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Goldman Sachs and the Rise of “AI Engineers”: Meet Devin, the Robot Coder

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In July 2025, Goldman Sachs took a bold step that may reshape how Wall Street tackles its most technical challenges: hiring an AI software engineer. But Devin isn’t a human—it’s an autonomous coder developed by startup Cognition. What happens when an AI becomes the newest “employee” at a major investment bank?

Introducing Devin: a tireless code-writing teammate

Goldman Sachs has started piloting an AI agent called Devin, created by AI startup Cognition. Unlike traditional AI assistants that suggest code snippets, Devin can independently handle complex, multi-step development tasks—essentially acting as a full-stack engineer under human supervision. The bank’s Chief Information Officer, Marco Argenti, described Devin as “like our new employee,” with plans to deploy hundreds to thousands of instances depending on business needs.

This marks a clear shift from basic developer copilot tools toward truly agentic AI—machines that execute rather than just assist. Cognition’s valuation has surged to around $4 billion as investor enthusiasm for the technology continues to grow. The startup’s AI agent has garnered industry-wide attention, not just for its autonomous capabilities but also for its performance under real-world conditions.

Why now? Productivity gains and the hybrid workforce

Goldman employs approximately 12,000 software engineers, and Devin aims to augment these teams, not replace them. Argenti envisions a “hybrid workforce,” where engineers shift from writing boilerplate code to supervising AI agents, turning problems into prompts and ensuring output quality.

Cognition claims productivity improvements of 3–4× over previous AI-assisted workflows. Meanwhile, other Goldman AI tools—from a generative AI assistant rolled out to 10,000 employees to code translators and investment research assistants—suggest a broad, enterprise-level commitment to AI.

Employees from junior analysts to managing directors have reportedly saved hours on tasks like drafting reports, understanding legacy code, or translating documents. Goldman’s internal AI assistant has already transformed day-to-day work across departments, signaling a readiness to embrace more autonomous tools like Devin.

But: can Devin really deliver?

Despite the hype, independent tests show Devin still struggles with certain complex assignments. In one benchmark, it succeeded in only 3 of 20 tasks, failing on the rest. While outperforming standard language-model bots, it fell short of fully replacing human oversight.

The tool appears most effective when operating on large, contextual codebases, where it can leverage ample context to make informed decisions. Still, human developers remain critical for supervision, revision, and strategic prompt-writing. Devin, in its current form, is less of a miracle worker and more of a high-efficiency intern—capable, tireless, and prone to occasional errors.

One of the key challenges facing the deployment of agentic AI like Devin is ensuring trustworthiness. Unlike deterministic software, AI agents can make unpredictable decisions based on subtle prompt changes or ambiguous instructions. For this reason, Goldman is taking a cautious approach: Devin doesn’t push to production autonomously. Instead, it operates in sandbox environments where human engineers approve or reject its output.

Broader implications: jobs, workflows, and Wall Street business models

Goldman Sachs isn’t alone. Firms like JPMorgan and Morgan Stanley have begun rolling out AI assistants or copilots. Goldman’s Devin experiment may be the first deep integration of autonomous AI engineers in finance.

Yet, the excitement is tempered by employment concerns. A Bloomberg study projects that 200,000 banking jobs could vanish within 3–5 years due to automation. Similarly, recent analysis suggests AI could fulfill 95% of an IPO prospectus task—formerly a multi-person, multi-week assignment—triggering major shifts in junior roles throughout finance.

The roles most at risk involve routine, mechanical work: financial modeling, data entry, compliance documentation, and code maintenance. At the same time, new roles are emerging—prompt engineers, AI supervisors, and trust validators. These jobs require a different skill set, blending domain knowledge with machine fluency.

Goldman’s strategy is not merely about efficiency. It’s about evolving the nature of financial work. As Argenti puts it, intelligence is no longer scarce—the value is in applying judgment. This reframing suggests a long-term vision: not fewer employees, but different ones.

Technical architecture: how Devin works

Devin is built atop a sophisticated LLM stack, combined with task planning, memory, tool integration, and execution capabilities. It can access version control systems like GitHub, navigate file systems, debug software, run test cases, and adapt its approach based on prior outcomes.

Unlike conventional models that generate static code snippets, Devin operates dynamically. For instance, when given a task to add a feature to a legacy codebase, Devin analyzes the structure, inserts the correct logic, tests the implementation, and updates the repository—all autonomously. Think of it as a software engineer with a photographic memory and the ability to iterate endlessly.

However, these capabilities raise operational questions: What if Devin introduces bugs that humans overlook? How do teams ensure transparency in AI-generated code? Who is accountable for software shipped by a non-human agent?

To address this, Goldman has integrated logging, audit trails, and human sign-off into Devin’s workflow. Every step taken by the AI is recorded and reviewed, turning the human supervisor into a strategic reviewer rather than a coder. The goal is not just speed, but accountability.

Cultural shift: adapting to AI colleagues

Beyond technology, the Devin pilot is forcing a cultural shift. Engineers accustomed to traditional coding must now become “AI conductors,” translating requirements into prompts and interpreting machine output. This requires both technical fluency and a mindset shift.

Goldman has launched internal training programs focused on prompt engineering, AI literacy, and best practices for supervising autonomous agents. The feedback loop between human engineers and Devin is being treated as a new form of collaboration, one where trust and clarity are paramount.

Interestingly, some junior employees report feeling less threatened and more empowered. With Devin handling routine grunt work, they have more time to focus on learning, strategy, and creativity. For many, the AI assistant is less a rival and more a highly skilled teammate.

Industry reactions and investor response

The finance sector is watching closely. Cognition’s valuation has jumped dramatically, attracting attention from both tech investors and traditional banking institutions. Other firms are evaluating whether to license Devin or build proprietary alternatives.

There is also a growing ecosystem of “agent ops” tools—software platforms designed to manage, monitor, and orchestrate fleets of AI agents. Goldman’s experience could serve as a blueprint for how to scale such deployments responsibly.

On the regulatory front, questions are emerging. Should AI agents be subject to the same compliance reviews as human developers? How do institutions prevent hallucinated code or biased outputs? The answers will likely shape how agentic AI is adopted across other regulated industries, from healthcare to insurance.

Looking ahead: where this could lead

Goldman’s bold rollout of Devin, alongside its GS AI Assistant and other tools, reflects a two-pronged strategy:

1. Automate routine, mechanical tasks, allowing humans to focus on higher-value thinking.

2. Build AI fluency across employees, empowering them to prompt, supervise, and refine AI-generated output.

    But success will depend on continuous improvement of the agents, rigorous supervision protocols, and strategic deployment. The hybrid model hinges on human judgment deciding what to ask, what to trust, and how to act. As Argenti emphasizes, the goal isn’t just to replace effort with automation, but to elevate the entire organization’s intelligence quotient.

    If Devin and tools like it can prove their value, we may see a transformation in how engineering, finance, and knowledge work are organized. Teams could evolve from static units of labor to dynamic orchestrators of algorithmic labor, with AI as both tool and teammate.

    Conclusion

    Goldman Sachs stepping into agentic AI with Devin represents a significant milestone: not just coding assistance, but distributed automation at scale. Whether this becomes a template for the financial industry—or a cautionary tale—depends on careful execution, real-world performance, and the ability of human teams to supervise, adapt, and innovate alongside these new colleagues.

    As AI agents begin writing code, analyzing data, and making decisions, the very nature of work is being rewritten. Goldman Sachs isn’t just testing a tool; it’s pioneering a new workforce paradigm. The rest of the industry is watching—and taking notes.

    AI Model

    How to Prompt Nano Banana Pro: A Guide to Creating High-Quality Images with Google’s AI

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    Why Nano Banana Pro Matters

    Nano Banana Pro is Google DeepMind’s most advanced image generation model, built on the powerful Gemini 3 Pro architecture. It delivers high-resolution outputs (up to 4K), understands complex prompts with layered context, and performs exceptionally well when generating realistic lighting, textures, and dynamic scenes. It also supports image referencing — letting you upload photos or designs to guide the visual consistency.

    In short, it’s not just a toy — it’s a tool for designers, marketers, illustrators, and creatives who want to build professional-grade images fast. But to unlock its full potential, you need to learn how to prompt it properly.


    Prompting Basics: Clarity Beats Cleverness

    The secret to powerful results isn’t trickery — it’s clarity. Nano Banana Pro doesn’t need keyword spam or obscure syntax. It needs you to be specific and structured.

    Here are the key rules to follow:

    • Be descriptive, not vague: Instead of “a cat,” write something like “a ginger British shorthair cat sitting on a marble countertop under soft morning light.
    • Layer your descriptions: Include details about the subject, setting, atmosphere, materials, lighting, style, and mood.
    • State your format: Tell the model if you want a photo, digital painting, cinematic frame, 3D render, infographic, comic panel, etc.
    • Use reference images: Nano Banana Pro supports multiple uploads — useful for matching styles, poses, faces, characters, or branding.

    This is how professionals prompt: not by hacking the system, but by being precise about what they want.


    Crafting Prompts by Use Case

    📸 Realistic Photography

    Want a product photo, fashion portrait, or cinematic still? Then your prompt should include lens type, lighting style, subject age, composition, and color grading.

    Example:

    Professional studio portrait of a 35-year-old woman in natural light, soft cinematic lighting, shallow depth of field, 85mm lens look, natural skin tones, soft shadows, clean background, editorial style.

    Another example:

    A 3/4 view of a red sports car parked in a luxury driveway at golden hour, realistic reflections, soft shadows, DSLR-style image, bokeh background.

    These prompt structures help the model replicate not just the subject but the feel of a professionally shot image.


    🎨 Illustration, Comic Art, and 3D Concepts

    If you want stylized work — like a retro comic, anime-style character, or matte painting — the style must be part of the prompt.

    Example:

    Comic-style wide cinematic illustration, bold black outlines, flat vibrant colors, halftone dot shading, a heroic female astronaut on Mars with a pink sky, dramatic lighting, wide aspect ratio.

    More styles to try:

    • Fantasy concept art, a medieval knight riding a dragon above stormy mountains, painted in the style of Frank Frazetta, high detail, dramatic lighting.
    • Cyberpunk anime character in a rain-soaked Tokyo alley, glowing neon lights, futuristic fashion, overhead perspective, digital painting.

    Tip: Reference known artistic styles (e.g., Art Nouveau, Impressionism, Pixar, Studio Ghibli) to guide the tone.


    🔄 Editing Existing Images

    Nano Banana Pro can also transform existing images by changing backgrounds, lighting, or adding/removing objects.

    Examples:

    Replace the background with a rainy city street at night, reflect soft blue and orange lights on the subject, keep original pose and composition, cinematic tone.

    Add a glowing book in the subject’s hands, soft magical light cast on their face, night-time indoor setting.

    Best practices:

    • Use clear “before/after” language.
    • Indicate what must stay unchanged.
    • Specify the mood or lighting effect you want added.

    Common Mistakes to Avoid

    • Too generic: A prompt like “a girl standing” tells the model almost nothing. Who is she? Where is she? What’s the style?
    • Keyword stuffing: Don’t use outdated tricks like “masterpiece, ultra-detailed, trending on ArtStation.” They’re mostly ignored.
    • Ignoring context: Don’t forget to describe how elements relate (e.g. “holding a glowing orb” vs. “glowing orb floating behind her”).
    • Unclear intent for text/logos: If you want branded material, say exactly what the logo or label should look like, and where.

    Prompt Templates You Can Use Right Now

    Try adapting these for your needs:

    • “Cinematic 4K photo of a mountain climber reaching the summit at sunrise, orange glow on snowy peaks, lens flare, dramatic sky.”
    • “Retro-futuristic 3D render of a diner on Mars, neon signs, dusty surface, stars in the background, warm ambient light.”
    • “Isometric vector-style infographic showing renewable energy sources, solar, wind, hydro, with icons and labels.”
    • “Realistic photo of a smartwatch product on a floating glass platform, minimalistic white background, soft shadows.”

    These prompts are short but rich in visual instruction — and that’s the key to strong output.


    Going Further: Advanced Prompting Tips

    • Use cinematic language: Words like “soft light,” “overhead shot,” “close-up,” “medium angle,” “shallow depth of field” guide the AI like a film director.
    • Test with reference images: Upload an image of your brand, product, or character to maintain continuity.
    • Iterate: If your first image isn’t right, adjust one or two variables (e.g., lighting, background, subject age) and regenerate.
    • Define aspect ratios: Use “cinematic,” “vertical portrait,” “square crop” if you need a specific format.
    • Stay natural: Write prompts like you’re briefing a professional illustrator or photographer.

    Final Thoughts

    Nano Banana Pro is one of the most powerful visual AI tools available — but it’s only as good as your prompts. Whether you’re an art director, a solo founder, or a content creator, learning to prompt well is the fastest way to unlock its full creative range.

    Focus on clarity, visual language, and style specificity. Add references when needed. Think like a photographer, art director, or storyteller. The better your brief, the better the image.

    Want more? Ask for our expanded prompt pack: 50+ ready-made formulas across categories like product design, sci-fi art, fantasy scenes, infographics, editorial portraits, and more.

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    AI Model

    Qwen vs. ChatGPT — Which AI Assistant is Better — and For What

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    Why This Comparison Matters Now

    Qwen, the large language model developed by Alibaba Cloud, has recently been gaining significant attention. The release of Qwen 2.5-Max and its successors has sparked comparisons across benchmarks covering reasoning, coding, long-context handling, and multimodal tasks. Meanwhile, ChatGPT continues to dominate as the default choice for many users who prioritize conversational quality, creative tasks, and ease of use. Comparing the two is increasingly important for anyone deciding where to invest their time, money, or infrastructure in 2025.

    Let’s explore how Qwen and ChatGPT compare across major performance categories — and which model might suit your needs better.


    Where Qwen Shines: Power, Context, and Flexibility

    One of Qwen’s strongest features is its ability to handle long-context reasoning and document-heavy workflows. With larger context windows than many competitors, Qwen is particularly adept at analyzing long reports, writing consistent long-form content, summarizing legal or technical material, and managing multi-layered input without losing coherence. It’s a powerful tool for users who need depth.

    Qwen also excels in structured logic and code-related tasks. In independent evaluations, it has shown impressive results in mathematical reasoning, data extraction, and code generation. For developers and technical users looking for an AI assistant to support real engineering workflows — rather than simply explain code snippets — Qwen is a highly capable alternative to established incumbents.

    Multimodal and multilingual flexibility is another area where Qwen stands out. It supports text, image input, and multiple languages, enabling it to serve as a true assistant across varied communication and media formats. That’s particularly useful for global users or teams operating in bilingual or multilingual environments.

    Finally, the open-source accessibility of Qwen is a major advantage. While not every version is fully open, many variants are freely available and can be run locally or fine-tuned. For users prioritizing data control, customization, or cost-efficiency, that’s a serious point in Qwen’s favor.


    Where ChatGPT Excels: Conversation, Creativity, and Ecosystem

    ChatGPT continues to lead when it comes to polish and user experience. Its conversational flow is smooth, stylistically natural, and often feels more human than any other model on the market. That’s invaluable for creative writing, ideation, storytelling, or any application that requires tone, style, and nuance. It’s also why many casual users prefer ChatGPT over more technical models.

    ChatGPT’s integration with live data, APIs, and tools (depending on the version) provides a dynamic and extensible platform for users who need real-time insights or app-level functionality. If you’re looking for an assistant that can browse the web, generate code, search documentation, or plug into third-party services, ChatGPT is often the more mature choice.

    Consistency, reliability, and safety mechanisms also remain a strength. For teams or individuals who don’t want to think about model drift, hallucination tuning, or backend parameters, ChatGPT offers a plug-and-play solution that’s hard to beat. It’s a tool that just works — and that simplicity matters more than benchmark scores for a wide audience.

    The scale and maturity of ChatGPT’s ecosystem also give it a clear edge. From community guides to business integrations, apps, and workflows — it’s supported nearly everywhere, and that makes it easy to adopt regardless of your skill level.


    Limitations and Trade-offs

    That said, Qwen and ChatGPT each come with their own trade-offs.

    Qwen, while powerful, sometimes lacks the fluency or stylistic finesse that makes ChatGPT feel so natural. It can hallucinate in edge cases, and while some versions are open-source, the most powerful iterations may still depend on Alibaba’s infrastructure, limiting portability for privacy-centric users.

    ChatGPT, for its part, is a closed model, with cost barriers and fewer customization options. It also has a more constrained context window in some versions, making it less ideal for ultra-long documents or advanced reasoning across large data structures.


    Which Model Should You Use?

    If your work involves processing long documents, building tools, working with code, or requiring multilingual support — and you value the ability to run models locally or integrate them deeply — Qwen is an excellent fit. Its performance is strong, and it offers more technical freedom for advanced users.

    If your needs are creative, conversational, or content-driven — and you want something intuitive, responsive, and polished out of the box — ChatGPT is still the best experience available today. It’s perfect for brainstorming, writing, email generation, and any task where clarity, creativity, and tone matter.

    For enterprise teams, researchers, and power users — using both might be the optimal solution. Qwen can handle the heavy lifting in development and data, while ChatGPT takes care of interaction, presentation, and ideation.


    Final Verdict

    There’s no absolute winner in the Qwen vs. ChatGPT debate — only better fits for different tasks. Qwen brings muscle, flexibility, and context awareness. ChatGPT delivers fluency, elegance, and seamless usability.

    In the AI race of 2025, the smartest move isn’t to pick a side — it’s to pick the right tool for the job.

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    Alibaba’s AI Coup: Qwen App Hits 10 Million Downloads in One Week — And the AI Wars Just Escalated

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    A Meteoric Debut for Qwen

    Alibaba’s freshly launched Qwen app has crossed 10 million downloads in just its first seven days — a staggering adoption rate that places it among the fastest-growing AI applications globally. The explosive start signals more than consumer interest. It marks Alibaba’s transition from infrastructure giant to serious AI contender in the public arena.

    Qwen Isn’t Just Another Chatbot

    At the core of Qwen’s early success is its engine: the Qwen model family, developed in-house by Alibaba. These large language models (LLMs) are multimodal — capable of processing not just text, but also images, audio, and potentially video. Unlike other AI tools that remain sandboxed in niche applications, Qwen is designed as a true all-in-one assistant.

    From drafting documents and summarizing reports to answering questions and managing multimedia tasks, Qwen is built to be useful — not just entertaining. And critically, it launched with a free-access model, eliminating the subscription paywall that often hinders adoption in early-stage AI apps.

    From E-Commerce to Everyday AI

    This launch represents a clear strategic pivot for Alibaba. Historically known for e-commerce dominance and its powerful cloud infrastructure (via Alibaba Cloud), the company is now positioning itself as a top-tier player in the AI space — not just at the backend, but at the consumer-facing layer.

    Qwen is not just a product — it’s a platform play. It ties into Alibaba’s cloud resources, shopping ecosystem, productivity tools, and eventually, financial services. By releasing it as a standalone, viral consumer app, Alibaba is laying the groundwork for a much bigger AI ecosystem play.

    Global AI Ambitions, Starting in Asia

    While Qwen’s initial rollout is concentrated in China and Southeast Asia, there are clear signs Alibaba intends to push the app globally. With Western alternatives like ChatGPT, Claude, and Gemini facing geopolitical and regulatory barriers in some regions, Qwen could capitalize on being both regionally accessible and locally optimized.

    Additionally, the app’s early traction reflects strong demand for AI solutions tailored to regional languages, customs, and ecosystems. As Chinese tech continues to look outward, Qwen may become a cultural as well as a technological export — one capable of competing head-to-head with the biggest names in global AI.

    The Next Phase: Monetization and Market Power

    Crossing 10 million downloads in a week is only the first milestone. The real test lies in retention, monetization, and integration. Alibaba will now focus on converting casual users into power users, offering advanced features, integrating payments, cloud-based services, and potentially leveraging the app to strengthen its broader commercial footprint.

    There is already speculation that Qwen could evolve into the “WeChat of AI” — a super-assistant that combines messaging, productivity, shopping, and finance in a single intelligent interface. If that vision materializes, Alibaba may have just positioned itself as the most powerful AI consumer company outside the West.

    Final Thought

    The Qwen launch is not just about downloads. It’s about direction. Alibaba has made its move — not with hype or vague roadmaps, but with a working, useful, and widely adopted AI assistant. The global AI race is officially more competitive than ever.

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