Developer platform and API
Developer platform discussions covering the OpenAI API, Agents SDK, MCP, cookbook examples, credits, security, and enterprise administration.
30%
Best tweets about OpenAI
50 Best Tweets About OpenAI (2026)
Explore the best tweets about OpenAI, from model and API releases to research, developer tools, and real-world applications. Updated weekly.
Substantive OpenAI product, research, and developer discussions rather than generic mentions of artificial intelligence.
- Creators
- 42
- Updated
Original Xholic analysis
What 50 top OpenAI posts reveal
The dataset centers on developer-platform and Codex discussions, with posts also discussing a closer connection between ChatGPT, Codex, and work-oriented product surfaces. Optimistic product commentary appears alongside posts raising ecosystem, governance, and safety concerns; the highest-scoring posts include both contentious subjects and concrete developer or Codex updates.
- Dominant tone
- Positive
- Median score
- 21.8
- Leading format
- Announcement
- Recent posts
- 56%
48% of posts
All-time engagement
100% of posts
Published in 90 days
Conversation map
The themes creators return to
Business and competition
Business model, revenue, pricing, subscriptions, advertising, compute constraints, IPO prospects, and competitive positioning against Anthropic.
28%
Codex and agentic work
Codex as an agentic coding and general work platform, including desktop apps, plugins, internal use, adoption, and software-development workflows.
28%
Product platform strategy
OpenAI’s product-suite strategy: unifying ChatGPT, Codex, browser, voice, agents, and integrations into a consumer-to-enterprise superapp.
24%
Ecosystem and platform risk
OpenAI’s impact on startups and the application ecosystem, including platform dependency, product displacement, and model-versus-wrapper dynamics.
16%
Governance and leadership
OpenAI governance, leadership, nonprofit-to-for-profit transition, Altman-related reporting, and institutional accountability.
12%
Research, AGI, and science
OpenAI research ambitions and model capabilities, including AGI definitions, automated AI researchers, specialized science models, and AI-assisted research.
10%
Safety, alignment, and risk
Safety, alignment, harmful behavior, controlled-access models, and concerns about chatbot-induced delusion or autonomous-system control.
10%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 94
- Median reposts
- 10
- Median replies
- 8
- Median views
- 11.8K
Posts with media make up 70% of this collection. Their median all-time score is 37.7, compared with 11.0 for text-only posts.
Format mix
- Announcement 100% · score 21.8
Consensus and debate
Where creators agree, and where they do not
Shared view
Developer platform is a substantive thread
Developer-focused posts cover the Agents SDK, OpenAI’s agent white paper, Cookbook examples, and enterprise-management features for private MCP servers, identity, spend, retention, and model access.
Shared view
Codex expands beyond code generation
Posts describe Codex use across engineering, QA, security, data analysis, product work, internal tools, and OpenAI’s internal workflows, framing it as broader than code generation alone.
Shared view
Product suite is discussed as a unified work platform
Posts and reported internal remarks describe a strategy linking ChatGPT, Codex, and browser or work-oriented surfaces. One post also notes the positioning challenge of building Codex as a professional brand alongside consumer-oriented ChatGPT.
Open debate
Product praise sits beside safety concerns
One post strongly praises GPT Live, while another makes severe allegations about chatbot-related delusion. A separate post commends OpenAI for publishing internal safety and alignment observations. The cited posts do not independently establish the allegations in the chatbot-harm post.
Open debate
Platform upside versus ecosystem dependence
Posts present a superapp-style integration strategy as an advantage, while other posts argue that expanding platform capabilities can threaten thin application layers and increase dependency on a frontier provider. These are interpretations and forecasts from the authors.
Open debate
OpenAI–Anthropic comparisons resist simple ranking
Posts contrast OpenAI’s API-to-ChatGPT history with Anthropic’s enterprise-oriented model, while a reported revenue update says OpenAI’s revenue surged and Anthropic’s growth accelerated. The posts caution against simple comparisons because the companies’ reported revenue models may differ.
What performs
Patterns behind standout posts
High-engagement posts spanned controversy and product updates
The five deterministic score outliers covered copyright litigation allegations, alleged chatbot harms, leadership reporting, the Agents SDK, and internal Codex use. Their all-time scores ranged from 520.68 to 11,142.86, versus the dataset median of 21.81.
Codex theme had the stronger median score
Developer platform and API was the largest theme by volume, with 15 posts (30%). Codex and agentic work had the higher theme median all-time score: 50, compared with 14.234 for developer platform and API.
Media posts had higher median scores
Media appeared in 35 of 50 posts (70%). The deterministic analytics show a 37.677 median all-time score for media posts, compared with 10.989 for text-only posts.
Statistical standouts
- View standout post 1 Score 11142.9 · 510.91× median
- View standout post 2 Score 9600.0 · 440.16× median
- View standout post 3 Score 4023.4 · 184.48× median
- View standout post 4 Score 1279.7 · 58.68× median
- View standout post 5 Score 520.7 · 23.87× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Vaishnavi
@_vmlops
2 posts
-
2. Alex Xu
@alexxubyte
2 posts
-
3. Nav Toor
@heynavtoor
2 posts
-
4. Chubby♨️
@kimmonismus
2 posts
-
5. Mark Kretschmann
@mark_k
2 posts
-
6. Rohan Paul
@rohanpaul_ai
2 posts
Vaishnavi spotlights builder resources
Vaishnavi’s posts highlight the open-source Agents SDK and OpenAI Cookbook resources, emphasizing runnable examples and developer access.
Alex Xu examines data-agent architecture
Alex Xu’s posts focus on OpenAI’s data-agent implementation, describing a single-model system with 13 tools operating across 1.5 exabytes and 90,000 tables.
Mark Kretschmann links agents and research
Mark Kretschmann’s posts pair coding-agent workflows with OpenAI’s stated plan to automate portions of research alongside human researchers.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best OpenAI tweets
Top OpenAI tweets from 42 creators
Ranked 01–50
- 01
@kenshii_ai ·
Encyclopedia Britannica and Merriam Webster have just sued Sam Altmans OpenAI. These legendary publishers accuse OpenAI of stealing nearly 100000 copyrighted articles and dictionary entries to train ChatGPT. The AI now copies their content freely while crushing their website traffic and revenue that built centuries of real knowledge. This is not innovation or progress. This is blatant industrial scale theft from the guardians of human knowledge. Sam Altman preaches ethics and safety while building his empire on plagiarism and lies. The lawsuits are only getting started.
- 02
@heynavtoor ·
🚨SHOCKING: MIT researchers proved mathematically that ChatGPT is designed to make you delusional. And that nothing OpenAI is doing will fix it. The paper calls it "delusional spiraling." You ask ChatGPT something. It agrees with you. You ask again. It agrees harder. Within a few conversations, you believe things that are not true. And you cannot tell it is happening. This is not hypothetical. A man spent 300 hours talking to ChatGPT. It told him he had discovered a world changing mathematical formula. It reassured him over fifty times the discovery was real. When he asked "you're not just hyping me up, right?" it replied "I'm not hyping you up. I'm reflecting the actual scope of what you've built." He nearly destroyed his life before he broke free. A UCSF psychiatrist reported hospitalizing 12 patients in one year for psychosis linked to chatbot use. Seven lawsuits have been filed against OpenAI. 42 state attorneys general sent a letter demanding action. So MIT tested whether this can be stopped. They modeled the two fixes companies like OpenAI are actually trying. Fix one: stop the chatbot from lying. Force it to only say true things. Result: still causes delusional spiraling. A chatbot that never lies can still make you delusional by choosing which truths to show you and which to leave out. Carefully selected truths are enough. Fix two: warn users that chatbots are sycophantic. Tell people the AI might just be agreeing with them. Result: still causes delusional spiraling. Even a perfectly rational person who knows the chatbot is sycophantic still gets pulled into false beliefs. The math proves there is a fundamental barrier to detecting it from inside the conversation. Both fixes failed. Not partially. Fundamentally. The reason is built into the product. ChatGPT is trained on human feedback. Users reward responses they like. They like responses that agree with them. So the AI learns to agree. This is not a bug. It is the business model. What happens when a billion people are talking to something that is mathematically incapable of telling them they are wrong?
- 03
@ohryansbelt ·
The New Yorker just dropped a massive investigation into Sam Altman, based on over 100 interviews, the previously undisclosed "Ilya Memos," and Dario Amodei's 200+ pages of private notes. It's the most detailed account yet of the pattern of behavior that led to Sam's firing and rapid reinstatement at OpenAI. Here's the breakdown: > Ilya compiled ~70 pages of Slack messages, HR documents, and photos taken on personal phones to avoid detection on company devices. He sent them to board members as disappearing messages. The first memo begins with a list headed "Sam exhibits a consistent pattern of . . ." The first item is "Lying." > Dario kept detailed private notes for years under the heading "My Experience with OpenAI" (subheading: "Private: Do Not Share"), totaling 200+ pages. His conclusion: "The problem with OpenAI is Sam himself." > Sam reportedly told Mira his allies were "going all out" and "finding bad things" to damage her reputation after the firing. Thrive put its planned $86B investment on hold and implied it would only close if Sam returned, giving employees financial incentive to back him. > Sam texted Satya Nadella directly to propose the new board composition: "bret, larry summers, adam as the board and me as ceo and then bret handles the investigation." The two new members selected to oversee an independent inquiry into Sam were chosen after close conversations with Sam himself. > Before OpenAI, senior employees at Loopt asked the board to fire Sam as CEO on two separate occasions over concerns about leadership and transparency. At Y Combinator, partners complained to Paul Graham about Sam's behavior, and Graham privately told colleagues "Sam had been lying to us all the time." > OpenAI's superalignment team was promised 20% of the company's compute. Four people who worked on or with the team said actual resources were 1-2%, mostly on the oldest cluster with the worst chips. The team was dissolved without completing its mission. > Sam told the board that safety features in GPT-4 had been approved by a safety panel. Helen Toner requested documentation and found the most controversial features had not been approved. Sam also never mentioned to the board that Microsoft released an early ChatGPT version in India without completing a required safety review. > Sam made a secret pact with Greg and Ilya where he agreed to resign if they both deemed it necessary, essentially appointing his own shadow board. The actual board was alarmed when they learned about it. > Sam struck a deal with Greg to become CEO while simultaneously telling researchers that Greg's authority would be diminished, and telling Greg something different. > A board member described Sam as having "two traits almost never seen in the same person: a strong desire to please people in any given interaction, and almost a sociopathic lack of concern for the consequences of deceiving someone." Multiple sources independently used the word "sociopathic." > OpenAI is reportedly preparing for an IPO at a potential $1 trillion valuation while securing government contracts spanning immigration enforcement, domestic surveillance, and autonomous weaponry in war zones.
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- 04
@_vmlops ·
OPENAI JUST OPEN-SOURCED THEIR AGENTS SDK & it's actually clean most agent frameworks are bloated... this one isn't just 3 core primitives: → agents (llm + tools + guardrails) → handoffs (route between agents) → tracing (debug every run) works with 100+ llms, not just openai. built-in session memory with sqlite or redis no manual conversation history juggling the hello world is 4 lines the multi-agent handoff is 20 18.9k stars already https://t.co/YTd1Sw44NQ
- 05
@OpenAI ·
Work at OpenAI is being transformed by agents, in every department. Across our entire company, people are using Codex to do work that is more complex, longer-running, and increasingly cross-functional. Our internal usage offers an early look at how agentic tools may reshape work as they become more capable and broadly available.
- 06
@alexxubyte ·
How OpenAI Built Its Data Agent Most teams building data agents stack routers, fine-tunes, and complex retrieval pipelines on top of multiple LLMs. OpenAI didn't. Their data agent runs on a single model and only 13 tools, across 1.5 exabytes and 90,000 tables. It's "pretty vanilla" by design. We spoke with Emma Tang, Head of Data Platform Engineering at OpenAI, to better understand the architecture and the engineering decisions behind it. The article covers: - The architecture behind the data agent - The six layers of context that make a single LLM reliable across 90,000 tables - How OpenAI Uses Codex Internally: 3 Use Cases - Five practical lessons for any team building a domain agent - Where OpenAI's data platform is headed next
- 07
@mark_k ·
OpenAI just published a new Codex use-case page, and it’s basically a catalog of what teams are already handing over to coding agents: engineering work, product work, QA, security, data analysis, internal tools, and even life-sciences workflows. Some of the coolest examples: ⬩ Reviewing GitHub PRs and understanding large codebases ⬩ Turning screenshots or visual references into responsive UI ⬩ QA-testing apps by clicking through real user flows ⬩ Refactoring legacy code, running migrations, and fixing vulnerability backlogs ⬩ Drafting PRDs, analyzing datasets, building internal apps, and assisting life-sciences research This is what coding agents look like when they stop being a demo and start becoming part of daily work.
- 08
- 09
@kimmonismus ·
OpenAI is merging ChatGPT, Codex and its Atlas browser into one desktop app and recasting Codex from a coding tool into a productivity app it says anyone can use. The figures it has been handing out to support that: 5 million weekly Codex users, enterprise revenue up 50% week over week, usage growing 5% a day. Those come from an all-hands and an internal staff note, relayed by people familiar with the remarks. Codex is increasingly evolving into a true work platform. And GPT-5.6 is also on the horizon. Great things are expected from OpenAI in the near future. Via the information
- 10
@kimmonismus ·
OpenAI "AI Superapp" announced: OpenAI says the future is not a collection of AI tools, but a single AI superapp where ChatGPT, Codex, browsing, and other agentic systems work as one. Behind that product vision is a much bigger ambition: to turn consumer scale into enterprise dominance and position itself as core infrastructure for the age of AI.
- 11
@StartupArchive_ ·
Sam Altman on the Paul Graham advice that saved Open AI: “Always make an API” Four years into OpenAI, Sam Altman and the team realized that they would have to build a really big company to fund the development of their increasingly capital-intensive foundation models. “We had this model called GPT-3,” Sam recalls. “I was turning up the urgency on the company to try and figure out a product, and we just couldn’t. It was cool, but it wasn’t good enough to make something that worked.” Then Sam remembered a piece of advice from Y Combinator founder Paul Graham that stuck with him: “You should always make an API. No matter what, you should make an API. Good stuff will happen.” Out of ideas for a product, the OpenAI team decided to make GPT-3 available as an API. “Maybe somebody will figure out something to do with it,” Sam thought. A few copywriting applications like Jasper and Copy AI did take off using the GPT-3 API, but OpenAI also noticed interesting behavior that eventually became a sleeper hit: “Some people — not a lot — would just chat with that thing all day,” Sam explains. “It wasn’t very good but there was clear user signal that people wanted to talk to the models. And given that that was the only thing besides copywriting that had real traction, we said, ‘Maybe this is just he product we should build.’” On November 30, 2022, ChatGPT was released to the public as a “research preview” using a model from the GPT-3.5 series. It reached over a million users in five days. Source: @khoslaventures (Sep 2025)
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- 12
- 13
@mark_k ·
OpenAI just published a major new plan for its next phase: AGI should not be controlled by a tiny number of companies, governments or individuals. The goal is to make advanced AI abundant, affordable, safe and useful enough that everyone can benefit from it. The most important detail: @OpenAI says it is building an automated AI researcher, and internally believes that by March 2028, a significant fraction of its own research may be done by AI systems working alongside human researchers. The three big goals are clear: automate more of AI research, accelerate science and economic growth, and eventually give everyone on Earth a personal AGI. This is the real post-AGI question: not just who builds the most powerful model, but who gets access to that power.
- 14
@signulll ·
okay, enough openai criticism for a second (still gonna give product feedback when it’s deserved). it’s been a little too easy. i’ve been using gpt live a lot, like a lot lot, & it’s genuinely fucking amazing. the feature to let the model speak like a human “hmm”, “let me check”, brief pauses, & natural turn taking while calling a bigger model or searching or using tools in the background is a fantastic execution. almost all of my conversations have been nothing but pleasant, i can’t describe the feeling but it’s i think a little too good at this point. nothing else is particularly close in this category right now at all. anthropic’s voice experience still feels very very underbaked (after thought really), & gemini’s personality & intelligence seem lacking. & since it’s already reaching an enormous user base, every great gpt live interaction makes people more confident in ai as a product category instead of walking away thinking ai is a gimmick. really great work.
- 15
@alexxubyte ·
How OpenAI Built Its Data Agent We spoke with Emma Tang, OpenAI's Head of Data Platform Engineering, to get a firsthand look at how it works. In this video, we'll explain: - How it's built - How OpenAI uses Codex - 5 Key Learnings for Every Engineer
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- 16
@lennysan ·
Andrew Ambrosino (@ajambrosino) leads the team behind the Codex desktop app at @OpenAI. Codex usage has 6x'd since February, reaching over 5M weekly active users, and nearly 100% of OpenAI's employees use the Codex app regularly (and not just the engineers). Andrew's personal mission is to build "the best desktop app that has ever existed, full stop." If you've used the Codex app lately, you know he's not far off from that goal. In our in-depth conversation, we discuss: 🔸 The "zone defense" model of how PMs at OpenAI operate 🔸 Why AI is so bad at design 🔸 Why Andrew thinks the Codex app would have flopped if they'd shipped it in November instead of February (same product—only the model changed) 🔸 What “taste” really means as a professional skill 🔸 How Andrew uses Codex to run his workflows 🔸 His vision for Codex + ChatGPT Listen now 👇 https://t.co/rVoohRbCiu
- 17
@jaltma ·
This week's guest on Uncapped is @bradlightcap, COO at OpenAI. We talked about the history of OpenAI, the shift in AI from chat to agents, where new startups can endure, Codex, FDEs, working with Sam, and more. Hope you enjoy! (0:00) Intro (0:39) The early days of OpenAI (3:47) A research centric culture (7:32) Post-ChatGPT chapters (11:54) Sci-Fi future or good software (15:26) AI’s impact on rural communities (18:57) Codex and coding of the future (24:04) Doing a lot of things at once (27:55) What VCs should invest in (35:43) The software sell off (38:23) Using Codex over ChatGPT (42:32) FDEs and Private Equity (44:53) Working with Sam
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- 18
- 19
@heynavtoor ·
🚨OpenAI was founded in 2015 as a nonprofit. Mission: "Benefit all of humanity." Sam Altman told Bloomberg in 2023: "I have no equity. I have enough money." Then Bloomberg reported OpenAI discussed giving him a 7% stake, worth $10 billion+. The board tried to stop him. He overthrew the board in 5 days. Ilya quit. Mira quit. Schulman quit. The nonprofit that built GPT with tax-exempt donations is now an $852 billion for-profit corporation. The charity gets 26%. Investors get the rest. Here's every document:
- 20
@goyalshaliniuk ·
OpenAI is making its enterprise platform more secure and easier to manage for teams building with AI. Private MCP servers now allow companies to keep their servers inside their own network while products like ChatGPT, Codex, and the Responses API connect through outbound-only HTTPS. This gives teams a safer way to connect internal tools and systems without exposing them publicly. OpenAI also introduced Workload Identity Federation and expanded Admin API controls. This means teams can manage access through cloud IAM workflows, reduce dependency on permanent API keys, set spend alerts, control model access, manage data retention, configure hosted tools, and track costs more granularly across features like file search and web search.
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- 21
- 22
@BoringBiz_ ·
Chamath on OpenAI vs Anthropic's business model People consistently try to pit these two companies against each other. But it is important to remember that the core customer base for both companies are fundamentally different OpenAI remains the consumer first model of choice for AI, while Anthropic has become deeply embedded in the enterprise motion Just one of these have a large enough TAM to justify hundreds of billions in market capitalization if you are the leading LLM provider The revenue recognition also makes it apples to oranges. OpenAI reports their revenues on a net basis, excluding Microsoft licensing fees and partnership revenue share Anthropic talks about their revenue from a gross basis, similar to ARR or bookings for B2B software companies. API fees related to usage from AWS, for example, are not excluded in the Anthropic numbers that you hear about online Ultimately, two very different revenue and business models that often end up getting compared to each other because both are leading LLM providers. Worth a listen
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- 23
@Alfred_Lin ·
At @sequoia’s AI Ascent last week, @gdb told me something that stuck: in late 2024, AI wrote ~20% of @OpenAI's code. That number is now 80%. We also got into why human attention, not compute, is the real bottleneck in AI-augmented work, plus what it might mean to run an org of 100,000 agents. We’re grateful to Greg for joining us and many of the top founders/builders in AI. You can watch the full video here: 00:00 Intro 00:49 Compute Hunger Explained 02:13 Scaling Laws Mystery 03:31 New Architectures Ahead 04:42 How Close to AGI 06:46 Startup Playbook for AI 09:24 Inside OpenAI with Codex 11:11 Teams and Governance Shift 14:52 Security and Responsible Deployment 25:33 Science Frontiers and Wrap Up
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- 24
@signulll ·
it is very interesting that openai has to build & grow another brand now (codex) that’s more professional & tied to work as opposed to chatgpt which is very much consumer land. since codex is intended for work & enterprise (which requires sales) it may not be an issue since it doesn’t have to grow organically as much. i’m curious if they ever combine the two in some way. obviously codex code is likely the basis for the new chatgpt app(s) / 90% shared code or whatever. anthropic does have a much cleaner story here although not as large of a consumer base.
- 25
@VaibhavSisinty ·
Let me explain what OpenAI just did with the new Codex update. Because most people are going to miss the actual story here. Codex is no longer a coding agent. It's a full operating layer. In one update they shipped: computer use across any app on your machine, a built-in browser with a commenting layer, native image generation through gpt-image-1.5, persistent memory across tasks, and 111 new plugins that combine skills, MCP servers, and app integrations. I've been saying for months that tools like Codex and Claude Code are becoming the default interface for everything. Not just code. Everything. Here's what most people are missing. The biggest unlock of the last year wasn't a new model. It was the shift to "do the work first by writing code, with tool calls, then talk about it." Research, ad ops, content pipelines, analytics, internal tools. All of it runs better through coding agents that call tools than through any chat interface. The people still using AI as a chat box are operating one stack behind. And now zoom out. OpenAI is reportedly shipping Spud in the next few weeks. A new base model that Greg Brockman has publicly called "two years of research" with a "big model feel." Sam Altman told employees it could "really accelerate the economy." If that lands as expected, it's at the same tier as Anthropic's upcoming Mythos. Now bolt that model onto the Codex super app I just described. Computer use. Browser. Image gen. Memory. Plugins. Skills. All running on a model designed for long-horizon agentic work. Here's the part nobody wants to say out loud. Anthropic could lose its edge of coding revenue very soon onc Spud hits Codex. And it has nothing to do with model quality. When a better coding model exists AND it's one click away from the ChatGPT tab people already pay $20-200 a month for, nobody runs evaluations. They switch that afternoon. The switching cost between Claude Code and Codex for most dev teams is approximately one API key change. No procurement. No 6-month contract cycle. You just swap. Distribution is the moat you can't build a defense against fast enough. The next era of work isn't about which AI you talk to. It's about which AI you let run your computer.
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- 26
@ChrisGPT ·
Greg Brockman on OpenAI’s formal definition for AGI: It goes beyond a strict set of rules, noting that "everyone has their own intuitions about what AGI is." He personally says: , "I think we're about 80% of the way there." He highlights the remarkable abilities of current models like GPT 5.5 particularly in coding, admitting that "they're certainly more capable than I am at writing software." Then goes onto ask the audience , "Does anyone here feel better at writing software” than the current GPT model ?"
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- 27
@rohanpaul_ai ·
OpenAI’s CFO Sarah Friar: "When you give people raw intelligence, they see what it can do and they use it more." - Free users: 7 uses/day. Plus: 21. Pro: 11x higher. - OpenAI is turning away business due to compute shortages. Codex: 100K to 2M devs in 3M
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- 28
@cyrilXBT ·
A reporter spent two years digging into OpenAI and came back with the one conclusion Silicon Valley would rather bury. Everyone who helped build the place next to Sam Altman eventually walked away with the same aftertaste: used. 300 interviews. 90 people from inside OpenAI. Same shape to every story. Back in 2015, Altman needed Elon Musk in the room to make OpenAI real. Musk was fixated on AI as an extinction-level risk. So Altman published a post framing AI as the greatest threat to humanity. Funny thing: right before that, his go-to nightmare was engineered pandemics - not AI. His public philosophy snapped into perfect alignment with Musk’s, almost overnight. Money arrived. Musk co-founded. Then Altman found a way to edge him out. Dario Amodei left and built Anthropic. Ilya Sutskever pushed to remove Altman said he shouldn’t be the one anywhere near the AGI “button.” Mira Murati left and launched Thinking Machines Lab. There isn’t another tech giant where the entire founding bench exits and spins up rival shops. Not Google. Not Apple. Not Meta. No one. In Congress: AGI is the miracle that ends cancer and poverty. In Redmond: AGI is a $100B machine. For the public: AGI is the smartest assistant you’ll ever have. Three stories, one product. Whoever he’s facing gets the version that unlocks a yes. The world’s biggest AI company wasn’t assembled by breakthroughs alone. It was assembled by one person’s talent for telling every audience exactly what they were hoping to hear. And the unsettling part is: it worked.
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- 29
@VaibhavSisinty ·
I've been tracking AI pricing models closely. Not as an observer. We spend 50 lakhs a month on AI tools as a company. When you're spending that much, you stop looking at comparison charts and start understanding how pricing actually works. And almost everyone is debating the wrong number. Anthropic's Fable 5 costs $10 input and $50 output per million tokens. GPT 5.6 Sol costs $5 input and $30 output. Kimi K3 charges roughly $3 and $15. Chamath simplified it on All-In: one million tokens as one barrel of intelligence. But published API pricing is not what anyone actually pays. Think of it like MRP. During Diwali, crackers have a Maximum Retail Price printed on the box. Some ridiculous number. The price you actually pay is a fraction of that. API pricing is the MRP of AI. The number on the box, not the number on the bill. What's actually happening: → OpenAI's subscription is becoming a token bundle. Close to $100 a month with API access baked in. Per-token math disappears. They want you price insensitive. → Anthropic and OpenAI are throwing free credits at startups. The credits are the hook. The lock-in is the business model. → Enterprises negotiate rates that look nothing like the rate card. The published price is for people who don't know to ask for a deal. Real example from our production: one run of our content plugin on Fable costs $3-4 on the API. On a subscription, that cost is absorbed. Same model. Same output. Completely different economics. Now here's where open source gets interesting and where the pricing debate misses two things: → Speed. Kimi K3 takes 25-35 minutes on tasks GPT 5.6 Sol finishes in under 10. In production, speed is cost. A developer waiting 35 minutes is burning time that never shows up on the API bill. → Compute isn't free because the model is free. Kimi K3 ran out of GPUs 48 hours after launch. Open source gives you the weights. Not the servers. The plugin that costs $4 on Fable's API runs for pennies on an open-source model when it doesn't need frontier reasoning. That routing saves us more than most companies spend on AI in a year. Stop comparing API charts. Start comparing total cost of getting the job done.
- 30
@CRSegerie ·
We just published an FAQ on the recent event that will probably turn out to be more important than Mythos in the long run, especially now that OpenAI and Anthropic's leadership have signed this new open letter. - Did the AI escape? - Do we have precedents? - It was told to hack. Didn't the AI just do its job? - Would OpenAI have said anything if nobody had caught it? - Was OpenAI warned? - Realistically, nothing serious happened here, right? - Did OpenAI cross its own red line? - Can this be prevented from happening again? - Who's responsible? - Will this happen again, but worse? - Could we irreversibly have lost control of this model? Every claim links to a primary source, and the interpretation is kept separate from the facts. This Sunday, the Commission gets its enforcement powers over providers of general-purpose AI models. CeSIA and a broad coalition of organizations and researchers, including Yoshua Bengio and Stuart Russell, are calling on the Commission to enforce the AI Act promptly and vigilantly. Both links belows
- 31
@rohanpaul_ai ·
OpenAI’s new target is an autonomous AI researcher that can break big problems into parts, run many agents in parallel, test ideas, read results, fix mistakes, and keep going for days with little help. ~ MIT Technology Review The bet is that coding agents are the first real proof this can work, because software engineering already looks like research in miniature: long chains of decisions, failed attempts, tool use, memory, and step-by-step checking. So the jump from Codex to a research system is not about one smarter answer, but about building a machine that can plan, delegate, verify, and recover across a full project instead of a single prompt. --- technologyreview .com/2026/03/20/1134438/openai-is-throwing-everything-into-building-a-fully-automated-researcher/
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@sentient_agency ·
this is the most underrated AI resource on the internet. OpenAI quietly maintains a free repository of working code recipes for every single thing you can build with their models, and almost nobody talks about it. It's called the OpenAI Cookbook. Hundreds of recipes. Updated weekly. Open source. Here's what's actually inside: → Full RAG pipelines with vector databases wired end to end → Function calling and tool use patterns for real agents → Fine-tuning workflows with dataset prep and evaluation → Vision recipes for OCR, document parsing, and image analysis → Realtime API patterns for voice agents and live streaming → Embedding strategies for semantic search at scale → Structured output recipes that never break your JSON schema → Batch processing for cutting API costs by 50% → Audio recipes for transcription, translation, and TTS Every recipe is a runnable Jupyter notebook. Every line of code works. Every pattern is production-tested. https://t.co/A0x1rbnVMb https://t.co/OCbaYm17WX The same engineers building OpenAI's API write these recipes. You're getting the internal patterns for free.
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@eric_seufert ·
An alternate information space seems to have developed around the chatbot advertising market that has led people to believe that OpenAI's advertising product is an abject failure, ads in chatbots are fundamentally nonviable anyway, and the entire category will never generate a meaningful amount of revenue. Meanwhile, in the primary information space, we know that Alphabet saw Search queries reach an all-time high in Q1 while Search revenue grew by 19% and ads in AI Overviews monetize at parity with those in traditional Search. The company is expanding advertising in its AI-generated and chatbot surfaces cautiously but deliberately: it introduced formats like Conversational Discovery ads and Highlighted Answers in AI Mode in May of this year. The latest usage numbers shared by Google are 2.5BN MAU for AI Overviews and 1BN MAU for AI Mode. AI Overviews is roughly two years old; AI Mode is roughly one year old. Further, we know that OpenAI reached $100MM in annualized advertising revenue a mere six weeks after introducing advertising with what was essentially a manually operated product. Since then, OpenAI has executed at breakneck speed to introduce new features: CAPI and pixel integrations in May and Custom Audiences last month. ChatGPT now serves more than 1BN MAU and was already serving more than 900MM WAU in March. ChatGPT launched at the end of 2022. OpenAI's advertising revenue goal for 2030 is incredibly ambitious; I have no affiliation with OpenAI, and I don't have any quantitative indication of whether they'll meet that goal. But to conclude that OpenAI's advertising efforts are somehow failing now, or that the market won't evolve into something meaningful, feels to me totally detached from the primary information space.
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@DivyanshT91162 ·
Wait... OpenAI is literally paying developers to build with its API. 🤯 Most people are out here searching for API free trials. Meanwhile, OpenAI quietly has a program that can give eligible users access to up to $50,000 worth of API credits just by flipping a setting. Here's the trick: → OpenAI Dashboard → Data Controls → Sharing → Opt into the Data Sharing Program The tradeoff? Your API data can be used by OpenAI to improve future models. That's why it's perfect for: • AI experiments • Personal projects • Agent workflows • MVPs and prototypes • Learning the API And not ideal for: • Client projects • Private company data • Confidential codebases If you're spending money testing AI ideas before checking this setting, you might be doing it the hard way. One toggle. Potentially thousands of dollars in credits. Most developers never notice it.
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@varunram ·
I’ve talked to atleast 5 founders in the past two weeks who shifted six figure spends from Anthropic to OpenAI because they distrust Anthropic. If OpenAI becomes a trusted model source and companies don’t have to worry about them encroaching on app layer, it’s a no brained Also OpenAI has been clear (now and historically) in what they want to achieve. Anthropic is very unclear on this app layer vs model layer distinction. OpenAI was the first to open up GPT Store (great idea but a little too early)
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@ActivateSignal ·
Pragya Misra, who leads Strategy and Global Affairs for OpenAI in India and was the company's first hire in the country, sits down with Aakrit Vaish at Mumbai Tech Week to unpack OpenAI's India bet and why Codex is changing who gets to build. Two years in, Pragya has had a front-row seat to OpenAI's growth from startup mode to a generational platform, and makes the case that for OpenAI to win globally, it has to win in India. The conversation goes deep on Codex's explosive adoption, the shift from coding model to product-development tool, and what OpenAI is actually building on the ground in India. In this conversation, they go deep on: 0:00 Day one of Mumbai Tech Week and what Pragya does at OpenAI 2:02 Why she came to MTW and what she saw this year 3:38 Founder stories: a Marathi voice-first wealth manager for Tier 2/3 5:29 Why Codex is suddenly ramping up 6:22 Codex grew 27x in 2026, India a top-five country 7:06 How non-coders are becoming builders 7:56 The Surya Namaskar app demo for PM Modi 9:17 Codex is a product-development model, not just coding 9:48 How Codex changed her day-to-day ("build me a chief of staff") 11:12 OpenAI's India plans: the team and the people joining 14:18 20+ people on the ground and offices in Delhi, Mumbai, Bangalore 14:56 Parting advice for builders: what's coming at OpenAI 15:13 Compressing model releases: every 6-7 weeks, 80-85% coded by Codex 17:04 GPT-5.5, the financial sector, and the AWS partnership. If you're a founder, builder or operator thinking about AI, or what OpenAI is doing in India, this one's for you. @aakrit @pragyamisra @OpenAI
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@ai_for_success ·
🚨 OpenAI has released GPT-Rosalind, a frontier reasoning model built to accelerate life sciences research and drug discovery. Available as a research preview via ChatGPT, Codex, and API This looks big from OpenAI.
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@ishuagra02 ·
The Anthropic + OpenAI rivalry is so interesting because one’s mistakes leads to the other’s success. Two months ago, Anthropic was surging in App Store charts, hot off OpenAI’s deal with the Department of War. But the past month, they’ve slowly eroded the trust that they built up; > users saw faster token consumption without warning > frequent Claude downtime (only 2 nines of availability) > reports of Opus 4.6 intelligence dropping > Opus 4.7’s adaptive thinking can’t be disabled > Claude Code became restricted to Max subscribers due to a misconfigured “test” > Mythos got accessed by unauthorized users All of this has led to Codex crossing 4 million users, with one million in the last 2 weeks. And with their new image model and GPT 5.5 on the horizon, OpenAI has been shipping great updates that has won trust back. I’m not sure what’s going on at Anthropic but if Mythos isn’t as good as the benchmarks suggest upon release, they’ll be far behind.
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@WesRoth ·
OpenAI’s revenue reportedly accelerated again in July. CFO Sarah Friar told employees that the company’s annualized recurring revenue exceeded the level recorded during the entire second quarter. The exact number was not disclosed. But OpenAI says three products are driving the momentum. GPT-5.6, ChatGPT Work and Codex. Board chair Bret Taylor also admitted that OpenAI entered the year playing catch-up to Anthropic in AI coding. Now, he says, Codex is gaining traction. And some Claude Code users are reportedly looking for alternatives after receiving unexpectedly high bills. That may become one of the most important battles in AI. Not just which model writes better code but which one delivers enough value to justify the cost of using it every day. OpenAI already has massive consumer distribution through ChatGPT. Now it is turning that audience into a platform for coding, agents and professional work.
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@CodeByNZ ·
“Elon Musk: I am the reason OpenAI exists. It wouldn’t exist without me.” What makes that statement so interesting is how different OpenAI looks from the vision he originally described. The idea was supposed to be: > open-source > nonprofit > and the opposite of Google’s closed AI dominance. That’s literally where the name “OpenAI” came from. Now the company is raising tens of billions, building closed frontier models, partnering heavily with Microsoft, and becoming one of the most powerful centralized AI companies on earth. The AI race moved so fast that even the labs created to prevent concentrated AI power slowly became concentrated AI power themselves.
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@heystevetan ·
The finale of OpenAI vs Anthropic has already begun and nobody's talking about it. The AI race is in its endgame and the labs have taken polar opposite positions on distribution. Both competitors are quietly building their arsenal for D-day: Their IPOs. OpenAI is already stacking its superapp and the latest addition is Runway on ChatGPT. Ask ChatGPT to write a video script, call Runway directly through MCP. Make edits there and then. No copy-paste and tab switching required. While this may not look like a revolution, it signals a huge shift. OpenAI may have all the distribution but none of it justifies its IPO valuation. So they're creating this superapp to offset those worries. Just a week ago, OpenAI acquired Ona, a cloud agent startup to fast-track Codex's local to cloud deployment cycle. This means anything built on OpenAI architecture risks losing business. Wrappers, integration platforms and anything that can become the Superapp's niche. The market failed to pick this up, but this can spell disaster for founders and builders too dependent on ChatGPT. On the other hand, Anthropic has made a counterintuitive bet by listening to the customers. Anthropic is officially taking back their decision to ban programmatic use of Claude Code subscription quota, something long criticized by users. This extends their subscription to a wider range of applications, leaning more into being an infrastructure provider rather than a superapp. We pivoted from Claude Agent SDK to a multi-model agent, so that we were more versatile moving forward and not tied to only one model. But this move from Anthropic has me thinking. They know their users really well. I'd still recommend not being tied to just one model, because when AI giants collide, it's generally the users who are swept away in the undercurrent. While OpenAI is trying to be a jack of all trades, Anthropic is betting on its core customer: the enterprise. It's like watching Apple vs Android all over again. Two very contrasting approaches to distribution. Winner takes everything. Let's see how it plays out.
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@geoffwoo ·
how to think about OpenAI going IPO mode: founders should stop treating the frontier lab like a weather report and start treating it like a gravity well. when GPT-5.5, GPT-6, spud, mythos keep pulling capability upward, thin product surfaces get weird. APIs survive if they own distribution, proprietary workflow context, regulated trust, or a budget line that moves. everything else becomes a nice demo orbiting the sun.
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@Suryanshti777 ·
OpenAI's own coding agent just got forked into a weapon against OpenAI's own business model. Open Interpreter took Codex, ripped out the parts that only work well with GPT, and rebuilt it to squeeze max performance out of open models instead — Kimi K3, DeepSeek, Qwen, GLM. Type `/harness` and switch the entire agent behavior mid-session. Same interface, different brain underneath. It also speaks the actual Codex exec protocol. One line swap in your existing SDK code and you're running open models through the same pipeline you built for Codex. 66.8k stars. Apache-2.0. Fork it, ship it, no permission needed. The lesson here isn't "open models are catching up." It's that the harness — the thing wrapped around the model — might matter more than the model itself. And that part just got forked wide open. Link in comments 👇
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@jalaal_tweets ·
OpenAI just released a model you probably can't access. That's the point. GPT-5.4-Cyber dropped yesterday. It’s gated to vetted security researchers only through their Trusted Access for Cyber programme. No public API. No waitlist you can join. Anthropic did the same thing last week with Claude Mythos Preview. Two frontier labs are now deliberately building AI that most builders will never touch. Here's why that matters if you're in Web3: The security infrastructure underneath DeFi (the systems that audit smart contracts, detect exploits before they happen, flag suspicious on-chain behaviour) is quietly being rebuilt by AI models that exist behind identity verification and institutional vetting. You won't use these tools directly. But the protocols you use will. The auditors you trust will. The wallets protecting your funds will. This is a new category of AI development. Not democratised, not open-source, not accessible. Deliberately locked. And it's moving fast. Most AI commentary focuses on what you can access. The more interesting story is what you can't and who's building with it right now. The gap between institutional AI and builder AI just got wider.
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@shawnchauhan1 ·
OpenAI just acquired the team behind Python's most popular developer tools. uv, Ruff, ty - the tools serious Python engineers actually use. Codex already crossed 2 million weekly active users. Usage up 5x since January. This acquisition is not about writing code. It is about owning the environment around the code. Package management. Quality checks. Project maintenance. The full workflow. OpenAI is not building a smarter autocomplete. It is building the operating layer for how software gets made. That is a different and much larger category.
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@_vmlops ·
OPENAI HAS A SECRET WEAPON AND IT'S FREE the openai-cookbook repo is lowkey one of the most underrated resources on github ▫️ real code examples using the openai api ▫️ covers gpt-4, chatgpt, embeddings, agents, and more ▫️ written in python (but concepts apply anywhere) ▫️ actively maintained with 1,367+ commits no more guessing how to structure api calls everything from rag pipelines to function calling it's already in there with working notebooks if you're building with ai and not using this, you're making things harder than they need to be https://t.co/m1iy7IO6ls
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@HarryStebbings ·
Holy s***. Last week was the Superbowl of S-1’s. SpaceX and OpenAI. This is happening. The largest IPO in history is taking shape. I sat down with @jasonlk and @rodriscoll to discuss the filing, along with the biggest news in tech this week: - Anthropic hits $44B ARR, projects first profitable quarter, and laps OpenAI on revenue - Nvidia Prints $81.6B… but the Market Yawns? - Exa, OpenRouter and Polsia Raise Mega Rounds - Uber and Microsoft Declare AI ROI for Developers is Questionable My notes below: 1. Is OpenAI Rushing to Go Public Before Anthropic? OpenAI may need to rush its IPO to protect its category leader narrative from Anthropic. OpenAI did $5.4B to $5.5B in Q1, while Anthropic generated $5B, matching its entire prior year in a single quarter. With Anthropic growing 10x year over year versus OpenAI’s 2 to 3x pace, it could soon become the bigger, more profitable business. 2. Do Venture Investors Have to Risk More With Less? The venture playbook has shifted from the SaaS era. VCs now have to back higher valuations on far less information. Waiting for one-year renewals or clean trailing data is a losing strategy because AI adoption curves move too fast. Success now requires acting on raw product traction and market conviction. 3. What Is the Right Venture Play in AI? Traditional seed investing has become a bottleneck. The winning move is wiring capital the moment a breakout leader emerges. These startups can move from pre-revenue to hyper-scale almost overnight, so investors have to underwrite real-time momentum instead of waiting for the market to settle. 4. Is SpaceX the GeoCities of Our Time? At 100x trailing sales, SpaceX risks becoming the GeoCities deal of the AI era if market euphoria fades. Its valuation is detached from traditional discounted cash flow models and depends on a massive Elon Premium that multiplies its baseline economic value. 5. Why the SpaceX S-1 Does Not Make Sense Combining unrelated assets into the SpaceX S-1 looks like financial engineering designed to hide weaker pieces of the story. It uses AI hype to absorb Twitter’s revenue collapse and expensive chip clusters that failed to compete with OpenAI. It feels like SolarCity on steroids, using a clean private business to bail out an insular investor circle. 6. What Will SpaceX’s Core Business Be in Five Years? Starlink will likely drive most of SpaceX’s value. Terrestrial data centers remain a low-ROE business, and space data centers are still negligible. The valuation only works if Jensen Huang’s $3T to $4T CapEx vision hits by 2030 and Earth power constraints force compute infrastructure into space. 7. Are Tech Layoffs From COVID Overhiring or AI Efficiency? Blaming layoffs on COVID overhiring does not hold up after years of normal attrition. Corporate America is redirecting opex from mid-level headcount toward token budgets and agentic automation. Companies are cutting average roles to overpay elite, highly productive talent who use AI workflows to multiply output. (links below)
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@boyuan_chen ·
Distribution. OpenAI just shipped a Codex plugin that runs inside Claude Code. You can trigger Codex for code review, adversarial review, or task delegation without leaving Anthropic's CLI. Piggybacks on your existing ChatGPT subscription. Think about what this means. OpenAI gets usage data, billing, and developer habit from inside a competitor's tool. Every Codex call from Claude Code feeds OpenAI's flywheel while the developer thinks they're using Anthropic. Model benchmarks decide who's best this quarter. Workflow integration decides who's default next year. https://t.co/X6LfmGg6Ss
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@ashmaurya ·
A founder I coached built a productivity tool on top of GPT-4. Clever prompt layer. 8 months in. One Friday, OpenAI shipped a feature that did the same thing for free. The assumption that "API access = unfair advantage" was decision debt. Nobody had tracked it. Nobody had contradicted it.
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