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

marsbitPubblicato 2026-05-17Pubblicato ultima volta 2026-05-17

Introduzione

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

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

Domande pertinenti

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.

Letture associate

Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

**Title:** Analyzing the US Quantum Computing Race: IonQ, Rigetti, D-Wave – Which Concept Stock is Worth Betting On? **Summary:** The podcast discusses the resurgence of quantum computing as a national priority for both the US and China, driven by its potential to break current encryption, revolutionize drug discovery, finance, and logistics. The core challenge is commercializing the technology, which is hampered by high error rates in quantum bits (qubits). Quantum error correction, requiring thousands of physical qubits per reliable logical qubit, is key but years away. The analysis compares three main publicly traded US quantum computing firms: * **IonQ (Ion Trap):** Considered the most financially stable with the fastest commercial progress (2025 revenue: $130M, +202%) and high-quality clients. Its valuation is very high, pricing in significant future growth. * **Rigetti (Superconducting):** Seen as the highest-risk, highest-potential-reward bet. It has the smallest revenue but recently launched a 108-qubit system. Its valuation multiples are extreme, making it highly sensitive to news. * **D-Wave (Quantum Annealing):** Has the most unique positioning with real-world enterprise clients today (e.g., Mastercard, Volkswagen) solving optimization problems. Its recent acquisition moves it into general-purpose quantum computing ("dual-platform"), adding execution risk. Major tech giants like Google, IBM, and Microsoft are also heavily invested, pursuing various technical approaches. Nvidia is positioning itself as the essential bridge between classical and quantum computing. The investment phase is likened to AI in 2018-2020: promising underlying technology with accelerating breakthroughs but a commercial inflection point still 3-7 years away, suggesting potential for a market correction ("bubble washout"). For investors, suggested approaches include gaining exposure through tech giants with quantum divisions (e.g., Google, IBM) or using niche ETFs like WQTM for pure-play quantum exposure, rather than direct stock picks in the highly volatile pure-play companies at this early stage.

marsbit8 min fa

Deconstructing the U.S. Stock Quantum Computing Sector: IonQ, Rigetti, D-Wave, Which of These Concept Stocks is Worth Betting On?

marsbit8 min fa

Ethereum Bears In Pole Position: $1,850 Could Be The Next Landing Spot

Ethereum's price remained bearish in May, with analysts predicting a potential drop to around $1,850. Crypto analyst Burak Kesmeci notes that ETH is trapped in a descending channel on the 4-hour chart, indicating seller dominance. If it fails to break the $2,033 resistance, a decline to the $1,822-$1,850 range is likely. While a breakout above $2,033 could target $2,400, the downward move appears more probable. This bearish outlook is reinforced by significant outflows from spot Ethereum ETFs, totaling over $241 million in the past week—the third consecutive week of major outflows. ETH has lost nearly 15% of its value over the last three weeks and is currently trading around $2,023.

bitcoinist20 min fa

Ethereum Bears In Pole Position: $1,850 Could Be The Next Landing Spot

bitcoinist20 min fa

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

From Parallel Finance to Mainstream: The Dawn of On-Chain Securities For over a decade, the crypto industry has operated as a parallel financial system with its own currencies, markets, and assets—from Bitcoin and ICOs to DeFi, NFTs, and memecoins. Despite building a robust internal ecosystem, a wall has separated it from the traditional financial world. That barrier is now crumbling. The industry's first act was one of internal evolution: ICOs streamlined fundraising, DeFi recreated financial services on-chain, and layer-2 networks competed for scalability—all within the crypto bubble. While innovative, this cycle remained closed, with capital and users circulating internally, leading to volatile boom-bust cycles. Even Bitcoin ETFs, while attracting Wall Street capital, merely provided a channel to buy crypto assets without bridging the systems. The next, larger narrative is Real-World Assets (RWA) moving on-chain. This involves tokenizing stocks, bonds, funds, and future cash flows. Blockchain can compress the complex traditional processes of trading, settlement, clearing, and custody into a seamless, automated network operating in seconds. This shift is creating a new financial gateway: the native crypto securities broker. This entity will combine functions of an exchange, broker, bank, and custodian into a unified global financial operating system. Consequently, the next major battleground won't be the "public chain wars" focused on speed and cost, but the competition to build the financial infrastructure capable of hosting high-quality, liquid real-world assets. Access to global equities, index funds, or stakes in companies like SpaceX could erase the boundary between crypto and traditional finance, unlocking a market orders of magnitude larger than crypto's current valuation. In summary, after years of creating a separate financial world, crypto's next decade will be defined by its integration into the existing global financial system, marking the true beginning of its largest growth story.

marsbit29 min fa

From Parallel Finance to Mainstream Finance: The On-Chain Securities Era Ushers in a Historic Window

marsbit29 min fa

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

Wang Chuan: When the Neighbor Lao Wang Made 30x on Storage Stocks, How to Stay Anxiety-Free (Part 6) - The Trap of Commoditized Goods. This essay uses historical and current examples to analyze the cyclical and high-risk nature of the data storage industry. It begins with the 1990s rise and dramatic fall of Iomega, whose stock soared over 160x in 18 months before collapsing 97% from its peak, illustrating the fleeting success of storage "meme stocks." The core problem is that storage products, like DRAM and flash memory, are highly commoditized. This leads to extreme volatility: prices have plummeted over 80% multiple times, and company stocks often crash 95% or go bankrupt. The industry's dynamic is defined by "elastic demand facing heavy-asset, long-cycle, rigid supply." When demand spikes and supply is fixed, prices skyrocket, as seen recently with AI-driven demand for High Bandwidth Memory (HBM). Companies like Sandisk and Micron have reported massive revenue and gross margin jumps (e.g., Sandisk's gross margin rising from 22.5% to 78.3%) despite minimal increases in production volume. However, these high margins are self-defeating. They incentivize massive new capacity investments (hundreds of billions planned from 2026), with supply expected to surge by late 2027. Once new supply meets demand, prices and profits will crash, potentially leading to a scenario where "selling more results in earning less." The article debunks the safety of long-term supply agreements, comparing them to fragile non-aggression pacts easily broken when market conditions shift. It warns that when an industry is highly profitable but trades at low P/E ratios, the risk is greatest, as plummeting prices quickly erase those earnings. Multiple asymmetric risks loom, including economic recession, reduced AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and technological innovations that reduce memory requirements. In conclusion, the storage sector is a cyclical trap where periods of euphoric profits are often precursors to devastating downturns, luring unprepared investors into a "wealth incinerator."

marsbit38 min fa

Wang Chuan: When the Neighbor Old Wang Made 30x on Memory Stocks, How to Avoid Anxiety (Part Six) - The Trap of Commoditized Goods

marsbit38 min fa

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

The article, "Wang Chuan: How to Remain Unanxious After Neighbor Lao Wang's Thirty-Fold Gain on Storage Stocks (Part 6) - The Trap of Commoditized Goods," analyzes the cyclical and perilous nature of the data storage industry through historical and current case studies. It begins with the example of Iomega, whose Zip drives led to a stock surge of over 160x in the mid-1990s before collapsing over 97% from its peak due to competition from cheaper CD-R technology. This pattern is characteristic of storage, where products like DRAM are highly commoditized, leading to extreme price volatility. The sector has seen prices crash over 80% multiple times, with companies often facing bankruptcy. The core dynamic is "elastic demand facing heavy-asset, long-cycle, rigid supply." High prices attract new capacity, but the long lead time means supply eventually overshoots, causing sharp price corrections. The current AI-driven boom, exemplified by surging demand for High-Bandwidth Memory (HBM), has led to skyrocketing prices and profit margins for companies like SanDisk and Micron, despite relatively flat production volumes. However, the author warns this high-margin environment is self-defeating. The high profits are already triggering massive new capacity investments (hundreds of billions starting 2026), with supply expected to ramp up by late 2027. When supply catches up, total revenue and profits may fall even as more units are sold. Long-term supply agreements offer little protection, as buyers can find ways to renegotiate if market prices drop, similar to fragile political treaties. Key risks include economic downturns, cuts in AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and innovations in chip/algorithm design that reduce memory needs. A critical trap is that at the cycle's peak, storage stocks often appear cheap with low P/E ratios, luring value investors just before an impending downturn where profits evaporate. The conclusion cautions that for commoditized goods like storage, high margins inevitably destroy themselves, and the current asymmetry favors downside risk over further upside. The neighbor's dream of easy wealth from storage stocks is portrayed as a precarious illusion.

链捕手56 min fa

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

链捕手56 min fa

Trading

Spot

Futures

Articoli Popolari

Come comprare BILL

Benvenuto in HTX.com! Abbiamo reso l'acquisto di Billions Network (BILL) semplice e conveniente. Segui la nostra guida passo passo per intraprendere il tuo viaggio nel mondo delle criptovalute.Step 1: Crea il tuo Account HTXUsa la tua email o numero di telefono per registrarti il tuo account gratuito su HTX. Vivi un'esperienza facile e sblocca tutte le funzionalità,Crea il mio accountStep 2: Vai in Acquista crypto e seleziona il tuo metodo di pagamentoCarta di credito/debito: utilizza la tua Visa o Mastercard per acquistare immediatamente Billions NetworkBILL.Bilancio: Usa i fondi dal bilancio del tuo account HTX per fare trading senza problemi.Terze parti: abbiamo aggiunto metodi di pagamento molto utilizzati come Google Pay e Apple Pay per maggiore comodità.P2P: Fai trading direttamente con altri utenti HTX.Over-the-Counter (OTC): Offriamo servizi su misura e tassi di cambio competitivi per i trader.Step 3: Conserva Billions Network (BILL)Dopo aver acquistato Billions Network (BILL), conserva nel tuo account HTX. In alternativa, puoi inviare tramite trasferimento blockchain o scambiare per altre criptovalute.Step 4: Scambia Billions Network (BILL)Scambia facilmente Billions Network (BILL) nel mercato spot di HTX. Accedi al tuo account, seleziona la tua coppia di trading, esegui le tue operazioni e monitora in tempo reale. Offriamo un'esperienza user-friendly sia per chi ha appena iniziato che per i trader più esperti.

87 Totale visualizzazioniPubblicato il 2026.05.07Aggiornato il 2026.05.12

Discussioni

Benvenuto nella Community HTX. Qui puoi rimanere informato sugli ultimi sviluppi della piattaforma e accedere ad approfondimenti esperti sul mercato. Le opinioni degli utenti sul prezzo di BILL BILL sono presentate come di seguito.