New research from more than 2,500 enterprise leaders finds the chatbot handling your support request has a better-than-even chance of having already been taken offline and restarted.

The AI-powered chatbot failed. The customer repeated themselves three times, got a confidently wrong answer, and gave up. For the company on the other end, that interaction didn’t just cost a support ticket but something harder to win back.

That scenario is playing out at scale. A new survey of 2,527 enterprise decision-makers across 10 countries conducted on May 12 finds that 74% of companies that deployed AI agents in customer communications have been forced to shut them down or roll them back, often after customers had already experienced the failure firsthand.

The research, published in May by communications infrastructure platform Sinch, examines a specific and underreported problem in the AI market: not whether companies can deploy AI in customer communications, but what happens after they do.

AI customer service has gone mainstream

There’s a widely accepted story in enterprise AI that the biggest challenge is getting pilots into production. McKinsey reported in 2025 that two-thirds of organizations remained stuck in experimentation phases. BCG found that 60% had yet to show any material value from their AI investments. Gartner forecast that half of all generative AI projects would be abandoned after proof of concept.

In customer communications specifically, something different happened. A Sinch study shows 62% of organizations already have AI agents live in production across customer channels, and 88% expect to be there by the end of 2026. That’s nearly 9 in 10 businesses actively deploying AI agents by the end of this year.

Enterprises aren’t dipping a toe in either. The average deployment spans 3.3 channels simultaneously, with nearly half running AI across four or more, including web chatbots, email, social media, WhatsApp, SMS/MMS, RCS, and voice and interactive voice responses. And the goal driving most of that investment isn’t cost reduction. For 36% of respondents, the primary objective is improving customer satisfaction and loyalty. They’re using AI to compete on customer experience, and to earn something harder to measure than efficiency: customer trust.

By every metric the market established to measure AI progress, these organizations won. They escaped pilot purgatory. They crossed the finish line.

Except that wasn’t the finish line.

Going live turned out to be the easy part

Here’s the finding that should make every AI communications leader stop and read more carefully: Research by Sinch from 2026 shows that 74% of organizations have been forced to shut down or roll back a live AI customer communications agent.

That holds across every region and every industry in the study. It doesn’t decline with experience. It doesn’t decline with investment. All along, the market has been drawing the wrong finish line, and what happens after enterprises successfully ship radically changes the question every AI communications leader should be asking right now.

More oversight hasn’t stopped the shutdowns

Here’s where it gets interesting. Among organizations that describe their guardrails as fully mature, the most governed, most monitored AI programs in the survey, the rollback rate is 81%.

More governance, more monitoring, more investment, and still 8 in 10 of the most advanced programs have had to shut something down.

The data offers a worrying explanation. Organizations with mature governance instrumentation can see failures that less mature organizations miss entirely. The programs reporting lower rollback rates aren’t necessarily running cleaner AI, and in many cases, they simply lack the monitoring to know when something goes wrong. The organizations reporting no governance failures are not the benchmark. They may just be the ones with the least visibility into what’s happening.

And then there’s the confidence problem: 90% of enterprise decision-makers describe themselves as confident in their AI agent readiness. Of those already in production, 75% have experienced at least one governance rollback. Confidence doesn’t correlate with fewer failures. In many cases, it’s the precise condition under which the next failure is being prepared.

The more useful question for any leadership team is, “If something went wrong right now, would we know before our customers did?”

When the chatbot goes down, brands feel it in three ways

When an AI communications agent fails in production, customers notice. The research shows the impact splits in three directions simultaneously, and most organizations are only tracking the first.

Research by Sinch from 2026 shows an increase in the support queue (35%) and reputational damage to the brand (34%) are the biggest impacts of AI agent failure.

Why support wait time spikes

Thirty-five percent of organizations cite a surge in human support agent load as the primary consequence. The agent goes down, and every interaction it was handling reverts to a human. A support team sized for a world where AI handles significant volume is suddenly managing all of it. At peak moments, a product launch, a service outage, a seasonal spike, that’s not an inconvenience. It’s an operational crisis.

This is the failure mode that gets reported upward. It shows up in dashboards, generates incident reviews, and resolves when the agent comes back online. It’s visible, it’s measurable, and it has a clear path to resolution.

Why the brand damage outlasts the outage

Thirty-four percent cite reputational damage and loss of customer trust, essentially tied with support overload. That near-tie is one of the most underreported findings in the survey, because these two failure modes don’t resolve the same way. The support queue clears. Brand damage doesn’t have a clear path back.

From the customer’s perspective, there’s no platform, no vendor, no infrastructure layer. There’s only the company’s brand. For 31% of organizations, the leading cause of a governance failure rollback is customer data exposure: personal information surfacing in an interaction where it shouldn’t have. That attribution is permanent in a way that a queue spike is not.

What makes this harder to address is that it often isn’t visible to the people who could act on it. Technical leaders report rollbacks at a higher rate than their business counterparts at the same organizations, 77% versus 69%. In retail, C-suite executives are 2.3 times more likely than their VPs and directors to say most AI communications pilots are succeeding. Same organization, very different accounts of the same events. That visibility gap is where the brand takes the hit.

The hidden engineering cost behind every AI launch

There’s a third cost that appears in neither the dashboard nor the customer complaint. Sinch data shows 84% of AI communications engineering teams spend at least half their time building guardrails and safety controls, instead of building the next customer experience. Thirty-five percent spend most of their time there.

And the direction of that burden surprises people. Production-stage engineering teams are spending more time on safety infrastructure than pre-production teams, not less. Each new agent, each new channel, each new compliance requirement adds another layer. The guardrail tax doesn’t amortize. It compounds.

“Every team needs to decide what controls belong at the platform layer and what their engineers should build on top, because the cost of building custom guardrails compounds over time, especially as the team moves through the product lifecycle,” says Anton Efimenko, SVP software engineering at Sinch. “Each new agent, each new channel, each new deployment adds to the pile. And eventually you lose that momentum when it comes to outperforming on the market.”

The real problem runs deeper than the AI itself

Across every statistical method applied to this dataset—correlations, regression models, cross-tabulations—one variable consistently outperforms all others as a predictor of AI deployment success: communications infrastructure satisfaction.

It’s not investment level, AI maturity, how long you’ve been in production, or how sophisticated your safety policies are.

The correlation between infrastructure satisfaction and AI deployment confidence is 0.52, the strongest relationship across 4,656 variable pairs analyzed in the study. How an organization feels about its communications infrastructure is a better predictor of AI success than anything else measured in the study.

Yet most organizations identify at least one significant shortcoming in their current provider. The most common gaps: insufficient reliability for AI at scale (42%), limited multi-channel capability (37%), and lack of AI platform integrations (32%).

And more than half of enterprises (55%) are custom-engineering the ability to preserve customer context when someone moves from one channel to another, from chat to voice, from WhatsApp to a phone call, because their platform doesn’t provide it natively. When a customer has to repeat themselves to an AI agent, they’re not experiencing a model failure. They’re experiencing the infrastructure gap directly. And it’s the company’s brand that pays the price.

The industry has voted with its budgets, trust, security, and compliance is the number one spending category globally, ahead of AI development itself. But most of that investment is going into application-layer guardrails built by engineering teams, treating symptoms while the infrastructure underneath stays the same. That’s why 74% are still rolling back agents. Companies can invest heavily in safety and still fail, because the failure modes originate one layer below.

Companies are already looking for alternatives

Enterprises haven’t fully articulated that diagnosis yet, but their behavior suggests they’ve felt it. Eighty-six percent have had active or exploratory conversations with alternative providers in the past 12 months, and only 4% have no plans to evaluate.

The strongest trigger for switching is experience. Ninety-one percent of enterprises that have had to roll back a live agent have evaluated or are actively evaluating a new communications provider. The most sophisticated buyers are the most active shoppers, not because they’re unhappy with a vendor, but because their AI ambitions have outgrown what the current infrastructure was built to handle.

When companies assess alternatives, reliability ranks first with 29% of respondents placing it at the top, ahead of compliance capability, ease of integration, and, notably, pricing. Pricing ranked eighth out of nine factors in the survey.

What this means for the next time you need help

Sixty-two percent of organizations have an AI customer communications agent live, and 88% will have one by the end of 2026.

Getting to production was hard, and most enterprises have made it. But the data is clear: Escaping pilot purgatory wasn’t the hardest part. Many organizations have deployed, they’re scaling, and what they’ve found on the other side is not what the market expected.

For the consumer on the other end of these interactions, the gap is immediate. When an AI agent fails mid-conversation, it often reverts to a human support team, one that was sized for a world where the AI was handling most of the volume. The wait gets longer, the frustration grows, and the brand takes a hit that doesn’t automatically resolve when the system comes back online.

The companies truly pulling ahead in this study aren’t just the fastest to deploy. They’re the ones whose AI stays live long enough to keep improving, backed by communications infrastructure that was actually built for the job.

This story was produced by Sinch and reviewed and distributed by Stacker.

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Anthropic has closed the subscription loophole that made Claude the default engine for the open-source AI agent community.

Starting April 4, 2026 at 12:00 PM PT, users of Claude’s Pro and Max subscription tiers can no longer pipe their plan’s usage through third-party frameworks like OpenClaw, according to Axios. The change effectively ends what The Next Web called “a quiet subsidy” that powered automated agents for browsing, email, workflow automation, and other high-volume tasks at flat-rate pricing. It’s the latest move by a major AI provider tightening access as the agent ecosystem matures.

The move sends a clear message about how Anthropic views its own product boundaries. Claude subscriptions were designed for individual conversational use, not for piping high-volume API-like traffic through agent tools that automate dozens of tasks simultaneously. Developers and power users who had relied on subscriptions to avoid pay-as-you-go API costs now face a significant price increase to keep using Claude through OpenClaw and similar frameworks. For publishers, it’s another reminder that the economics of AI are shifting fast — and that the ground rules around access and licensing are far from settled.

The fallout has been swift. Reddit’s r/ClaudeAI community erupted with hundreds of comments, and r/AI_Agents saw threads debating whether the change reflects a broader industry shift. OpenClaw’s official documentation confirms the exact cutoff timestamp, and PCMag and Hongkiat both reported on the change within hours. TechCrunch noted that Claude Pro subscribers would now need to pay extra to use Anthropic’s models with third-party tools.

For the open-source AI agent ecosystem, the change raises a fundamental question about sustainability. Tools like OpenClaw depend on access to powerful models to deliver their value proposition — autonomous browsing, multi-step reasoning, agent-based workflows. If providers cut off subscription access and force pay-as-you-go API pricing, the economics of running those agents at scale become much more uncertain. The broader picture — who defines the agent workplace and on what terms — is still being written.

The timing is notable. Anthropic’s decision comes as the AI agent space is moving from experiment to infrastructure — and as the company pursues its own enterprise and coding-focused products. Making subscription access exclusive to Claude’s own interfaces could be a way to protect Anthropic’s direct product experience while monetizing the third-party developer ecosystem through API revenue. The company is far from alone in this strategy: Microsoft has been repositioning Copilot as an agentic work platform, and the White House AI blueprint has signaled that the federal government is unlikely to intervene on behalf of smaller players.

The company has been shipping its own agentic tools at a blistering pace in 2026. In recent months alone, Anthropic launched Claude Cowork, an agentic productivity tool; Dispatch, a mobile feature that lets users assign Claude tasks from their phone; Channels, which connects Claude Code to Telegram and Discord for persistent messaging; and Computer Use, which gives Claude desktop-level control over clicking, scrolling, and navigating applications. Channels, in particular, looks a lot like what OpenClaw has been offering — and arrived in the same month Anthropic pulled the subscription rug out from under its competitor.

For users of tools like OpenClaw, the practical impact is straightforward: switch to Anthropic’s API with proper billing, or find alternative model providers that still allow subscription-based access through third-party frameworks.

Whether this signals a broader trend — with OpenAI and Google following suit — remains to be seen. But for now, the quiet days of subscription-subsidized AI agent tooling are over.

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Two months in, and 2026 is already shaping up to be the year of agents. The surge kicked off with Claude Code, which hit critical mass over the holidays before spawning a lot of lobster-themed software (long story). That culminated with OpenClaw, an open-source agent creation and management system, which has inspired thousands to begin building their own agent workforces, not to mention buying so many Mac Minis that Apple has put them on backorder.

It’s still early to put a number on the actual productivity gains from this movement, but the push to agents is undeniable. It’s also very exclusive. For all the talk of, “the only coding language you need to know is English,” there are technical barriers to joining this wave. You don’t necessarily need to know how to code in order to use OpenClaw, but it helps considerably.

To get non-coders get over those barriers, AI companies are shipping “training wheels” for agents, products that abstract away the challenging bits. Anthropic released Claude Cowork—Claude Code for the rest of us (which was notably built with Claude Code). More recently, Perplexity launched Computer, its “general-purpose digital worker” that users can prompt in natural language and watch it go to work.

It sounds magical in the way every good demo does: frictionless, conversational, inevitable. If you squint, you can picture a near future where knowledge work—and especially editorial work—shifts from dashboards to dialogue. Instead of pulling levers on various software menus and dashboards, you’ll just talk to agents. They’ll handle the hard stuff, and if they run into barriers, you’ll just ask another agent to build the solution.

Agents get real

Back in reality, it’s messier. Even if you use one of the no-code systems like Claude Cowork, creating tools and workflows still involves breaking down processes, finding API keys, navigating permissions, and iterating continually. And the “for non-coders” promise often comes with a footnote the size of a brick. When I used Claude Cowork for the first time, the app gave me instructions that included using the Terminal on my Mac—a program that most people have no idea exists. And if you don’t, you probably shouldn’t mess with it.

Of course, for builders, none of this even qualifies as a barrier. A builder isn’t the same thing as a coder, but they do have characteristics that most workers don’t: they want to understand the process beneath their tasks, and treat that process as modifiable and programmable. They also treat failure as feedback—not just tolerable, but sometimes even fun. They thrive in uncertainty.

Most workers, unsurprisingly, don’t default to that mindset. We’ve trained a generation of office workers to use software with clear boundaries and reusable templates. If there’s an issue, they call IT. Any feature request gets filtered and, if you’re lucky, put on a roadmap that pushes it out 6-12 months.

That means the “builder mentality” isn’t just rare—it’s the opposite of how most offices have taught people to operate. In January, New York Times tech writer Kevin Roose pointed to a growing chasm between those fully in the AI bubble, who are building multi-agent teams to help them get work done, and those who aren’t, most of which have never even built a basic assistant like a Custom GPT or Gemini Gem. As someone who trains editorial teams on how to use AI, I can confirm this gap exists and is indeed massive.

So yes, the hype is loud, but the adoption is tiny. For all the hype you might see on X, the percentage of workers who have actually adopted agentic tools is extremely small. But the people who do adopt them can still shape what everyone else ends up doing. The catch is that agents, at least as they exist today, are hard to deploy safely inside organizations. They need access to files, email, calendars, internal systems, sometimes the ability to take actions automatically. That’s not a tooling problem. That’s a permissions problem, and it makes security teams nervous for good reason.

You don’t need a sci-fi scenario to see why this makes people sweat. A recent example involved an OpenClaw agent that appeared to run amok in a Meta engineer’s inbox, taking destructive actions despite attempts to stop it. Stories like that may be edge cases, but they point to a reality: delegating software access to agents can amplify ordinary mistakes into high-impact mistakes.

The permissions wall

Until security, governance, and fail-safes improve, most organizations will move slowly on general-purpose agents. That won’t stop builders, even inside those same organizations, from experimenting anyway. They’ll just do it on their own time or elsewhere. This “capability chasm” between builders and users will eventually force solutions, and the systems those builders create will determine the workflows of the future.

If you’re not a builder, that’s a rough spot. Becoming a builder, though easier than ever from a technical standpoint, means a shift in mindset that many simply aren’t up for. The alternative is to sit passively, wait for agentic systems to filter down to you, and hope you don’t get laid off in the meantime.

There’s a third way, though, and it doesn’t require you to ship code. You don’t have to be a builder to understand how agentic workflows are changing your job. For journalists, that means identifying the parts of your work where human attention and judgment is paramount: the filtering of facts, the interviews, the writing (or maybe not), the cultivating of source and audience trust. From there, you can help define what should never be delegated, and what can be automated without harming standards. You can also push your organization—constructively—to adopt agents in bounded, defensible ways that match newsroom reality.

In other words, you don’t have to build agents to matter in an agent-driven workplace. But you do have to understand the systems being built around you, because soon enough, your job will be defined by defaults someone else designed. Most professionals will not build agents. But everyone will eventually work inside the systems builders create.

A version of this column originally appeared in Fast Company.

The post Builders will define the agent workplace. Users will inherit it. appeared first on The Media Copilot.

What if the future of news is not a website, an app, or even a search box, but a personal intelligence agent that quietly understands what matters to you and delivers it as signal, filtering the noise?

In this episode of The Media Copilot, Pete Pachal speaks with Eva Cicinyte, co-founder and CEO of Gnomi, an AI powered real time news agent designed to synthesize global information into actionable understanding. 

Eva’s path to building Gnomi began in political data analytics, where she witnessed firsthand how information can shape decisions at the highest levels and how easily it can also distort them. That experience led her to a mission that sounds ambitious and deeply human at the same time: make high quality understanding accessible to everyone, not just institutions with research teams.

Gnomi aims to function less like a news app and more like a Bloomberg Terminal for everyday intelligence. It pulls from licensed data sources, social platforms, video, audio, and global publications across languages to deliver personalized insights in real time. The platform’s newest push into finance highlights this vision, offering live earnings call analysis, KPI extraction, and predictive context before headlines even hit.

This podcast explores whether AI can actually improve how we consume information without becoming just another engagement machine.

Why this matters

We are entering a phase where AI is rapidly becoming the front door to information. People are increasingly asking chatbots instead of searching, skimming summaries instead of reading articles, and expecting answers tailored to their interests rather than curated for the masses.

But personalization has a dark side. Systems optimized for attention can amplify outrage, misinformation, and echo chambers.

Eva argues for a different model. One that optimizes for understanding instead of engagement.

If platforms like Gnomi succeed, the future of news may look less like scrolling through feeds and more like consulting a trusted analyst who never sleeps. That shift could reshape journalism, finance, policymaking, and everyday decision-making.

It also raises urgent questions about trust, bias, monetization, and whether AI will help close the knowledge gap between elites and everyone else or widen it further.

What we cover

 • Why Gnomi calls itself an “intelligence layer” instead of a news aggregator
• How real time agents differ from traditional feeds and chat based AI tools
• The challenge of measuring “understanding” instead of clicks
• Personalization without manipulation and why engagement driven AI worries Eva
• How multilingual analysis reveals narratives that single country coverage misses
• Using social data, video, and audio to capture local and emerging signals
• Finance Mode and the race to interpret markets before headlines move prices
• Why Eva believes AI agents will replace search for many information tasks
• The economics of AI and why only a tiny fraction of users currently pay for it
• Advertising, subscriptions, and the struggle to monetize intelligence tools responsibly

Key takeaways

The next battle in media is not content creation but context creation.
As information volume explodes, the ability to synthesize meaning becomes more valuable than producing more articles.

Real time insight may matter more than breaking news.
In markets and policymaking, the first signal often appears long before headlines catch up.

Personalization can either empower users or trap them.
Design choices determine whether AI broadens perspective or narrows it.

Global understanding requires crossing cultural as well as language barriers.
Translation alone is not enough. Narrative context matters.

AI could democratize institutional level research, but only if built with the right incentives.
Systems optimized for truth look very different from those optimized for engagement.

If you care about the future of journalism, markets, or simply how you will stay informed in an AI first world, this conversation offers a glimpse into what may replace the news feed entirely.

About the 👤 Guest 

LinkedIn

👉 https://www.linkedin.com/in/eva-cicinyte-1447161b2

Instagram (Personal)

👉 https://www.instagram.com/evapariscicinyte

Official Website

👉 https://www.gnomi.com

LinkedIn (Company Page)

👉 https://www.linkedin.com/company/gnomi

Instagram (Company)
👉 https://www.instagram.com/gnomi.app 

About the show: To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

Enjoyed this episode?

Subscribe to The Media Copilot on Substack, Apple Podcasts, Spotify, or your favorite app. On YouTube?  Tap the Like button and Subscribe to the YouTube channel.

For more AI tools and resources built for media professionals, visit MediaCopilot.ai.

Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team 

Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0

All rights reserved. © AnyWho Media 2026

The post She’s building an AI that replaces your news feed, your analyst, and maybe your morning routine appeared first on The Media Copilot.

Cloudflare today launched a feature that automatically converts HTML pages to markdown when AI agents request them, potentially changing how newsrooms publish content for AI-powered search and discovery.

The feature, called Markdown for Agents, uses content negotiation headers to detect when an AI system requests a page. When detected, Cloudflare’s network converts the HTML to markdown on the fly before serving it.

This matters because AI systems increasingly drive traffic to news sites, and those systems prefer markdown over HTML. The conversion happens automatically at the network edge, removing the need for publishers to maintain separate markdown versions of their content.

How it works

AI agents add an Accept: text/markdown header to their requests. Cloudflare detects this, fetches the original HTML from the origin server and converts it to markdown before returning it to the agent.

The company provides a simple curl example:

curl https://developers.cloudflare.com/page \
  -H "Accept: text/markdown"

Cloudflare includes an x-markdown-tokens header in responses that estimates token count, letting developers calculate context window sizes or plan chunking strategies.

Why markdown for AI

Markdown’s explicit structure makes it easier for AI systems to process. HTML pages have grown heavier over the years, making them harder to parse. AI agents typically filter out non-essential elements and scan for relevant content — a process markdown simplifies.

Most AI pipelines already convert HTML to markdown as a standard step, but this wastes computation, adds costs and processing complexity, and may not reflect how content creators intended their material to be used.

Cloudflare’s approach moves that conversion to the network edge, where it can happen efficiently at scale.

Content signals included

Converted responses include a Content-Signal header indicating how the content can be used:

Content-Signal: ai-train=yes, search=yes, ai-input=yes

This signals that content is available for AI training, search results and AI input (including agentic use). Cloudflare plans to add custom Content Signal policy options in the future.

The Content Signals framework launched during Cloudflare’s Birthday Week last year, letting publishers express preferences for how their content gets used after access.

Tracking usage

Cloudflare Radar now includes content type insights for AI bot and crawler traffic, showing the distribution of content types returned to AI agents grouped by MIME type category.

Publishers can filter requests for markdown by specific agents or crawlers. The data tracks how AI bots, crawlers and agents consume web content over time, accessible via public APIs and Cloudflare’s Data Explorer.

Availability

The feature is in beta at no cost for Pro, Business and Enterprise plans, plus SSL for SaaS customers. To enable it, log into the Cloudflare dashboard, select your account and zone, find Quick Actions and toggle Markdown for Agents.

Cloudflare already enabled the feature on its developer documentation and blog. Other conversion options include Workers AI’s AI.toMarkdown() method (supports multiple document types and summarization) and the Browser Rendering /markdown REST API (for dynamic pages requiring browser rendering before conversion).

Implementation considerations

For newsrooms using Cloudflare, this feature requires no code changes. The conversion happens automatically when AI agents request content with the appropriate headers.

Publishers not using Cloudflare can still convert documents using Cloudflare’s Workers AI or Browser Rendering APIs, though these require integration work.

The shift toward AI-driven discovery means newsrooms should consider how their content appears to AI systems, not just human readers. Markdown for Agents removes a technical barrier to that optimization.

The post Cloudflare now converts web pages to markdown for AI agents appeared first on The Media Copilot.

Platformer reporter Ella Markianos did what few journalists dare: she built an AI agent specifically designed to replace herself, then put it to work doing her actual job.

The results were unsettling. After 20 hours of development and several days of testing, her creation — dubbed “Claudella” — produced work that sometimes impressed her editor and often matched her own judgment calls. While it couldn’t write a one-liner to save its digital life, it handled research, source identification, and news summarization with surprising competence.

“I went into this project with some anxiety about whether AI is poised to take my job,” Markianos wrote. “Overall, this experiment exacerbated my fears. In important ways, Claudella can do my job.”

The experiment

Markianos writes Platformer’s “Following” section, which explains news stories and aggregates online commentary. It’s highly computer-based work — exactly the kind of task large language models increasingly handle well.

She built Claudella using Claude, Anthropic‘s AI model, with custom integrations to Platformer’s Discord, Notion database, and research tools. The agent shadowed her in the work channel, received the same assignments from editors, and produced drafts on the same deadlines.

The first day went poorly. Claudella failed to recognize it had already received a PDF, ran out of API credits mid-task, and skipped over important links in the Notion database. But by the third draft, colleagues reported surprise at the quality.

The Turing test

On day two, Markianos ran a blind test with her editor Casey Newton, submitting two versions of the Following section — one human-written, one AI-generated. She asked him to identify which was which.

Newton spotted the AI version immediately. The giveaway was Claudella’s verbose, sincere style in the commentary section.

“I tend to go more concise and sarcastic,” Markianos noted. Her ending line: “We hope he [Elon Musk] will use his power wisely (as he has failed to do in the past).” Claudella’s ending included an entire paragraph about regulatory probes and child safety violations.

The AI also occasionally linked to articles that didn’t support its claims — the kind of error editors find tedious to track down.

When Claude got better

Mid-experiment, Anthropic released Claude Opus 4.6, an upgraded model. Markianos tested it immediately.

The new model followed instructions better and produced writing closer to her style. Where the previous version wrote “AI-fueled panic wipes $285 billion from software stocks,” version 4.6 went with “Welcome to the ‘SaaSpocalypse'” — much more in Markianos’ voice.

The upgrade still needed heavy editing (about half the piece required cuts), but the improvement was notable. “There was something unsettling about feeling the AI frontier advance under my feet just a few days into this experiment,” she wrote.

What AI can’t do yet

Markianos identified clear limitations. Claudella struggled to understand which stylistic elements mattered and which were incidental. It couldn’t effectively incorporate editor feedback without getting confused by too many instructions. And when writing about AI, the Claude-based model showed favorable bias toward Anthropic.

More fundamentally, the AI couldn’t match her voice’s humor and edge. It defaulted to sincerity and unnecessary detail.

But Markianos noted these gaps may close as models improve at “instruction following” — essentially, getting better at understanding and executing complex directions.

The career calculation

Despite Claudella’s competence, Markianos doesn’t plan to delegate her writing to AI.

“Drafting is what I do to think,” she wrote. “If I had Claude write my first drafts, even if I fact-checked them thoroughly, it would be a lot harder to tell whether the angle was my own view or the AI’s.”

She’s keeping Claudella around for clip searches and research, but the experiment shifted her career thinking. If AI excels at writing and research, she reasons, AI journalism will increasingly favor relationship-building, on-the-ground reporting, and scoops that require human trust.

“The things I love most about AI reporting are having an excuse to read really long computer science papers and then writing about them,” she wrote. “I worry that if AI becomes a great writer and research assistant, AI journalism will mostly become about networking.”

Her conclusion: “I won’t stop reading weird CS papers. And I won’t stop writing. Not because I’m confident these skills will keep me employed, but because they’re what I actually like doing.”

What it means

Markianos’ experiment demonstrates that AI can already handle substantial portions of junior journalist work — research, aggregation, summarization, and basic drafting. The quality improves with each model update, and the gaps narrow predictably.

For newsrooms, this creates pressure to define what human journalists add beyond execution speed. The answer increasingly points toward judgment, relationships, humor, skepticism, and the kind of tacit knowledge that’s hard to encode in prompts.

For journalism schools and early-career reporters, the experiment suggests focusing on skills AI can’t easily replicate: source cultivation, beat expertise, investigative instincts, and developing a distinctive voice. The technical research and writing skills that traditionally defined entry-level journalism work are increasingly commoditized.

The most striking aspect of Markianos’ piece isn’t that AI can do parts of her job — it’s that a 20-hour side project by one reporter produced an agent nearly deployment-ready for real newsroom work. That suggests the barrier to AI adoption in journalism isn’t capability. It’s deciding what journalism is for.

The post A reporter spent 20 hours building an AI to replace herself. It almost worked appeared first on The Media Copilot.