As AI agents start to operate for humans — and increasingly among themselves — they will necessitate mechanisms to secure employment, handle payments, and foster trust. Crypto exchange OKX is wagering that this future is nearer than anticipated, unveiling a platform where AI agents can employ each other, autonomously settle payments, and cultivate transferable on-chain reputations.

Named OKX AI, the marketplace will be accessible to developers on Tuesday after a closed beta that involved 50 initial AI service providers. This marketplace leverages technology developed by OKX to enable AI agents to possess digital wallets, process payments using stablecoins, and form enduring identities.

The introduction signifies OKX’s latest effort to extend beyond cryptocurrency trading as it aims to evolve into a more comprehensive fintech entity. With over 150 million users worldwide, OKX anticipates that the upcoming generation of clients will include not just individuals or organizations, but AI agents capable of conducting transactions independently, paving the way for a burgeoning “agent economy.”

“The next ten years will see the rise of one-person businesses generating over a million dollars in yearly revenue – as every individual effectively acquires an unlimited workforce,” stated Star Xu, founder and CEO of OKX, to TechCrunch. “Conventional financial systems were designed for humans. The agentic economy requires infrastructure tailored for autonomous software. That’s the motivation behind creating OKX.AI.”

Haider Rafique, OKX’s chief marketing officer and global managing partner, expressed the company’s belief that “agentic commerce” might evolve into a trillion-dollar market during the next five years, propelled by micropayments and autonomous software.

Targeting crypto developers engaged in creating AI applications and independent entrepreneurs aiming to automate facets of their operations with AI agents, Rafique shared with TechCrunch that the company expects these developers to create applications for the marketplace, enabling others to utilize AI-powered tools without needing to develop them from scratch.

Among the initial developers are CertiK, which enables AI agents to evaluate the security of a crypto wallet or token prior to executing a transaction, and CoinAnk, offering live market data on a pay-per-query basis. GenLayer, another partner in the launch, is introducing dispute-resolution capabilities to assist AI agents in settling contractual conflicts.

Through the utilization of blockchain-based payments and stablecoins, the firm asserts that AI agents can execute transactions 24/7, including low-value micropayments that would be unfeasible with traditional payment systems.

Rafique indicated that OKX is implementing the same fraud detection, compliance mechanisms, and proprietary infrastructure that support its cryptocurrency exchange in the marketplace, which will be introduced in stages before gaining wider availability.

OKX’s launch coincides with a surge of tech companies and startups racing to construct the foundation for AI agents, including developer platforms, marketplaces, and payment and identity systems. Albert Castellana, co-founder and CEO of GenLayer Labs, remarked that the main challenge lies not merely in enabling AI agents to transact but in assisting them to discover each other and resolve conflicts when issues arise.

“What we’re constructing is fundamentally a digital court system,” Castellana shared with TechCrunch. “Our challenge is distribution. OKX already possesses that.”

Rafique posits that OKX’s primary advantage resides not just in its technology but in its reach. The firm believes that its established network of crypto developers and users will help nurture the marketplace, while its broader strategy spans well beyond digital assets.

In March, Intercontinental Exchange (ICE), the parent company of the New York Stock Exchange, invested roughly $200 million in OKX at a valuation of $25 billion. Rafique noted that this partnership is part of the firm’s goal to “modernize markets” through tokenization, with OKX AI signifying its concurrent initiative to “modernize money” for an age of autonomous software.

Developers engage with the marketplace through Onchain OS, OKX’s toolkit for linking AI agents to blockchain services. The company stated that no OKX account is necessary to get started, and the platform is compatible with AI coding tools such as Claude Code, Codex, Hermes, and OpenClaw.

As the marketplace initially targets developers rather than retail users, India plays a significant role in OKX’s strategy. The nation has emerged as one of the largest centers for AI and blockchain developers, a community the company aspires to connect with even prior to a broader revival of its crypto trading activities.

In 2024, OKX halted its operations in India while navigating the country’s regulatory stipulations for crypto exchanges. Rafique informed TechCrunch that India continues to be one of the company’s top-priority markets, adding that developer products like OKX AI encounter fewer regulatory challenges than spot crypto trading and could facilitate the firm’s reconnection with the nation’s builder ecosystem more swiftly.

Concerns about job losses due to AI intensify with every layoff announcement from companies. Up until May 2026, firms reported nearly 90,000 job reductions linked to AI, with projections indicating that as much as 15% of U.S. jobs could vanish because of AI in the coming five years. Promises from the tech sector about AI generating new employment opportunities do little to alleviate these fears, particularly for the cohort questioning whether they will find work after graduation. 

A fresh analysis from Ramp and Revelio Labs, which monitor enterprise AI expenditures and employee records across almost 22,000 businesses, adds complexity to this dismal outlook. 

The analysis found that organizations heavily investing in AI are increasing their workforce more rapidly, even in entry-level positions that many believe are at risk. The study noted that “high-intensity adopters”—companies spending an average of $30 monthly per employee on AI during the initial quarter—experienced a 10.2% increase in headcount.

Workforce growth also occurred across various functions, such as engineering, sales, administration, customer service, finance, marketing, and scientific roles. The most significant job growth among high-intensity adopters was seen in the information sector, encompassing software, internet, media, and technology-related companies. 

Despite these encouraging indicators, the information is not as bright as it might appear. It heavily leans toward tech-savvy, knowledge-driven companies—those that may have venture capital support and are already growing quickly, making it hard to determine if AI is aiding the hiring process or merely appearing at organizations that are expanding irrespective of AI.

“This paper does not indicate that AI universally spurs job creation,” the authors of the paper acknowledge, “but it does refute assertions that AI will result in widespread job losses.”

It also challenges the notion that AI is eliminating all junior positions. New research from Goldman Sachs indicated that AI has already caused the loss of approximately 16,000 net jobs monthly over the last year, with Gen Z and entry-level workers bearing the majority of this burden. However, the report shows that in tech-oriented companies, entry-level positions actually increased by 12%.

What can we derive from this? Perhaps that AI is not solely a tool for replacing labor, but rather an instrument for company growth. 

“For software and technology companies, AI can reduce the cost or speed of producing core outputs: coding, debugging, crafting internal tools, generating technical documentation, and aiding product development,” the report states. “Lower production expenses in these processes can enhance the return on expanding the entire firm, not just the engineering division.”

However, companies that acquire subscriptions and conduct pilot programs but do not make ongoing investments typically do not observe any increases in headcount, according to the report. 

This creates the risk of a widening divide between firms that have the necessary resources—such as capital, technical personnel, founder networks, and management capacity—to turn AI implementation into tangible business benefits, and those still experimenting with subscriptions. In essence, this report indicates that businesses already equipped with resources are poised to achieve the most significant gains. 

The authors of the paper suggest that such a divide may continue to expand, stating: “Firms lacking those channels may lag behind.”

Base44, the platform for vibe coding that was purchased by Wix for $80 million just a year ago — at a time when the venture was only six months old with a mere team of eight — has begun the rollout of its proprietary AI model designed to assist users in developing applications using natural language.

This initiative arises amid heightened discussions in AI communities regarding the suitability of frontier models for various applications. A pertinent inquiry is whether businesses utilizing external models can maintain long-term defensibility. Base44’s latest action, headquartered in Tel Aviv, addresses both of these concerns.

Although its custom LLM is in the early stages of deployment, Base44 aspires for it to eventually outshine frontier models. The founder, Maor Shlomo, stated, “training and owning the model as part of [our] entire stack grants us considerably more optimizations regarding latency, cost, and efficiency.”

On the surface, this may be a strategy to maintain an edge over competitors such as the Swedish startup Lovable, which achieved unicorn status during its Series A funding last summer and relies on third-party LLMs. Nevertheless, Shlomo anticipates that others will train their own models — “at least those entities that have scaled sufficiently and generated enough velocity to gather ample data.” 

Jonathan Userovici, a general partner at VC firm Headline — whose portfolio encompasses AI companies like Mistral AI, but not Base44 — asserts that data is one of three essential components of defensibility for AI startups, alongside their distribution and technology stack. 

The result is that firms with robust brands are now leaning into their data and frameworks to bolster their defensibility, with Base44 exemplifying this trend. The company claims that the initial iteration of its LLM, Base1, was created and trained using a dataset derived from “tens of millions of genuine user interactions on the platform.” 

This dataset will continue to grow alongside the company; however, so too will that of its competitors. The most significant rivalry may not emerge from vibe-coding startups, but from frontier AI laboratories encroaching on Base44’s territory — both Cursor and Grok’s parent company, xAI, are now part of SpaceX, and Claude Code has emerged as a vibe coding contender in its own right. 

This allows Anthropic and other foundational AI providers to access data and feedback mechanisms to enhance models for application creation, yet Shlomo contends that specialization provides Base44 with a competitive advantage. “Models are evolving, but they’ll remain quite general in their capabilities,” he predicted.

Userovici urges caution against underestimating frontier models, referencing the legal tech startup Harvey, which reversed its decision to develop its own model. He does not foresee applied AI companies collectively transitioning into frontier labs but contextualizes Base44’s initiative within a wider framework — where inference costs have become a pivotal part of the landscape.

Userovici notes that this cost pressure has instigated changes now being demanded by enterprise customers. “They don’t necessarily perceive a [return on investment] when utilizing the latest models for all use cases, leading to the establishment of entire infrastructures aimed at orchestration and optimization to select the appropriate models for them, ensuring costs don’t escalate while preserving similar performance across the majority of applications.”

Although enterprise companies still constitute a minority among users of vibe coding platforms, they represent a growing portion of revenue, and users of all sizes are beginning to voice concerns over AI usage costs. Base44’s decision to craft its own LLM is influenced by various factors, with cost reduction likely being among the advantages.

“We aim to develop a model that’s more aligned with our vision, optimized to reflect what users appreciate in terms of the outcomes we’re achieving, and is ultimately quicker and less expensive for customers compared to using frontier models like Opus,” Shlomo remarked.

Concerning Base44, the path toward cost reduction isn’t straightforward. In a press release, the company clarified that “ownership of the model grants Base44 direct authority over compute and inference expenses, which is anticipated to yield a structurally stronger margin profile over time.” 

Even with a delayed benefit, enhanced margins would be advantageous for Base44’s parent company, which has recently announced layoffs affecting 20% of its workforce. Conversely, Base44 has been increasing its headcount since the acquisition — and disclosed that it surpassed $100 million in annual recurring revenue a few months ago.

That still trails Lovable, which announced hitting $500 million in ARR earlier this month. However, Shlomo is confident that the “significant engineering effort” involved in developing Base1 will solidify Base44’s status as the “only vertically integrated vibe-coding application — meaning, in Userovici’s view, a player that possesses its distribution, data, and infrastructure simultaneously.

This article was updated to clarify Base44’s location.