A recent social media experiment by artist SHL0MS exposed something deeper than just another online argument about AI art — it revealed how strongly perception now shapes artistic judgment.

The setup was simple.

SHL0MS posted an image online and claimed it was AI-generated in the style of famed impressionist painter Claude Monet. He then challenged users to explain, in detail, why the “AI image” was inferior.

The internet responded exactly as expected.

Thousands of comments poured in criticizing the artwork:

• “Emotionless”
• “Soulless”
• “AI slop”
• “Flat composition”
• “Bad reflections”
• “No depth”
• “Lacks human feeling”

There was just one problem.

The image was not AI-generated at all.

It was an authentic Monet painting from the famous Water Lilies series, created around 1915.

The Experiment Wasn’t About Monet

The painting itself was almost irrelevant.

What mattered was the label.

The moment viewers believed the image came from AI, many immediately switched into critique mode — actively searching for flaws, artificiality, and emotional emptiness. The exact same image, if presented honestly as a Monet masterpiece, would likely have received admiration instead of ridicule.

That reaction says a lot about the current state of the creative industry.

Today, “AI-generated” often functions less as a technical description and more as an emotional trigger.

For many artists, designers, writers, and creatives, AI represents:

• automation,
• disruption,
• loss of identity,
• economic anxiety,
• or even cultural replacement.

As a result, discussions around AI art are increasingly driven by assumptions before the work itself is even evaluated.

The Psychology Behind the Reaction

Interestingly, this phenomenon is not just anecdotal.

Research from Norwegian academics in 2024 found that people frequently rated AI-generated artwork positively when they did not know its origin — but once told the work was AI-created, their perception dropped significantly.

In other words:
people often dislike the idea of AI art more than the art itself.

This is a classic cognitive bias problem.

Humans rarely judge creative work in a vacuum. Context matters:

• the artist’s identity,
• the perceived effort,
• the story behind the work,
• and now increasingly, whether AI was involved.

The Monet incident simply made that bias visible in public.

The Creative World Is Entering a New Era

This backlash is understandable.

AI is rapidly changing creative workflows across:

• illustration,
• music,
• writing,
• film,
• software development,
• animation,
• photography,
• and design.

Many creators fear being replaced or devalued.

But history shows that major technological shifts almost always trigger resistance before normalization:

• photography disrupted painting,
• digital art disrupted traditional illustration,
• synthesizers disrupted music production,
• desktop publishing disrupted print design.

AI is now the next disruption layer.

The tension comes from the fact that AI challenges something deeply personal: the belief that creativity is uniquely human.

The Real Question Isn’t “AI vs Human”

The Monet-gate experiment accidentally revealed a more important truth:

People are increasingly reacting to labels instead of evaluating the work itself.

That creates a dangerous environment where:

• assumptions replace analysis,
• outrage replaces curiosity,
• and tribal identity replaces artistic discussion.

The future of creativity likely won’t be purely human or purely AI.

It will be collaborative.

Artists who learn how to direct, refine, and integrate AI tools may become more powerful creators — not lesser ones. Meanwhile, audiences will eventually need to develop more nuanced ways of judging authenticity, originality, and craftsmanship in an AI-assisted world.

Because if thousands of people can mistake a real Monet for “AI slop,” the conversation clearly isn’t just about art anymore.

It’s about perception, fear, identity, and the uncomfortable speed of technological change.

OpenAI has expanded its AI coding ambitions by rolling out Codex preview support inside the ChatGPT across all plans — a move that signals the next phase of long-running AI development workflows.

The update allows developers to monitor, manage, and interact with AI-powered coding tasks directly from their phones while the actual execution continues on a laptop or remote environment.

This is not just a convenience feature. It is part of a rapidly evolving competition between OpenAI and Anthropic for ownership of the emerging AI developer tooling ecosystem.

What Codex Mobile Actually Changes

Instead of requiring developers to stay physically connected to their machines, Codex now enables:

• Live monitoring of long-running coding sessions
• Reviewing code changes remotely
• Approving actions and workflows
• Managing plugins and execution context
• Dispatching new development tasks from mobile

The actual agent runtime still operates on the developer’s computer or remote host, but the phone becomes the orchestration and supervision layer.

That distinction matters.

We are starting to move from:

• “AI chat assistants”
  to:
• persistent AI execution environments with human oversight loops.

The Bigger Architectural Shift

One of the most interesting parts of OpenAI’s announcement was the mention of a “secure relay layer” that avoids exposing the developer’s machine directly to the public internet.

That is a subtle but important architecture decision.

As AI agents begin operating continuously for hours — or eventually days — security, orchestration, synchronization, and approval governance become core platform concerns.

This starts looking less like:

• a chatbot feature

and more like:

• distributed agent infrastructure.

The mobile device effectively becomes:

• a control plane,
  while the developer workstation becomes:
• an execution plane.

That separation is very similar to patterns we already see in cloud-native systems and modern distributed architectures.

OpenAI vs Anthropic: The Agent Platform Race

Anthropic has already been pushing in this direction with:

• Remote Control
• Dispatch
• expanded mobile accessibility for Claude

OpenAI’s messaging appears intentionally competitive, emphasizing that Codex is:

“more than the ability to remotely control a single task.”

That wording feels directly aimed at the broader race to define how developers will manage autonomous AI workflows in the future.

The real competition may no longer be:

• which model writes better code

but instead:

• which ecosystem manages persistent AI work most effectively.

Why This Matters

The quality-of-life improvement is obvious:
developers no longer need to stay glued to a desk while long-running AI tasks execute.

But strategically, this points toward something larger:

• persistent AI workers
• asynchronous software development
• mobile orchestration of cloud-hosted agents
• human approval checkpoints
• distributed execution environments
• AI operational governance

As models improve at reasoning, debugging, refactoring, and tool usage, the ability to supervise and steer agents remotely becomes increasingly valuable.

We may be approaching a future where developers spend less time “typing code” and more time:

• directing,
• validating,
• governing,
  and
• orchestrating AI systems.

The desk is no longer the center of the workflow.

https://openai.com/index/work-with-codex-from-anywhere

At its recent Android Show event, Google unveiled one of its strongest signals yet that the future of computing will not revolve around standalone AI apps — but around AI-native operating systems and devices.

The announcements went far beyond chatbot upgrades.

Google introduced a new generation of Gemini-integrated “Googlebook” laptops, expanded Gemini deeper into Android, demonstrated AI-driven interface concepts like the “Magic Pointer,” and previewed a broader vision where AI acts less like a tool and more like an intelligent execution layer across devices and applications.

This feels less like adding AI features to products and more like redesigning the computing experience around AI itself.

Gemini-Native Laptops: AI as a Core Device Layer

Google announced a new family of Gemini-native laptops developed alongside hardware partners including:

• Dell
• HP
• Lenovo
• Acer
• ASUS

Unlike traditional laptops where AI assistants exist as isolated applications, these devices are positioned as “Gemini-native,” meaning AI is integrated directly into the interaction model, workflows, and operating experience.

One of the more interesting concepts shown was the “Magic Pointer” — an AI-enhanced cursor capable of understanding screen context, user intent, and interaction patterns.

This is important because it shifts AI from conversational-only interfaces into contextual computing.

Instead of:

• opening an assistant,
• typing prompts,
• and manually moving data between apps,

the operating system itself becomes aware of what the user is doing.

That is a major architectural shift.

Android + ChromeOS + Gemini = Platform Convergence

Another significant development is Google’s increasing convergence of:

• Android
• ChromeOS
• Google Play
• Gemini AI services

The new devices are expected to support Android apps and Android-native workflows directly on laptops, blurring the boundaries between mobile and desktop ecosystems.

This resembles a broader industry trend:

AI is becoming the orchestration layer across platforms rather than an isolated feature inside them.

The operating system increasingly acts as:

• a context engine,
• an execution orchestrator,
• and an intelligent workflow coordinator.

This is especially relevant for enterprise and productivity scenarios where users continuously switch between:

• email,
• browsers,
• documents,
• messaging,
• business systems,
• and cloud applications.

An AI layer capable of understanding cross-application context has enormous implications for productivity and automation.

Gemini Intelligence: Toward an Agentic Computing Model

Perhaps the most strategically important announcement was “Gemini Intelligence” — described as a cross-device AI system capable of operating within apps and understanding on-screen context.

This moves closer to what many in the industry are calling agentic computing.

Instead of only answering questions, the AI can potentially:

• navigate interfaces,
• coordinate workflows,
• perform multi-step actions,
• and interact with applications on behalf of users.

That distinction matters.

Traditional assistants are reactive.

Agentic systems become operational participants inside workflows.

This is the same direction increasingly appearing across the industry:

• AI copilots
• autonomous workflow orchestration
• context-aware execution systems
• multi-agent coordination models

Google appears to be embedding these concepts directly into Android infrastructure itself.

Smaller Features That Actually Matter

Some of the smaller announcements may ultimately become the most impactful in daily use.

Create My Widget

An AI-generated customization system for dynamically creating Android widgets.

Rambler Dictation

A dictation tool that automatically removes filler words and conversational noise.

This is particularly interesting for:

• meetings,
• executive communication,
• documentation,
• and professional content generation.

Gemini Auto-Browse in Chrome

An AI browsing capability operating locally on-device.

On-device inference is increasingly important because it improves:

• privacy,
• latency,
• responsiveness,
• and offline capability.

This is likely where AI platform competition will increasingly move over the next few years.

Why This Matters

Many companies are still treating AI as an add-on feature.

Google appears to be moving toward something larger:

AI as an operating system capability.

That changes the competitive landscape significantly.

While the industry continues waiting for Apple to fully reinvent Siri for the AI era, Google is aggressively integrating Gemini directly into:

• Android,
• Chrome,
• hardware,
• productivity workflows,
• and user interaction models.

The strategic advantage here is ecosystem depth.

Google already controls:

• mobile OS infrastructure,
• browser infrastructure,
• cloud AI infrastructure,
• productivity tooling,
• and a massive app ecosystem.

If Gemini becomes the orchestration layer across all of those surfaces, Google could establish one of the first truly AI-native consumer computing ecosystems.

Final Thoughts

The biggest takeaway from the Android Show event is not any single feature.

It is the architectural direction.

We are moving from:

• apps → intelligent workflows
• assistants → execution systems
• operating systems → AI orchestration layers

The companies that successfully integrate AI into the actual fabric of computing — instead of treating it as a side feature — are likely to define the next platform era.

Google’s Gemini strategy suggests they understand that race very clearly.

https://blog.google/products-and-platforms/platforms/android/gemini-intelligence

Artificial intelligence is rapidly evolving from a passive assistant into an active research collaborator — and Google DeepMind just demonstrated one of the clearest examples yet.

In a newly published paper, DeepMind introduced an AI co-mathematician system built on Gemini 3.1, designed specifically to help mathematicians tackle difficult and unsolved research problems. The system achieved state-of-the-art results on research-level mathematics benchmarks and even contributed to a real breakthrough involving an open mathematical problem.

From AI Chatbot to AI Research Team

What makes this system different is that it does not behave like a single chatbot answering questions sequentially.

Instead, DeepMind modeled the architecture after modern AI coding agents such as Anthropic’s Anthropic Claude Code-style workflows — using coordinated teams of AI agents working in parallel.

The architecture includes:

• A coordinator agent that breaks large mathematical problems into smaller research tracks
• Multiple specialized sub-agents assigned to explore different solution paths simultaneously
• Built-in review and critique loops where agents evaluate and reject weak approaches
• Capabilities for:
  • writing code
  • searching mathematical literature
  • generating proof attempts
  • testing conjectures

This represents a shift from “answer generation” toward something much closer to a distributed research environment.

The Most Interesting Part: A Rejected Idea Led to a Discovery

One of the most fascinating outcomes came from Marc Lackenby of the University of Oxford.

While reviewing outputs from the system, Lackenby identified what he described as a “really, really clever proof strategy” hidden inside an output that had actually been rejected by the AI review process.

That insight helped resolve an open problem from the Kourovka Notebook — a long-standing collection of unsolved problems in group theory.

This detail matters because it highlights something important about the future of AI research systems:

The value is not only in perfect final answers.
It is increasingly in the generation of novel intellectual directions that human experts can recognize, refine, and complete.

Benchmark Performance Was a Major Leap

The system was also evaluated on FrontierMath Tier 4 problems from Epoch AI.

Results were striking:

• The co-mathematician system scored 48%
• Gemini 3.1 Pro alone scored 19%

That means the agentic research workflow more than doubled the raw performance of the underlying foundation model.

This reinforces a growing industry trend:

The orchestration layer around frontier models is becoming as important as the model itself.

We are seeing the same pattern emerge in:

• software engineering agents,
• cybersecurity tooling,
• research automation,
• scientific discovery systems,
• and enterprise workflow automation.

Why This Matters Beyond Mathematics

Mathematics is one of the hardest domains for AI because it requires:

• long-horizon reasoning,
• abstraction,
• symbolic consistency,
• proof validation,
• and exploration of multiple competing paths.

Success here suggests that agentic AI systems may soon become meaningful collaborators in:

• physics,
• chemistry,
• engineering,
• medicine,
• finance,
• and systems architecture.

For experienced engineers and architects, this is especially important because it validates a broader industry direction:

The future is likely not a single “super AI,” but orchestrated ecosystems of specialized agents operating together with humans in the loop.

Human Expertise Still Matters

Despite the impressive benchmark scores, the most important takeaway may actually be the human role in the process.

The breakthrough came because an expert mathematician recognized value in an imperfect output that the system itself discarded.

That mirrors what many senior engineers already experience with AI tooling today:

• AI accelerates exploration,
• proposes novel directions,
• automates repetitive reasoning,
• and expands idea generation,
• but expert humans still provide judgment, validation, prioritization, and contextual understanding.

Rather than replacing experts, these systems are increasingly becoming force multipliers for highly skilled people.

Final Thoughts

The DeepMind co-mathematician project is another signal that AI is moving beyond conversational assistance into structured, multi-agent problem solving.

Just as AI coding agents transformed software development workflows, agentic research systems may fundamentally reshape scientific and mathematical discovery over the next decade.

The most powerful future may not be human vs AI.

It may be elite human expertise amplified by coordinated AI systems operating at research scale.

https://arxiv.org/pdf/2605.06651