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a16z: TradFi is not embracing the DeFi model, but rather accelerating the adoption of blockchain technology

a16z published a blog post stating that as traditional financial institutions accelerate their exploration of blockchain technology, the market generally believes that the future will see a comprehensive integration of DeFi (Decentralized Finance) and TradFi (Traditional Finance), forming a new financial model through the combination of decentralized finance and institutional distribution systems.However, the reality may not be so. The core motivation for traditional financial institutions to adopt blockchain is not to embrace decentralization, but to value its commercial benefits in reducing costs, improving settlement efficiency, expanding distribution channels, and optimizing customer relationship management.What is more likely to emerge in the future is a new type of "programmable financial infrastructure" based on underlying blockchain technology, optimized for institutional needs, rather than a simple integration of traditional finance and DeFi. Institutions are selectively absorbing certain technological capabilities from DeFi and modifying them according to their own regulatory, risk management, and operational requirements.For example, atomic settlement can reduce counterparty risk, shared ledgers can lower back-office reconciliation costs, programmable funds can automatically execute processes such as interest payments, margin management, and corporate actions, and automated market-making models are also being applied to on-chain foreign exchange and tokenized asset pricing.At the same time, the native DeFi features of open access, anonymity, and trustless execution often conflict with institutional requirements for compliance, control, and accountability. Therefore, cases such as JPMorgan's institutional blockchain project, BlackRock's and Franklin Templeton's tokenized funds, are essentially not traditional finance entering DeFi, but rather using blockchain technology to improve existing financial business processes.In the future, the blockchain industry will have two development paths: on one hand, enterprises and financial institutions will continue to promote the implementation of blockchain infrastructure that meets regulatory requirements, expanding the industry scale through applications such as stablecoins, tokenized assets, and on-chain settlements; on the other hand, open networks will continue to play the role of a source of innovation, continuously generating new financial primitives and market mechanisms, providing technical reserves for future institutional infrastructure.TradFi and DeFi are not in competition but are developing together in different directions. Traditional finance may not fully adopt the DeFi model but will gradually adopt parts that suit its own needs. The true integration may ultimately occur at the underlying blockchain network level, rather than one side replacing the other.For developers, the key is not to chase all markets simultaneously but to clarify the target audience: for institutions, products need to be built around compliance, risk control, and long-term business processes; for open networks, there is a need to continue exploring innovation, liquidity, and network effects. The future financial system may operate on blockchain infrastructure, but the most important innovations may still come first from open networks.

Cybrid: Enterprise-level stablecoin applications see significant growth, with over 80% of surveyed companies planning to adopt them within the year

The latest report from payment infrastructure company Cybrid shows that the adoption of stablecoins by enterprises is accelerating towards becoming mainstream. Among the 468 corporate executives and business leaders surveyed, as many as 42% of companies are already using stablecoins for cross-border payments, and 88% of respondents indicated they are very likely to adopt them within the next 12 months, while only 2% of respondents said they would rely entirely on traditional payment networks.Data shows that companies using stablecoins save an average of 35% on cross-border payment costs; for large enterprises processing over $100 million monthly, cost savings can reach up to 47%. The most common use cases for companies using stablecoins are: payroll disbursement, vendor payments, and customer payments. In addition, 71% of respondents emphasized that clear regulatory policies (such as the recently passed stablecoin regulatory bill GENIUS Act in the U.S.) are the most critical factor driving their expansion of stablecoin usage, with its importance even surpassing the level of infrastructure improvement.With the growth in demand, the supporting infrastructure in the industry is also continuously expanding. Data from payment platform Paybis shows that in the first four months of 2026, B2B transactions accounted for nearly 98% of the total stablecoin payments on its platform. This Monday, Bank of New York Mellon (BNY) also announced the expansion of its digital asset custody platform, allowing institutional clients to store and circulate Circle's USDC directly through the bank.

first_img The Ramp AI report shows that the adoption rate of Anthropic has surpassed that of OpenAI, with top companies' employees averaging an AI monthly expenditure of $7,449

The economic laboratory of the fintech company Ramp has released a new version of the Ramp AI Adoption Index report. Based on spending data analysis from over 70,000 U.S. enterprise customers, the adoption rate of enterprise-level AI for Anthropic increased by 2.5 percentage points to 41%, officially surpassing OpenAI, which slightly decreased to 39.5%, establishing a leading position in the field of commercial applications.The report focuses on analyzing the spending trends of top enterprises that "deeply adopt AI." Data shows that the top 1% of enterprises spend as much as $7,449 per employee per month on AI, with this figure still achieving a 14.1% increase last month; in contrast, the top 10% of enterprises have an average monthly spending of $611 per employee, while the median enterprise spends only $11.38 (approximately equal to the cost of a single basic subscription).Additionally, the research points out that enterprises that deeply apply AI do not experience "vendor lock-in" and generally adopt multiple cutting-edge large models, open-source platforms, and vertical AI solutions simultaneously. Although enterprises are beginning to experiment with more cost-effective models (such as DeepSeek) in the face of cost pressures, overall AI spending remains on an upward trend.

Coinbase: Has reduced AI spending by nearly 50% and is trying to default to adopting open weight models

Coinbase CEO Brian Armstrong published an article introducing the company's latest progress in AI cost optimization.Armstrong stated that as the usage of AI and Token consumption continues to grow, the key to controlling costs is not to restrict employee usage or frequently send budget reminders, but to optimize default model selection, task routing mechanisms, and caching strategies.He revealed that Coinbase is trying to use open-weight models such as GLM 5.2 and Kimi 2.7 as default options through an internal LLM gateway, while still allowing engineers to choose other models based on specific task requirements. Data shows that 91% of the company's employees have never reached the AI usage quota limit, so Coinbase has not chosen to tighten quotas but instead improved overall efficiency through lower-cost model solutions.In terms of model routing, Coinbase preprocesses prompts and, combined with cache hit rates and the pricing of different models, automatically assigns tasks to the most suitable model. Armstrong believes that complex tasks such as planning and reasoning may require support from cutting-edge models, but execution tasks do not necessarily need to invoke higher-cost models. In the future, the model selection process should be more automated by AI rather than relying on manual decisions.Additionally, he pointed out that cache hit rate is one of the important factors affecting AI costs. Coinbase has incorporated a cache-aware mechanism into the request process to improve the reuse rate of historical results. For example, in the case of LibreChat, after optimizing the caching solution, its cache hit rate has increased from 5% to 60%.Armstrong also stated that the company requires engineers to keep context as concise as possible, including starting new sessions when switching tasks, narrowing the context scope of files, and closing unused tools, to reduce unnecessary Token consumption.According to him, through these measures, Coinbase has successfully reduced AI spending by nearly 50%, while Token usage continues to grow.
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