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first_img Goldman Sachs restricts traders and bankers from using certain AI programming tools

According to Bloomberg, Goldman Sachs has restricted most employees from using advanced AI tools aimed at programming after evaluating production efficiency and risk management, leading to dissatisfaction among some employees. Internal guidelines were updated in October, and bankers had previously been able to use Anthropic's Claude Code and Microsoft's GitHub for several months, but the scope of these tools has now been narrowed to engineers.Sources say that some traders had used these tools to accelerate monitoring of client trading flows, modeling market changes and positions, as well as creating text and graphic content, and this restriction has left them feeling dissatisfied. Goldman Sachs' Chief Information Officer Marco Argenti stated in July that non-technical personnel such as traders and bankers had developed solutions using AI tools, which were then handed over to the company's approximately 12,000 engineers for improvement.Goldman Sachs spokesperson Nick Carcaterra stated that the company provides employees with certain AI tools to determine applicable scenarios in different businesses; after evaluating factors such as production efficiency and risk management during the trial period, the tools will be given to the most suitable personnel for long-term use. Additionally, sources indicated that the latest directive does not apply to strats who are simultaneously engaged in trading, quantitative research, and software engineering. Earlier this year, Goldman Sachs also restricted employees in Hong Kong from using Anthropic's Claude.

first_img Proton CTO: Programming is dead, engineers should help models write code

The privacy email and VPN service provider Proton publicly shared an internal memo titled "AI @ Proton, Engineering Manifesto," written by Chief Technology Officer Bart Butler about a month ago. The memo states that software engineering has undergone irreversible changes this year, as large language models and their tools can now write code. A senior Proton engineer indicated that no one in their business unit is writing code by hand anymore. Butler believes that the era of manually writing excellent code is over; time-consuming tasks such as documentation, testing, and boilerplate code can be handled by models, which can also participate in architecture and system design discussions.Butler pointed out that although engineers are widely using large language models, Proton's productivity has not significantly improved for three reasons. First, the engineering culture needs to shift; the work of engineers is no longer to write code but to help models write better code faster. Each engineer should become a team leader for models and agents, promoting their autonomous and continuous work, and reducing repetitive failures through repository-level context, shared skills, and prompts. Second, the company is still in the first phase of using models to accelerate existing processes and needs to enter the second phase, which involves eliminating bottlenecks across continuous integration, design, approval, specifications, and code review, with the expectation that most manual code reviews will come to an end. Third, there is a collective action problem between individual use of models and the overall transformation of company processes, as the organization has not invested enough resources.The memo emphasizes that this is not the end of expertise or good engineering practices; maintainability, documentation, and testing remain critical, but best practices will adjust with changes in cost and capability. Butler wrote, "Programming is dead," but engineering and professional skills have not vanished. The new responsibility of engineers is to continuously optimize and maximize automation as builders, and Proton will organize and allocate resources to complete this transformation. Proton stated that the memo is not an in-depth discussion of engineering documents related to security, reliability, or infrastructure. It also mentioned that technology companies have laid off over one million people in recent years, and Microsoft proposed 60,000 buyout offers last week.

first_img Cardano founder is developing a new programming language that integrates transaction intentions with Groth16 proofs

According to CoinDesk, Cardano founder Charles Hoskinson stated that he is personally developing a new programming language for the Cardano network and collaborating with Input Output Global engineer Lucas Rosa. Lucas Rosa is a co-creator of Aiken, one of Cardano's main smart contract languages. Hoskinson posted on the X platform that this will be the first programming language to combine user transaction completion intentions with cryptographic proofs.It is reported that the language will support three tools: transaction intent, data with carry proofs, and Groth16 proofs. Transaction intent allows users to specify the goals they want to achieve, while leaving the specific steps to the application, for example, traders can request that a token be exchanged for another only when the final price meets certain conditions, without specifying the exact path.Data with carry proofs can attach cryptographic evidence to information, proving that it complies with specific rules, allowing other applications to verify without recalculating. Groth16 is a widely used proof system that can generate small-sized, fast-verifying proofs, demonstrating that conditions have been met without exposing private data.With the combination of the three, developers can build applications that coordinate transactions, check complex conditions, and pass verified information between services, ultimately verified by Cardano with a small proof. Currently, Cardano developers mainly use Haskell-based Plutus and Aiken to write smart contracts, while Input Output Global, founded by Hoskinson, has its own privacy blockchain Midnight with its language Compact. The name, public code, and release date of this new language have not yet been announced.

first_img Analysis: The demand for AI infrastructure is longer than that of the internet, and general programming still drives ARR

Analysis of the AI semiconductor and infrastructure cycle indicates that the demand for AI infrastructure will continue to exceed that of the internet era, as user penetration and per capita token are multiplied and converted into tokens, significantly raising the ceiling. User penetration has surpassed 50%, with growth primarily coming from the still-early per capita token; the median monthly AI spending per employee in U.S. companies is about $12, which could long-term approach around 10% of white-collar salaries, approximately $1,000 per month, leaving nearly two orders of magnitude of space in between. Unlike the flat subscriptions and extremely low marginal hardware consumption of the internet, the high costs of inference make the marginal cost of a single access higher, requiring greater infrastructure intensity.After developers program, the next ARR growth will still mainly come from broad programming: non-programmers use programming infrastructure to complete non-programming tasks across industries, with programming becoming the default execution kernel for agents. Tasks related to broad programming account for about 60% to 70% of ARR. As of June 2026, Codex accounted for 64% of the total output tokens from Codex and ChatGPT among OpenAI's enterprise clients; since February, Codex has seen a much higher weekly growth in verticals such as law, sales recruitment, and marketing compared to engineering. In Anthropic's revenue, narrow development/software accounts for about 40%, while finance and insurance exceed 20%, with law, life sciences, retail, and others also having considerable shares.The demand-side token growth logic remains, with a high overlap between funders and beneficiaries.

AMD launches enterprise-level AI programming platform Instinct Coder

AMD officially launched the on-premises AI programming platform AMD Instinct Coder for enterprises, integrating AMD EPYC processors, AMD Instinct GPUs, Supermicro AI servers, Spectro Cloud PaletteAI inference launch platform software, and the GLM-5.2 model optimized by AMD inference microservices, forming a comprehensive solution that is pre-integrated from hardware to software and can be quickly deployed.AMD stated that the total cost of ownership of this platform can reduce costs by up to 70% compared to using cloud-based cutting-edge models, with a capital recovery period as short as 6 months, directly addressing the two core pain points that enterprises commonly encounter in AI programming assistant pilots: the cost impact of cloud models and the data security risks of third-party services.Spectro Cloud's PaletteAI platform provides intelligent routing capabilities, allowing for strategic automatic switching between on-premises inference and cloud-based cutting-edge models from Anthropic, OpenAI, Google, and xAI, and is equipped with token quotas, audit tracking, and Grafana dashboards to visualize cost comparisons between on-premises and cloud environments, fully compatible with standard operating systems and Kubernetes. Each node can support up to 50 users and is compatible with mainstream development tools such as Claude Code, OpenAI Codex, Visual Studio Code, and Cursor.IDC executives commented that this platform "directly addresses the core needs of enterprises wanting AI programming productivity without the unpredictable cloud costs or risks of exposing source code." AMD's move shifts the AI programming assistant from a pure service model billed by usage to an infrastructure investment model with pre-planned costs.
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