Every passage

“7.0 billion Agentic Work Units (“AWUs”) delivered to date across Agentforce and Slack, with 3.2 billion in Q2”

SalesforcePublished by the organization itself (regulatory filing), no named author

“Nearly 45% of issues that start with our AI assistant are now resolved without a human agent while delivering much faster resolution times.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“reduced the time from concept to delivery by as much as 60%”

AirbnbPublished by the organization itself (regulatory filing), no named author

“we've increased the number of features and improvements we shipped this year by nearly 80%.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“we are seeing doubling in the code output for engineers”

Balaji KrishnamurthyChief Financial Officer · Executive, Uber

“Customer agent now resolves 72% of support tickets without human escalation.”

HubSpotPublished by the organization itself (earnings call), no named author

“7.1 million conversations handled by our digital assistant Cora in H1 2026, of which 3.8 million were fulfilled entirely digitally, up by 23% compared with H1 2025.”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“one in three pull requests on GitHub now involves an agent”

Satya NadellaChairman and Chief Executive Officer · Executive, Microsoft

“Revenue per employee reached nearly $1.4 million, four times the 2022 level”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Nearly 60% of the code our engineers produce is now written by AI, which we estimate is about twice the industry average.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“when guests contact us through our AI aid assistant, over 40% of issues are now resolved without a human agent.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“About 10% of our codes now is committed. That committed is built by agents, autonomous agents out there. Obviously, we check the code before it gets committed.”

Dara KhosrowshahiChief Executive Officer · Executive, Uber

“AI right now writes well over 50% of our code today, and that number is going up significantly, not down.”

Harley FinkelsteinPresident · Executive, Shopify

“850 full-time agents”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our AI assistant has handled 80% of customer service chats in the year ended December 31, 2025 (according to our service chat log data)”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our average annual revenue per employee at period end has increased from approximately $344,000 in 2022 to approximately $1,240,000 in the year ended December 31, 2025.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Since launch, our AI assistant has had 31 million conversations, handling 80% of customer service chats in the year ended December 31, 2025”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Revenue per employee reached $1.24 million, up 3.6 times since 2022 as headcount declined 49%”

KlarnaPublished by the organization itself (regulatory filing), no named author

“nearly 30% of tickets in North America that are English-based are handled by an AI agent”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“Since the beginning of 2025, we've seen a 30% increase in output per engineer, with the majority of that growth coming from the adoption of agentic coding, which saw a big jump in Q4.”

MetaPublished by the organization itself (earnings call), no named author

“We're seeing even stronger gains with power users of AI coding tools, whose output has increased 80% year over year.”

MetaPublished by the organization itself (earnings call), no named author

“When AI is used for Software Development, more than 40% of the new code is either AI-Generated or AI-Assisted.”

WalmartPublished by the organization itself (earnings call), no named author

“our AI assistant has had 25.3 million conversations, solving 69% of customer service chats in the last twelve months ended June 30, 2025”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our AI assistant handled 69% of customer service chats in the last twelve months ended June 30, 2025, according to our service chat log data”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our average annual revenue per employee at period end has increased from approximately $344,000 in 2022 to approximately $972,000 in the last twelve months ended June 30, 2025.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“We estimate that our AI assistant does the equivalent work of over 700 full-time agents”

KlarnaPublished by the organization itself (regulatory filing), no named author

“the last time I said the number was like 25% of code that's checked in. It involves people accepting AI-suggested solutions. That number is well over 30% now.”

Google (Alphabet)Published by the organization itself (earnings call), no named author

“supported by AI-enabled productivity gains and continued cost discipline.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our AI assistant is now available in more than 50 languages.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“our customer support related cost per booking declined approximately 16% year-over-year”

AirbnbPublished by the organization itself (regulatory filing), no named author

“we don't need to grow our headcount. at levels that we did in the past because we're getting so much more output and speed from our existing workforce”

Ellie MertzChief Financial Officer · Executive, Airbnb

“For example, groceries took eight months, nine months, and airport pickups took about six weeks to develop.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“it's not about how many tokens you use, it's about the throughput of your product and the quality of your product designs and how much you're shipping.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“it obviously does assume a material increase in terms of the AI spend over the course of the year”

Ellie MertzChief Financial Officer · Executive, Airbnb

“we now have a number of distilled models where we take a teacher model, usually a big frontier model, then teach a specific use case to a smaller model”

Harley FinkelsteinPresident · Executive, Shopify

“On R&D, the majority of our internal AI spend is allocated here, so you've seen a modest uptick in year-over-year growth.”

Jeff HoffmeisterChief Financial Officer · Executive, Shopify

“We were able to hold gross margins at a relatively consistent level quarter over quarter while Sidekiq usage scaled, which reflects some cost efficiencies in support”

Jeff HoffmeisterChief Financial Officer · Executive, Shopify

“Customer support is a clear area where we should be able to up the quality of our customer support interactions as well as reduce the cost of our effort there.”

Balaji KrishnamurthyChief Financial Officer · Executive, Uber

“at a minimum, it has allowed us to moderate the pace with which we are adding headcount there.”

Balaji KrishnamurthyChief Financial Officer · Executive, Uber

“We are seeing at the moment near 100% adoption with our engineers on AI-based coding tools.”

Balaji KrishnamurthyChief Financial Officer · Executive, Uber

“Inside the business, we also use AI, and that is more closer to the $10 million range internally.”

DuolingoPublished by the organization itself (earnings call), no named author

“We also expanded AI-enabled capabilities across onboarding, operations and customer servicing, with five agents now live supporting our colleagues in core processes and our first customer-facing GenAI capability launched to help customers with Bankline queries.”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“Colleague usage of AI tools increased threefold during H1 2026 and we launched AI-powered client intelligence which converts advisor calls into actionable insights”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“five agents now live supporting our colleagues in core processes”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“Our associates are generating more than 400,000 prompts a day, and as of last week, we had over 300 AI use cases approved, all of which have good economics, of which 114 are live generative AI use cases.”

Brian MoynihanChair and Chief Executive Officer · Executive, Bank of America

“Our bankers automate the research and presentation materials. Our developers code more efficiently, and all our teammates improve productivity, consistency, and client service”

Brian MoynihanChair and Chief Executive Officer · Executive, Bank of America

“New AI capabilities have improved service, increased efficiency, and allowed teammates to focus on higher-value client interactions.”

Alastair BorthwickExecutive Vice President and Chief Financial Officer · Executive, Bank of America

“Some of the technology is letting our people do more and be more productive.”

Goldman SachsPublished by the organization itself (earnings call), no named author

“It started with productivity and a more narrow component of the firm called engineering, but based on leveraging the firm's relationships with a number of different large language model providers”

Goldman SachsPublished by the organization itself (earnings call), no named author

“We have had discrete areas where we did reduce jobs by 30% or 40%, and most of those people offered jobs elsewhere.”

JPMorgan ChasePublished by the organization itself (earnings call), no named author

“I think there's almost 1,000 use cases today, though we'd say that the really important ones are 50 across risk, fraud, marketing, hedging, prospecting, note-taking, idea generation, document reading.”

JPMorgan ChasePublished by the organization itself (earnings call), no named author

“Employee engagement is measured through Amplifier, Klarna's AI-powered engagement survey.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“We continue to enhance our toolkit for ad buyers, including AI features that help to dynamically adjust content mix to optimize campaign performance”

WalmartPublished by the organization itself (earnings call), no named author

“This is supported by AI-enabled productivity gains and continued cost discipline across the business.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“In a new world, an engineer can spin up 10 agents and those agents can work 24 seven. I mean, I'm kind of exaggerating a little bit.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“We've seen the cost per booking decrease about 10% year over year in Q1.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“our investment in AI tools and infrastructure is increasing. That will be offset by slower head count growth.”

Dara KhosrowshahiChief Executive Officer · Executive, Uber

“we're seeing uptake of these tools, whether it's our legal team or marketing team or developers. And we think it's creating employees with superpowers.”

Dara KhosrowshahiChief Executive Officer · Executive, Uber

“We shipped over 300. new products and features last year alone. We kept our flat headcount, which we're very proud of”

Harley FinkelsteinPresident · Executive, Shopify

“So it's really not even a question of where are we using AI, but where aren't we using AI? Because it's been an extensive usage in pretty much all departments within the company.”

Jeff HoffmeisterChief Financial Officer · Executive, Shopify

“We've obviously been disciplined for three years now. We're, on any given year, we're in fact slightly down from the year before. I don't see that changing.”

Jeff HoffmeisterChief Financial Officer · Executive, Shopify

“normally that would have taken 40 or 50 people about a year to do. And we took five really smart people, AI forward thinking people building on agentic coding tools. And those five people rebuilt it in 65 days.”

AmazonPublished by the organization itself (earnings call), no named author

“Agentforce can resolve customer issues swiftly and accurately with answers grounded in our trusted, unstructured content unified in Data 360”

SalesforcePublished by the organization itself (regulatory filing), no named author

“By automating simple and repetitive tasks, our support engineers are able to augment and scale their time in a more valuable way”

SalesforcePublished by the organization itself (regulatory filing), no named author

“A significantly smaller team, using the tools we're building, can do more and do it better.”

BlockPublished by the organization itself (regulatory filing), no named author

“in 2025 delivered approximately $59 million”

KlarnaPublished by the organization itself (regulatory filing), no named author

“We also reduced or canceled contracts with over 1,700 suppliers following our AI adoption and standardization”

KlarnaPublished by the organization itself (regulatory filing), no named author

“We decreased spending on external marketing suppliers, such as translation, production, CRM and social agencies, by $7 million in the first quarter of 2024 (as compared to the first quarter of 2023) and we attribute 37% of this decrease to the use of AI”

KlarnaPublished by the organization itself (regulatory filing), no named author

“our engineers use an AI-assisted case log classification tool that organizes documents and categorizes over one million monthly chat conversations.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“At the same time, appreciating that certain consumers may nevertheless prefer to interact with human representatives, we continue to offer all of our customers that option.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“While we continue to utilize well-established ML techniques in our underwriting processes, we do not use generative AI for credit underwriting.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“have handheld devices, and they're using computer vision to map our inventory, to know what we have, to know exactly where it is”

WalmartPublished by the organization itself (earnings call), no named author

“We have accelerated AI and data transformation with partnerships with Open AI, AWS and Accenture.”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“AI tooling has reduced call summarisation time by more than 70%”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“New AI capabilities enabled quicker responses to customer complaints, saving around 90,000 hours per annum through automated summarisation and AI-generated complaint responses.”

NatWest GroupPublished by the organization itself (regulatory filing), no named author

“Consistent with prior years, we relied on a global network of approximately 13,000 third-party workers as of December 31, 2025, to support the majority of our community support contacts.”

AirbnbPublished by the organization itself (regulatory filing), no named author

“During 2025, we introduced new artificial intelligence (“AI”) features to help deliver customer support in certain countries and languages.”

AirbnbPublished by the organization itself (regulatory filing), no named author

“More than 80% of engineers are now using AI tools.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“We're helping our associates build the skills they'll need to thrive in an AI-Powered workplace through things like embracing OpenAI Certifications and rolling out ChatGPT Enterprise Licenses.”

WalmartPublished by the organization itself (earnings call), no named author

“For over two years, total headcount has consistently been flattened down, both sequentially and year over year”

Jeff HoffmeisterChief Financial Officer · Executive, Shopify

“Scout is an internal voice of the customer system that indexes hundreds of millions of merchant feedback items, making them searchable within our tools.”

Harley FinkelsteinPresident · Executive, Shopify

“Our AI assistant handled 69% of customer service chats in the last twelve months ended June 30, 2025”

KlarnaPublished by the organization itself (regulatory filing), no named author

“As of June 2025, 80% of our engineers and data scientists connected to their work software an AI copilot that can create and review code”

KlarnaPublished by the organization itself (regulatory filing), no named author

“Our legal teams use AI to expedite document review.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“we operate an internal knowledge chatbot, which we call Kiki, that helps employees find information in real time across internal systems.”

KlarnaPublished by the organization itself (regulatory filing), no named author

“We built a custom agent built on 13 different models that have been tuned off of tens of thousands of conversations.”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“Currently at 2 million weekly resolutions and growing fast”

Intercom (renamed Fin in May 2026)Published by the organization itself (post), no named author

“We've been seeing around 560,000 resolutions a month with Fin, at about a 79% resolution rate,”

Isabel LarrowHead of AI Support (title as shown on Intercom's homepage) · Leader, Intercom (renamed Fin in May 2026)

“In 2025, Cora handled 12.9m retail banking customer conversations, 50% of which needed no human intervention.”

NatWest GroupPublished by the organization itself (post), no named author

“830M AI interactions”

ZendeskPublished by the organization itself (post), no named author

“Automate up to 80% of interactions with Zendesk AI agents.”

ZendeskPublished by the organization itself (post), no named author

“Built to improve with every resolution. Proven across 4.8B+ resolutions.”

ZendeskPublished by the organization itself (post), no named author

“19,000 software developers have experienced efficiency gains of over 20% through real-time coding assistance”

Bank of AmericaPublished by the organization itself (post), no named author

“Clients now interact with Erica nearly 200 million times per quarter.”

Bank of AmericaPublished by the organization itself (post), no named author

“has surpassed 3.8 billion client interactions since its 2018 launch.”

Bank of AmericaPublished by the organization itself (post), no named author

“In May 2026, Anthropic averaged around 2,500 code changes merged per day; as of September, we merged over 30,000 per day.”

AnthropicPublished by the organization itself (post), no named author

“Take new feature requests. They used to be copied by hand into Salesforce, about 300 a month. In May, an account manager saved a prompt to Glass that pulls them straight from customer calls. Now we capture 20,000 a month.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“A third emails customers when something they asked for ships. More than 5,000 of those emails have gone out so far.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“94% Automated resolution (during tax season) 72% Verified resolution (during tax season)”

ZendeskPublished by the organization itself (post), no named author

“At Atlassian, agent loops in Jira automatically resolve 51% of potential vulnerabilities”

AtlassianPublished by the organization itself (post), no named author

“Fin has industry leading resolution rates, averaging 76% across 12,000+ customers, with many seeing over 85%.”

Intercom (renamed Fin in May 2026)Published by the organization itself (post), no named author

“we achieved a 4.6 CSAT rating and a 74% resolution rate by empowering AI agents and humans to work as one unified team”

SalesforcePublished by the organization itself (post), no named author

“Agentforce on Help has now surpassed five million customer conversations, resolving more than 63% of them instantly.”

SalesforcePublished by the organization itself (post), no named author

“answering more than 3 million support requests in the first year”

SalesforcePublished by the organization itself (post), no named author

“You get a pretty high degree of confidence on merge. Today, almost a fifth of all the pull … are merged without any human review or without any human testing. … that is a number that we care a lot about and want to bring higher and higher, of course, within the guardrails of producing really high quality code.”

Daksh GuptaCo-founder · Executive, Greptile

“Today, … used by several tens of thousands of engineers every single week to review all of their code. So we also know, generally speaking, the number of pull requests that each of these people are writing. The median … user writes 50 pull requests a month, so call it about … per work day. The 90th percentile writes 500 pull requests per month. That is a drastic difference between the median and the P90. The P99 is in the thousands of pull requests.”

Daksh GuptaCo-founder · Executive, Greptile

“about a quarter of all the pull requests … in any given month were completely or at least largely generated by AI. Then I backtracked this data to the last 12 months. And it turns out that this number is growing really fast. In fact, early last year, fewer than 1% of pull requests had any evidence of being completely … generated.”

Daksh GuptaCo-founder · Executive, Greptile

“85% of questions that are being asked to PMs now are fully answered with AI.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“93% of our PRs are now automatically handled by Review Buddy so that the highest quality engineers are spending time on the 7% of PRs that truly matter,”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“Inspect has a million sessions, 75% of our PRs is actually built by Inspect, and 1,000 of those PRs over the last month was submitted by a non-engineer.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“Testo, in the last 30 days, caught 425 bugs.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“Thanks to these autonomous loops, 60%, 60% of UX issues that are identified by either a customer or a salesperson or a CX person or ourselves are fixed within 24 hours.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“We're around 70% AI-generated code”

DropboxPublished by the organization itself (interview), no named author

“automated resolutions for its highest-performing product increased from 39 to 46 percent”

ZendeskPublished by the organization itself (post), no named author

“Total merged changes more than doubled year over year in August, from roughly 8,100 to 17,000.”

SpotifyPublished by the organization itself (post), no named author

“Stripe’s user-facing AI agent that holds 100k+ conversations per week”

StripePublished by the organization itself (post), no named author

“In the past four weeks, OCA assisted with 575 of the 1,220 PRs merged to address incidents.”

Richard MarmorsteinCo-author of Ramp's OCA post (job title not stated) · Practitioner, Ramp

“In the first 11 days of running the dependency workflow, the backlog of open issues fell by about 70%.”

Erin SonSenior Engineer · Practitioner, Shopify

“Since launch, security merges through our freshness-gated merge queue went from about 10% to 80%.”

Erin SonSenior Engineer · Practitioner, Shopify

“all of a sudden all the design docs and all the Slack messages and all the emails have gotten three or four times as long and … gotten two or three times as many emails and they say basically as much as they did before.”

Eyal BlumSoftware Engineer · Practitioner, Figma

“I aligned with three other teams for another week on that and then I just sent it to an agent to implement overnight and it came back, this is probably from two … not one, but it's basically six weeks of coding work … only one week. So that's where I get … 5X speed up if I include the review cycle at the end…”

Eyal BlumSoftware Engineer · Practitioner, Figma

“More than 70% of pull requests are attributed to local or cloud agents.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“the number of issues with a pull request attached by someone in an engineering, product, or design function has grown sevenfold”

LinearPublished by the organization itself (post), no named author

“the share of work they create has grown from 3% a year ago to 50% today”

LinearPublished by the organization itself (post), no named author

“And all of this led to twice the number of lines of code per engineer year over year.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“Over the last year, all of the investments we made in agentic AI have led to more than seventy percent of our PRs now either by local or cloud agents.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“We've already shipped 75 integrations this way, reducing work that took weeks or months down to hours - or minutes for the customer just trying to get unblocked.”

Shubhayan SrivastavaSoftware Engineer Intern (summer 2026) · Practitioner, Ramp

“authoring ~10% of all merged PRs at Monzo”

MonzoPublished by the organization itself (post), no named author

“routinely running more than 1800 tasks every day”

MonzoPublished by the organization itself (post), no named author

“Between 25-35% of the PRs we merge on a weekly basis are now authored by `ai-sdk-factory` agents.”

VercelPublished by the organization itself (post), no named author

“At PostHog, for example, 63% of changed lines are in files that already exist, and fix is our largest commit type at 40%.”

Jina YoonAuthor, PostHog newsletter · Practitioner, PostHog

“PostHog is just a little further than that with agents writing about 70% of our PRs, but humans still skim at least 80% of those.”

Jina YoonAuthor, PostHog newsletter · Practitioner, PostHog

“When Account Executives use Kai, they produce 2x the sales activity, create 17% more opportunities, generate 26% more revenue opportunities, and close 39% more deals when compared to the same sellers in weeks they don't use it.”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“Over roughly six weeks, we’ve run thousands of scans, produced over 300 findings ranging from defense-in-depth improvements to resolved security incidents.”

Zack DeveauTitle not stated on Deveau's author page; the post is written from Shopify's Application Security team · Practitioner, Shopify

“More than 70% of pull requests are attributed to local or cloud agents.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Organizations running this model report 3 to 5x productivity gains. Legacy backlogs that once ran to thousands of work days shrink to hundreds. Time to market compresses from quarters to weeks.”

BCG PlatinionPublished by the organization itself (post), no named author

“Before I trusted this workflow, I back tested the scoring against months of clean up history. Then I ran it live. Seven for seven PRs with green CI. Now it runs daily on my laptop and hands work back for me to review. … $1.26 a pull request. A backlog of around 520 flags a year runs for under $700. If you had engineers working on this by hand, at least $26,000 that you're looking at.”

Andrew OrobatorSenior Software Engineer · Practitioner, Reddit

“We had an initial goal of about 30 to 35 percent automated resolutions when we first launched. We went up to 60 percent within the first two weeks—and then we had peaks of 85 percent.”

Luca GiaccardiSenior Operations Excellence, E-Commerce · Practitioner, Qantas Loyalty

“Inside of Airbnb, we're double this. So we're closer to 59% today. So more than half of code inside of Airbnb today is primarily authored by AI.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“We have like 50 users and I don't want to scare people, but the PR throughput for these folks is like 3X higher again.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“Our PR throughput inside of Airbnb is 65% higher than it was before we introduced agentic AI.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“And today at Uber, one in nine of our PRs is fully production ready with zero human authoring from our background agents.”

Ty SmithPrincipal Engineer, AI & Developer Productivity · Practitioner, Uber

“Builderbot executes over 200,000 operations per day and merges approximately 1,500 pull requests per week, about 15% of all production code changes across Block.”

BlockPublished by the organization itself (post), no named author

“A year ago, agents triggered less than 3% of the deployments on Vercel. Now, they trigger around 29%, and we expect half of all deployments to come from agents soon.”

VercelPublished by the organization itself (post), no named author

“As of May 2026, more than 80% of the code we merge into Anthropic’s codebase was authored by Claude.”

AnthropicPublished by the organization itself (post), no named author

“Inspect is an internal software factory that writes more than two-thirds of Ramp's code.”

RampPublished by the organization itself (post), no named author

“we're seeing a 76% increase in pull request frequency”

SpotifyPublished by the organization itself (post), no named author

“now automates 91% of support tickets”

VercelPublished by the organization itself (post), no named author

“accounting for roughly 1 in 12 pull requests at Dropbox today”

DropboxPublished by the organization itself (post), no named author

“3,536 River-coauthored pull requests merged in that window.”

Burke LibbeyPrincipal Engineer · Practitioner, Shopify

“About one in eight pull requests merged into our codebase last week was authored by River, reviewed by us.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“we complete about 189 tickets for this sprint, which is about four times as much or two to three times more than what we usually accomplish”

Xiucheng QuekSoftware engineer · Practitioner, Genentech

“if you look at the 180 tickets the bot completed about 122 tickets”

Xiucheng QuekSoftware engineer · Practitioner, Genentech

“Today, 75% of all new code at Google is now AI-generated and approved by engineers, up from 50% last fall.”

Google (Alphabet)Published by the organization itself (post), no named author

“The week of March 23 hit 10,952, nearly double the Q4 baseline.”

CloudflarePublished by the organization itself (post), no named author

“67% Automated resolution rate 86% AI understood conversations 19% Escalations to a human agent”

ZendeskPublished by the organization itself (post), no named author

“1,800 code changes per week are now written entirely by Uber's internal background coding agent”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Even within traditional IDEs, ~70% of committed code is now AI-generated.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Our internal background coding agent went from <1% of all code changes to 8% in just a few months. There is zero human authoring. Engineers review and approve, but the code is written entirely by AI agents.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“GitHub Copilot populates approximately 80% of the issue's metadata automatically”

GitHubPublished by the organization itself (post), no named author

“Last year, 20.6 million users interacted with Erica nearly 700 million times.”

Bank of AmericaPublished by the organization itself (post), no named author

“Since Erica's launch in 2018, total client interactions have surpassed 3.2 billion.”

Bank of AmericaPublished by the organization itself (post), no named author

“Thirty-five percent of the PRs we merge internally at Cursor are now created by agents operating autonomously in cloud VMs.”

Cursor (Anysphere)Published by the organization itself (post), no named author

“Early testers have built over 21,000 agents.”

NotionPublished by the organization itself (post), no named author

“Over 1,300 Stripe pull requests (up from 1,000 as of Part 1) merged each week are completely minion-produced”

Alistair GraySoftware engineer, Leverage team · Practitioner, Stripe

“building and shipping an internal beta of a software product with 0 lines of manually-written code”

OpenAIPublished by the organization itself (post), no named author

“roughly 1,500 pull requests have been opened and merged with a small team of just three engineers driving Codex.”

OpenAIPublished by the organization itself (post), no named author

“This translates to an average throughput of 3.5 PRs per engineer per day, and surprisingly the throughput has increased as the team has grown to now seven engineers.”

OpenAIPublished by the organization itself (post), no named author

“Over a thousand pull requests merged each week at Stripe are completely minion-produced”

Alistair GraySoftware engineer, Leverage team · Practitioner, Stripe

“In 2025, Cora handled 12.9 million conversations, with 50% of these interactions requiring no human intervention from our teams”

NatWest GroupPublished by the organization itself (post), no named author

“we have merged over 7,000 fully AI-generated pull requests”

HubSpotPublished by the organization itself (post), no named author

“~30% of all pull requests merged to our frontend and backend repos are written by Inspect.”

Zach BruggemanPrincipal engineer; founding engineer of Inspect · Practitioner, Ramp

“We're seeing 71 percent automated resolution—far exceeding the other use cases that we have on average, which sit at around 50 percent.”

Emily DowseHead of Support · Leader, Arbor Education

“engineers using it ship about 70% more merged pull requests”

OpenAIPublished by the organization itself (post), no named author

“roughly corroborates the 67% increase in merged pull requests”

AnthropicPublished by the organization itself (paper), no named author

“our agents have generated more than 1,500 pull requests that teams across Spotify have merged into our production codebase.”

SpotifyPublished by the organization itself (post), no named author

“~40% of daily code written at Coinbase is AI-generated. I want to get it to >50% by October.”

Brian ArmstrongCEO · Executive, Coinbase

“70% of inquiries have been resolved with a digital assistant”

IBMPublished by the organization itself (post), no named author

“AskHR answers 94% of common employee inquiries in minutes or less, without human intervention, any time of the day or night.”

IBMPublished by the organization itself (post), no named author

“assisting nearly 50 million users since launch, surpassing 3 billion client interactions, and now averaging more than 58 million interactions per month.”

Bank of AmericaPublished by the organization itself (post), no named author

“now averaging more than 58 million interactions per month”

Bank of AmericaPublished by the organization itself (post), no named author

“uReview today analyzes over 90% of the weekly ~65,000 diffs (equivalent of pull requests) landed at Uber.”

Sonal MahajanStaff Engineer, Programming Systems team · Practitioner, Uber

“2,000+ workflow-heavy service requests automated per quarter”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“Each quarter, we resolve over 60,000 support requests with AI agents that carry out full workflows, execute backend actions, and adapt in real time to what our customers need.”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“In 2024, 21 percent of requests were fully automated and resolved without any agent intervention”

ZendeskPublished by the organization itself (post), no named author

“In 2024, Cora, our AI virtual assistant, handled 11.2 millionLA Retail Banking conversations, almost half of which required no human input.”

NatWest GroupPublished by the organization itself (post), no named author

“Around half of Cora conversations are now entirely contained and we achieved a Cora CSAT of 66%.”

NatWest GroupPublished by the organization itself (post), no named author

“Resolved 86% of queries with AI agents.”

IBMPublished by the organization itself (post), no named author

“With 7% of incoming tickets handled by AI agents, Jaycar has saved around AUD85,000.”

ZendeskPublished by the organization itself (post), no named author

“Klarna has built an AI-powered copywriting tool, Copy Assistant, which allows the company to use AI for 80% of all copywriting.”

KlarnaPublished by the organization itself (post), no named author

“To date, Erica has responded to 800 million inquiries from over 42 million clients”

Bank of AmericaPublished by the organization itself (post), no named author

“More than 2 billion since 2018. That's how many interactions Bank of America clients have had with Erica”

Bank of AmericaPublished by the organization itself (post), no named author

“In 2023, Cora, our AI virtual assistant, handled 10.8 million(*) Retail Banking conversations, almost half of which required no human input.”

NatWest GroupPublished by the organization itself (post), no named author

“Cora, fully supported 49% of customer queries through 10.8 million(*) conversations handled without colleague intervention.”

NatWest GroupPublished by the organization itself (post), no named author

“I've made the decision to reduce our workforce by 8% at Zendesk.”

Tom EggemeierChief Executive Officer · Executive, Zendesk

“We’re reducing the size of our team by around 14%”

Patrick CollisonCEO · Executive, Stripe

“We can teach you what to do because we have fully transformed our own customer experience”

Intercom (renamed Fin in May 2026)Published by the organization itself (post), no named author

“A resolution is a type of outcome that is counted when, following Fin's last answer in a conversation, the customer either confirms the answer was satisfactory (confirmed resolution), or exits the conversation without requesting further assistance (assumed resolution).”

Intercom (renamed Fin in May 2026)Published by the organization itself (post), no named author

“~9,000 contact centre colleagues are using AI to transcribe customer conversations and reducing time spent to search relevant data.”

NatWest GroupPublished by the organization itself (post), no named author

“GitHub labels serve as the state machine that connects the agents. Each agent watches for a specific label, does its work, and applies the next label:”

WarpPublished by the organization itself (documentation), no named author

“A human intervenes at two points: approving the spec before implementation begins, and merging the PR after review. Everything else runs automatically.”

WarpPublished by the organization itself (documentation), no named author

“This is the most important human checkpoint in the loop. An accurate spec leads to a correct implementation; a wrong spec leads to a correct implementation of the wrong thing.”

WarpPublished by the organization itself (documentation), no named author

“warpdotdev/oz-for-oss is Warp's complete, deployable software factory for GitHub-hosted repositories. It includes all four agent roles, a Vercel webhook layer that handles GitHub events without GitHub Actions boilerplate, and self-improvement loops that propose skill updates based on maintainer feedback.”

WarpPublished by the organization itself (documentation), no named author

“The reviewer agent surfaces issues and inconsistencies; the human makes the final call on quality and correctness.”

WarpPublished by the organization itself (documentation), no named author

“Resolve up to 80%+ of even the most complex service issues across any channel.”

ZendeskPublished by the organization itself (post), no named author

“Conversations that don’t pass this verification are considered a Contained resolution.”

ZendeskPublished by the organization itself (post), no named author

“Conversations that pass this verification are considered a Verified resolution.”

ZendeskPublished by the organization itself (post), no named author

“Messaging: By default, the conversation ends 2 hours after the first message in the conversation, though this can be increased up to 72 hours.”

ZendeskPublished by the organization itself (post), no named author

“Only conversations that were not escalated are evaluated for automated resolutions.”

ZendeskPublished by the organization itself (post), no named author

“Paying per automated resolution means you pay only for customer requests that were successfully resolved by an AI agent, without any escalation to a human agent.”

ZendeskPublished by the organization itself (post), no named author

“The system flags the interaction as an automated resolution because the live conversation ended with the AI agent rather than a human representative.”

ZendeskPublished by the organization itself (post), no named author

“Humans helped prioritize 38 ideas, then agents implemented the changes, trained models, debugged failed runs, and evaluated the results.”

Ryan DrapeauMachine learning engineer, Intelligent Commerce · Practitioner, Stripe

“18,000 customer service representatives receive real-time guidance in seconds, helping them resolve client needs faster”

Bank of AmericaPublished by the organization itself (post), no named author

“94% of client interactions are now digital.”

Bank of AmericaPublished by the organization itself (post), no named author

“Anthropic uses Claude to write software far more than almost any other company”

AnthropicPublished by the organization itself (post), no named author

“Very often, we found that the role of the engineers was to tell Claude “no.””

AnthropicPublished by the organization itself (post), no named author

“One week in May, we burned $1.5 million in tokens.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“Say a support agent is stuck on a ticket that needs an engineer. She shares her Glass session. The engineer jumps in, sees everything, they finish it together.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“So far, 323 employees (PMs, CX agents, salespeople, marketers, analysts) have made 6,500+ edits to what Glass knows.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“When we moved our own work onto it, our bill fell 30% overnight.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“Since launching with our Legal team, we’ve seen an over 20% reduction in average contract review time and 91% accuracy in AI-generated decisions.”

Austin GrecoStaff Software Engineer, Michelangelo (Uber's AI platform) · Practitioner, Uber

“Uber lawyers then review each suggestion, deciding whether to accept, reject, or modify it before final incorporation into the client response.”

Austin GrecoStaff Software Engineer, Michelangelo (Uber's AI platform) · Practitioner, Uber

“AI-native development processes at Atlassian have cut issue cycle time by nearly half, from 11.5 days to 6.7 days.”

AtlassianPublished by the organization itself (post), no named author

“In internal benchmarks, agents with access to the Teamwork Graph delivered 44% more accurate results while using 48% fewer tokens compared to agents operating without it.”

AtlassianPublished by the organization itself (post), no named author

“MCP Gateway unlocked a scalable, fast, and consistent path to building AI agents within Uber and is currently hosting over 800 MCP servers and over 5000 tools.”

Alok SrivastavaPrincipal Engineer, Business Platform · Practitioner, Uber

“a 5% decline in case volume —and 12% fewer cases than forecasted—even while product adoption grew”

SalesforcePublished by the organization itself (post), no named author

“Agentforce conversations that result in a transfer to a human agent”

SalesforcePublished by the organization itself (post), no named author

“Agentforce conversations where Agentforce answered the customer's inquiry”

SalesforcePublished by the organization itself (post), no named author

“Agentforce conversations where the user did not engage with Agentforce after starting the conversation”

SalesforcePublished by the organization itself (post), no named author

“available 24/7, in seven languages, and now that same help is live not just over chat, but on 1-800-NO-SOFTWARE too.”

SalesforcePublished by the organization itself (post), no named author

“Discover how we enable our engineers to provide full resolutions in as little as one hour by bypassing discovery steps”

SalesforcePublished by the organization itself (post), no named author

“initially opening it to just 200 authenticated users within a four-week period”

SalesforcePublished by the organization itself (post), no named author

“Salesforce Help gets over 60 million visits annually from customers in need of assistance”

SalesforcePublished by the organization itself (post), no named author

“our support team still receives around two million support requests each year”

SalesforcePublished by the organization itself (post), no named author

“the vanilla setup required roughly 20 days of engineering effort, compared to 4 days when using the reusable prompts.”

David DunneSoftware engineer, Local Payment Methods · Practitioner, Stripe

“So there didn't seem to be very big difference between the rate at which pull requests were reverted from people versus agents in my study.”

Daksh GuptaCo-founder · Executive, Greptile

“every time there's a pull request, we have a swarm of agents. And they go and look at every file that's changed, every file that's related to the files that change to figure out if there's bugs. And it also spins up your code in a sandbox, … dependencies, spins up local host, clicks around to try to break things, mocks inputs, and everything else to figure out if the code is broken.”

Daksh GuptaCo-founder · Executive, Greptile

“So then I started tracking the counts of P0s, P1s, and P2s in all of these changes. And interestingly, once again, there was not that much of a difference. In fact, three out of the four agents that we tested performed better than humans in terms of the rate at which … were producing P0s. They were producing fewer P0s than humans were. Similarly, the case with P1s and P2s. Broadly speaking, human-generated PRs were about equal in quality to … PRs based on this data.”

Daksh GuptaCo-founder · Executive, Greptile

“And so I started tracking the number of iterations, the number of review rounds between when the pull request was opened and when it was merged. Sure enough, very little difference. … Once again, very little if any statistical difference between human-generated and AI-generated pull requests in terms of how many iterations before they're ready to merge.”

Daksh GuptaCo-founder · Executive, Greptile

“and 1,000 of those PRs over the last month was submitted by a non-engineer.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“Or another question, sales. Asking questions about, what is this feature? How do I sell it? Is it available in Brazil? What's the use case? What's the price? All of these things are fully understood. And gadget, if it does not understand, routes it to the right person.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“We built an actual customer's insight agent that agent pulls from all the data sources at a company.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“It finds the right human reviewer to loop in.”

Geoff CharlesChief Product Officer (CPO) · Executive, Ramp

“a recent Java migration across backend services completed in three days”

SpotifyPublished by the organization itself (post), no named author

“Since May 2026, 3,000+ Stripes have created 12,000 prototypes, and 25% of Stripes have created at least one prototype in Harbor.”

Cristian RiveraStaff engineer, Web Presence and Platform team · Practitioner, Stripe

“Some are seeing automated resolution rates spike 10% post-deployment.”

ZendeskPublished by the organization itself (post), no named author

“Assisted by AI, the team was able to go from a proof of concept to a fully rebuilt native app published in the app stores in just 12 weeks.”

Max Da SilvaVP of Engineering, Growth · Leader, Shopify

“Engineers reviewed requirements and plans before proceeding with implementation.”

Max Da SilvaVP of Engineering, Growth · Leader, Shopify

“Right now, OCA is human-in-the-loop.”

Richard MarmorsteinCo-author of Ramp's OCA post (job title not stated) · Practitioner, Ramp

“the adoption is uneven. We have teams that are very AI-forward and have already transformed their entire workflows and then we have teams that are still experimenting in the earlier acts or have lost confidence”

Eyal BlumSoftware Engineer · Practitioner, Figma

“It's actually our best engineer. … they know where all the pitfalls are. They are … together with like their mental duct tape, all the places that agents are not working well and they're preventing all the really bad stuff from coming in … slower to adapt because they see all the problem …”

Eyal BlumSoftware Engineer · Practitioner, Figma

“Figma has pivoted very strongly from being a traditional tool to an AI-first tool. But in this talk, I'm not … talk about our product, I'm … talk more about our internal organization and how our engineering org has been adopting AI agents.”

Eyal BlumSoftware Engineer · Practitioner, Figma

“So for example, when … playwright … came out instead of having humans navigate the code now, the agent can explore the code. That was a big win unlock for productivity in a lot of our team.”

Eyal BlumSoftware Engineer · Practitioner, Figma

“we've decided we always, every PR description will start with something like that, something that I wrote by hand … could be very short to describe what this is doing and what this is doing.”

Eyal BlumSoftware Engineer · Practitioner, Figma

“because they're seeing … where you're lacking validation, where your tools fail. So, and their feedback is basically the roadmap of how to improve your agent …”

Eyal BlumSoftware Engineer · Practitioner, Figma

“And so it's not uncommon to spend a week writing a very detailed plan, making all the decisions, flushing it out, iterating, sending it out to teammates to review. And then only when it's read and you've … out all the decision, … to the agent, the agent will send it back to you when it's implemented … has been really successful also in accelerating and also really restoring some of the joy into the development process.”

Eyal BlumSoftware Engineer · Practitioner, Figma

“Engineers have built over 3,600 agent skills across the software development life cycle, and executed more than 30K agent skill executions per day.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“from February to Aug 2026, weekly active users across all agentic offerings across all our employees (engineers & non-engineers) grew 7x, and weekly agentic requests grew 9.4x.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“a growing share of sessions aren’t initiated by humans, but by automated managed agents handling code review, self-healing CI failures, completing E2E PRs with visual validation, triaging on-call alerts, debugging incoming bugs, and handling a variety of code maintenance tasks with human reviews/escalations.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“This single entry point encompasses more than 1,000 MCP servers across internal and third-party SaaS MCP”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“if this is an autonomous diff coming from Minion, we want to give a human reviewer some confidence that this diff has gone through a lot of self-improvement already”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“we've saved more than forty percent fleetwide savings.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“Alert investigation: automatic investigations of paging alerts in production”

MonzoPublished by the organization itself (post), no named author

“the key infrastructure for us to spawn a sandboxed container that would run an agent”

MonzoPublished by the organization itself (post), no named author

“nearly all of our engineers now using at least one coding agent regularly”

MonzoPublished by the organization itself (post), no named author

“A human stays in control throughout the entire process, including merging every change.”

VercelPublished by the organization itself (post), no named author

“Connect the factory with your GitHub issues, tag the ones you want it to pick up (choose trivial issues and bugs first), run it, and review the resulting PR and the agents' PR comments.”

Noah BlauensteinerAuthor, Xebia articles · Practitioner, Xebia

“Entry-level software engineer work that largely consisted of "here's the ticket, go implement it" can now probably be replaced by some form of AI or factory.”

Noah BlauensteinerAuthor, Xebia articles · Practitioner, Xebia

“The planning agent is responsible for the actual thinking, so it should run on your most powerful model. I initially used Fable for this purpose when it was available, as it excels in UI/UX design. When Fable was no longer accessible, I switched to Opus. Since the planner writes the specifications, the implementation can be handled by a more cost-effective and faster model like Sonnet.”

Noah BlauensteinerAuthor, Xebia articles · Practitioner, Xebia

“Running several agents in parallel burns tokens quickly, so set budgets and keep an eye on spending from day one.”

Noah BlauensteinerAuthor, Xebia articles · Practitioner, Xebia

“You could figure out which ones took less tool calls, … which one made less errors, which one produced the correct outcome, and deterministically know what's going on. The point is you can start building data-driven systems without ever writing a single line of code. … in order to build a programming language, it wouldn't have taken eight people. It wouldn't have taken less than two years. It would have taken hundreds and thousands and tens of thousands of man hours, and then you would still have a broken system. And today, we can just spend billions of tokens and make it work, and we can make it stable.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“What we did was we built a system that actually has agents constantly running and creating BAML programs. … We then look at the entire … transcripts, see what tools they use, see what happened, and obviously we as humans can inspect them. But more importantly, we can have agents go inspect them. And agents find what was good, what was bad. … And we can have humans collaborate with these issues to figure out which ones are real, which ones are hallucinations, which ones don't have taste as much as I hate to use that word. And then we can have agents go ahead and create fixes to these problems and go to address them.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“But the real problem with all of this is I built this and I hit a little bit of AI psychosis. And I started shipping 10 design docs a day. And soon, the team was fighting my slop. … This last rule was … ship a design doc, you require people to actually go read it. And with this last standard, we suddenly had design docs … incredibly high quality.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“We built an architecture.md file. Instead of using … MD, just pick something that every model can just understand. This file has to be incredibly small, and it can only have things that will not change for months or for years. In our case, it's the layers of the compiler. … So now we have standards so anyone can use whatever they want.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“This tool basically visualizes our dependency graph internally with some external dependencies as well, … But what's more interesting is we can go build CLI tools that guarantee that certain invariants can't be broken. And what this does is when Claude builds a new package or adds a dependency that's leaky, we now have CI/CD changing or a simple git commit history that tells us exactly where things break. And by this, we're actually able to make our architecture change. We haven't changed our architecture in the last three or four months.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“we have a very simple rule in our team. Code can be slop, writing cannot. … What this design doc tool does, it's a replacement for both Notion and GitHub, effectively for design docs. It allows versioning, commenting, all the stuff you want. … We built another tool on top of that. And this tool was a slack integration for that tool. Every time a design doc got updated, this channel got notifications. And what ended up happening is this channel became the most popular channel in our company really fast.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“When we started building BAML, I didn't think it would be possible to build some of the software we did. And just yesterday, one of … engineers built a partial C compiler, purely in BAML. … Our processes have to evolve if we're going to ship at agent speed.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“We do no code reviews. We require every engineer to work on things in parallel, and we have no standardization how people do AI.”

Vaibhav GuptaCo-founder and Chief Executive · Executive, Boundary (BoundaryML)

“And across Stripe, more than 5,000 sessions everyday center on data analysis.”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“In aggregate, Kai has helped shift 25,000 hours per year from administrative work, to revenue generating work.”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“Anyone could build and deploy workflow-specific agents that could use tools. Over 4,000 agents were built using this system.”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“In finance and operations, Kai is helping Stripes analyze messy data, generate recurring digests, and turn fragmented context into usable artifacts.”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“Today, 83% are weekly active users, including nearly all of GTM (marketing, sales, customer success managers, and technical account managers).”

Sharadh KrishnamurthyEngineering manager, Agent Foundation team · Leader, Stripe

“ensures human oversight, transparency, and accountability for all outcomes”

Bank of AmericaPublished by the organization itself (post), no named author

“EricaAssist reduces average call times by nearly one minute per interaction”

Bank of AmericaPublished by the organization itself (post), no named author

“EricaAssist works alongside employees during calls – summarizing and surfacing relevant guidance in real time”

Bank of AmericaPublished by the organization itself (post), no named author

“Later this year, Bank of America plans to expand EricaAssist to support additional servicing scenarios and business lines.”

Bank of AmericaPublished by the organization itself (post), no named author

“summarize why a client is calling, pull together relevant information, and recommend next steps based on the employee's role and the client's relationship with the bank”

Bank of AmericaPublished by the organization itself (post), no named author

“Used by more than 18,000 customer service representatives”

Bank of AmericaPublished by the organization itself (post), no named author

“Capital allocation across 150 cities: 15 hours → 30 minutes. • Financial pacing reports: 2 days → 10 minutes.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“In just the past two months, we've run 16 Agentic Pods across 16 different business functions.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Marketing web quality assurance: 2 weeks → 50 minutes.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Support workflow creation: 9,000 manual workflows → self-service automation.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“This is not just a tech topic anymore—it's changing the economics of industries. It belongs on the agenda of every CEO.”

Axel GriewelManaging Director, BCG Platinion · Leader, BCG Platinion

“What I've seen is that when we started introducing … and loops and eventually more autonomous work within the whole organization, a new technical path opened. All of a sudden, we were helping the agent with tooling, building tooling for the agent and that kind of reignited some of the developers … kind of felt that it wasn't for them. Not all of a sudden, they were like, yes, we can do this, we have that knowledge, we're like somehow helping this even with a kind of programmatic way.”

Patrick DeboisProduct DevRel Lead · Practitioner, Tessl

“I think what I hear from most companies is they say, first step is we give them an exercise and we want them to really go nuts on the … to solve this. … Now, after they pass this, you do a walkthrough and you actually say, please explain me what happened. Why is this a good idea?”

Patrick DeboisProduct DevRel Lead · Practitioner, Tessl

“One is you start measuring how many human touches you still do to have the agent do the right thing. That's supposed to go down.”

Patrick DeboisProduct DevRel Lead · Practitioner, Tessl

“What I started seeing in some of the more advanced kind of teams is that … rituals of, Hey, we're doing a planning and we're doing a retro in a team that they weren't about like, Hey, we had issues with the code, but we're seeing we had issues with the system. So on the retro part is like, Hey, the agent went over and over hit this problem. Can we fix the system? That's something you'll learn in the retro.”

Patrick DeboisProduct DevRel Lead · Practitioner, Tessl

“With this architecture, agentic reasoning is used for intake and context gathering, but every autonomous risk decision is still made by an auditable model, governed under an approved policy.”

Blake WilliamsProduct Manager · Practitioner, Ramp

“I got tired of watching old feature flags pile up… I built an agent for cleaning up stale feature flags. It runs in my local workflow and it doesn't merge any code. The models, the easy part. The judgment in front of it is the whole game. Every flag gets scored the way an experienced reviewer would analyze it. How many modules does it touch? Is it multi-variant? Does it share a component? Then it checks the experiment data. Is the rollout frozen? Is there a sample ratio mismatch? Is there a variant already rolled out to 100 percent?”

Andrew OrobatorSenior Software Engineer · Practitioner, Reddit

“The job changed, and most of us didn't even notice, we're harness engineers now. Our work is the system that helps produce and verify the code. The constraints, the gates, the skills, the verification.”

Andrew OrobatorSenior Software Engineer · Practitioner, Reddit

“91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults.”

Brian ArmstrongCEO · Executive, Coinbase

“97% of active engineers inside of Airbnb are using Agentic AI on a weekly basis so far, 90% daily.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“we haven't done any mandates inside of Airbnb. It's all been organic adoption.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“People that were using agentic AI for four hours or more per day have seen PR output more than double from before AI.”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“So as of now, we have about double the amount of users of agentic AI tools as we do active developers”

Christopher SansonProduct Lead, AI Developer Experience · Leader, Airbnb

“Early data shows that 70% of all our in house background agents, tasks which is Minions are toil.”

Abhishek TibrewalSenior Data Scientist, Platform Engineering · Practitioner, Uber

“It also operates on source code and system configuration only; it does not access or process customer data, payment information, or personally identifiable information.”

BlockPublished by the organization itself (post), no named author

“Builderbot is an orchestration layer that coordinates multiple AI agents across our entire codebase. It works inside Slack: anyone can tag @builderbot with a short description of what they need, and it gets to work right there in the thread, whether it’s a bug fix, a migration across services, or a new feature.”

BlockPublished by the organization itself (post), no named author

“It picks up tickets directly from Linear and Jira, creates the branch, writes the code, opens the pull request, watches CI, and iterates based on feedback.”

BlockPublished by the organization itself (post), no named author

“On the Square side, we took a list of features sellers had been waiting on for months and our engineers shipped them in days. Builderbot handled the scaffolding and the repetitive work, and our engineers made the decisions that shaped the product.”

Brad AxenHead of AI Capabilities · Leader, Block

“Today, 100% of Block's engineers regularly use AI in their work.”

BlockPublished by the organization itself (post), no named author

“No longer will they be the sole custodians of building the software. Instead, they will be responsible for building the factories that build the software. With this comes the responsibility of governance, safety, and the ownership of business outcomes.”

FactoryPublished by the organization itself (post), no named author

“No organization starts with a fully autonomous software factory. Autonomy is a maturation process that is gradual and specific to every organization’s readiness and comfort level.”

FactoryPublished by the organization itself (post), no named author

“The software factory starts with signals from the outside world: bug reports, internal conversations, customer feedback, business requirements. These signals get triaged and turned into planned changes. These changes are built, tested, reviewed, secured, shipped, and monitored. Monitoring that deployed software generates more signals. The entire system is a continuous feedback loop.”

FactoryPublished by the organization itself (post), no named author

“Well-defined, measurable tasks run with simple Droid agents or skills. Automations coordinate recurring workflows with a shared objective and memory. Remote and persistent execution leverages Droid Computers for long running or local agents. Multi-agent autonomous execution called Missions solve complex tasks over hours or days by decomposing work into parallel tracks to handle.”

FactoryPublished by the organization itself (post), no named author

“The agent runs our cleanup skill and one-shots it nearly every time, because the task is well scoped and always the same shape. I just review and merge.”

LinearPublished by the organization itself (post), no named author

“so it lands the correct fix about a third of the time”

LinearPublished by the organization itself (post), no named author

“Glass gives every employee, from engineering to legal, a fully configured AI workspace, helping the company reach 99.5% AI adoption.”

RampPublished by the organization itself (post), no named author

“more than 99% of our engineers use AI coding tools every week, 94% report that AI has made them more productive”

SpotifyPublished by the organization itself (post), no named author

“we've merged more than 2.5 million automated maintenance PRs, the vast majority auto-merged with no human in the loop.”

SpotifyPublished by the organization itself (post), no named author

“automates 91% of support tickets and saves 5,000 engineer-hours a month”

VercelPublished by the organization itself (post), no named author

“now gets 45% of its questions from other agents, rather than people”

VercelPublished by the organization itself (post), no named author

“Mode · Automation: PR review. Durable session, woken by an external system, often no human in the loop.”

Burke LibbeyPrincipal Engineer · Practitioner, Shopify

“In a recent 30 day period: 59,918 River sessions happened in 5,170 Slack channels, touching the work of 7,000+ people inside the company”

Burke LibbeyPrincipal Engineer · Practitioner, Shopify

“Merged 160+ PRs using the automated upgrader”

Mahib HosainEngineer, Developer Productivity team · Practitioner, Stripe

“Spam and routine exchanges are excluded.”

ZendeskPublished by the organization itself (post), no named author

“An improvement from 36% to 77% over two months came from people watching River work”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“we are allowing the bot to merge that directly into our main branch because we have integration tests, unit tests, etc., canary tests.”

Xiucheng QuekSoftware engineer · Practitioner, Genentech

“We are now generating operation logs uh uh or daily summary of the platform”

Xiucheng QuekSoftware engineer · Practitioner, Genentech

“simply asking Ona or the bot on my research failed, can you my analysis failed, can you tell me why? And it will just go through the entire troubleshooting process.”

Xiucheng QuekSoftware engineer · Practitioner, Genentech

“In the last 30 days, 5,938 Shopify employees worked with River across 4,450 different Slack channels. It opened 1,870 pull requests in the last week alone in our main monorepo.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“Our engineers are orchestrating fully autonomous digital task forces, firing off agents and accomplishing incredible things.”

Google (Alphabet)Published by the organization itself (post), no named author

“In the last 30 days, 93% of Cloudflare’s R&D organization used AI coding tools powered by infrastructure we built on our own platform.”

CloudflarePublished by the organization itself (post), no named author

“Over the last 30 days: 100% AI code reviewer coverage across all repos on our standard CI pipeline.”

CloudflarePublished by the organization itself (post), no named author

“we run around 50,000 builds a week, a number that is only increasing with AI adoption among our engineering teams accelerating commit rates and productivity”

Aditya AnchuriTech lead, Ruby Infra Platform team · Practitioner, Stripe

“Normally, this sort of activity might take a team of 40 people about a year to carefully build. Instead, the Bedrock team spun up a separable group of six very skilled engineers who were excited about starting over and building on our agentic coding service (Kiro)”

AmazonPublished by the organization itself (post), no named author

“I used coding agents to build agents that automated part of my job.”

GitHubPublished by the organization itself (post), no named author

“Zendesk announced its intent to acquire Forethought on March 11, 2026.”

ZendeskPublished by the organization itself (post), no named author

“More than 300 are active every week.”

Andrii YakovenkoAuthor describing Intercom's internal Claude Code rollout · Practitioner, Intercom (renamed Fin in May 2026)

“84% of AI users are now working with agent-style workflows, not just tab completion. Claude Code usage nearly doubled in 2 months (32% → 63%), while IDE-based tools have largely plateaued.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“There is zero human authoring. Engineers review and approve, but the code is written entirely by AI agents.”

Praveen Neppalli NagaChief Technology Officer · Executive, Uber

“Pull requests arrive faster than human-driven review can stay meaningful.”

Bence A. TóthAuthor on the Zendesk Engineering blog (role not stated on the pages read) · Practitioner, Zendesk

“GitHub Copilot then posts a comment on the issue containing: A summary of the problem and user impact”

GitHubPublished by the organization itself (post), no named author

“Zendesk expects AI to manage more customer service interactions than humans in 2026.”

ZendeskPublished by the organization itself (post), no named author

“More than one-third of the PRs we merge are now created by agents that run on their own computers in the cloud.”

Cursor (Anysphere)Published by the organization itself (post), no named author

“We've spent the last few months using Custom Agents internally at Notion. We now have more agents than employees.”

NotionPublished by the organization itself (post), no named author

“Stripe’s enormous preexisting battery of tests—over three million of them—can provide this feedback.”

Alistair GraySoftware engineer, Leverage team · Practitioner, Stripe

“Escalate to a human only when judgment is required”

OpenAIPublished by the organization itself (post), no named author

“They pull review feedback, respond inline, push updates, and often squash and merge their own pull requests.”

OpenAIPublished by the organization itself (post), no named author

“we built a central internal MCP server called Toolshed, which hosts more than 400 MCP tools”

Alistair GraySoftware engineer, Leverage team · Practitioner, Stripe

“while they're human-reviewed, they contain no human-written code.”

Alistair GraySoftware engineer, Leverage team · Practitioner, Stripe

“The system peaked at ~1,000 commits per hour across 10M tool calls over a period of one week.”

Cursor (Anysphere)Published by the organization itself (post), no named author

“To better support our colleagues, we use AI summaries to help triage customer needs. In 2025, the technology benefitted approximately 9,000 customer support agents”

NatWest GroupPublished by the organization itself (post), no named author

“Cora, our digital assistant, deals with over 100 topics across mobile, online, and telephony banking.”

NatWest GroupPublished by the organization itself (post), no named author

“more than 12,000 engineers bankwide now have access to AI coding assistants, supporting efficiency and faster feature deployment.”

NatWest GroupPublished by the organization itself (post), no named author

“It offers a secure way to provide identity documents to NatWest digitally, reducing completion time from days to minutes.”

NatWest GroupPublished by the organization itself (post), no named author

“saving over 70,000 hours through automated call and chat summaries and another 17,500 hours through faster, high- quality complaint responses.”

NatWest GroupPublished by the organization itself (post), no named author

“we have seen around a 20-percentage-point increase in queries resolved without any human intervention, compared to the equivalent non-Gen AI supported Cora journey.”

NatWest GroupPublished by the organization itself (post), no named author

“code reviewed over 50,000 pull requests authored by humans”

HubSpotPublished by the organization itself (post), no named author

“even when you have hundreds of sessions running in parallel”

Zach BruggemanPrincipal engineer; founding engineer of Inspect · Practitioner, Ramp

“Dropbox saw AI usage among our engineering teams jump to over 90% this year”

DropboxPublished by the organization itself (post), no named author

“We recently rolled out the Employee Assistant, a personalized AI-powered agent to provide employees a single resource to get help and take action across the firm.”

JPMorgan ChasePublished by the organization itself (post), no named author

“While it will likely be the outcome in certain jobs, the goal is not fewer headcount; the goal is compounding performance”

JPMorgan ChasePublished by the organization itself (post), no named author

“Zendesk announced its acquisition of Unleash”

ZendeskPublished by the organization itself (post), no named author

“Codex reviews every internal pull request.”

OpenAIPublished by the organization itself (post), no named author

“@Copilot is constantly assigned to clean up deprecated feature flags”

GitHubPublished by the organization itself (post), no named author

“It gets assigned issues by human engineers, opens pull requests, and does the work assigned.”

GitHubPublished by the organization itself (post), no named author

“All the surrounding Fleet Management infrastructure — targeting repositories, opening pull requests, getting reviews, and merging into production — remains exactly the same.”

SpotifyPublished by the organization itself (post), no named author

“AI and Automation have helped IBM unlock extreme productivity gains across the company since January 2023, and we are on track to reach USD 4.5B in savings by the end of 2025.”

IBMPublished by the organization itself (post), no named author

“CashPro Chat is used by 65% of clients, with Erica handling greater than 40% of client interactions.”

Bank of AmericaPublished by the organization itself (post), no named author

“More than 98% of users find the information they need, significantly decreasing call center volume”

Bank of AmericaPublished by the organization itself (post), no named author

“with the virtual assistant having reduced calls into the IT service desk by 50%”

Bank of AmericaPublished by the organization itself (post), no named author

“uReview today analyzes over 90% of the weekly ~65,000 diffs (equivalent of pull requests) landed at Uber. Engineers who interact with the tool mark 75% of its comments as useful”

Sonal MahajanStaff Engineer, Programming Systems team · Practitioner, Uber

“we immediately activated them in our own support experience on day one.”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“with no human intervention required”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“Using our AI agent builder, we simply describe business procedures in natural language and the AI agent is ready to go.”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“When hiring for our service team, we were able to rescope the skillset to deemphasize the need for deep workflow expertise.”

Austin LaceyDirector, Chatbots, AI & Automation · Leader, Zendesk

“Before asking for more Headcount and resources, teams must demonstrate why they cannot get what they want done using AI.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“we reached 97% of the 3.5K original Enzyme files”

Charles Covey-BrandtAuthor of the Airbnb engineering blog post on LLM test migration (role not stated on the page) · Practitioner, Airbnb

“The AI agent helped contribute to a 40% reduction in the HR team’s operational costs over the past four years.”

IBMPublished by the organization itself (post), no named author

“Generated over 1,000 images in the first three months of 2024 using genAI, reducing the image development cycle from 6 weeks to just 7 days.”

KlarnaPublished by the organization itself (post), no named author

“9 out of 10 Klarna employees (87%) now harness the power of Generative AI in their daily work.”

KlarnaPublished by the organization itself (post), no named author

“Klarna's lawyers are using ChatGPT Enterprise to create the first draft of common types of contract, massively reducing the time it takes to draw up a contract.”

KlarnaPublished by the organization itself (post), no named author

“More than 98% of clients get answers they need from Erica within 44 seconds, on average.”

Bank of AmericaPublished by the organization itself (post), no named author

“When further assistance is needed, Bank of America's Mobile Servicing Chat capability connects clients with a live representative who can answer more complex servicing questions.”

Bank of AmericaPublished by the organization itself (post), no named author

“Ultimate’s AI agents automate up to 80 percent of support requests.”

ZendeskPublished by the organization itself (post), no named author

“Zendesk announced today it will acquire Ultimate”

ZendeskPublished by the organization itself (post), no named author

“Zendesk today announced it has signed a definitive agreement to acquire”

ZendeskPublished by the organization itself (post), no named author

“From 2020 – 2022, our hiring outpaced our business realities.”

Tom EggemeierChief Executive Officer · Executive, Zendesk

“So while we solve 50% of the tickets, we're actually deliberate about the tickets we don't choose to solve yet [with agents].”

Ahmad Al-DahleChief Technology Officer · Executive, Airbnb

“We're talking about roughly a 10x increase in load on some systems. At most companies, that kind of growth might happen over two or three years. At OpenAI, we see it in about six months.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader, OpenAI

“it’s probably up to about 50% of pull requests in Shopify, which again, pull requests, every time you change your production system, you write a pull request, are created now not by engineers doing engineering work in the traditional sense”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“about 30% of our PRs in that week came from Minions”

Will GaybrickPresident, Technology and Business (also styled President of Product and Business) · Executive, Stripe

“last week, I think 7,000 PRs came from minions”

Will GaybrickPresident, Technology and Business (also styled President of Product and Business) · Executive, Stripe

“seller productivity has increased by 20%”

Will GaybrickPresident, Technology and Business (also styled President of Product and Business) · Executive, Stripe

“close to 100% of our code, probably somewhere between 95% and 100%, is written by or with LLMs today.”

Rob WitoffHead of platform · Leader, Coinbase

“it grew like 7X over, the last, I think it was, two months, three months, something like that. And then you see our engineering headcount growth. It’s, gone up by, 10% or something.”

Walden YanCo-founder and Chief Product Officer · Executive, Cognition

“I think that we're processing over 100,000 expenses a day that are being reviewed agentically.”

Eric GlymanCo-founder and co-CEO · Executive, Ramp

“we have now done about a million and a half conversations with customers, and at the same time, that’s the agentic layer speaking to the customer”

Marc BenioffChair, CEO & Co-Founder · Executive, Salesforce

“50% are with agents, 50% are with humans.”

Marc BenioffChair, CEO & Co-Founder · Executive, Salesforce

“I'd say maybe 20%, 30% of the code that is inside of our repos today and some of our projects are probably all written by software,”

Satya NadellaChairman and Chief Executive Officer · Executive, Microsoft

“will be cutting almost 5pc of its staff worldwide.”

Elaine BurkeFormer editor of Silicon Republic (until 2023) · Outside the organization, Zendesk

“agents spin up to then triage and run your on-call”

Ahmad Al-DahleChief Technology Officer · Executive, Airbnb

“every engineer must be able to explain, even if an AI generated the PR, must be able to explain what they built.”

Ahmad Al-DahleChief Technology Officer · Executive, Airbnb

“We have our own internal agent that we call AirChat, which [includes] basically all the necessary MCP organizational context,”

Ahmad Al-DahleChief Technology Officer · Executive, Airbnb

“"This intelligence layer means I now have total information," he said. "I've cut my meetings in half."”

Brian CheskyCo-founder and Chief Executive Officer · Executive, Airbnb

“We're now seeing agentic code reviews that look at code changes through a series of different lenses. In the past, it would have been impractical for a cloud infrastructure engineer and a security engineer to review every single code change. With agents, that becomes possible.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader, OpenAI

“The big theme of the past months has been that everything is now a coding agent. Whether the visible code is your output or not, agents write your artifacts.”

Andrew AmbrosinoLead, Desktop · Leader, OpenAI

“We are building an agent that "handholds" a change all the way to production — whether it's a code change or a change behind a feature flag. It observes the relevant monitoring graphs, but can also build its own dashboard to monitor important signals. More of our code changes are going to production with this kind of agent monitoring.”

Venkat VenkataramaniVP of Engineering, Applied Infra · Leader, OpenAI

“For a long time, we had a 'capability overhang': the models were capable but the products didn't fully bring that out. Now, we're seeing an awareness gap. Some people have figured out they can use Codex to monitor Slack, update Airtable, or create onboarding materials. But many others still use it for one task and then discover more uses from teammates via word-of-mouth.”

Akshay NathanEngineering Lead, Productivity team · Leader, OpenAI

“The number one thing that is changing is that people are starting to use threads for much longer, and this longer usage has been a breakthrough. It's surprising to see the sheer length of time that people spend on a thread – even days! They often set a goal and then have the model crank.”

Andrew AmbrosinoLead, Desktop · Leader, OpenAI

“Inside of Shopify, the amount of people I know who really write code is vanishingly small now.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“So periodically at night or in off hours, we give River, “Here’s all the conversations you’ve had today. What went well? What did you struggle with?”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“Very often 10, 20, 30, 40, 50 instances of them all through either subagents or just different windows coordinating.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“You just tell the AI to go nuts. It makes a pull request. You just say, “Yep, that’s good.” You don’t really read it, and now it has to be reviewed by your colleagues.”

Tobi LütkeCo-founder and Chief Executive Officer · Executive, Shopify

“He's orchestrating 16 agents and he's going a whole lot faster.”

Will GaybrickPresident, Technology and Business (also styled President of Product and Business) · Executive, Stripe

“we're not saying, "Wow, we need fewer sellers." We're saying, "So, we need a lot more sellers."”

Will GaybrickPresident, Technology and Business (also styled President of Product and Business) · Executive, Stripe

“Effectively, 100% of our employees are using AI on a daily basis here.”

Rob WitoffHead of platform · Leader, Coinbase

“if you align the org chart too much to the individual projects, you're going to end up re-orging every two weeks, which would be a total nightmare. … And there is the concept of a bet lead or a directly responsible individual. But the interesting thing is that they don't manage usually any of the other people, which … breaks the … previous way in which a lot of these things were done. But I think it leads us to be really flexible when you say, OK, actually, this project is not going to work out. Let's disband and keep going, and it's not a big deal.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“We are definitely still bottlenecked on reviews, especially for things that are touching some architecture pieces. And it's actually more subtle than just being bottlenecked on review, … we can carve out time differently. It's … bottlenecked on human ability to even fully conceptualize what we're doing.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“I was like, OK, I need to port this whole thing from Python to TypeScript. Like, as a-- you know, if I put on my, like, 2010s engineering hat, or even my early 2020s, like, that's a dumb idea. … But I was like, I think this is doable now. And I basically created this dynamic workflow set up. And over the weekend, … port the whole thing, like, verify it, double check it, … and then came back Monday to complete a workflow that was a ported version of that thing.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“But especially in a labs-type group where our whole cadence is two-week reviews where every project goes up for, we call it persevere or pivot. So basically, every project is up for review, and either it's time to keep going, … or it's time to pivot it, or even shut down. And we shut down projects basically every single one of those cycles. And the more you do it, the less it's just like, oh, no, my project is shut down. I failed. It's like, no, that is definitely the intention of the lab team is to prototype quickly, try to ship internally, maybe get it to early access, and if it doesn't work, wind it down.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“actually talked to Claude about the code and say, all right, these are the questions that I would have. Can you go investigate it? So it is kind of … code review, but still human-driven, and for the really important ones. And for the ones that are cosmetic visual changes, it's much more like we'll fix forward if we need to fix forward.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“We have one of our Slack challenges, like Project Unship, which is like, what is in the product right now? … Like, we unshipped styles, I think, recently, … used by a small percentage of people … very, sort of, prescriptive in the way that it worked, and skills were a much better application, something like that.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“But most usage is actually much more delegating via tagging… And then more advanced version is really trying to think of it as a teammate that is actually … has memory and can be proactive. And that's just really changed how we operate internally. It's much more like this multiplayer async proactive way than it is most people off in their own CLIs.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“So one of the reasons we built … artifacts that we shipped a couple of weeks ago was partially for that, which is you would send somebody a PR, and then they'd be like, I don't know, man, this is like 2000 lines of code. It looks like code to me. And what we started doing instead is sharing much more like, here's a … artifact. Here's the explanation. Here's the intention of the change. Here's the trade-offs that were made.”

Mike KriegerHead of Anthropic Labs · Leader, Anthropic

“We’ve also internally done a big shift to make a lot of our core, components of code testable as purely local dev without needing to actually, integrate with, any live services for this reason. And honestly, the older the company, the more you have to change to shift in this direction. But you can use AI to help you perform this migration nowadays.”

Walden YanCo-founder and Chief Product Officer · Executive, Cognition

“So we have like some Slack channels internally, maybe a Slack channel dedicated to, a specific product like DeepWiki maybe. And you can imagine that, or you want a Devin that never stops, it’s just always awake, but it has this like memory dock that it can just maintain for itself about, okay, what are like the number one priorities of what we have to fix and prioritize? Who is responsible for some upcoming work?”

Walden YanCo-founder and Chief Product Officer · Executive, Cognition

“The SRE use case, internally we think about it as auto triage Because we just want every message that comes in, and that’s an alert, that’s a bug report, to have Devin just start triaging it before anything else. And we’ve leaned into this use case so much though that we’ve basically tried to make it so that you don’t ever have to leave Slack to interact with this.”

Walden YanCo-founder and Chief Product Officer · Executive, Cognition

“I think we found that the state-of-the-art in December was you can probably, run this for about two weeks. By the end of those two weeks, you’d find that, hey, you want to, change the color of a button. Well, it turns out this button is implemented in, 10 different places, and they, have All these different variations, and oh, you forgot one of them, and actually it’s a slightly different color in one spot.”

Walden YanCo-founder and Chief Product Officer · Executive, Cognition

“basically it just means can you create sort of like a, as automated as possible, a workflow for developing debugging. Mm-hmm. Merging, reviewing, and maintaining a code base”

Simon LastCo-founder and head of AI · Executive, Notion

“Literally over 30 of them”

Simon LastCo-founder and head of AI · Executive, Notion

“It’s got 800 employees working here, fully distributed, far more agents than there are people.”

Wade FosterCo-founder and Chief Executive Officer · Executive, Zapier

“pre that week we had roughly 10% of the employee base was using AI with some frequency. After that week it was over 50%.”

Wade FosterCo-founder and Chief Executive Officer · Executive, Zapier

“We ask them if they want to work part-time in customer service”

Sebastian SiemiatkowskiChief Executive Officer and co-founder · Executive, Klarna

“We need to rethink this and make customer service a human experience. Everyone who wants a human connection should get it.”

Sebastian SiemiatkowskiChief Executive Officer and co-founder · Executive, Klarna

“The reason for that is because I’ve seen the acceleration of AI, and I know we can ship all these things on the existing organization,”

Sebastian SiemiatkowskiChief Executive Officer and co-founder · Executive, Klarna

“an autonomous support agent that Zendesk believes will solve 80% of support issues without human intervention.”

Russell BrandomAI Editor · Outside the organization, Zendesk

“Because of the benefits and efficiencies of Agentforce, we’ve seen the number of support cases we handle decline, and we no longer need to actively backfill support engineer roles”

SalesforceNot attributed: no named speaker, and published by Fortune (news magazine), so we cannot yet show the organization said it

“Developers using Cursor commit three times more code than before”

NvidiaNot attributed: no named speaker, and published by Cursor customer story (a vendor writing about its customer), so we cannot yet show the organization said it

“Teams are using Cursor for writing code, code reviews, generating test cases, and QA.”

NvidiaNot attributed: no named speaker, and published by Cursor customer story (a vendor writing about its customer), so we cannot yet show the organization said it

“90% of these teams used Kiro, among other internal tools that we have, and what they found was it wasn't about the tools, it was about the way that they worked. The teams that achieved step function improvements intentionally changed the way that they worked, and the other simply kind of sprinkled Kiro and some of the other tools that we have on top of their existing way of working.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“And sometime last year, we knew, or I say we, but the bedrock team knew that they were going to need to build a new inference data plane. But they had estimated it at 30 people over 18 months. This is a big, big service, and it was … take time to build the new one, migrate customers over, migrate models over, and they decided to take a step back. They took six people and they built it in 76 days with …”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“And completely anecdotally based on my own experience, I've really only felt maybe 10 to 20% more productive with all of these phases that have come before. But now inside of Amazon, we've been running pilots with different teams across the company, and we've been seeing a median of 4.5x productivity improvement and sometimes more than 10x.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“So another experiment that I want to talk about is an experimental sprint that was done in the Prime Video Organization. They took a 10-day sprint and they … experiment where they put, again, six engineers in a room and they let them go wild with Kiro. They brought down the project delivery time estimate from what was going to be 90 weeks down to 24, based on all of the progress they had made in this 10-day sprint.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“They watched 50 teams that were totally … distribution of early career folks, mid-career senior engineers, and that worked on existing systems. … And they watched them for the better part of last year, and they found something super interesting. They found that there was a big difference in the productivity gains that they saw between half of the teams and the other half. And in this case, they used a productivity metric of deployment velocity to production. … And they saw that for half of the teams, they achieved less than 3X increase.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“Back in 2024, we were seeing that engineers would get their first review within three hours. Now, in 2026, that has grown to nine hours, in addition to all of the volume changes. So in short, code review is now the bottleneck that we are running into.”

Will BondStaff Engineer, AI Foundations team, Development Platform organization · Practitioner, Uber

“[uReview] does … around 25,000 comments a week, and we get 10% of them actually get some feedback … We also saw that the overall address rate was around 67%, and almost three quarters of the high severity issues were usually addressed by the developers”

Ameya KetkarSpeaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner, Uber

“we saw that against like a very naive implementation, our costs were down by 60%, and our quality and our accuracy was up by around 70%.”

Ameya KetkarSpeaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner, Uber

“The impact on developer velocity internally is clear: we’ve never seen a quarter-to-quarter increase in merge requests to this degree. As AI tooling adoption has grown the 4-week rolling average has climbed from ~5,600/week to over 8,700.”

CloudflarePublished by the organization itself (post), no named author

“we have a security agent that processes over 7 billion tokens per day on Kimi. That would cost an estimated $2.4M per year on a mid-tier proprietary model. But on Workers AI, it’s 77% cheaper.”

CloudflarePublished by the organization itself (post), no named author

“Previously, writing code manually was the bottleneck. I find that with an Amazon, we've found the speed of decision making becomes a new bottleneck. … When it used to take nine to 12 months to build a new product, it didn't matter so much in the overall wash of things if it took two months to make the decision to build the product and then two months to approve the launch. But now, those are the bottlenecks. … Often, I find that frontier engineering teams spend more time making decisions than they do writing code.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“And I like to define what we've been calling frontier developers inside of Amazon by three behaviors that I've been seeing. One is hands off coding, frontier developers write maybe … of the code that they produce. The rest is agents. The second is that they interact with their agents infrequently. They'll aim to get their coding assistant to run for up to hours at a time without their intervention. And third is that they minimize idle time. These frontier developers tend to run multiple agents in parallel, … through a backlog of tasks.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“The second one is slowing down to speed up. In almost every team that was interviewed, they reported that their productivity actually went down, as they intentionally adopted a new way of working. That's counterintuitive, right? You have to do intentional engineering work before you're gonna see that hockey stick curve in productivity improvement.”

Clare LiguoriSenior Principal Engineer, AWS · Practitioner, Amazon

“Currently, we still have humans approving the code, but we see a short path in the near future to a percentage of our code landing automatically, having automatic approvals, right?”

Will BondStaff Engineer, AI Foundations team, Development Platform organization · Practitioner, Uber

“One of the biggest learnings in this process was like the model doesn't know that it's wrong. It always confidently says 100% sure that, yeah, this is the review for your code. Go ahead. But we saw that, no, it actually needs a lot of guidance from the teams, because each team has its own style guide, its own patterns or … anti-patterns that they want to look for.”

Ameya KetkarSpeaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner, Uber

“One thing that we learned is that actually writing the skill was very easy, like teams just very quickly wrote a skill by asking Claude to write one, go over my previous PR reviews and write me a skill, but the hard part was how to run these skills at scale with consistent quality and low cost, and that required a lot of iterations, not only from the [uReview] team side, but also … for each team who was trying to write these rules.”

Ameya KetkarSpeaker, AI Engineer World's Fair 2026 (job title not stated) · Practitioner, Uber

“But rather than killing the outer loop, I think that we believe and the industry has just started to really kind of coalesce on this idea that … really expanding the outer loop. Rather than removing humans from the code review process, we are moving their responsibilities up a layer.”

Will BondStaff Engineer, AI Foundations team, Development Platform organization · Practitioner, Uber

“Uber spent the time to invest in building an in-house solution due to some of the constraints that we have. One of those is we currently use [Phabricator] and have for a long time and are in the process of migrating to GitHub. Most of the solutions do not provide support for [Phabricator].”

Will BondStaff Engineer, AI Foundations team, Development Platform organization · Practitioner, Uber

“we handled more than two hundred and fifty automated migrations, cumulatively nine million lines of code automatically for our engineers.”

Uday Kiran MedisettyDistinguished Engineer · Practitioner, Uber

“we've been trying to build … platform layer around AI tooling at Cloudflare, not just coding … but the stack that makes the tools useful and safe inside a real engineering organization.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“the code tells the model what's in front of it, but it does not tell the model who owns the service, what the dependencies are, … what databases are being talked to, what conventions are that the teams considers non-negotiable … they reduce the amount of guessing the models have to do. And one thing we learned the hard way is that even with the decent context file, it is better than having none. And when the model stops guessing, the quality goes up everywhere else.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“engineers don't trust AI opinions. They just trust clear findings tied to standards that we … agreed upon. … it stopped sounding more like, maybe this is a problem and started sounding more like this violates a standard that we actually care about.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“sometime in November, we started looking and seeing a lot more benefit for some of the models that we were trying to leverage. I think Opus 4.5 maybe was the inflection point where that then generally made it so that it was very easy to see the kind of results we were seeing and expecting.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“the good part was that we were ready with all the background work that we were doing with respect to the building of the context and building of the right sort of MCP infrastructure internally. So when that became a reality, we started seeing a lot more adoption and because it started helping in the real work that people were doing.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“requests and tokens tell us like usage, like merge requests and pipelines tell us about platform activity, but they do not tell us what value … providing. … the more meaningful long-term question really is like what is the future velocity and outcomes that we can measure with less friction, getting changes out safely.”

Rajesh BhatiaSenior Director of Engineering, Cloudflare · Leader, Cloudflare

“Our Network Firewall team audited rampartd using a multi-agent consensus process where every requirement was scored COMPLIANT, PARTIAL, or NON-COMPLIANT with specific violation details and remediation steps reducing what previously required weeks of manual work to a structured, repeatable process.”

CloudflarePublished by the organization itself (post), no named author

“It's now more than half. So more than 50% of deployments to Vercel are being made by coding agents, which is insane.”

Guillermo RauchFounder and Chief Executive Officer · Executive, Vercel