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Human-Centered AI

A follower is not an audience

Earlier today I posted something deliberately blunt on LinkedIn. I said the feed increasingly felt less like a professional network and more like Facebook: crowded with clickbait, personal-brand theater, and content optimized to provoke a reaction rather than help someone find work, exchange expertise, or make a useful professional connection.

Three hours later, the post showed 42 impressions. My profile has roughly 16,500 followers. Two people had reacted. Above the post, LinkedIn displayed an invitation to Boost it.

I took a screenshot because the juxtaposition was almost too perfect. A platform had offered me a number suggesting extraordinarily weak organic distribution and, in the same interface, offered to sell me more visibility. Then I wrote a second post criticizing that result. That complaint reached about 250 impressions in its first hour—roughly six times the number shown on the original post.

The easiest conclusion is also the most emotionally satisfying: LinkedIn suppressed my post so it could charge me to reach the audience I had already built.

The screenshot does not prove that. It reveals something more defensible, and in some ways more consequential: a follower on a ranked platform is not an addressable audience. It is a relationship that the platform may or may not activate, according to objectives the user cannot see.

That distinction matters on any social network. It matters more on a platform that mediates employment, expertise, reputation, recruiting, sales, and access to professional opportunity.

The interface should carry more of the context

Google DeepMind is experimenting with an artificial intelligence-enabled mouse pointer that can combine pointing, visual context, and natural language. Instead of describing an object at length or moving material into a separate chat window, a user can indicate “this” or “that” where the work already appears.

The concept addresses a real problem. People should not have to become amateur prompt engineers to communicate context that is already visible on the screen.

Financial agents will be proven in the exception queue

Anthropic has released agent templates for financial services, including work such as preparing pitchbooks, screening Know Your Customer (KYC) files, reviewing valuations, reconciling ledgers, and supporting the monthly close.

These are not toy tasks. They sit inside governed processes with source systems, deadlines, approvals, materiality judgments, and audit expectations. Their automation will be judged less by the clean case than by what happens when the evidence does not line up.

Verification is now part of the product surface

OpenAI has released Generative Pre-trained Transformer 5.5 (GPT-5.5), describing a model that can carry more of a complex task across coding, research, data analysis, document creation, and software tools. This continuing increase in agentic capability changes the user's job.

When a system produces a paragraph, review can happen at the paragraph. When it completes an hour of work across several applications, review has to cover a chain of actions, transformed data, and consequential choices.

Evaluator disagreement is information

Google Research has published work asking how many human raters an artificial intelligence benchmark needs. The question matters because many model evaluations rely on people to judge qualities that cannot be reduced to exact matching: helpfulness, factuality, relevance, safety, style, or the quality of an explanation.

Adding raters can improve reliability. But disagreement is not always noise that disappears when the sample grows. Sometimes it is the finding.

A scientific companion should strengthen the evidence chain

Google DeepMind has described new results using Gemini Deep Think for mathematical and scientific discovery. The work is another indication that advanced models can contribute more than polished explanations: they can explore candidate approaches, connect ideas, and help experts work through difficult problems.

The most useful interpretation is not that the scientist is leaving the loop. It is that the loop itself can become richer—if the system preserves the evidence needed for expert challenge.

Healthcare AI enters through the workflow

OpenAI has introduced OpenAI for Healthcare, bringing its models and products into an environment where information is sensitive, time is scarce, and an apparently useful answer can shape a consequential decision.

The announcement emphasizes administrative and clinical work as well as support for obligations under the Health Insurance Portability and Accountability Act (HIPAA). Those are important foundations. They do not, by themselves, make an artificial intelligence system fit for a particular healthcare decision.

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