Binance currently has over 380 job openings worldwide. Notably, one in five of its hires so far in 2026 is focused on AI technology and product development. Rather than contracting around AI, the company is expanding its capabilities.
This makes Binance a significant case study. The framework supporting these positions is detailed enough to analyze independently: examining what the tools accomplish, the number of trained personnel, and the overarching governance of the system. Understanding its current state is crucial.
The augmentation bet
Internally, Binance refers to AI as a “capability multiplier.” The concept behind this branding is that AI can manage mechanical tasks (data processing, pattern recognition, routine execution), freeing employees to focus on judgment, strategy, and creative work that is harder to automate.
This perspective is supported by ample evidence. McKinsey Global Institute’s 2025 report, Agents, Robots, and Us, discovered that over 70% of the sought-after skills by employers are applicable in both automatable and non-automatable roles. Skills do not disappear with the introduction of AI; they shift, necessitating that those who possess them understand their new application. The institute also reported a sevenfold increase in the demand for AI fluency in U.S. job postings within two years, the fastest growth amongst all skill categories in their analysis.
A June 2025 working paper from the OECD, reviewing numerous controlled experiments on generative AI and productivity, came to a similar conclusion. AI is most effective when used as a complement. Less experienced workers tend to gain the most from well-defined tasks, while seasoned professionals benefit when AI enhances their expertise rather than replacing it. In both scenarios, training is the key factor.
One particularly relevant finding from the OECD review is that in a controlled study, students who had access to ChatGPT and were later cut off scored 17% lower than those who had never used it before. AI may temporarily boost their performance, but their foundational capabilities did not evolve in tandem. When the tool was removed, they were left worse off than if they had never utilized it. Therefore, organizations rapidly adopting AI need to mitigate the risk of the tool becoming a crutch rather than an advantage. Binance’s approach, reflecting its investment strategy, focuses on structured and continuous training.
Three tools, built in-house
Binance operates three proprietary AI systems, each addressing distinct challenges and integrated into the company’s daily operations.
SAFUGPT stands out architecturally. It is a custom large language model interface developed and maintained not by an AI team, but by Binance’s Security Operations unit. The origin of this approach is significant: in May 2023, employees at Samsung’s semiconductor division inadvertently exposed proprietary source code by using ChatGPT, a widely cited cautionary tale in the tech world. This incident shaped Binance’s decision to create a secure alternative instead of restricting AI use.
With SAFUGPT, employees can access a variety of AI models, including a self-hosted DeepSeek instance for sensitive queries and Microsoft-hosted GPT-4 instances under enterprise agreements that prevent third-party data access and disable external monitoring. Internet connectivity is off by default and only activated as needed for specific tasks. Data from conversations remains within Binance’s security perimeter and is neither stored nor externally reviewed. Access is controlled based on roles and logged for audits. Teams can upload internal resources, such as FAQs, training guides, legal documents, and policy files, to develop custom AI agents with searchable knowledge bases. Daily applications range from report summarization and code synthesis to document translation and marketing preparation.
Yi He, co-founder of Binance, articulates a broader vision: “AI will magnify the skillsets of individuals. For those with creativity and strong critical thinking, opportunities will multiply.”
Hexa, the second tool, provides a no-code platform allowing non-engineering teams to create AI assistants and chatbots without coding. Common use cases include internal knowledge chatbots and agents streamlining operational reviews. Clawbot, the third tool, is more specialized, automating repetitive workflows in daily operations. Collectively, these three form the foundational layer for Binance’s training initiatives.
Twenty-eight sessions, 87% participation
Implementing tools is merely a procurement choice. Ensuring thousands of employees utilize them effectively is a training challenge, underscored by the OECD’s research. Binance’s 2026 program comprises eight types of AI training across 28 sessions, with multiple time slots to cater to a global workforce. Two tracks are dedicated to prompt engineering, while four Clawbot-specific programs span 16 sessions.
In addition to formal training, the company has released 22 weekly AI micro-learning pieces since December 2025. Each piece encapsulates a practical AI insight or technique intended to be read in under three minutes. A fifth Clawbot training module and two additional use-case sharing sessions were set to launch the week following the program’s latest public update. This reflects a pattern of sustained investment rather than a one-time push.
Adoption and the culture of showing your work
Clawbot usage among employees stands at approximately 72%, while Hexa is around 57%.
More telling than these percentages is the emerging culture around them. In 2026, there were thirteen live Clawbot use case sharing sessions and three Hexa roadshow sessions. The format is intentionally peer-to-peer: departments showcasing useful tool applications guide the rest of the organization on their processes, functionalities, and takeaways. Binance has also created structured knowledge libraries documenting real-world applications across functions, including catalogues of Hexa and SAFUGPT use cases that act as evolving playbooks. When a team successfully automates a process or creates an internal chatbot, the implementation is documented for other departments to replicate easily.
McKinsey’s estimate that AI could unlock $2.9 trillion in economic value in the U.S. by 2030 includes a crucial caveat: this potential hinges on organizations redesigning workflows for human-AI collaboration, rather than merely installing new technologies with the expectation of benefits. The use-case sharing sessions, knowledge repositories, and peer-led format indicate Binance’s efforts toward that level of workflow redesign. The depth and scalability of these changes will become clearer in the coming year.
Governance as infrastructure
Binance recently attained ISO/IEC 42001 certification, the global standard for responsible AI governance. SAFUGPT operates under a Privacy by Design framework, while the company’s prompt engineering initiatives and structured oversight practices aim to place human judgment at the forefront of AI deployment.
Governance might not be the most glamorous aspect of any AI strategy, which is why it warrants focused attention. The OECD’s review highlighted consistent risks associated with swift AI adoption: overreliance on generated outputs, gradual decline in critical thinking, and vulnerability to erroneous content when verification practices weaken. Expanding AI tools across a global workforce without corresponding safeguards poses these risks. The ISO certification and SAFUGPT’s SecOps-led architecture serve as formal responses. Their effectiveness will depend on whether they can evolve alongside the tools and the workforce’s increasingly adept interactions with them.
The bet
Currently, every company in the tech industry is making a bet on AI. Most bets resemble each other: fewer employees, greater automation, reduced costs. Binance, however, is placing a different wager. It contends that companies that educate their workforce to collaborate with AI will outperform those that opt to replace their workforce with it.
The more challenging question pertains to sustainability. No internal initiative can ensure that an augmentation-first model will persist as AI capabilities accelerate and the economics of automation shift. With 380 positions still unfilled and training programs advancing to their next stage, Binance is proactively working to validate its approach.
