AI use cases
2997 ways people actually use AI, compiled from the videos in this
library. Each names the job and why it is worth doing, and links to the
moment in the source video.
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Embodied reasoning
— Self-supervised bootstrapping identifies which reasoning traces are useful for a particular robot embodiment and removes distracting or non-action-predictive reasoning.
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Long-horizon robot control
— Multiscale embodied memory gives policies task history, time awareness, progress tracking, and the ability to adapt after mistakes.
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Using a large language model with tools in an autonomous loop
— To let an agent perform tasks, access information, execute commands, call APIs, and take actions without continuous human oversight.
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Sending emails
— Send autonomous or automated messages to recipients
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Booking flights to Milan under $500
— Create a travel-booking plan involving flight search, filtering, booking, and sending a confirmation email
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Finding a document
— Search, evaluate results, replan, and retry until the agent finds an answer
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Generate a hero image and other website media.
— Create visual assets matching the website's look and feel.
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Use an AI agent to build and deploy the directory website from an uploaded CSV.
— Create searchable pages, listing pages, categories, business submissions, and paid featured listings.
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Generate a table of real estate sale cleanout businesses in the greater Dallas-Fort Worth area.
— Populate the directory with business names, categories, phone numbers, and one-line descriptions.
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Execute delegated development and operational tasks
— Allow users to manage systems that write or operate software rather than performing every coding or operating task directly.
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Generate and review software
— Support a hybrid development model in which AI writes code and humans review or edit it.
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Augment engineers handling open source vulnerabilities
— Streamline vulnerability reporting and resolution, validated patch deployment and upstream disclosure coordination.
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Chain low-severity vulnerabilities into attack patterns
— Identify system-level exploit paths that individual vulnerability scanners may miss.
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Prepare, schedule, and assign complex maintenance work orders
— Reduce manual preparation and give the maintenance manager a ready-to-approve work order
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Operate a coding assistant
— Search a repository, open files, run tests, and help debug problems.
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Build and orchestrate AI agents
— Structure agent instructions, tool use, reasoning loops, memory, workflows, and multi-agent coordination.
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Connect to external tools and data
— Use APIs, databases, files, web resources, and other external sources to answer questions or perform tasks.
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Reasoning, communication, and execution across connected systems
— AI agents use an LLM and agentic components to respond to users, interact with an MCP server, and connect to processes or data.
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Support enterprise workflows
— Help organizations apply agents to changed, better-documented workflows and improve access to information.
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Automate social engineering
— Match tone and generate deepfakes, making deceptive communications more convincing.
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Verify patches and find vulnerabilities
— Accelerate defensive testing and identify bugs before they reach production or shortly afterward.
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Discover and exploit software vulnerabilities
— Scan files for vulnerabilities, chain multiple exploit steps, and conduct longer penetration tests.
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Generate and execute agent skills
— Use natural language for authoring while compiling deployment logic into structured Python programs with deterministic behavior and selective language-model calls.
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Provide an investment thesis and conditions for selling.
— Make portfolio decisions more systematic and explain the rationale behind each trade.
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Generate stock, ETF, options, and cryptocurrency trade recommendations.
— Manage five separate real-money portfolios in a performance competition.
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Generate artwork
— Potentially reduce artwork production costs, although Print Our Pet's artwork was still being created by human artists.
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Generate images for blog posts
— Create visual content for blog publishing.
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Automate customer-facing business services
— Support potential upsells such as AI voice reception, lead generation, customer service, booking, follow-up, and email marketing.
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Integrate website functions with business systems
— Connect the website to a CRM, calendar, booking schedule, and related workflows.
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Build and customize a local-business website
— Produce a handyman website by adapting a reference site, changing the business name and location, and including local cities and suburbs.
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Generate a detailed website-building prompt
— Create a prompt for an AI website tool that includes CRM, calendar, workflows, SEO, metadata, design, and local service areas.
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Synthetic data generation
— Create training data for predictive models when real data is scarce or sensitive.
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Summarization
— Distill long documents into their most important content.
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Conversational assistance
— Handle customer queries through conversational systems.
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Code assistance
— Write, explain, and debug code.
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Content generation
— Create emails, marketing copy, product descriptions, images, and other new content.
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Forecasting measurable outcomes
— Predict fraud, sales, customer cancellation, inventory demand, equipment failure, or credit default.
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Scientific assistance
— Brainstorm ideas, iterate on experiments, and improve paper write-ups.
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Customer support
— Provide live transcription, surface relevant knowledge articles, generate smart replies, and summarize calls.
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Research and report writing
— Search for relevant references, outline them, summarize their content, and synthesize a comprehensive report.
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Code generation and testing
— Generate code, run unit tests, and use test results to determine whether the output is correct and guide correction.
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Generate and select answers
— Repeatedly sample a fixed language model and use a verifier or selection mechanism to identify correct outputs.
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Debugging
— AI-assisted debugging traces execution paths and surfaces state changes during a run, reducing the need to step through code line by line.
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Continuous code analysis
— AI-assisted analysis identifies issues during development before they reach testing.
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Drafting implementations from plain-language instructions
— AI provides a starting implementation based on intended behavior, such as sending a welcome email when a new customer signs up.
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Generating code suggestions
— AI uses surrounding code and existing code-base patterns to produce suggestions that fit established conventions.
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Refactoring code
— AI helps update and simplify code within the IDE, including targeting duplication and logic that can be simplified.
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Generate responses from user prompts.
— Return outputs shaped by framing, constraints, context, prior information, and expressed intent.
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Predict token sequences and statistical language patterns.
— Produce outputs that appear to demonstrate understanding without inherently grounding symbols in lived reality.
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Generate a web page
— Create a usable web page quickly.
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Analyze financial data
— Gain insight into how the business is operating.