from the willingly-burning-libraries dept

Imagine a newspaper publisher announcing it will no longer allow libraries to keep copies of its paper. 

That’s effectively what’s begun happening online in the last few months. The Internet Archive—the world’s largest digital library—has preserved newspapers since it went online in the mid-1990s. The Archive’s mission is to preserve the web and make it accessible to the public. To that end, the organization operates the Wayback Machine, which now contains more than one trillion archived web pages and is used daily by journalists, researchers, and courts.

But in recent months The New York Times began blocking the Archive from crawling its website, using technical measures that go beyond the web’s traditional robots.txt rules. That risks cutting off a record that historians and journalists have relied on for decades. Other newspapers, including The Guardian, seem to be following suit. 

For nearly three decades, historians, journalists, and the public have relied on the Internet Archive to preserve news sites as they appeared online. Those archived pages are often the only reliable record of how stories were originally published. In many cases, articles get edited, changed, or removed—sometimes openly, sometimes not. The Internet Archive often becomes the only source for seeing those changes. When major publishers block the Archive’s crawlers, that historical record starts to disappear.

The Times says the move is driven by concerns about AI companies scraping news content. Publishers seek control over how their work is used, and several—including the Times—are now suing AI companies over whether training models on copyrighted material violates the law. There’s a strong case that such training is fair use. 

Whatever the outcome of those lawsuits, blocking nonprofit archivists is the wrong response. Organizations like the Internet Archive are not building commercial AI systems. They are preserving a record of our history. Turning off that preservation in an effort to control AI access could essentially torch decades of historical documentation over a fight that libraries like the Archive didn’t start, and didn’t ask for. 

If publishers shut the Archive out, they aren’t just limiting bots. They’re erasing the historical record. 

Archiving and Search Are Legal 

Making material searchable is a well-established fair use. Courts have long recognized it’s often impossible to build a searchable index without making copies of the underlying material. That’s why when Google copied entire books in order to make a searchable database, courts rightly recognized it as a clear fair use. The copying served a transformative purpose: enabling discovery, research, and new insights about creative works. 

The Internet Archive operates on the same principle. Just as physical libraries preserve newspapers for future readers, the Archive preserves the web’s historical record. Researchers and journalists rely on it every day. According to Archive staff, Wikipedia alone links to more than 2.6 million news articles preserved at the Archive, spanning 249 languages. And that’s only one example. Countless bloggers, researchers, and reporters depend on the Archive as a stable, authoritative record of what was published online.

The same legal principles that protect search engines must also protect archives and libraries. Even if courts place limits on AI training, the law protecting search and web archiving is already well established.

The Internet Archive has preserved the web’s historical record for nearly thirty years. If major publishers begin blocking that mission, future researchers may find that huge portions of that historical record have simply vanished. There are real disputes over AI training that must be resolved in courts. But sacrificing the public record to fight those battles would be a profound, and possibly irreversible, mistake. 

Republished from the EFF’s Deeplinks blog.

Filed Under: ai, archives, copyright, culture, fair use, history

Companies: internet archive, ny times, the guardian

Most organizations are not failing at AI because of technology. They are failing because they do not know which data actually matters, and they are scaling that confusion faster than ever. At a time when investment continues to surge, the expectation is that more intelligence will naturally follow. Instead, many teams are finding themselves overwhelmed. The issue is the inability to distinguish between signal and noise in a way that leads to confident decisions. 

The broader landscape makes this tension hard to ignore. According to the State of Enterprise AI 2026, global spending is projected to reach $2.52 trillion, yet only 14% of CFOs report measurable returns. At the same time, 42% of companies abandoned most of their AI pilots in 2025. These point to a systemic disconnect between ambition and execution. As boards demand accountability and leaders look for proof of value, many organizations are confronting a difficult reality: they invested in capability without first ensuring clarity. 

The usual explanation is that the data is not clean enough. That is not wrong, but it misses something more fundamental. Clean data has limited value if it is not relevant, connected, or usable in the context of real decisions. Over time, organizations have accumulated dashboards, reports, and tracking systems that create the appearance of visibility while leaving critical questions unresolved. Teams often cannot explain why a metric moves, how it connects to outcomes, or what action should follow. That gap between information and understanding is where progress stalls.

Part of the problem is scale. The volume of data has expanded faster than the systems used to interpret it. Teams track what they can, often without a clear view of why it matters, and the result is an environment filled with metrics that compete for attention. Definitions vary across departments, events are recorded inconsistently, and reporting relies on manual interventions that introduce further distortion. In that environment, it becomes difficult to form a single, coherent narrative. People operate from fragments, and those fragments rarely align. 

This fragmentation becomes more consequential as AI is introduced into the workflow. Systems trained on inconsistent inputs do not resolve ambiguity; they extend it. According to a report, 61% of data leaders say better data quality is helping move AI initiatives into production, yet 50% still identify data quality and retrieval as major barriers. There is also a concerning dynamic emerging around trust. While 65% of leaders believe employees trust the data used for AI, 75% acknowledge gaps in data literacy. That combination creates a situation where decisions are made with confidence but not necessarily with understanding.

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There is a belief in some circles that better tools will eventually close this gap. We have seen the opposite. Organizations struggle because their operational systems were never designed to produce reliable signals. When processes are inconsistent, ownership is unclear, and metrics are loosely defined, the data generated from those systems reflects that ambiguity. Signals, which are meant to guide decisions and automation, end up reflecting fragmented realities instead of coherent ones. The outcome is hesitation and misalignment.

The effects show up in subtle but persistent ways. Teams spend more time reconciling numbers than acting on them. Leaders request additional reporting to compensate for uncertainty, which adds more layers without resolving the underlying issue. Priorities shift based on partial views of performance, and coordination across functions becomes more difficult. Over time, this erodes confidence, not just in the data, but in the systems that produce it. The organization moves, but without a shared understanding of direction.

A useful way to think about this is through navigation. Having more instruments in a cockpit does not guarantee a better flight if those instruments are not calibrated to the same reality. Pilots rely on a small number of trusted signals that are consistently defined and clearly understood. In many organizations, the opposite is true. There is an abundance of instrumentation, but little agreement on which signals matter or how they should be interpreted. The result is constant adjustment without meaningful progress. 

The urgency of this issue is reflected in broader research. A report shows that improving data governance has become a top priority for over 40% of leaders, even surpassing some AI-specific initiatives. The reasoning is straightforward: AI and automation amplify the condition of the data they rely on. When that condition is poor, the impact grows quickly, affecting both operational performance and strategic outcomes. This is a question of how organizations define, manage, and use information in practice.

Addressing this requires a shift in focus. The goal is to build more sophisticated dashboards. It is to establish clarity around what decisions need to be made and what information is required to support them. That begins with defining ownership so that data is tied to accountability. It involves standardizing processes so that events are captured consistently across teams. It requires designing metrics that reflect how work actually happens, not just how it is reported. And it depends on building a data layer that brings these elements together into a coherent, usable view. 

Even more important is the human dimension and understanding how people actually work in their day-to-day. Without that capability, even well-structured data will fall short of its potential. People need to understand not just how to access information, but how to apply it in the context of daily decisions. This is where change management becomes critical. It is the ability to help teams separate meaningful signals from background noise and to act with confidence based on that distinction. 

For those trying to move forward, there is a practical starting point that often gets overlooked. Identify the questions that are difficult to answer today. These are usually the questions that require excessive effort, multiple sources, or reliance on individual knowledge. They reveal where the gaps exist in how information is captured and structured. Once those gaps are visible, it becomes possible to design systems that address them directly, focusing on relevance and usability instead of volume.

AI will continue to advance, and its potential remains significant. But its effectiveness will always depend on the environment it operates within. Organizations that invest in clarity, clear processes, clear ownership, and clear signals will find that technology enhances their capabilities. Those who do not will continue to struggle, regardless of how advanced their tools become. The difference comes down to discernment and whether it is treated as a priority or an afterthought.

Presented by OutSystems

After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on the practical matters of governance, orchestration, and iteration, along with integrating agents into the systems they’ve spent decades building.

Enterprise leaders are increasingly focused on fundamentals. The priority is using new AI technologies

to accelerate productivity, improve delivery, and produce measurable business results.

Three elements shape this work:

• The move from AI agent prototypes to agentic systems that deliver measurable ROI in production

• The growing role of enterprise platforms in governing, orchestrating, and scaling AI agents safely

• The rise of the generalist developer and enterprise architect as the most valuable technical profiles in an era of AI-generated code

Against this backdrop, the panel discussed governance frameworks, the economics of enterprise AI, and the limits of large language models without orchestration. The conversation ultimately turned to how leading organizations are building multi-agent systems grounded in existing enterprise data and workflows.

Agents in the real world

Enabling agents to work in production across the enterprise is best accomplished with a unified platform that handles development, iteration, and deployment. And that’swhere capabilities like the Agent Workbench in the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific. It provides the infrastructure to learn, iterate, and govern agents at scale.

His team at Thermo Fisher has moved away from single-task AI assistants in customer service to building a coordinated team of specialized agents using the workbench. When a support case arrives, a triage assistant classifies the request and dynamically routes it to the right specialist agent, whether that’s an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.

“We don’t have to think about what will work and how. It’s all pre-built,” he explained. “Each agent has a narrow role and clear guardrails. They stay accurate and auditable.”

Governing the risks of shadow AI

A new category of risk emerges when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, this is ungoverned shadow AI. These homegrown products are prone to hallucinations, data leakage, policy violations, model drift, and agents taking actions that were never formally approved.

To get ahead of the risk, leading organizations need to do three things, said Luis Blando, CPTO of OutSystems.

“Give users guardrails. They’re going to use AI whether you like it or not. Companies that seem to be getting ahead are using AI to govern AI across their full portfolio,” he explained. “That is the difference between shadow AI chaos and enterprise-grade scale.”

Eric Kavanagh, CEO of The Bloor Group, noted that governance requires a layered set of disciplines that includes securing data, monitoring models for drift, and making deliberate choices about where AI connects to existing business processes.

“Companies don’t have to be manually creating these controls,” he added. “A lot of those guardrails and levers are baked in to platforms like OutSystems.”

Why the real orchestration challenge is models vs. platforms

Much of the early excitement around enterprise AI focused on selecting the right large language model. Now the harder challenge, and far more durable source of value, is orchestration. This includes routing tasks, coordinating workflows, governing execution, and integrating AI into existing enterprise systems.

Scott Finkle, VP of development at McConkey Auction Group, noted that LLMs, however impressive, are pieces of complex workflows, not final solutions. Organizations should be ready to hot-swap between Gemini, ChatGPT, Claude, and whatever emerges next without having to rebuild the agentic system around it.

A platform with orchestration capabilities makes that possible. It manages the lifecycle, provides visibility, and ensures processes execute reliably, even as AI handles the reasoning layer on top.

“The AI and the models change, the workflows can change, but the orchestration remains the same,” Finkle said. “That’s how we’re going to extract value out of AI.”

The economics of enterprise AI investing

Security, compliance, governance, and platform-level AI capabilities will all command greater investment in 2026, particularly as AI moves into core workflows like finance and supply chain. Enterprises should favor incremental wins rather than expect big, immediate gains.

“We’re focusing on base hits,” Finkle said. “The way it counts is by getting something into production and having it make an impact. Big investments in pilot projects that don’t make it into production don’t save any money. It’s not going to happen overnight, but over time I think we’ll see tremendous savings.”

There’s still a split in how enterprises are approaching AI transformation. Some start from scratch and reimagine every process. Others, especially those with billions of dollars in existing infrastructure depreciating in-house, want AI to integrate with their systems. They want agentic systems to reuse data, APIs, and proven processes while speeding up delivery. The agent platform approach serves both camps, but particularly the latter. Organizations can deploy agents where they add clear value while preserving the integrity of established, deterministic workflows.

The rise of the enterprise architect and the generalist developer

As AI accelerates code generation, bottlenecks in software delivery are dissolving. In its place is a premium on systems thinking. This is the ability to understand the broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Kavanagh pointed to enterprise architects specifically as the professionals best positioned to capitalize on this moment.

“We’re entering a very interesting age of the generalist,” he explained. “The better you know your enterprise architecture and your business architecture and how those things align, the better off you’re going to be. ”

“The result is faster delivery with fewer interruptions and fewer bugs,” Kavanaugh said. “You can focus on the non-repetitive tasks. It’s a benefit to the developer, to the business, and to the whole IT organization.”

Catch the entire webinar here.

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Blossom Health, a New York-based telepsychiatry startup founded in 2024, has raised $20 million in combined seed and Series A funding to scale an AI-powered platform that pairs psychiatrists with clinical copilots and automated administrative support. The round was led by Headline, whose co-founder and managing partner Mathias Schilling is joining the company’s board. Village Global and TA Ventures returned from earlier rounds, with Operator Partners and Correlation Ventures joining as new institutional backers alongside angel investors including founders from General Catalyst, Flatiron Health, Sword Health, and Zip.

The company, founded by CEO John Zhao, is built around a specific premise: that the bottleneck in psychiatric care is not a shortage of clinical knowledge but a shortage of time. Psychiatrists in the United States spend roughly half their working hours on non-clinical tasks, including documentation, billing, insurance authorisation, and scheduling. Blossom’s platform automates much of this through a network of AI agents that handle billing, reception, care coordination, and medical scribing, while a separate set of clinical copilots assist with symptom evaluation, diagnosis refinement, and medication selection during patient encounters.

The scale of the problem

The psychiatric workforce shortage in the United States is severe and worsening. More than 122 million Americans live in federally designated mental health professional shortage areas, according to the Health Resources and Services Administration. The national psychiatrist-to-population ratio stands at one provider for every 5,058 residents. Roughly 60 per cent of practising psychiatrists are 55 or older, meaning a significant portion of the existing workforce will retire within the next decade. Wait times for an initial psychiatric appointment range from three weeks to six months depending on location, and in many rural counties there are no psychiatrists at all.

This gap has created a market. US digital health startups raised $14.2 billion in 2025, the highest total since 2022, with AI-powered companies accounting for 54 per cent of that funding. Within mental health specifically, Talkiatry, an in-network telepsychiatry platform, raised $210 million in February 2026. Spring Health, which uses AI for personalised treatment recommendations, is valued at $3.3 billion. Ambient clinical scribes, the category of AI that automatically generates notes from patient conversations, produced $600 million in revenue last year alone.

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Blossom is small by comparison. The company says its tools are used by hundreds of clinicians treating more than 10,000 patients across multiple US states. Most patients are seen within 48 hours, with many receiving same-day appointments. Blossom accepts all major commercial insurers, including Optum UnitedHealthcare, Aetna, Cigna Evernorth, and Blue Cross Blue Shield, with average copays of around $22.

Copilot, not replacement

The “copilot” framing is deliberate and important. Blossom is not building a therapy chatbot. Its AI tools sit alongside licensed psychiatrists during clinical encounters, surfacing relevant information, helping evaluate symptoms against diagnostic criteria, and suggesting medication adjustments based on the patient’s history and current presentation. The psychiatrist retains clinical authority over every decision.

Between appointments, the platform uses AI agents to maintain contact with patients through text-based check-ins on sleep, mood, medication adherence, and other indicators. Fortune reported that in the case of postpartum depression, for example, the system follows up with conversational prompts that surface warning signs and prepare information for clinicians ahead of the next visit. This approach converts what has traditionally been episodic care, where a patient sees a psychiatrist for 15 minutes every few months and is otherwise unsupported, into something closer to continuous monitoring.

The clinical claims are plausible but early. Blossom says it has demonstrated the ability to stabilise mental health conditions and prevent progression toward more intensive care, but the company has not published peer-reviewed clinical evidence. At 10,000 patients, the dataset is meaningful for a company this young but far too small to draw population-level conclusions about clinical efficacy.

The Cerebral cautionary tale

Any startup operating at the intersection of AI, telepsychiatry, and controlled substance prescribing inherits the reputational burden of what came before. Cerebral, the telemental health company that raised $300 million at a $4.8 billion valuation in 2022, became the subject of a Department of Justice investigation into its prescribing practices for controlled substances and paid a $7 million settlement to the Federal Trade Commission over allegations of misleading cancellation policies and data sharing. The company’s rapid growth, which prioritised patient volume over clinical rigour, damaged trust across the sector.

Blossom’s architecture is different in important ways. It works through licensed psychiatrists rather than nurse practitioners prescribing independently, and its AI tools are positioned as decision support rather than decision-makers. But the fundamental tension remains: scaling psychiatric care through technology requires maintaining clinical quality at volumes that a traditional practice model was never designed to handle. The AI copilot must be good enough to genuinely assist clinicians without introducing errors that a time-pressed psychiatrist might not catch, particularly in medication selection, where psychiatric pharmacology is notoriously complex and highly individual.

The $20 million will fund expansion into additional US states, new insurance partnerships, clinician recruitment, and continued research and development. For a company founded less than two years ago, treating over 10,000 patients with in-network insurance coverage is a notable operational achievement. Whether the clinical copilot meaningfully improves outcomes, or simply makes it faster to deliver care at the same quality, is the question the next round of funding will need to answer.

Anyone looking for a new opportunity in the engineering space should consider one of the following courses as part of their upskilling process.

While it is an innovative, exciting and dynamic sector, the STEM space and the careers that exist within it demand consistent and up-to-date training in order to ensure professionals are operating at their peak. Upskilling is an essential element of engineering careers and with that in mind, the following courses could be an ideal way for you to stay in the know and ahead of the game. 

Coursera

For those looking for an introductory course to be undertaken flexibly, IBM via Coursera is running a free Introduction to Software Engineering programme. It consists of six modules, is aimed at beginners and can be managed at the learners’ own pace. It also comes with a certificate upon completion of the course.

Also available via Coursera, with free online enrollment, is a 12-week introduction to Systems Engineering Specialisation, offered by the University of Colorado, Boulder. The beginner-level programme can be accessed in eight different languages and aims to teach the fundamentals, methods, practices and processes of industry-standard systems engineering.

Coursera also offers a range of courses designed for more advanced learners. For example, a Microsoft AI & ML Engineering Professional Certificate. The free six-month programme aims to prepare students and professionals for a career in artificial intelligence and machine learning. Coursera itself also has a free four-week Deep Learning Engineering Specialisation course for advanced students. 

Further Education and PLC

For students based in Louth, looking to earn their level five qualifications, there is a Further Education and PLC, level five QQI course in Engineering Technology. The programme is said to equip learners with the skills and knowledge needed to gain employment, access apprenticeship programmes and progress to further study in universities and institutes of technology. Successful graduates will have the opportunity to find employment in areas such as engineering machine operations, civil engineering, electrical engineering and advanced manufacturing, alongside others. 

In Dunboyne, Meath there is a year-long level five Engineering Technology course open to prospective students looking towards future education. It is a pre-university engineering course designed to to prepare the student for work in the field of engineering via entry into a third-level institution. Students who graduate from this course can pursue further study at degree level and will be well placed to gain apprenticeships in the many different engineering sectors through Generation Apprenticeship Ireland. There are a range of level five opportunities available nationwide, so be sure to find one that is convenient. 

South East Technological University

South East Technological University (SETU) has dozens of opportunities for engineering students and professionals, to fit a range of lifestyles and ambitions. Courses for those looking for their bachelor’s degree include standard four-year programmes in areas such as agricultural systems engineering, electronic engineering, aerospace engineering and automation engineering, among others. 

For more established students, there are also several one-year master’s degrees, such as the Master of Science in Engineering Research and Innovation and Master of Science in Sustainable Energy Engineering. Different courses will have unique requirements, commitments and prices, so make sure to read up on your chosen course first. 

Udemy

Educational platform Udemy has a free Engineering Mechanics Fundamentals to Proficiency course, which can be undertaken at the learners convenience online. The programme is aimed at beginners, covers a range of topics such as foundational mechanics and principles and will require an understanding of basic maths and physics. Udemy has dozens of free and paid options, to suit a variety of budgets and lifestyles, including, The Complete Full Stack AI Engineering Bootcamp, Site Reliability Engineering and The Complete Mechanical Engineering Course, which claims to offer 12 courses in one. 

Whether you are a complete novice, an enthusiast, a graduate or an established professional, there is really no incorrect way to engage with learning, provided you have a clear idea of what it is you hope to achieve from the experience. So make sure to do your research, identify your weaknesses and shop around for the course or learning materials that match your ambitions and available resources. 

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Have you ever asked a chatbot something and felt like it completely missed your point? You say something with a bit of nuance, and the AI misses the subtlety entirely. That is exactly the problem researchers are trying to solve.

Even though the emotional connection with AI can feel deeper than human conversation for many users, most AI systems today still treat a sentence as a single block of sentiment. If you mix praise and criticism, the nuance often gets lost.

The research, by Zhifeng Yuan and Jin Yuan, introduces a model that can break down a sentence and understand how you feel about each part, instead of generalizing everything into one response.

How this system helps AI read your intent better

Think about a sentence like, “The food was great, but the service was terrible.” A typical AI chatbot might struggle because the sentence has both positive and negative emotions.

The proposed model looks at each part of the sentence separately and connects each emotion to the right subject. It relies on an ‘emotional keywords attention network’ to do that.

In simple terms, it teaches AI to focus on words that carry strong emotions, such as “great” or “terrible.” These words guide the system toward understanding what matters most in the sentence.

The model then links those emotional cues to a specific aspect. It learns that “great” applies to food, while “terrible” applies to service. This process, known as aspect-level sentiment analysis, makes responses far more precise.

It also uses attention mechanisms to understand context, so it does not rely on keywords alone. It can figure out how different parts of a sentence connect. Researchers say this method performs better than existing models on standard benchmarks.

This approach can make AI chatbots feel more human

If adopted widely, this could change how AI responds in real-world situations. Chatbots could handle nuanced feedback more effectively instead of defaulting to generic replies. Customer support systems could pinpoint exactly what went wrong and respond with greater accuracy.

While concerns grow around AI chatbots mirroring human personality traits a little too well, one thing is clear. AI is here to stay, and if it is going to be part of everyday conversations, it needs to get better at reading the room.