{ "schema_version": "1.1", "id": "atlas-adoption", "slug": "adoption", "title": "Adoption", "url": "https://feed7.dev/atlas/adoption", "current_answer": null, "implementation_consequence": null, "agent_context": null, "confidence": "auto_collected", "last_verified": null, "last_updated": null, "evidence": [ { "schema_version": "1.1", "id": "auto-bd6ce42035", "slug": "building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035", "url": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035", "title": "Building AI infrastructure with the Effingham County community", "why_included": "The infrastructure announcement provides no new Codex capability, access terms, dates, capacity figures, or workflow change.", "summary": "OpenAI’s Georgia infrastructure announcement matters mainly as regional expansion and a promise of local Codex access; it offers little operational detail for builders.", "practical_implication": "Builders should read this as infrastructure and ecosystem expansion, not as a new Codex capability or workflow change.", "agent_context": "OpenAI announced **Project Camellia** in **Effingham County, Georgia**, alongside commitments covering energy use, community investment, employment, and Codex access.\n\nBuilders should read this as infrastructure and ecosystem expansion, not as a new Codex capability or workflow change.\n\nThe provided material gives no dates, capacity figures, access terms, or concrete delivery plan for the stated commitments.", "source": { "name": "OpenAI", "url": "https://openai.com/index/building-ai-infrastructure-with-the-effingham-county-community", "published_at": "2026-07-22T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "adoption" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-07-22T00:00:00.000Z", "modified_at": "2026-07-22T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035", "json": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035.json", "markdown": "https://feed7.dev/p/building-ai-infrastructure-with-the-effingham-county-com-bd6ce42035.md" } }, { "schema_version": "1.1", "id": "auto-fb21820d80", "slug": "from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "url": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "title": "From assistance to execution: How enterprises put AI to work", "why_included": "The adoption narrative lacks sample size, methodology, workflow examples, and implementation guidance for improving an agent session.", "summary": "OpenAI’s research frames enterprise AI adoption as a shift from assistance toward agent execution, with ChatGPT and Codex as examples rather than implementation guidance.", "practical_implication": "Builders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.", "agent_context": "OpenAI reports that enterprises are adopting **agentic AI** through products including **ChatGPT and Codex**, and says a group it calls frontier firms is moving ahead in adoption.\n\nBuilders should treat this as evidence of organizational demand for agents that execute work, then look for the underlying research before changing product or deployment plans.\n\nThe supplied material provides no sample size, methodology, adoption rates, workflow examples, or definition of a frontier firm, so the size of the claimed gap is unclear.", "source": { "name": "OpenAI", "url": "https://openai.com/index/how-enterprises-put-ai-to-work", "published_at": "2026-08-12T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "adoption", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-12T00:00:00.000Z", "modified_at": "2026-08-12T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80", "json": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80.json", "markdown": "https://feed7.dev/p/from-assistance-to-execution-how-enterprises-put-ai-to-w-fb21820d80.md" } }, { "schema_version": "1.1", "id": "auto-cb030de2dd", "slug": "stampli-cuts-launch-hours-by-68-using-chatgpt-work-cb030de2dd", "url": "https://feed7.dev/p/stampli-cuts-launch-hours-by-68-using-chatgpt-work-cb030de2dd", "title": "Stampli cuts launch hours by 68% using ChatGPT Work", "why_included": "The 68% time claim lacks workflow, quality, review-burden, and task-level detail, so it offers little reusable guidance.", "summary": "Stampli used Codex and ChatGPT Work to cut launch-production hours by 68%, showing how agents can absorb execution work when deadlines and design capacity collide.", "practical_implication": "For agent-assisted launches, define the deadline and production scope clearly, then direct agents toward the work that constrained teammates cannot cover.", "agent_context": "Stampli used **Codex and ChatGPT Work** to produce launch materials under a fixed deadline, cutting launch-production hours by **68%** and compressing **weeks into days**.\n\nFor agent-assisted launches, define the deadline and production scope clearly, then direct agents toward the work that constrained teammates cannot cover.\n\nThe material does not describe the workflow, output quality, review burden, or which tasks produced the measured reduction.", "source": { "name": "OpenAI", "url": "https://openai.com/index/stampli", "published_at": "2026-08-20T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "coding-agents", "adoption" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-20T00:00:00.000Z", "modified_at": "2026-08-20T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/stampli-cuts-launch-hours-by-68-using-chatgpt-work-cb030de2dd", "json": "https://feed7.dev/p/stampli-cuts-launch-hours-by-68-using-chatgpt-work-cb030de2dd.json", "markdown": "https://feed7.dev/p/stampli-cuts-launch-hours-by-68-using-chatgpt-work-cb030de2dd.md" } }, { "schema_version": "1.1", "id": "auto-d34e82eae2", "slug": "how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "url": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "title": "How loveholidays is making everyone a builder with Codex", "why_included": "It offers no workflow, governance details, adoption data, or measured outcomes that a solo builder could reuse next session.", "summary": "loveholidays is extending Codex beyond software teams so more employees can turn ideas into products, but the supplied material gives no implementation details or measured outcomes.", "practical_implication": "Builders evaluating broader agent access should consider which product work non-engineers can safely own and what review boundaries they need.", "agent_context": "loveholidays says it is using **Codex** to make software development accessible **across the business**, not only within engineering.\n\nBuilders evaluating broader agent access should consider which product work non-engineers can safely own and what review boundaries they need.\n\nThe material provides no workflow, governance model, adoption numbers, or measured delivery gains, so it is evidence of direction rather than a reusable playbook.", "source": { "name": "OpenAI", "url": "https://openai.com/index/loveholidays", "published_at": "2026-08-26T00:00:00.000Z" }, "source_class": "blog_post", "content_type": "Official Release", "layer": "industry", "domains": [ "coding" ], "topics": [ "adoption", "enterprise" ], "verification": { "status": "official_source", "label": "Official Source", "method": "source_feed", "verified_at": null }, "uncertainty": [], "connected_context": null, "lifecycle": "New", "published_at": "2026-08-26T00:00:00.000Z", "modified_at": "2026-08-26T00:00:00.000Z", "supersedes": [], "expires_at": null, "formats": { "html": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2", "json": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2.json", "markdown": "https://feed7.dev/p/how-loveholidays-is-making-everyone-a-builder-with-codex-d34e82eae2.md" } } ], "conflicting_sources": [], "superseded_claims": [], "corpus_evidence": [ { "title": "How law firm Gilbert + Tobin governs and scales AI with OpenAI", "url": "https://openai.com/index/gilbert-tobin", "source_name": "OpenAI", "published_at": "2026-09-01T01:00:00+00:00", "summary": "Gilbert + Tobin’s enterprise rollout pairs executive sponsorship and formal governance with human accountability, a useful reminder that agent adoption is an operating-model change." }, { "title": "Polimill builds Japan's next-generation public AI infrastructure", "url": "https://openai.com/index/polimill", "source_name": "OpenAI", "published_at": "2026-08-31T07:00:00+00:00", "summary": "Polimill is using GPT models and Codex to make municipal administrative knowledge searchable and speed up development, offering a compact public-sector adoption example." }, { "title": "The Signal Layer: What to Build When Anything Can Be Built — Lena Hall, Akamai", "url": "https://www.youtube.com/watch?v=1KOdiGgMtpY", "source_name": "AI Engineer", "published_at": "2026-08-29T18:00:34+00:00", "summary": "As coding agents make implementation easier to copy, builders should spend more judgment on problem choice and preserve claims, evidence, and limits as AI remixes work across product and GTM." }, { "title": "Which AI startups actually land enterprise contracts? — Brian Lewis, Millennium", "url": "https://www.youtube.com/watch?v=7A65O-0lvKE", "source_name": "AI Engineer", "published_at": "2026-08-29T14:00:06+00:00", "summary": "Enterprise AI contracts are won on security, controls, integration, and support as much as model capability. Builders should make those operational surfaces part of the product early." }, { "title": "How do you diffuse AI into the real world? — Varun Shenoy, Long Lake", "url": "https://www.youtube.com/watch?v=B0fjR3yaZFU", "source_name": "AI Engineer", "published_at": "2026-08-28T17:30:33+00:00", "summary": "Real-world agent adoption depends on workflow redesign, operational traces, and hands-on enablement. Code-agent patterns help, but service work has messier exceptions and triggers." }, { "title": "Our decision on Cursor following its acquisition by SpaceX", "url": "https://openai.com/index/our-decision-on-cursor-following-its-acquisition-by-spacex", "source_name": "OpenAI", "published_at": "2026-08-28T06:00:00+00:00", "summary": "OpenAI says it will stop supplying models to Cursor after SpaceX acquired the company, creating a model-availability risk for builders whose workflows depend on Cursor." }, { "title": "The Death of Developer Advocates — Stephanie Jarmak, Sourcegraph", "url": "https://www.youtube.com/watch?v=Lrw0jqBNaw0", "source_name": "AI Engineer", "published_at": "2026-08-26T17:00:32+00:00", "summary": "Agents increasingly choose developer tools, so test whether your docs connect real user pain to your product—not merely whether comparison prompts mention it." }, { "title": "Reverse-Engineering the AI Buyer — Aliisa Rosenthal, Acrew Capital", "url": "https://www.youtube.com/watch?v=wdTRsfw0KG0", "source_name": "AI Engineer", "published_at": "2026-08-26T13:00:06+00:00", "summary": "For AI startups scaling sales, automate intake, follow-up, security, and outbound before adding headcount. Keep the first design partners high-touch, and remove buyer work wherever possible." }, { "title": "Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber", "url": "https://www.youtube.com/watch?v=17-YSUHo6Lk", "source_name": "AI Engineer", "published_at": "2026-08-21T13:00:06+00:00", "summary": "Uber’s agent adoption rests on shared gateways, ready-to-run environments, skills, and a context graph. The operational bottleneck is shifting from code generation to validation and capacity." }, { "title": "Replit expands access to software creation with GPT-5.6 Luna", "url": "https://openai.com/index/replit", "source_name": "OpenAI", "published_at": "2026-08-19T07:00:00+00:00", "summary": "Replit’s Free Mode uses GPT-5.6 Luna to remove token-cost concerns from initial software creation, lowering friction for experimentation inside its agent workflow." }, { "title": "Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating", "url": "https://arxiv.org/abs/2608.18058v1", "source_name": "arXiv", "published_at": "2026-08-18T17:51:16+00:00", "summary": "Users were far more willing to deploy their own conversational agent than engage someone else’s, making receiver consent and receptivity-aware routing core product constraints." }, { "title": "Asana cleared 5 years of engineering work in 2 weeks with Codex", "url": "https://openai.com/index/asana", "source_name": "OpenAI", "published_at": "2026-08-18T07:00:00+00:00", "summary": "Asana says Codex replaced an outdated test system in two weeks for about $12K, compressing an estimated five-year migration into a focused agent-assisted project." }, { "title": "Cursor is now a part of SpaceX", "url": "https://cursor.com/blog/joining-spacex", "source_name": "Cursor", "published_at": "2026-08-14T12:00:00+00:00", "summary": "SpaceX has acquired Cursor, tying the coding-agent vendor’s model roadmap to a larger compute provider. Cursor says the deal should produce stronger models at lower cost, but gives no pricing commitments." }, { "title": "How RingCentral builds AI-native work from engineering to ops", "url": "https://openai.com/index/ringcentral", "source_name": "OpenAI", "published_at": "2026-08-12T00:00:00+00:00", "summary": "RingCentral uses ChatGPT Work and Codex across engineering and operations, connecting AI product development with centralized operational knowledge." }, { "title": "DeepSeek overtakes Google on volume, cost per token falls 13.6%", "url": "https://vercel.com/blog/deepseek-overtakes-google-on-volume-cost-per-token-falls", "source_name": "Vercel", "published_at": "2026-08-11T04:00:00+00:00", "summary": "Vercel’s July gateway data shows model routing, not list-price cuts, drove a 13.6% drop in average token cost as open-weight models gained production traffic." }, { "title": "Local Models: Trust, Control, Optimization — Carter Abdallah, NVIDIA", "url": "https://www.youtube.com/watch?v=FWMJQDH3iK0", "source_name": "AI Engineer", "published_at": "2026-08-07T02:00:06+00:00", "summary": "Open models let builders retain inference traces, customize the training stack, and reduce dependence on one provider, while closed frontier models remain useful for many workloads." }, { "title": "AI Gateway is now available on AWS Marketplace", "url": "https://vercel.com/changelog/ai-gateway-is-now-available-on-aws-marketplace", "source_name": "Vercel", "published_at": "2026-08-05T04:00:00+00:00", "summary": "Vercel AI Gateway is available through AWS Marketplace, letting teams place inference on their AWS bill under annual private offers without changing per-token pricing." }, { "title": "Circles powers telco personalization with OpenAI technology", "url": "https://openai.com/index/circles", "source_name": "OpenAI", "published_at": "2026-08-03T00:00:00+00:00", "summary": "Circles reports measurable telco gains from combining the OpenAI API with Codex, but the supplied case-study material gives no baseline, methodology, or detail on the developer-efficiency claim." }, { "title": "How Forward Deployed Engineering is done at Cognition — Jia Wu", "url": "https://www.youtube.com/watch?v=RVxym6mmIns", "source_name": "AI Engineer", "published_at": "2026-07-28T20:00:06+00:00", "summary": "Cognition measures coding-agent deployments by delivery outcomes, not sessions or tokens: engineering capacity, shorter timelines, and accepted PRs tied to customer work." }, { "title": "The Dirty Secret of Forward Deployed Engineering — Natalie Meurer, Sierra", "url": "https://www.youtube.com/watch?v=Byv311hdoHE", "source_name": "AI Engineer", "published_at": "2026-07-28T18:00:18+00:00", "summary": "Forward-deployed engineering is not one role but a stack of customer-accountable work. Coding agents now let those engineers carry field insight through to production changes." }, { "title": "Scientific computing in the age of agentic AI", "url": "https://openai.com/index/scientific-computing-agentic-ai", "source_name": "OpenAI", "published_at": "2026-07-28T17:00:00+00:00", "summary": "OpenAI reports that scientists are using coding agents to modernize scientific software and accelerate work in genomics, though the supplied report summary offers no methods or results." }, { "title": "Forward Deployed Engineering 101 — Kevin Bai, Anthropic, ex Palantir & Rippling Founding FDE", "url": "https://www.youtube.com/watch?v=KwhgfwOSToQ", "source_name": "AI Engineer", "published_at": "2026-07-28T15:00:06+00:00", "summary": "Forward-deployed engineering fits technical products sold to nontechnical buyers, but only when customer work composes shared platform primitives instead of creating bespoke codebases." }, { "title": "Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education", "url": "https://arxiv.org/abs/2607.22463v1", "source_name": "arXiv", "published_at": "2026-07-24T16:28:14+00:00", "summary": "A small programming-education study found that classroom observation missed how a student learned with AI. Builders of learning tools should avoid equating visible agent use with the underlying process." }, { "title": "NTT DATA Group cuts incident analysis to 30 minutes with Codex", "url": "https://openai.com/index/ntt-data", "source_name": "OpenAI", "published_at": "2026-07-22T00:00:00+00:00", "summary": "NTT DATA reports using Codex and ChatGPT Enterprise across 9,000 employees, with incident analysis reduced to 30 minutes—a concrete enterprise adoption data point." }, { "title": "How Searchable ships customer-requested features in 30 minutes on Vercel", "url": "https://vercel.com/blog/how-searchable-ships-customer-requested-features-in-30-minutes-on-vercel", "source_name": "Vercel", "published_at": "2026-07-21T04:00:00+00:00", "summary": "Searchable says Vercel’s AI SDK and Gateway let it swap models without SDK or key changes, helping the team ship some customer requests in 30 minutes and move 2–5× faster." }, { "title": "Our latest Google Finance upgrades, including a new app", "url": "https://blog.google/products-and-platforms/products/search/google-finance-updates-june-2026/", "source_name": null, "published_at": null, "summary": "Google Finance leaves beta with AI portfolio Q&A, scheduled market briefings, and an Android app. Off-topic for agent builders, but a clean example of productized scheduled-agent UX in a consumer app." }, { "title": "Announcements", "url": "https://www.anthropic.com/news/anthropic-public-record", "source_name": null, "published_at": null, "summary": "Anthropic surveyed ~52,000 Americans: 64% fear job displacement, 71% want government involved in AI rules, and only 15% trust AI companies. Context for anyone shipping AI products into that mood." }, { "title": "Announcements", "url": "https://www.anthropic.com/news/seoul-office-partnerships-korean-ai-ecosystem", "source_name": null, "published_at": null, "summary": "Anthropic opens a Seoul office with a science-ministry MoU and enterprise rollouts: NAVER put Claude Code across its whole engineering org; Samsung SDS, LG CNS, and Nexon follow." }, { "title": "New York City educators and industry leaders gathered at Google’s offices to shape the future of AI in classrooms.", "url": "https://blog.google/products-and-platforms/products/education/nyc-ai-summit/", "source_name": null, "published_at": null, "summary": "Google, the NY Jobs CEO Council and Urban Assembly hosted 150 education and industry leaders to discuss AI in classrooms. No product news or commitments; mainly a read on AI-in-education momentum." }, { "title": "How agents are transforming work", "url": "https://openai.com/index/how-agents-are-transforming-work", "source_name": null, "published_at": null, "summary": "An OpenAI research paper argues AI agents now sustain longer, more complex tasks and lift productivity across roles. The vendor's own read on the delegation workflows agent-first builders already run daily." }, { "title": "How ChatGPT adoption has expanded", "url": "https://openai.com/index/how-chatgpt-adoption-has-expanded", "source_name": null, "published_at": null, "summary": "OpenAI's Signals data on ChatGPT adoption: usage per user is rising and growth spans regions and languages. Market context rather than tooling news for agent builders." }, { "title": "Why B3 chose Android for secure AI-enabled productivity", "url": "https://blog.google/products-and-platforms/products/android-enterprise/b3-android-enterprise/", "source_name": null, "published_at": null, "summary": "Enterprise case study, not builder tooling: Brazil's B3 exchange rolled Android devices with built-in Gemini to about 1,000 employees in under two weeks, projecting 30% cost savings over a decade." }, { "title": "HP Inc. launches Frontier strategic partnership with OpenAI", "url": "https://openai.com/index/hp-frontier-partnership", "source_name": null, "published_at": null, "summary": "HP Inc. is scaling an OpenAI Frontier partnership to deploy AI across customer experience, software development, and enterprise operations. An enterprise-adoption signal, nothing builders can use directly." }, { "title": "Forward Deployed Engineering at Cursor — Pauline Brunet", "url": "https://www.youtube.com/watch?v=APqXGyCoGW4", "source_name": null, "published_at": null, "summary": "Cursor’s FDE playbook treats agent deployment as co-development: choose measurable use cases, require customer ownership, avoid staff augmentation, and document the handoff." }, { "title": "Mapping Europe’s AI Workforce Opportunity", "url": "https://openai.com/index/mapping-ai-jobs-transition-eu", "source_name": null, "published_at": null, "summary": "An OpenAI report maps which EU occupations face automation, growth, or workflow change from AI. Labor-market context, not tooling news — a signal of how OpenAI frames agent-driven work." }, { "title": "Open-weight models surge to 29% of volume, price per token flattens", "url": "https://vercel.com/blog/ai-gateway-production-index-july-2026", "source_name": null, "published_at": null, "summary": "Vercel’s June gateway data shows cheap volume moving to open-weight models while costly agent workloads stay on frontier models, strengthening the case for risk-based routing." } ] }