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“Failing to Understand the Exponential, Again”: Why Some AI Observers Get Progress Wrong

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AI fatigue is real. After the initial waves of excitement around large language models, some industry watchers are beginning to murmur that progress is slowing. Updates feel more incremental, and new releases often seem like fine-tuned reruns of past breakthroughs. But beneath the surface, an entirely different story may be unfolding—one that points not to stagnation, but to a hidden acceleration.

Julian Schrittwieser, a former DeepMind researcher, has emerged as one of the most vocal critics of the “AI slowdown” narrative. Drawing on data from two rigorous evaluation sources—METR and OpenAI’s newly unveiled GDPval benchmark—he argues that AI capabilities are continuing to improve exponentially. In other words, just because the changes aren’t always visible in flashy demos doesn’t mean progress isn’t happening. It is. And it may be faster than most people think.

Measuring Autonomy: What METR Reveals About AI’s Hidden Progress

METR, which stands for Model Evaluation and Threat Research, is a nonprofit research organization dedicated to evaluating AI models on long-horizon tasks. These are challenges that require a model to maintain coherence and problem-solving performance over extended periods, typically several hours. One of METR’s core benchmarks measures the “time horizon” that an AI model can autonomously handle before failing. According to their findings, this time horizon has been doubling roughly every seven months.

Schrittwieser highlights recent results showing that GPT‑5, OpenAI’s latest unreleased model evaluated under controlled conditions, can now successfully complete software engineering tasks lasting over two hours with a 50 percent success rate. This is a significant leap compared to its predecessors and suggests that the model is becoming increasingly capable of tackling real-world, open-ended problems without constant human supervision.

Importantly, these gains are not sudden jumps tied to splashy releases. Instead, they follow a clear exponential trend, with each model iteration contributing a steady increase in autonomous task completion. This undermines the perception that progress has plateaued. Rather than diminishing returns, Schrittwieser sees evidence of compounding returns, especially in areas like planning, code synthesis, and long-form reasoning.

GDPval and the Rise of “Real Work” Benchmarks

To further reinforce his case, Schrittwieser draws on OpenAI’s GDPval benchmark, a novel attempt to measure how AI models perform on economically meaningful tasks across a broad range of professions. GDPval includes over 1,300 real-world tasks designed by experienced professionals in law, finance, engineering, consulting, healthcare, and other high-skill industries. These tasks are not mere academic puzzles; they reflect what experts actually do in their day-to-day work.

The performance of advanced models like GPT‑5 and Anthropic’s Claude Opus 4.1 on GDPval is striking. In many cases, these models approach or even match human expert performance. They demonstrate competence in tasks ranging from legal drafting and financial modeling to software debugging and clinical decision-making. While they are not flawless—and certainly not human replacements—they show a level of professional proficiency that was unthinkable just a few years ago.

What makes this particularly compelling is the alignment between METR and GDPval. One measures long-duration autonomy; the other assesses economic usefulness. When both point to rising capabilities, the case for a hidden acceleration becomes harder to ignore. Schrittwieser suggests that improvements in one domain reinforce the others, indicating systemic advances rather than isolated spikes.

The Illusion of Stagnation

Why, then, do so many commentators believe that AI progress is slowing? Schrittwieser argues that many are falling into a cognitive trap: underestimating exponential trends because they seem linear until suddenly they are not. Just as early internet observers in the 1990s dismissed the web for its slow load times and clunky design—failing to anticipate the compound impact of Moore’s Law—today’s AI skeptics may be misjudging the curve.

Another reason for the disconnect is that benchmarks like METR and GDPval don’t capture the public’s imagination the way a viral chatbot can. It’s easier to notice changes in personality or style than in abstract measures of planning depth or time horizon. But these are precisely the metrics that matter when considering how close AI is to performing complex work independently.

Furthermore, there is a growing divide between user experience and backend capability. ChatGPT, Claude, and other tools are increasingly fine-tuned for safety, alignment, and predictability, which can obscure the raw potential of the underlying models. This intentional dampening can make newer models appear less capable or “dumber,” even when their core abilities are dramatically stronger.

Caveats and Counterpoints

Schrittwieser’s argument is not without its critics. Some point out that a 50 percent success rate on a benchmark task does not equate to a production-ready AI system. Real-world applications often demand near-perfect reliability, especially in domains like healthcare or law. Others question whether one-shot evaluations, like those used in GDPval, truly reflect the iterative, messy nature of human work.

There’s also the issue of benchmark overfitting. As AI companies design models increasingly with these evaluations in mind, it becomes harder to tell whether we’re measuring genuine general intelligence or simply training for the test. Moreover, most of the available data still comes from controlled settings rather than open deployment. Until these systems prove their worth in the wild, some skepticism remains warranted.

Even so, Schrittwieser’s broader point stands: the trendlines suggest improvement, not stagnation. And if those trends continue, then today’s caveats may become tomorrow’s footnotes.

Rethinking the Narrative

The implications of this hidden exponential growth are profound. If AI capabilities continue to compound at current rates, we could see systems capable of autonomously completing full workdays within a year or two. That doesn’t mean mass unemployment or overnight superintelligence, but it does suggest that the pace of change may soon accelerate beyond what most institutions are prepared for.

Policymakers, business leaders, and the public would do well to recalibrate their expectations. The real risk may not be overhyping AI—it may be underestimating how quickly it’s evolving in ways that matter most. The challenge now is to shift the conversation away from flashy demos and toward deeper questions about deployment, safety, and integration.

Schrittwieser’s warning is clear: don’t confuse surface stillness with underlying inertia. The exponential, once again, is easy to miss—until it isn’t.

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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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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