Where AWS Sees Value in the AI Stack, What it Means for Investors

Andy Jassy’s recent letter to shareholders provides a way of thinking about where artificial intelligence startups will be created; where functional objectives can lead to new company revenue streams and how the value chain will develop. 

Jassy talks about bottom, middle and application layers of AI. Using the software stack as an analogy, this corresponds to infra, middleware, app layers. 

The bottom layer includes both hardware and software: AI foundation models (generative AI, for example);  the computing infra required to train models and generate inferences and the software that makes it easier to build these models. 

Jassy points out that virtually all the leading models have been trained on Nvidia chips, but that customers “have asked us to push the envelope on price-performance for AI chips.”

So Amazon Web Services has built custom AI training chips (Trainium) and inference chips (Inferentia). Those chips are used by Anthropic, for example. Other users include include Airbnb, Hugging Face, Qualtrics, Ricoh and Snap.

Customers building their own models must organize and fine-tune data, build scalable and efficient training infrastructure, and then deploy models at scale in a low latency, cost-efficient manner.

Amazon SageMaker is a managed, end-to-end service for preparing their data, managing experiments, training models faster, lowering inference latency and improving developer productivity, Jassy says. 

At a broader level, all that implies opportunities for rival graphics processor units, acceleration chips, generative AI models and AI “as a service” businesses, 

The middle layer is for customers seeking to leverage an existing model, customize it with their own data, and leverage a leading cloud provider’s security and features to build a GenAI application as a managed service, Jassy says. 

We might also liken the process of “rendering” to the middle layer as well. In computer graphics, rendering is the creation of 2D images from 3D models. In audio production, rendering refers to the process of creating a complete audio file from multiple different tracks. 

In video production, rendering (editing, formatting) might refer to the processes of creating a final version of the product, adding visual effects or formatting for specific delivery formats (resolution, frame rate). 

Amazon Bedrock is an example of this layer, including “Guardrails: to safeguard what questions applications will answer), “Knowledge Bases,” as well as “Agents” to complete multi-step tasks) and “Fine-Tuning” to keep teaching and refining models.

Customers using Bedrock include ADP, Amdocs, Bridgewater Associates, Broadridge, Clariant, Dana-Farber Cancer Institute, Delta Air Lines, Druva, Genesys, Genomics England, GoDaddy, Intuit, KT, Lonely Planet, LexisNexis, Netsmart, Perplexity AI, Pfizer, PGA TOUR, Ricoh, Rocket Companies, and Siemens, Jassy says. 

AI “as a service” presents one set of opportunities, but every commercially-viable AI model will require this sort of middle-layer support as well, often sourced from third parties. 

The top layer of the AI stack is the application layer. 

For Amazon that includes shopping assistants, smarter versions of Alexa, advertising, customer service and seller services, as well as coding support apps to write, debug, test and implement code. Such apps might also support moving platforms from older to newer versions, conducting queries across multiple data repositories, summarizing data, conducting conversations and taking actions as assistants.

We should already see investment shifting from generative AI models to applications (ways to use the models to solve business problems or conduct consumer operations and tasks. Some examples include apps aimed at industry verticals, horizontal functions such as customer service or coding, fraud detection, healthcare diagnostics or supply chain optimization. 

As always is the case for general-purpose technologies, early investment goes into creating infrastructure. Later investment broadens to create applications and use cases across multiple industries and functions. 

Startups will be the field for private equity firms, institutional investors and accredited investors. Most of the opportunities for consumer investors will come in the form of publicly-traded firms with some plausible involvement in bottom, middle and app layers (infra, middleware, end user and retail supplier use cases). 

Unless you own or work for a venture capital firm, or are an accredited investor, you will not be able to invest in startups oriented around artificial intelligence, leaving you with the task of identifying existing public firms that have some plausible direct relationship to AI. 

Eventually, as has proven to be true for the internet, most firms will have some indirect relationship to the internet, but that is not so helpful in identifying candidates for investment right now. 

As is true for just about any general-purpose technology before it (steam power, railroads, the internet, electricity) and other platforms that might not always be considered GPTs (highway systems, passenger air travel, mobile communications, telephone systems), infrastructure is where investments must be made first, before the full range of use cases develops. 

So for “regular people” the domain initially will be public firms with AI infrastructure operations: the compute power to run AI software, the products required to build AI models, make inferences, create applications or supply platforms and devices to run the models and make inferences, sustain the connectivity to processing nodes, create and run the data centers, provide AI functions as a service. 

Most of these infra segments includes firms one might own for other reasons as well (dividends, revenue growth, capital appreciation, real estate investments, software or information technology, content assets, connectivity). 

So, perhaps oddly enough, “AI investments” are pretty much the same as would be expected if one were instead searching for “digital economy” or “internet” investments, with perhaps a stronger weighting towards “picks and shovels” that create or sustain the infrastructure to run apps and provide experiences. 

Analyst Cody Willard suggests an 11-layer model focused on AI infrastructure, including some private firms or open-source initiatives, but also focusing on public firms plausibly involved in creating AI infrastructure, applications and content. 

Chips, servers, data centers, cloud computing, data management, algorithms, models, internet connectivity and end user devices are perhaps the clearest examples of AI infra. But some might also include content, enterprise AI-enabled applications or advertising as layers of the AI value chain. 

source: Cody Willard, MarketWatch 

Looking at infra as 11 or more layers, that might suggest a layer one (chips) including

• Silicon (Nvidia, duh!), Intel, AMD, Qualcomm and Broadcom (add Microsoft, Alphabet, Meta and Apple to the extent they are developing their own AI chips as well)

• Silicon design services such as Cadence Design Systems, Synopsis  or Autodesk

• Application Specific Integrated Circuit (ASIC) designer such as Broadcom , Marvel, Intel, Advanced Micro Devices and Qualcomm

• Silicon intellectual property including Arm, Intel

• Semiconductor equipment such as ASML, Advanced Materials, Lam Research, KLA Corp. and Teradyne

• Foundries including TSMC, Intel, Samsung, Global Foundries

• Memory (SK Hynix, Samsung, Micron, Western Digital, Seagate

• Machine Learning languages including PyTorch (open-sourced from Meta), TensorFlow (open-sourced from Google), Keras, Microsoft Cognitive Toolkit, Theano, Apache MXNet, Chainer, JAX, TensorFlow.js

Layer two might focus on servers, including:

• Server design (Dell, Hewlett-Packard Enterprises, Super Micro, IBM, Lenovo, Cisco, Oracle, Fujitsu, Quanta Cloud Technology, Inspur

• Server manufacturing including Foxconn, Flex, Jabil, Sanmina Corp., Pegatron Corp., Celestica, Wistron Corp., , Quanta Computer,, Compal Electronics, Inventec Corp..

• Distribution partners include Ingram Micro, Aero Electronics and CDW.

• Server installation services (IBM, Schneider Electric, Vertiv Holdings Co., Hewlett Packard Enterprise, Super Micro, Dell

Layer three can be viewed as data centers:

• Data center design and construction (Holder Construction, Turner Construction, Jacobs, Fluor Corporation, AECOM, Syska Hennessy Group, Corgan, Gensler, HDR, Mastek, Dycom

• Data center Infra, especially cooling (Schneider Electric, Johnson Controls, Carrier Global Corporation, Honeywell International Inc., Siemens AG, Super Micro, Dell, Hewlett-Packard Enterprises

• Electric components, including renewable energy, including Enphase Energy, Inc., Solaredge, First Solar, Tesla, Inc., SunPower Corporation, Schneider Electric, ABB Ltd., Eaton Corporation

• Electrical power suppliers (PNM Resources, NextEra Energy, Duke Energy Corporation, Dominion Energy), Southern Company, Exelon Corporation, American Electric Power Company, PG&E Corp., Consolidated Edison, Xcel Energy, Entergy Corp.

• Electric utility infra (General Electric, Siemens Energy, Mitsubishi Heavy Industries, Toshiba Corporation, Hitachi Ltd., ABB Ltd., BWX Technologies, Doosan Heavy Industries & Construction

• Raw materials such as copper, gold, plastic (oil) and silver (Freeport-McMoran, Newmont Corp., Barrick Gold Corp., Franco-Nevada Corp., Freeport-McMoRan Inc., Southern Copper Corporation, BHP Group, ExxonMobil, Chevron, ConocoPhillips

• Networking and interconnect gear (Cisco Systems, Arista Networks, Inc., Juniper Networks, Inc., Broadcom Inc., NVIDIA Corp.), F5 Networks, Extreme Networks, Inc., Dell Technologies, Marvel, Applied Optoelectronics, Viavi Solutions, MaxLinear, Emcore, Nlight

Layer four might be envisioned as the “cloud” layer, including: 

• Cloud data centers (Amazon Web Services, Microsoft Azure, Google Cloud Platform, Alibaba, Oracle, Tesla, Meta

• Data center real estate investment trusts (Equinix, Digital Realty Trust, Inc., CyrusOne Inc. (KKR), CoreSite Realty Corporation (subsidiary of American Tower), QTS Realty Trust (Blackstone)), Iron Mountain Inc., DigitalBridge

• Cloud computing as a service providers (AWS, Microsoft Azure, GCP, Oracle, Meta, Tesla, Alibaba

• Inference As A Service (NVIDIA, Amazon Web Services, Google Cloud AI Platform, Microsoft, Cloudflare, Akamai

Layer five might be viewed as the data layer, including functions such as data gathering and input, machine vision, data sources:

• Machine vision including Tesla, Rockwell Automation, Zebra, Cognex Corp., Keyence Corp., OMRON Corp., Basler AG, Teledyne Technologies, ISRA VISION AG

• Consumer data sources (shopping, other behavior including Meta, ByteDance, Google, Apple, Amazon, Snap, Pinterest, Yelp, Tencent, Reddit, Etsy,, Wayfair, Walmart

• Financial data (Apple Pay, Google Pay, JPMorgan Chase & Co., Visa Inc., Mastercard Inc., Discover Financial Services, PayPal Holdings, Inc., Square, Inc., Robinhood Markets, Inc., Morgan Stanley, The Charles Schwab Corp., Fair Isaac Corp., TransUnion, Equifax Inc.

• Location, travel data (Apple Inc., Alphabet Inc., Verizon Communications Inc., AT&T Inc., T-Mobile US, Inc., Uber Technologies, Inc., Lyft, Inc., Expedia, Tripadvisor

• Enterprise Data (ServiceNow, Apple, Salesforce.com, Inc., Oracle Corp., Microsoft Corp., Alphabet Inc., Dropbox, Inc., Box, Inc., Workday, Inc., AutoDesk, Adobe, Dassault Systèmes, PTC Inc., Ansys, Inc., Trimble Inc., Siemens AG, AVEVA Group plc, Bentley Systems, Inc..

• Content (Disney, Sony, Spotify, Netflix, Warner Brothers, Paramount, New York Times, Fox, Simon & Shuster, Random House

• Data Management (Amazon Web Services, Alphabet Inc., Microsoft Corp., Snowflake Inc, Datadog, MongoDB, Inc., Oracle Corp., Confluent, Inc., Broadcom Inc., Alteryx, Inc., International Business Machines Corp., Cisco Systems, Inc. 

Layer six is the algorithm and model layer:

• Algorithms (OpenAI, Google Deepmind, Tesla AI, Meta Labs

• Large language models (OpenAI, Google, Microsoft, Anthropic, Perplexity) and training

• Libraries: Hugging Face

Layer seven is the applications layer:

• Generative AI as an app (ChatGPT, Gemini, Anthropic, xAI Grok, LLaMA, Stability AI, Mistral, Mosaic, Amazon

• Apps using LLM (Microsoft Copilot & Github, Office365, Google Workspace, Duolingo, Alexa, Siri, Spotify, Palantir, Autodesk, Unity

• Data analysis (Snowflake, Oracle, Datadog, AWS, Google, Azure

• Content Creation (The Walt Disney Company, Netflix, Inc., Electronic Arts Inc., Warner Bros. Discovery, Inc., Paramount Global, Sony Group Corp. , Comcast Corp. , Activision Blizzard, Inc. (Microsoft), Electronic Arts, Take-Two Interactive Software, Inc., Spotify Technology S.A., Lions Gate Entertainment Corp, Fox

• Cybersecurity (Palo Alto Networks, Fortinet, , CrowdStrike Holdings,, Zscaler, , Check Point Software Technologies Ltd. , CyberArk Software Ltd., Okta, Inc., FireEye, Inc.

Layer eight might be edge networking:

• Hardware and servers (Intel, AMD, NVIDIA, Dell, HPE)

• Content Delivery Networks (Cloudflare, Akamai, Fastly, AWS, GCP, Azure)

Layer nine might be advertising:

• Venues (Meta, Google, Amazon, Snapchat, Pinterest)

• Ad placement and services (The Trade Desk, Unity, Applovin)

Layer 10 might be networking:

• Tower infra (Crown Castle International Corp., American Tower, SBAC)

• Access providers (Starlink, Verizon Communications, AT&T, Lumen Technologies, Charter Communications, T-Mobile, Comcast, Comtech, ViaSat, Iridium Communications,  Gogo

Layer 11 includes end user devices:

• Consumer devices including Apple iPhone, iPad, Mac, PCs, Google Pixel phones, Meta Glasses, Lenovo PCs

• Enterprise devices such as robots (Tesla Optimus, Mitsubishi, Kawasaki, Epson, Universal Robots, Omron, Yaskawa Electric, Fanuc, Kuka, Denso, ABB

• AI machinery (tractors, cranes, containers, and boats made by Caterpillar, John Deere, Trimble)

• AI satellites and spacecraft: SpaceX, Rocket Lab, Intuitive Machines, Optimus by Tesla)

• AI Drones by AeroVironment, Lockheed Martin, Boeing, Northrop Grumman

Some of us might argue that mass market investors should view virtually all these assets as categories we’d consider for other reasons, despite their AI potential, as it might be some time before AI revenues are material. 

In Willard’s 11-layer model, some of us might consider much of layer seven and virtually all of layers eight through 11 as being part of the broader computing and internet value chains, and not specifically powered by AI potential. 

And parts of layers one through six would be required to support modern computing, even if AI did not exist. The point is that the AI value chain overlaps substantially with the internet value chain. With a few specific exceptions, Nvidia being the primary example, virtually all the other assets in the developing AI value chain would also be candidates for ownership as part of the internet value chain.

Can AI Make Social Media More Pleasant?

Many of us would say we avoid social media because of the amount of content that seems impolite, not respectful; immature or worse. Perhaps no amount of content moderation is going to stop some people from behaving in ways that are rude; uncivil and lacking in grace. -

Among the ways generative artificial intelligence could help reduce the amount of hostile and uncivil social media content, Generative AI models can be trained to identify patterns of language commonly used in hateful, abusive, or harassing content. This can help flag such posts for further human review by moderators.

The caveat is that some people seem to believe ideas themselves are inherently abusive, or “violent” or “threatening” when they might arguably simply reflect a difference of opinion. So “protection” for some might seem to be censorship by others. 

Assuming that sort of bias can be largely avoided (a big “if”), then perhaps AI can go beyond simple keyword matching and analyze the overall sentiment and context of a post. This can help identify nuanced forms of negativity or attacks that might bypass simpler filters, again assuming we are dealing with some agreed-upon sense of the difference between free speech; different ideas and bad behavior. 

AI might be able to analyze conversations and identify situations where a disagreement is escalating towards something more than hostility and bad manners, and the AI might suggest alternative phrasings or ways to reframe arguments to promote more civil discourse and respectful dialogue that some might call simple good manners and politeness. 

On a different level, Gen AI might be used to identify and showcase positive and constructive interactions on a platform, creating a more positive atmosphere and nudge users towards more civil behavior.

Or perhaps AI could provide users with personalized prompts to encourage them to reconsider potentially offensive language before posting.

Of course, value judgments always are involved. Some might consider certain subjects or keywords “offensive,” while others might consider those subjects or keywords merely descriptive. And historical context might matter as well. Some ideas or words might have been historically common in the past, but considered inappropriate in a modern context. 

Language also can be nuanced, and sarcasm or humor might be misinterpreted by AI. Comedians, almost by definition, make fun of lots of things. 

And there's a delicate balance between filtering harmful content and stifling free speech. Just because some people do not like some ideas does not mean they are “hate speech” or somehow “violence” or “hostility.” 

AI Value from "Smarter or Faster" Rather than Virtual?

Some obvious ways artificial intelligence will provide value are by making existing business analytics and decision support software “smarter.”

Online furniture retailer Wayfair used AI to change its lost-sales KPI. “We used to think that if you lost the sale on a particular product, like a sofa, it was a loss to the company,” says CTO Fiona Tan. “But we started looking at the data and realized that 50 percent to 60 percent of the time, when we lost a sale, it was because the customer bought something else in the same product category.”

So now the lost-sales KPI formerly was “item” oriented, it now is “category” oriented. 

Other applications might similarly rely on AI to generate insights the existing structured software packages do not provide, or which humans do not have the time to discover. 

source: BCG, MIT Sloan School of Management 

Analytics Provider	Current Value Proposition	AI Possible Future Value Proposition
Microsoft	Power BI: User-friendly data visualization and reporting. Azure Synapse: Scalable data warehousing and analytics platform.	Power BI: Personalized insights and recommendations through AI-powered data exploration. Azure Synapse: Automated data preparation and anomaly detection for proactive decision-making.
Oracle	Oracle Analytics Cloud: Comprehensive suite for data analysis, reporting, and collaboration.	Oracle Analytics Cloud: AI-driven business simulations and scenario planning for future-proof strategies. Autonomous data management: Self-optimizing databases for improved efficiency and reduced costs.
SAP	SAP Analytics Cloud: Integrated platform for business intelligence, planning, and predictive analytics.	SAP Analytics Cloud: Hyper-personalized dashboards with AI-driven insights tailored to individual user roles. Real-time decision support: AI-powered recommendations and alerts based on dynamic market conditions.
IBM	Watson Analytics: Easy-to-use suite for data exploration, visualization, and predictive modeling.	Watson Analytics: Explainable AI models that provide clear reasoning behind recommendations and predictions. Automated data governance: AI-powered data quality checks and regulatory compliance management.
Tableau (Salesforce)	Tableau: Powerful data visualization tools for interactive dashboards and storytelling.	Tableau: AI-powered data storytelling with automatic chart selection and narrative generation. Conversational analytics: Natural language interaction with data through voice commands or chatbots.
SAS	SAS Viya: Cloud-based platform offering a wide range of analytics capabilities.	SAS Viya: Advanced anomaly detection and root cause analysis using AI and machine learning. Prescriptive analytics: AI-powered recommendations for optimizing business processes and maximizing outcomes.
Qlik	Qlik Sense: Associative analytics platform for fast data exploration and self-service BI.	Qlik Sense: AI-driven anomaly detection and automated insights generation. Augmented data discovery: AI-assisted search and exploration to uncover hidden patterns and relationships within data.
MicroStrategy	MicroStrategy Workstation: Powerful platform for enterprise-grade data analysis and reporting.	MicroStrategy Workstation: AI-powered data preparation and automated data cleansing for improved data quality. Generative AI: Automatic generation of reports and insights based on user queries and preferences.
Domo	Domo Workbench: Cloud-native platform for data integration, visualization, and collaboration.	Domo Workbench: AI-powered data storytelling with automated narrative generation and data-driven recommendations. Predictive forecasting: Continuous forecasting models that adapt to real-time data and market changes.
Sisense	Sisense Fusion Platform: Embedded analytics platform for integrating data insights into applications and workflows.	Sisense Fusion Platform: AI-powered personalization of embedded analytics dashboards based on user behavior and preferences. Automated data monetization: AI-driven identification and recommendation of data-driven revenue opportunities.

What Will Be the Primary AI Impact?

Right now, one might argue that the greatest impact of artificial intelligence will come in the realm of efficiency: allowing humans to do all sorts of things faster. To some extent, that has been at least a secondary feature of most computing technologies since 1980.

But it might be argued that the primary outocmes of new computing technologies have centered on digital product substitution for analog or physical products, removing constraints of time and place.

Products such as music, newspapers, magazines, books, television and movies were changed from physical to virtual products, or from physical delivery to virtual delivery. 

Since virtual goods are often cheaper to create, distribute and replicate than physical goods, new business models are possible. Video and music streaming; online publishing; user-generated content; social media and search are examples. 

Traditional Industry	New Challengers	Competitive Advantage
Retail (Brick-and-Mortar Stores)	Online Retailers (Technology)	E-commerce platforms, data analytics for targeted marketing, efficient logistics networks.
Media (Newspapers)	Social Media Platforms (Technology)	Real-time news updates, user engagement through interactive features, targeted advertising.
Taxis (Regulated Industry)	Ride-Sharing Apps (Technology)	Mobile app for booking rides, efficient matching of drivers with passengers, dynamic pricing models.
Financial Services (Traditional Banks)	Fintech Startups (Technology)	Mobile banking apps, online payment processing, data-driven credit scoring models.
Hospitality (Hotels)	Home-Sharing Platforms (Technology)	Online booking platform, user reviews and ratings, lower lodging costs for travelers.

Aside from new “products,” we also saw at least a few new business models, such as ad-supported technology, which did not exist prior to the internet. You might not think of social networks; messaging or search as “technology” products, but they are. Likewise, we saw the development of commerce-supported technology models as well. 

Though AWS and Google Cloud might use a traditional fee-for-service revenue model, that is possible only because the prior creation of ad-supported search and commerce produced excess capacity that underpinned cloud computing “as a service.”

Likewise, earlier waves of innovation removed time and place constraints. E-commerce allows shopping anytime, anywhere while communication tools such as messaging, email and video conferencing enable collaboration across great geographical distances almost for free. 

So how might AI alter business models, consumer experience and industries? Right now, it seems as though extreme personalization; customization and automated functions will be the primary effects. 

AI will further personalize software experiences, creating hyper-personalized experiences for consumers, and therefore supplier opportunities across many industries. 

Automation and efficiency should be the other key AI contribution, allowing firms to optimize and reduce costs across their operations. Aside from the consumer price benefits, that will enable new possibilities for cross-industry disruption. 

Cloud computing “as a service” allowed Amazon (retailer) and Google (search provider) to emerge as suppliers of computing services in competition with traditional suppliers of computing hardware and software, for example. 

Microsoft, until recently a primarily a supplier of enterprise and consumer software, emerged as a supplier of computing services and content. Apple the PC company became a leading mobile phone supplier. 

Cable TV firms became full-fledged suppliers of fixed and mobile communications services. Many non-banks essentially became “banks.” 

Traditional Industry	Firm	Services Offered
Retail (Large Chains)	Walmart MoneyCard, Amazon Cash	Prepaid debit cards for purchases and bill pay.
Technology (Payment Apps)	PayPal, Venmo, Square	Money transfer, bill pay, debit card linked accounts.
Retail (Fintech Startups)	Chime, Current, SoFi	Mobile banking accounts, debit cards, potential credit products.
Retail (Fintech Startups)	Klarna, Afterpay	Point-of-sale financing and "buy now, pay later" options.
Finance (Investment Firms)	Charles Schwab, TD Ameritrade	Robo-advising, checking accounts, debit cards.
Retail (Ride-Sharing)	Uber Debit Card	Debit card with rewards and features for drivers.

It is hard to tell, at this moment, whether AI will enable entirely new categories of products and services in the same way that the internet produced “search” and “social media,” with their different revenue models. 

All we know now is that AI will  be applied to virtually every existing industry, business process and consumer product in some way. So AI will be a feature of most products; an application in other cases. AI will have vertical industry forms, where AI-optimized processes are industry-specific, as well as horizontal applications supporting marketing, operations or finance for any industry. 

AI might in some cases be used as an interface, in the same way that graphical user interfaces changed the human interaction with personal computers. In other cases AI might be an alternative replacement for “search.” 

There are lots of other analogies. Generative AI, for example, might function as a word processor; a photo editing app; a musical instrument; a mini version of an operating system, human subject matter expert or code writer. 

Generative AI Function	User Analogy	Description
Text Generation	Word Processor	Instead of typing from scratch, AI generates different creative text formats like poems, scripts, musical pieces, or code based on prompts and user input.
Image Generation	Photo Editing App	AI acts like a powerful photo editor that can create new images from scratch based on descriptions or edit existing ones by adding elements or changing styles.
Music Generation	Musical instrument	AI generates new music pieces in various genres or moods based on user preferences.
3D Modeling	CAD Software	Like Computer-Aided Design (CAD) software, AI can generate 3D models of objects for various purposes, from prototyping to video game design.
Data Augmentation	Operating System	Imagine an OS feature that automatically creates synthetic data (like images or text) to supplement existing datasets, improving the training of other AI models.
Personalization	App Feature	Think of an app feature that personalizes your experience. Generative AI can personalize content feeds, product recommendations, or even tailor learning materials based on individual user preferences.
Code Completion	Programming Language Feature	Similar to a programming language's code completion feature, AI can suggest or even generate entire sections of code based on the context of the program being written.
Creative Ideation	Brainstorming Session Assistant	Imagine having an AI assistant during a brainstorming session. It can generate new ideas, variations on existing concepts, or unexpected connections to spark creative thinking.

AI might be described as a machine-based system that can make predictions, recommendations, or decisions. 

Machine learning then might be defined as data-driven approaches that allow computers to learn from data without being explicitly programmed. 

Neural networks are computer systems inspired by the structure and function of the human brain, able to learn from data and improve their ability to perform tasks such as image and speech recognition, as well as natural language processing. 

So neural networks underlie generative models designed to create entirely new content, including text, images, videos, music or software code. 

source: Wikipedia