3 People with 100 AI Programmers, Burning Through $1.3 Million a Month! OpenAI: I'll Foot the Bill

marsbitPublished on 2026-05-17Last updated on 2026-05-17

Abstract

In a striking demonstration of AI-powered development, Peter Steinberger (creator of OpenClaw) shared that his three-person team spent $1.3 million in one month to run approximately 100 AI agents (primarily Codex instances). OpenAI covered the cost. The expenditure consumed 6.03 trillion tokens across 7.6 million requests. Steinberger argues that, with "fast mode" disabled, the cost falls below that of a single engineer while providing significantly greater output. This "cloud programmer army" handles core but tedious software engineering tasks: reviewing pull requests, finding security vulnerabilities, deduplicating issues, fixing bugs, monitoring benchmarks, and even generating PRs after meetings. This shifts AI's role from merely writing code to maintaining the entire collaborative fabric of a project. Steinberger's tool, CodexBar (a macOS menu bar app), tracks usage and costs across various AI coding services, highlighting how token consumption is becoming a key metric—a new "means of production." The experiment poses a profound question: if token cost ceases to be a barrier, how will software development transform? As model prices fall, the capability for small teams to leverage large numbers of AI agents could become commonplace, fundamentally altering the scale and speed of development. The future, Steinberger suggests, is arriving rapidly.

Peter Steinberger

Editor: Solomon

[New Zhiyuan Report] 3 people, 100 AI agents, burning through $1.3 million a month — The father of OpenClaw has turned software development into an "AI assembly line," with OpenAI picking up the tab.

While others show off their pay stubs, he shows off the bill — $1.3 million a month!

That's nearly 9 million RMB per month. It's left netizens utterly dumbfounded.

OpenClaw father Peter Steinberger casually posted a screenshot on X.

Peter Steinberger

But the numbers on the screenshot were anything but casual:

30-day spend: $1,305,088.81. Consumed 603 billion tokens. Made 7.6 million requests.

You read that right, 1.3 million U.S. dollars. And it's not a quarterly AI budget for some big tech company — it's the monthly usage of a three-person team.

Even more explosive: OpenAI is reimbursing this cost.

The comment section instantly went wild.

Some were amazed, some skeptical, some whipped out their calculators to figure out "how many programmers this equals."

Steinberger himself calmly responded: "With fast mode off, my cost is less than an engineer, and it really helps a lot more."

Translation: — It's genuinely cost-effective!

Other netizens were shocked by the $400k/month engineer — "The San Francisco job market is insane."

Netizen comment

Netizen comment

Others were curious about where this massive token usage went.

Peter responded that most was used for OpenClaw development.

Netizen comment

A Cloud-Based Programmer Army

The most outrageous thing is that Pete's small team only has 3 people.

They have about 100 Codex instances running long-term in the cloud, handling the dirtiest, most grueling, most mind-numbing work in software engineering —

Reviewing PRs, finding security vulnerabilities, deduplicating issues, fixing bugs, monitoring benchmarks, posting to Discord upon discovering regressions, even opening PRs directly after listening to meetings.

Thus, AI isn't just "helping you write code," but is infiltrating every crevice of software collaboration.

This is terrifying.

Because what's truly expensive in software development is communication, comprehension, context switching, review, regression, fixes, waiting, and repetitive tasks.

In the past, a team spent a huge amount of time each day on these things that don't seem like "creation" but without which the project would rot.

Now, Peter has tossed all these processes to a bunch of AI agents at once.

This is AI starting to maintain the nervous system of an organization for you.

Illustration

There's another important detail in this screenshot: it's not the OpenAI backend, but CodexBar made by Peter.

CodexBar is a macOS menu bar tool for tracking usage windows, credits, costs, and reset times for various AI programming tools.

It supports a bunch of services like Codex, Claude, Cursor, Gemini, Copilot, etc.

What used to be in a programmer's menu bar? CPU, memory, battery, network speed.

Now there's one more thing: tokens. Tokens are becoming a new "means of production."

CodexBar

A Final Word

$1.3 million a month, 3 people, 100 AI agents.

Ponder this set of numbers — three living humans, leading a hundred digital employees who don't eat, sleep, or demand raises, doing the work of an entire engineering team.

Some felt invigorated after reading this: AI finally isn't just a decorative vase for chatting! Others felt a chill down their spine: Wait, so what do we coders do in the future?

But honestly, what keeps me up at night is Steinberger's casual remark: "I'm exploring what software development would look like if token cost wasn't an issue."

Peter Steinberger

Everyone, he said "if."

The problem is, this "if" is visibly turning into "when" at an astonishing speed.

The work that costs $1.3 million today, after one price cut for models, becomes $130k. Another cut, $13k.

On that day, having 100 AI agents working simultaneously is no longer a game exclusive to Silicon Valley big shots, but a basic operation for any three-person startup team.

Three young people in a garage, holding a hundred tireless AI programmers — this image, just thinking about it is absurd.

Peter Steinberger has revealed the bottom card.

On the card it says: The future is already knocking, and it doesn't plan to wait for you to be ready.

References:

https://the-decoder.com/for-1-3-million-a-month-openclaw-founder-peter-steinberger-runs-100-ai-agents-that-code-review-prs-and-find-bugs/

https://x.com/steipete/status/2055346265869721905

https://developers.openai.com/codex/speed

This article comes from the WeChat public account "New Zhiyuan", author: New Zhiyuan

Trending Cryptos

Related Questions

QHow much money did the three-person team spend on AI development in one month, and who covered the cost?

AThe three-person team spent $1,305,088.81 (approximately 1.3 million USD) in one month on AI development, and the cost was covered by OpenAI.

QWhat is the name of the tool Peter Steinberger created to track AI development costs and usage?

APeter Steinberger created a tool called CodexBar, a macOS menu bar application that tracks usage windows, credits, costs, and reset times for various AI programming tools like Codex, Claude, Cursor, Gemini, and Copilot.

QApproximately how many AI agents (instances) does Peter Steinberger's team run for development tasks?

APeter Steinberger's team runs approximately 100 AI Codex instances to perform various development tasks.

QWhat kinds of software development tasks do the AI agents in the article handle?

AThe AI agents handle tasks such as reviewing pull requests (PRs), finding security vulnerabilities, deduplicating issues, fixing bugs, monitoring benchmarks, reporting regressions to Discord, and even opening PRs after listening to meetings.

QWhat is the core implication Peter Steinberger highlights regarding the future of software development with AI?

APeter Steinberger highlights that the core implication is exploring what software development would look like if token cost were not a limiting factor, suggesting a future where small teams can leverage large numbers of AI agents as a standard practice, dramatically changing the development landscape.

Related Reads

Will the Ethereum Foundation Evolve into a 'Mascot'? Diversified Organizations Are Fragmenting Its Functions

The Ethereum Foundation (EF) is undergoing significant internal turmoil and functional erosion. Following its largest-ever layoff of 54 staff (20% of its workforce) and a major organizational restructuring announced in June, its Protocol Support Team has been officially dissolved. This comes alongside the high-profile resignation of key figures like co-executive director Xiaowei Wang, bringing senior departures this year to at least eight. Criticism of EF's rigid structure, opaque decision-making, and perceived lack of a clear value narrative for ETH has intensified within the community. The layoffs have catalyzed the emergence of independent, non-profit organizations like Ethlabs and Ethereum Institutional, founded by former EF researchers and members. These entities are now taking on core functions such as protocol research/development and institutional adoption, effectively fragmenting the EF's traditional leadership role. Concurrently, EF's security team is adapting to technological change, deploying specialized AI agents to audit Ethereum's codebase, which successfully discovered a critical vulnerability (CVE-2026-34219). While EF states AI complements rather than replaces researchers, it signals a potential future shift in its operational model. Faced with these challenges—internal restructuring, talent drain, the rise of competing organizations, and AI integration—the Ethereum Foundation appears to be stepping back from a central commanding role. Analysts and community observers speculate it may increasingly transition towards a symbolic "ecosystem mascot" function, while decentralized initiatives drive Ethereum's future growth and institutional adoption.

marsbit14m ago

Will the Ethereum Foundation Evolve into a 'Mascot'? Diversified Organizations Are Fragmenting Its Functions

marsbit14m ago

Nearly a Hundred Players Rush into Embodied Data: With 4.47 Billion Yuan in Financing in One Year, Who Can Really Make Money by 'Selling Data'?

The domestic embodied AI data industry has attracted nearly 100 players, with 70 focused on data collection and 27 on data infrastructure. In the past year, 15 independent embodied data service providers raised approximately 4.47 billion yuan. Despite this growth, the sector remains early-stage, fragmented, and faces significant challenges. Data collection methods are diverse, categorized into four main routes: teleoperation of real robots, human demonstration without a robot (using motion capture, exoskeletons, etc.), simulation synthesis, and distillation from internet videos. Most companies (43%) adopt hybrid approaches, combining multiple routes, as no single method can meet all training needs. Teleoperation alone is pursued by 31% of players, often by state-owned platforms and robot companies, while newer firms favor asset-light, no-hardware human demonstration. Independent data service providers now form the largest player group (40%), indicating the emergence of a distinct industry segment rather than just a subsidiary function for robot makers. Two-thirds of all players are "embodied-native" startups, while one-third are companies that pivoted from fields like AI data annotation, which are more prevalent in the data infrastructure layer. Current annual industry capacity is estimated at 1.6-1.8 million hours plus 70-80 million data points, with a short-term goal to increase this 15-20 fold within 1-3 years. Data collection factories are spread across 20 provinces in China, concentrated in the Yangtze River Delta, Beijing-Tianjin-Hebei, and Pearl River Delta regions. Financially, the 4.47 billion yuan raised in the past year pales compared to the 43.8 billion yuan raised by the broader embodied intelligence sector in just the first half of 2026, highlighting that data remains a less "sexy" bet for investors. The 15 funded independent providers show clear stratification: a top tier led by a unicorn (Lightwheel Intelligence, 3.1 billion yuan), a middle tier of 11 firms raising tens to hundreds of millions, and an early-stage tier of 3 companies. Sixty-nine investment institutions have participated, but none have made concentrated bets, reflecting uncertainty about viable business models. Over half of these funded companies are less than a year old, most are at pre-A or A rounds, and profitability remains largely unproven. In summary, the embodied data industry has become an independent track creating jobs and local economic activity. However, it is still nascent, with unformed consensus, unsolved problems, and unproven business models. The coming 1-2 years will be a critical validation window to see if companies can build sustainable, profitable businesses purely by "selling data."

marsbit3h ago

Nearly a Hundred Players Rush into Embodied Data: With 4.47 Billion Yuan in Financing in One Year, Who Can Really Make Money by 'Selling Data'?

marsbit3h ago

Dialogue with Multicoin Partner: The Crypto Market Has Bottomed Out, Favoring Three Cryptocurrencies in This Cycle

In a recent interview, Multicoin Capital managing partner Tushar Jain shared his views on the crypto market. He believes the market has bottomed and is at an inflection point, citing that negative news no longer causes significant price declines and application adoption continues to grow. Jain remains highly bullish on Solana, viewing it as the correct architectural choice for internet capital markets, particularly for spot and tokenized security trading. He is also positive on Hyperliquid, noting its leadership in decentralized derivatives trading. His investment approach focuses on concentrating capital in top convictions rather than equal allocation. A distinct opportunity he highlights is Zcash (ZEC), which he sees as a return to the industry's cypherpunk ethos and a potential top-five asset by market cap. For assets like Zcash without cash flows, his valuation framework is based on relative market cap ranking. Regarding investment strategy, Jain employs a "three-part" entry method to avoid timing pitfalls and emphasizes long-term "active management" over "active trading." He outlines four sources of investment edge: informational, analytical, behavioral/psychological, and structural. On portfolio management, the fund uses Bitcoin as its "cash," selling assets into Bitcoin during market euphoria to reduce beta risk and using Bitcoin to buy dips. Sales occur only if a better opportunity arises, the investment thesis breaks, or valuations become excessively overheated. While respectful of Ethereum's resilience, he questions its unclear scaling roadmap. Finally, Jain reaffirms his commitment to the thesis that blockchains will form the foundational architecture for future capital markets.

marsbit3h ago

Dialogue with Multicoin Partner: The Crypto Market Has Bottomed Out, Favoring Three Cryptocurrencies in This Cycle

marsbit3h ago

Trading

Spot

Hot Articles

How to Buy BILL

Welcome to HTX.com! We've made purchasing Billions Network (BILL) simple and convenient. Follow our step-by-step guide to embark on your crypto journey.Step 1: Create Your HTX AccountUse your email or phone number to sign up for a free account on HTX. Experience a hassle-free registration journey and unlock all features.Get My AccountStep 2: Go to Buy Crypto and Choose Your Payment MethodCredit/Debit Card: Use your Visa or Mastercard to buy Billions Network (BILL) instantly.Balance: Use funds from your HTX account balance to trade seamlessly.Third Parties: We've added popular payment methods such as Google Pay and Apple Pay to enhance convenience.P2P: Trade directly with other users on HTX.Over-the-Counter (OTC): We offer tailor-made services and competitive exchange rates for traders.Step 3: Store Your Billions Network (BILL)After purchasing your Billions Network (BILL), store it in your HTX account. Alternatively, you can send it elsewhere via blockchain transfer or use it to trade other cryptocurrencies.Step 4: Trade Billions Network (BILL)Easily trade Billions Network (BILL) on HTX's spot market. Simply access your account, select your trading pair, execute your trades, and monitor in real-time. We offer a user-friendly experience for both beginners and seasoned traders.

2.0k Total ViewsPublished 2026.05.07Updated 2026.06.02

How to Buy BILL

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of BILL (BILL) are presented below.

活动图片