, AI might indeed be poised to “change everything.” “AI features” are not simply being added to existing software. 

AI might conceivably disrupt entire value propositions, change user expectations and alter the economics of software. AI should make it easier for non-technical people to produce apps, as the internet enabled many content creators to flourish outside the established media firms. 

The cost of creating content or code should drop. And the way people pay for use of software could keep evolving in the direction of consumption-based pricing rather than flat-fee licenses. And advertising might be a new “pricing” tool, allowing use of software to be defrayed by advertising exposure. 

For consumers, AI arguably leads to more dynamic, adaptive experiences, shifting  focus from manual input to automation and personalization. For business software, the ability to make decisions is probably more important. 

In either case, there might be an argument to be made that software now begins to be experienced more as a “service.” 

Beyond that, software becomes more adaptive, learning from user behavior. Software also becomes less of a tool and more of an “assistant.” 

And it always is possible that whole new categories of apps are created, as once was the case for search and social media; ride-hailing and food delivery.

Invest in AI Value Chain or Just Ignore it?

Though institutional and retail investors alike are investigating opportunities in artificial intelligence outside the venture capital area, iit often is hard to target such investment since the AI value chain is so broad. To some extent, the advice to invest in firms and sectors you would choose for other reasons seems logical enough, especially if one believes AI will eventually affect every industry. 

Of course, some segments of the AI value chain have been more obvious direct participants. Nvidia’s graphics processing units, for example, have driven its equity value in recent years. The “AI as a service” providers including AWS, Azure and Google also have been early investor “infrastructure” or “picks and shovels” favorites. 

Value Chain Segment	Estimated Value Creation (%)	Example Public Companies	Explanation
AI Chips & Hardware	~15%	Nvidia, Intel, AMD	Specialized semiconductors and hardware accelerators power AI computations. Their performance improvements drive overall AI efficiency and capability.
Cloud & Data Infrastructure	~30%	Amazon (AWS), Microsoft (Azure), Alphabet (Google Cloud)	Scalable computing, storage, and data processing services form the backbone of AI deployment. They enable vast data collection and processing at scale.
Core AI Software & Tools	~20%	Alphabet (TensorFlow), Microsoft (Cognitive Services), IBM (Watson)	AI frameworks, libraries, and algorithm platforms that underpin model development, training, and deployment across various industries.
AI Applications & End-User Solutions	~35%	Salesforce (Einstein), Adobe (Sensei), Meta Platforms, Tesla	Direct consumer and enterprise applications—ranging from recommendation engines and personalized marketing to autonomous vehicles—that capture final user value.

But the issue is that if AI is a general-purpose technology, it will affect virtually all industries. So one way of looking at investment is simply to put money where you think growth or dividends are, depending on one's investing perspective, and ignore AI, assuming it will become part of the value of every product and every industry.

Internet and AI: It's "Different This Time"

Investors, as all humans, tend to see the future through the lens of the past. And the thinking that "it is different this time" tends to be dangerous. So many have warned of an investment  “dot com bubble” in artificial intelligence.

So some worry about the size of AI infra investments, compared to the near-term and immediate revenue generation from those investments. 

source: Seeking Alpha 

But investment in AI stands on much-firmer ground than did internet startup investing a quarter century ago. 

To be sure, the past emergence of general-purpose technologies (assuming AI will one day be deemed to be a GPT), have led to over-investment. But it also is true that the past GPTs did emerge as transformative and profitable, even if there was a period of investment excess. 

And it might also be correct to say concern over the present investment boom is not anchored in the magnitude of the investment so much as the magnitude of the near-term revenues. 

Would-be leaders of the coming AI markets have a different perspective, of course. They believe the future markets will be huge and will be led by just a handful of firms. So the risk of falling behind is commensurately great. 

There is a risk of over-investment, to be sure. But that might be deemed the lesser of evils. The risk of some temporary over-investment has to be weighed against the risk of losing out on permanent, long-term market leadership. 

Some over-investment is temporary and quantitative. Missing out on the chance to lead in AI markets is lasting and qualitative. 

General-Purpose Technology	Time Period	Investment Boom/Bubble	“Boom”	“Bust”
Railroads	1840s	Railroad Mania	Rapid expansion of rail networks, speculative investments	Many companies went bankrupt, but rail infrastructure remained
Automobiles	Early 20th century	Automotive boom	Proliferation of car manufacturers, increased road construction	Industry consolidation
Internet	Late 1990s	Dot-com Bubble	Excessive speculation in internet-related companies, skyrocketing valuations	NASDAQ crashed 78%, many startups failed
Artificial Intelligence	2020s-present	AI Boom	Massive investments in AI companies, high valuations for AI-related stocks	?

But there might also be many differences between the “internet” investment bubble of the last turn of the century and the current AI investment trend. For starters, AI infrastructure is so hugely expensive that most of the leading investors are deep-pocketed, profitable firms with established businesses and huge cash flows. 

The internet investment bubble was much more speculative, with a greater role played by venture capital and even retail investors, where AI investment is led by established technology giants and institutional investors. 

Internet firms often raised money on the assumption they would “find a business model.” Today’s AI leaders already have logical avenues to  monetize their investments, for the most part. And, for the most part, all those models hinge on vast improvements to the performance of existing use cases, not the creation of new use cases. 

Aspect	Internet Bubble (Late 1990s)	AI Investment Wave (2020s)
Investor Composition	Primarily speculative retail investors and venture capital	Predominantly established, profitable tech giants and institutional investors
Company Financials	Many dot-com startups with no proven business models	AI companies backed by companies with substantial existing revenue streams
Revenue Potential	Highly speculative, based on potential internet reach	More concrete, with clear applications in existing industries
Technology Maturity	Nascent internet infrastructure and capabilities	More advanced technological foundation with demonstrable AI capabilities
Valuation Basis	Primarily "eyeballs" and website traffic	Tangible metrics like AI model performance, integration potential, and efficiency gains
Market Penetration	Theoretical internet transformation	Proven AI applications across multiple sectors (healthcare, finance, technology)
Investment Sources	Retail investors, IPOs, venture capital	Large tech companies (Microsoft, Google, NVIDIA), institutional investors, strategic corporate investments
Economic Context	Emerging digital economy	Established digital infrastructure with clear productivity enhancement potential
Risk Profile	Extremely high speculative risk	More measured risk with clearer value proposition
Competitive Landscape	Numerous undifferentiated internet startups	Fewer, more technologically advanced AI companies with distinct competitive advantages

And where internet metrics often were indirect or non-financial (usage, attention), AI metrics already are largely operationally quantifiable (time saved, code generated, output per hour increased), even if direct revenue increases are harder to measure. 

And even if some parts of the AI infrastructure must be created (graphics processing unit as a service; model training and inference as a service), most of the rest of the infrastructure (broadband internet access; high-capacity cloud computing and data transport facilities; high existing use of applications and devices) is basically in place. 

The internet investment occurred when broadband access had yet to be created; when smartphones were not common; search, social media, e-commerce and content streaming were still developing; and the widespread availability of cloud computing as a service had yet to develop. 

Perhaps the point is that the internet and AI investment context is quite different. There will be over-investment, but by many large, profitable firms that can take the short-term hit. The fate of many would-be startups remains unknown. 

But there are many significant differences between the internet and AI investment contexts. While firms might still falter for any number of reasons, monetization paths are quite a bit clearer; the finances of big investors are sturdier; the use cases clear, in principle. 

We do not have to guess at the value of AI embodied in the form of robo-taxis or autonomous vehicles; factory and other robots. We already know AI can enhance all personalization efforts for all types of software and consumer processes. We are aware of the many ways AI can speed up output by automating repetitive processes. 

The value of the internet was far less clear in early days.