2025-12-12 10:49:03
Cloud systems don’t fail simply…
They fail cryptically.
And today, we finally decode the mystery.
Subscribe here
In the previous edition, CAAT taught your observability pipeline what to collect intelligently.
Now, T-RAG goes a level deeper — it explains
why your system failed,
where it originated,
and what context makes it meaningful.
T-RAG introduces a Trace-Native Retrieval-Augmented Generation engine that transforms raw spans into explainable, LLM-powered RCA.
It embeds traces, retrieves historical patterns, and reasons like your smartest SRE — but with perfect recall.
This is not traditional observability.
This is Cognitive Observability.
If CAAT optimized your telemetry…
T-RAG decodes your failures.
And together, they form the foundation of the MindOps journey — an autonomous, AI-driven future for cloud operations.
Full article, architecture, and walkthrough: (link your newsletter)
Full source code now available on GitHub:
https://github.com/Huzefaaa2/MindOps
Brace yourself — the next MindOps projects will redefine everything you know about AIOps.
2025-12-12 10:49:01
Observability tools are crucial to large, complex and scaling applications. They help engineers complete the following tasks with more precision and efficiency:
Analyze system performance
Investigate bugs and errors
Resolve issues and identify points of failure
This article will compare Multiplayer, and Datadog, focusing on debugging and issue resolution.
Multiplayer is a full-stack session recording tool that provides engineers with complete, correlated replays, capturing everything from the frontend to the backend, without requiring external integrations. Multiplayer natively supports all observability stacks, giving you full debugging context from day one. Multiplayer supports these use cases:
QA engineers: Automatic step-by-step capture of issues, the moment they occur, limiting the need for elongated reproduction and investigation.
Developers: Complete error tracing that is both full-stack and AI-ready, for contextual debugging.
End-users: Easy bug reporting and less time spent on explaining issues to support engineers.
Datadog is a monitoring and observability platform designed for performance evaluation, and primarily aimed at DevOps teams. Datadog’s biggest strength is helping support teams identify performance bottlenecks and keep an eye on system health and availability. Datadog also provides a session replay addon to their Real User Monitoring (RUM) platform, however, this is a separate platform that requires a different subscription and is more geared towards identifying web application trends, bottlenecks, and performance, as opposed to fixing bugs.
Let’s analyze Multiplayer and Datadog’s features by using a simple example: an online banking application that is showing users that their debit transactions have failed, but debiting money nonetheless.
Multiplayer allows users to capture sessions through their browser extension, in-app widget, or automatic capture by integrating a library SDK. Multiplayer can be either self-hosted or cloud-hosted.
If “continuous” or “conditional” recording mode is enabled, the session would automatedly be flagged in our example of the banking application producing a transaction failure. Developers can then examine the automatically captured session data. If the issue is reported retrospectively, the support team can request a session recording via the widget or browser extension (“on-demand” recording mode). For teams handling sensitive financial data, self-hosting Multiplayer is an option to ensure full control over data residency and compliance requirements. Multiplayer offers a number of masking and data privacy features to further increase security over confidential data.
Datadog session replay is a SaaS solution integrated via an SDK as part of their Real User Monitoring (RUM) platform. It is fundamentally designed for always-on recording, capturing all user sessions. Consequently, investigating a specific reported transaction failure is a time-consuming process, as teams must filter through all recorded sessions using criteria such as timestamps, user IDs, or error patterns to locate the relevant event:
After the transaction issue is identified, support engineers would need to escalate it to the development team along with context for investigation.
Multiplayer provides a shareable link to each full-stack session recording that includes:
RUM and APM can be linked, in the form of links to the other platform on the respective explorer, but this still involves developers hopping between tools.
Let's assume the transaction failure is caused by a race condition in the payment processing microservice, where duplicate idempotency checks are failing due to insufficient database transaction isolation. The below logic creates a race condition where simultaneous requests using the same idempotency key could both bypass the duplicate check. This vulnerability exists in the gap between verifying an existing transaction and initiating a new one, potentially leading to the user being double-charged:
# Backend Bug: Race condition in payment processing
@transaction.atomic
def process_payment(user_id, amount, idempotency_key):
# Check if transaction already processed
existing = Transaction.objects.filter(
idempotency_key=idempotency_key
).first()
if existing:
return existing # Duplicate request
# Race condition window here - another request might pass the check before this transaction commits
account = Account.objects.get(user_id=user_id)
if account.balance >= amount:
account.balance -= amount
account.save()
transaction = Transaction.objects.create(
user_id=user_id,
amount=amount,
idempotency_key=idempotency_key,
status='completed'
)
return transaction
else:
raise InsufficientFundsError()
Multiplayer allows developers to immediately access the full-stack session recording:
Furthermore, with Multiplayer's MCP (Model Context Protocol) server, developers can feed this complete session context directly to AI coding assistants like Cursor or Claude Code, asking "What caused this duplicate transaction?" and getting an immediate analysis based on the full stack trace.
Developers can auto-generate test scripts from the session, using Multiplayer, to ensure this race condition doesn't reoccur, capturing the exact sequence of events that triggered the bug.
Datadog would require a more manual investigation process:
While Datadog's monitoring would potentially show aggregate metrics indicating increased payment errors, investigating the specific root cause requires significant tool-hopping and manual correlation. The developer might spend 30-45 minutes just gathering all the necessary context before even beginning the fix.
We can fix the race condition by using database-level locking:
# Backend: Fix race condition with proper locking
@transaction.atomic
def process_payment(user_id, amount, idempotency_key):
# Use select_for_update with nowait to prevent race conditions
try:
existing = Transaction.objects.select_for_update(
nowait=True
).filter(
idempotency_key=idempotency_key
).first()
if existing:
return existing # Duplicate request
except DatabaseError:
# Another transaction is processing this key
raise DuplicateRequestError()
account = Account.objects.select_for_update().get(user_id=user_id)
if account.balance >= amount:
account.balance -= amount
account.save()
transaction = Transaction.objects.create(
user_id=user_id,
amount=amount,
idempotency_key=idempotency_key,
status='completed'
)
return transaction
else:
raise InsufficientFundsError()
When to use Multiplayer:
When you need to quickly resolve specific technical support issues and bugs with full-stack context
When your support workflow involves escalations between customer success, support, and engineering teams
When you need flexible recording modes (on-demand, continuous, conditional) rather than always-on capture
When you want to integrate session context with AI coding assistants for faster debugging
When data residency, compliance, or privacy requirements make self-hosting essential
When your primary need is comprehensive infrastructure and application performance monitoring at scale
When you need to track metrics, trends, and system-wide observability across distributed systems
When your focus is on proactive monitoring and alerting rather than reactive debugging
When DevOps teams are the primary users and deep monitoring integration is more important than support workflows
When session replay is a supplementary feature to broader monitoring needs, not the core use case
2025-12-12 10:45:27
Converting PDF pages into PNG images directly inside the browser is completely possible today thanks to modern rendering engines. Most developers use PDF rendering libraries that draw each page on a canvas element and then export the image from it.
This technique allows websites to show PDF previews, generate thumbnails, or export images on the client side—without relying on backend servers. It also improves privacy, because documents do not need to be uploaded anywhere.
However, building this pipeline takes time. You need to handle PDF loading, scaling, rendering quality, and browser compatibility.
If you prefer an instant solution, a practical option is:
👉 https://pdftopng.io
It creates clean, high-resolution PNG files without requiring any setup. It’s helpful for both developers testing features and everyday users who simply need fast conversion.
2025-12-12 10:43:39
Salesforce has become the core operational platform for enterprise sales, service, and operations teams, managing critical customer relationships and business processes. However, at the final stage of legal and commercial delivery, the PDF remains the universal standard format for all official documents—quotes, contracts, invoices, purchase orders, service agreements are almost exclusively created, reviewed, revised, and signed as PDFs.
Despite its power in data management and process automation, Salesforce’s native functionality does not support direct editing of PDF file content. This creates a critical "digital disconnect": whenever even a minor modification to a PDF is needed, users are forced to exit Salesforce and resort to external tools. This break not only slows down processes but also introduces errors, security risks, and a lack of process control.
To make the core business chain of "Quote-to-Contract" (QTC) truly smooth, efficient, and controlled, native Salesforce PDF editing capability must be accomplished within Salesforce itself.
Salesforce natively supports file upload, storage, and preview, making it an excellent document repository. However, its limitations become apparent when the business needs to modify document content. Native functionality does not support:
Consequently, users are forced into a cumbersome "outsourced" process: Download PDF from Salesforce → Edit locally with other software (e.g., Adobe Acrobat) → Re-upload the new version to Salesforce.
This process triggers numerous problems:
In essence, editing PDFs outside the CRM means placing the most critical business documents outside the core business process and management control, severely hampering overall operational efficiency and compliance.
"Quote-to-Contract" is a precise process involving multiple departments and steps. Here are the critical junctures where direct PDF editing is essential:
After initial quote generation, sales reps often need to make quick adjustments based on client feedback: modifying prices, discounts, terms, client information, or adding product notes. Inability to do this directly within Salesforce drastically slows response times.
Managers or finance during approval may need to directly amend clause wording, adjust tax notes, or add limitations. Direct editing on the PDF is more efficient than writing long comments.
Even with templates, each contract requires personalization: filling in unique agreement numbers, adjusting company addresses and signatory information for both parties, adding or deleting specific clauses. This is one of the most edit-intensive stages.
The legal team needs to perform redlining, add revision comments, or directly modify legal text on the PDF. Using external tools is not only inefficient but may also increase compliance risks due to version control issues.
During negotiations, clients often return marked-up PDFs. Sales or legal need to work directly on the client's version, accepting or rejecting changes. Frequent exports and imports at this stage create significant time waste.
Before signing, the final version may require filling in dates, inserting e-signatures or initials. Ensuring this is completed within the system is the final crucial step for process integrity and audit compliance.
Integrating PDF editing capability seamlessly into Salesforce can transform the QTC process:
Users can move from creating a quote to finalizing a contract without ever leaving the Salesforce interface. The entire document lifecycle (creation, modification, approval, signing, archiving) is tracked and auditable within the same platform.
Once a PDF edit is complete, subsequent workflows can be triggered automatically: updating record status, initiating approvals, notifying the client, generating contract copies. This turns manual steps into automated rules.
All edits are made within Salesforce based on a single source of truth. Critical information (e.g., final terms) can be configured to automatically write back to Opportunity, Quote, or Contract object fields, ensuring 100% alignment between system records and paper documents.
Sensitive documents do not need to be downloaded to local devices or sent via email. All editing occurs in a controlled cloud environment with a complete audit trail, meeting enterprise security and compliance requirements.
Sales teams can respond instantly to client requests, shortening deal cycles. Internal collaboration (sales, finance, legal) becomes smoother due to unified versions and transparent processes.
Not all PDF solutions are suitable for embedding into a CRM. Enterprises should evaluate:
Salesforce is the central nervous system of the modern enterprise, but the lack of native PDF editing capability leaves it "limbless" at the critical document-handling stage. The Quote-to-Contract process is highly dependent on the dynamic generation and modification of PDF documents.
Native PDF editing capability is the key to bridging this gap. It is no longer a "nice-to-have" add-on but an indispensable core productivity tool for enhancing operational efficiency, ensuring data compliance, and achieving process automation.
For enterprises committed to truly realizing digital, automated workflows, the answer is clear: The editing, collaboration, and management of PDFs must be accomplished natively within Salesforce. This is not just a technological upgrade but a significant evolution in work philosophy and business processes.
2025-12-12 10:43:38
The war over who owns the raw material of artificial intelligence has moved from Twitter arguments to federal courtrooms, and the stakes couldn't be higher. On one side stand artists, photographers, writers and musicians whose life's work has been scraped into massive training datasets without permission or payment. On the other, AI companies insist they're building transformative technology protected by centuries-old fair use doctrine. Between them lies a legal grey zone that could reshape copyright law, determine the future of creative industries, and decide whether machine learning development continues at breakneck speed or grinds to a licensing-fee halt.
The fundamental question isn't particularly complex: can you build a billion-dollar AI model by copying millions of copyrighted works without asking? The answer, it turns out, is fiendishly complicated.
Sarah Andersen discovered the problem the way many artists did: by searching for her own name. The popular webcomic creator typed her name into Stable Diffusion and watched as the AI generated new images in her distinctive style. Not inspired by her work or reminiscent of it, but unmistakably hers in every brushstroke, every emotional beat, every quirk that had taken years to develop.
In January 2023, Andersen joined illustrators Kelly McKernan and Karla Ortiz in filing what would become one of the landmark copyright cases of the AI era. Their class action lawsuit against Stability AI, Midjourney and DeviantArt alleged that these companies had built their image generation systems on a foundation of copyright infringement, training on 5 billion images scraped from the internet without consent or compensation.
The legal trajectory has been winding. U.S. District Judge William Orrick initially dismissed most of their claims in October 2023. But the artists persisted, refining their arguments. In August 2024, Judge Orrick reversed course in a ruling that sent shockwaves through Silicon Valley. He found it plausible that image-diffusion models like Stable Diffusion contain "compressed copies" of their training datasets, and that training, distributing and copying such models could constitute copyright infringement. The case will proceed to trial in September 2026, where a jury will examine how AI companies use copyrighted works and whether these practices violate existing law.
If the artists ultimately succeed, the precedent could reshape how AI art is regulated across the United States.
Whilst artists filed class actions in California, Getty Images pursued Stability AI in both American and British courts. The dual-track strategy offered a natural experiment in how different legal systems handle identical technology.
The UK case, filed in January 2023, hinged on whether Stability AI had infringed Getty's copyrights and trademarks by training Stable Diffusion on millions of Getty-watermarked images. Getty's original claims were ambitious: primary copyright infringement, secondary infringement, database rights violations, trademark infringement, and passing off.
As the litigation progressed, Getty's case narrowed dramatically. The company accepted there was no evidence that training had occurred in the UK. Getty abandoned its primary copyright and database rights claims before closing submissions. By trial's end in June 2024, only two issues remained: whether making model weights available for download constituted secondary copyright infringement, and whether certain Stable Diffusion outputs infringed Getty's trademarks.
The High Court of England and Wales delivered its judgement on 4 November 2024, and both sides claimed victory. Judge Smith rejected Getty's core argument, ruling that AI model weights are not a "copy" of training images in the sense required by copyright law. The court found that Stable Diffusion "doesn't store or reproduce any Copyright Works (and has never done so)." On trademarks, the court found limited infringements for early versions of Stable Diffusion, but determined these were neither widespread nor persistent beyond version 2.x.
Getty maintained the result was "a significant win for intellectual property owners." Stability AI's General Counsel Christian Dowell called it a win that "ultimately resolves the copyright concerns that were the core issue." The divergent interpretations highlight how muddied these legal waters remain.
Getty's parallel US case continues, refiled in San Francisco federal court in August 2023.
When The New York Times sued OpenAI and Microsoft in December 2023, it marked an escalation in both the calibre of plaintiff and the sophistication of legal arguments. The Times presented a multi-layered theory of how AI development creates liability at multiple stages.
The newspaper's first claim is straightforward: when OpenAI scraped Times articles from the web to build its training corpus, it made unauthorised copies. Moving data from The New York Times's servers to OpenAI's servers constitutes literal copying under copyright law.
The second claim addresses what happens during model use. The Times demonstrated that ChatGPT sometimes produces near-verbatim reproductions of Times articles when prompted, a phenomenon researchers call "memorisation." Large language models occasionally regurgitate substantial portions of their training data, particularly when that data appears frequently or distinctively in the corpus.
The third claim is more subtle: that ChatGPT's ability to answer questions by synthesising information from Times journalism creates a market substitute. Why pay for a Times subscription when ChatGPT can summarise the paper's reporting for free?
OpenAI's defence rests heavily on fair use doctrine, arguing that training AI models on publicly available text is transformative, serving different purposes than the original journalism. The company contends that occasional memorisation is a rare bug being actively addressed, and suggests The Times may have deliberately engineered prompts to produce copied text.
In March 2025, Judge Sidney Stein rejected OpenAI's motion to dismiss the lawsuit, allowing the case's main copyright claims to proceed. In May 2025, Magistrate Judge Ona T. Wang issued a preservation order requiring OpenAI to retain all ChatGPT conversation logs, affecting over 400 million users worldwide. When OpenAI objected, Judge Stein affirmed the order, signalling the court's willingness to impose significant discovery burdens.
The Times case will force detailed examination of fair use's four-factor test in the AI context. The first factor examines whether the use is transformative or merely duplicative. The second considers the nature of the original work. The third asks how much of the original was used. The fourth, and often decisive, factor assesses market harm: does the allegedly infringing use substitute for the original?
OpenAI insists its use is "quintessentially transformative," creating new capabilities rather than replacing journalism. The Times counters that transformation requires commentary or new expression, not just different functionality, and that market substitution is demonstrable when users get Times reporting through ChatGPT instead of subscribing.
Fair use doctrine has become the central battleground. Recent rulings demonstrate profound judicial disagreement about whether AI training qualifies.
In June 2025, Judge Alsup ruled in Bartz v. Anthropic that training large language models on copyrighted books constitutes fair use. He called the process "quintessentially transformative" and "spectacularly so," finding that any alleged market harm was pure speculation.
Also in June 2025, Judge Vince Chhabria reached a similar conclusion in Kadrey v. Meta, holding that Meta's training of its Llama model on books from "shadow libraries" was "highly transformative" with insufficient evidence of market harm.
Yet a different judge reached the opposite conclusion. In February 2025, the Third Circuit Court of Appeals ruled in Thomson Reuters v. ROSS Intelligence that AI training is not fair use. Judge Bibas found that ROSS's use wasn't transformative because it served the same purpose as Thomson Reuters's legal research tool. The companies were competitors, and ROSS had set out to create a directly competing product.
Judge Bibas also rejected the "intermediate copying" argument that had succeeded in earlier technology cases like Google v. Oracle. He noted those cases involved copying computer code for interoperability, not ingesting written works to build competing products.
Courts finding for AI companies emphasise the generative and creative capabilities of modern models. Courts finding against AI companies focus on competitive harm and market substitution. Both cite longstanding precedent; both claim fidelity to copyright's purposes.
This judicial split virtually guarantees appeals and eventual intervention by higher courts, possibly the Supreme Court.
Whilst litigation grinds forward, legislators and regulators worldwide are crafting frameworks to address AI training.
The European Union's AI Act, which entered into force on 1 August 2024, mandates unprecedented transparency. Article 53 requires providers of general-purpose AI models to publish "sufficiently detailed summaries" of their training data, including content protected by copyright. The European Commission released a mandatory template requiring disclosure of data sources, collection methods, and the complete data lifecycle from pre-training through fine-tuning.
Compliance becomes mandatory on 2 August 2026, with potential fines reaching €15 million or 3% of global annual revenue, whichever is greater.
The UK has taken a more cautious approach. In December 2024, the government launched a consultation proposing an opt-out system for text and data mining. Under this framework, AI developers could use copyrighted works for training unless rights holders explicitly object. The proposal met fierce resistance from creative industries. The Creative Rights in AI Coalition, formed by organisations including the Society of Authors, the Publishers Association, and the Association of Illustrators, insists AI developers should only use copyrighted material with express permission.
California passed Assembly Bill 2013 in September 2024, requiring developers of generative AI systems released after 1 January 2022 to post detailed information about training data on their websites by 1 January 2026. The law emphasises data provenance, requiring developers to trace the lineage of training data and maintain transparency about their development practices.
These regulatory approaches share a common theory: transparency enables accountability. If rights holders can see what's in training datasets, they can enforce their rights, negotiate licensing deals, or pursue litigation with better information.
Some observers believe litigation and regulation are addressing symptoms rather than causes. The real solution, they argue, is licensing markets that compensate rights holders whilst allowing AI development to continue.
Several licensing models are emerging. OpenAI has signed direct licensing deals with Politico, The Atlantic, Time, and the Financial Times, granting training rights in exchange for upfront payments or ongoing royalties.
Revenue-sharing arrangements are gaining traction. Perplexity AI's Publishing Program, launched in July 2024, offers publishers a portion of subscription revenue based on how often their content is cited in AI-generated responses.
Collective licensing offers another path. Sweden's STIM announced in September 2024 what it called the world's first collective AI music licence, allowing AI companies to train on copyrighted music lawfully with royalties flowing back to original songwriters. Germany's GEMA presented a similar model at the Reeperbahn Festival.
The Artists Rights Society in the United States has proposed extended collective licensing schemes for visual artists. The UK's Copyright Licensing Agency is developing an AI training licence for text-based published content, scheduled for release in 2025.
Human Native AI, a startup, is building a marketplace specifically for AI training data. Rights holders upload content and connect with AI companies for revenue share or subscription deals. CEO James Smith described the current environment as "the Napster-era of generative AI."
Yet licensing faces significant challenges. Training datasets comprise billions of works from millions of creators. Clearing rights individually would be prohibitively expensive and time-consuming. Even collective licensing struggles with orphan works, international jurisdictions, and creators who refuse to license at any price.
Moreover, licensing markets only function if AI companies face meaningful legal or business pressure to participate. If courts ultimately find training is fair use, why would companies pay for what they can legally take for free?
Beyond legal and market mechanisms, technical solutions are emerging to make training data visible and verifiable.
The Coalition for Content Provenance and Authenticity (C2PA) provides an open standard for establishing the origin and editing history of digital content. Content Credentials function like nutrition labels, documenting who created content, whether it can be used for AI training, and what modifications have occurred.
The C2PA specification enables AI developers to reference training data transparently and allows creators to assert restrictions. A "data mining assertion" can specify whether a work is allowed, constrained or prohibited for AI training.
Founded in 2021 by Adobe, Microsoft, Intel, ARM and Truepic, C2PA has expanded to include Google, OpenAI, Meta and Amazon. The specification is expected to achieve ISO international standard status in 2025.
Researchers at MIT launched the Data Provenance Explorer after auditing more than 1,800 text datasets. They found over 70% omitted licensing information, whilst about 50% contained errors. Their tool automatically generates readable summaries of a dataset's creators, sources, licences and allowable uses.
The Data Provenance Initiative conducted the largest longitudinal audit across text, speech and video datasets, analysing nearly 4,000 public datasets from 1990 to 2024. The research revealed that multimodal machine learning has overwhelmingly turned to web-crawled content, synthetic data and social media platforms like YouTube since 2019.
These transparency tools share a critical limitation: they depend on voluntary adoption. Less scrupulous AI developers can simply ignore standards, scrape without documenting provenance, and proceed unless caught.
Several platforms and organisations have implemented opt-out systems, allowing creators to exclude their work from AI training datasets.
DeviantArt added opt-out preferences in November 2022, appending "noai" and "noimageai" directives to the HTML of opted-out artwork. The preferences are embedded in image file metadata, theoretically preventing third-party AI tools from using the content.
Spawning, an organisation building tools for artist ownership of training data, launched "Have I Been Trained?" This search engine lets creators check whether their images appear in datasets like LAION-5B, which trained Stable Diffusion. Stability AI announced it would honour opt-out requests through this tool for Stable Diffusion 3, and over 80 million artworks have been opted out.
As of April 2023, Spawning's API allows any website, app or service to automatically comply with opt-outs for images, text, audio and video.
Yet opt-out systems face profound challenges. They place the burden of protection on creators, requiring unpaid labour to safeguard their own rights. For millions of artists, musicians and writers, individually opting out work by work represents a massive time cost.
More fundamentally, opt-outs are voluntary for AI developers. No law currently requires companies to honour them. DeviantArt's terms of service create contractual obligations for authorised users, but don't bind parties scraping content without permission.
Former Stability AI audio lead Ed Newton-Rex noted there are currently no automatic content recognition tools enabling companies to consistently cross-reference scraped data with opt-out registries. Without such tools, there's no reliable way to determine if works collected from the internet are covered by opt-out requests.
Opt-out systems may work for responsible companies seeking to avoid controversy, but they provide limited protection against actors willing to scrape indiscriminately. Rights advocates argue this inverts the proper default: creators should opt in to training, not struggle to opt out.
Music rights holders have pursued an aggressive litigation strategy, potentially setting precedents for other creative fields.
In June 2024, Sony Music, Warner Music Group and Universal Music Group sued AI music generators Suno and Udio for alleged copyright infringement. Universal Music, Concord Music and others sued Anthropic in October 2023, asserting that Claude was trained on massive amounts of copyrighted lyrics scraped from the web.
Germany's collecting society GEMA sued OpenAI in November 2024 for reproducing copyrighted song texts, and sued Suno in January 2025 on similar grounds. In June 2025, Disney and NBCUniversal sued Midjourney, describing the AI engine as a "bottomless pit of plagiarism" trained on copyrighted works without permission. Warner Bros filed a similar suit in September 2025.
These cases follow a familiar pattern: assert that training on copyrighted works without licence is infringement, seek damages for past copying, and demand injunctions preventing future unauthorised training.
Recording Industry Association of America chairman Mitch Glazier stated that whilst immediate goals are to stop training and recover damages, the long-term vision is licensing: "music creators will enforce their rights to protect the creative engine of human artistry and enable the development of a healthy and sustainable licensed market."
This suggests strategic litigation: use lawsuits to establish liability risk, then pivot to licensing frameworks as the commercial resolution.
The U.S. Copyright Office weighed in with a May 2025 report confirming that building training datasets using copyrighted works "clearly implicates the right of reproduction," making it presumptively infringing unless defences like fair use apply. This settles a threshold question: copying for training is copying under the law.
The question then becomes whether fair use saves that copying from liability. As the divergent rulings in Bartz, Kadrey and Thomson Reuters demonstrate, judges disagree profoundly.
Several factors will prove decisive. First, how courts characterise AI capability matters immensely. If judges view models as generating genuinely new creative outputs, transformativeness arguments strengthen. If they view outputs as derivative recombinations of training data, fair use weakens.
Second, market harm evidence will be critical. Artists and publishers claiming substitution effects will need empirical data showing users choosing AI outputs instead of licensing or purchasing original works.
Third, the intermediate copying question looms large. Earlier technology cases like Google v. Oracle allowed copying for interoperability. Whether that logic extends to training AI models on creative works remains contested.
The Supreme Court may ultimately need to resolve these tensions.
Different jurisdictions are charting dramatically different courses, creating a fractured global landscape.
The EU's mandatory transparency regime creates significant compliance burdens for AI developers serving European markets. The AI Act's training data disclosure requirements apply regardless of where training occurred, asserting extraterritorial reach similar to GDPR.
The UK's proposed opt-out system would tilt toward AI developers, making copyrighted works available unless creators actively object. This diverges sharply from EU law, potentially creating competitive advantages for companies operating from Britain.
The United States remains the critical uncertainty. American fair use doctrine offers broader defences than most jurisdictions, but also greater unpredictability. The ongoing lawsuits will determine whether the US becomes a haven for AI training or joins Europe in requiring licenses.
China's approach emphasises state control and algorithmic governance rather than copyright, focusing on content filtering, data localisation and party oversight.
This fragmentation creates strategic choices for AI companies: train in jurisdictions with favorable laws, structure operations to minimise liability, or pursue global licensing to ensure market access everywhere.
The central tension is real: AI development benefits from access to broad, high-quality training data. Copyright protects creators' economic and moral rights in their works. Both aims are legitimate; both involve genuine social interests.
Several synthesis approaches deserve consideration:
Compulsory licensing schemes, where AI companies pay standardised fees for training rights, could provide certainty and compensation whilst avoiding transaction costs. The U.S. Copyright Office has cautioned against this approach. Rights holders worry compulsory rates would be set too low.
Extended collective licensing, where voluntary organisations negotiate on behalf of creators who can opt out but are included by default, splits the difference between opt-in and opt-out. The STIM and GEMA models represent this approach.
Tiered fair use, where small-scale research receives broader exemptions whilst commercial deployment requires licensing, could preserve research freedom whilst ensuring commercial beneficiaries pay. The challenge is defining boundaries.
Transparent training registries, where AI companies must document all training data sources publicly, would enable rights holders to discover use of their works and negotiate or litigate accordingly. The EU's AI Act moves toward this model.
Revenue sharing from AI outputs, where creators receive compensation when their work contributes to generated content, aligns incentives differently. The difficulty is attribution: how do you determine which training data contributed to any given output?
Each approach involves tradeoffs between compensation, efficiency, innovation, autonomy and fairness. None perfectly resolves the tension.
The next 24 months will prove decisive. Major trials are scheduled: Andersen v. Stability AI in September 2026, with The New York Times v. OpenAI likely following. Appeals in Bartz, Kadrey and Thomson Reuters will test whether different circuits reach different fair use conclusions, potentially triggering Supreme Court review.
The EU's AI Act training data disclosure requirements take effect in August 2026. California's AB 2013 disclosure requirements begin in January 2026. The UK's consultation on text and data mining exceptions will conclude, revealing whether Britain follows the EU's path or diverges.
Licensing markets will either flourish or founder. If enough AI companies sign deals with publishers, labels and agencies, negotiated solutions may outpace legal ones. If litigation creates sufficiently credible infringement risk, holdout companies may join licensing frameworks. Alternatively, if fair use rulings favour AI companies decisively, licensing markets may collapse.
The technical infrastructure for transparency and opt-outs will either achieve critical mass adoption or remain niche. C2PA's ISO standardisation in 2025 could prove a tipping point.
Beneath the legal technicalities lies a philosophical question: what should AI development cost?
One view holds that training AI is like reading books to learn, researching existing art to understand techniques, or studying others' code to grasp algorithms. These activities have never required licensing every work studied. Demanding it for AI training would impose costs that prevent beneficial technology from emerging.
The opposing view holds that AI training is industrial-scale copying for commercial advantage, not individual learning. Companies aren't reading to understand; they're ingesting millions of works to build products they'll sell. The scale, purpose and commercial nature distinguish it from traditional fair use. Allowing free copying creates "content kleptocracy," as News Corp termed it, where creators' life work becomes raw material for others' profit.
Both narratives contain truth. AI models do transform their training data into genuinely new capabilities. They also depend utterly on that data, and their commercial success is built on creators' unreimbursed labour.
Perhaps the real question is who should bear uncertainty costs whilst these issues resolve. Current practice places that burden on creators, who must watch their work fuel AI systems without compensation or control unless and until they win lawsuits or achieve regulatory protection.
An alternative would place uncertainty costs on AI companies: if you want to train on copyrighted works at scale, license them or accept liability risk if courts later determine you should have.
There's no neutral ground. Any legal framework, or absence of one, allocates risks and rewards. Pretending otherwise obscures whose interests are being served.
The battles over AI training data will reshape multiple domains simultaneously: copyright doctrine, technology regulation, creative industries' economics, and the norms governing what we owe each other when building on existing culture.
Courts will determine whether fair use stretches to cover machine learning's massive copying, or whether that breaks copyright's traditional bounds. Legislators will decide whether to mandate transparency, require licensing, or step back and let litigation resolve uncertainties. Markets will either develop licensing frameworks that satisfy both developers and creators, or fail to overcome transaction costs and strategic holdouts.
Rights holders will continue seeking legal protection and market compensation. AI companies will continue pushing boundaries, launching new capabilities that outpace regulation and testing how much copying courts will tolerate. Creators will keep making art, writing stories, composing music, and taking photographs, unsure whether doing so in public means contributing to datasets they never agreed to join.
The fundamental tension won't disappear. Technology enabling machines to learn from existing human creativity is here to stay. So are creators' rights to control and benefit from their work. We're negotiating the boundary between these claims, and the negotiation is just beginning.
What emerges will determine not just who profits from AI, but what we believe creativity is worth, whether authorship matters in an age of machine generation, and what obligations we owe to those whose expression trained the algorithms now seeking to augment or replace them.
These aren't just legal questions. They're questions about what kind of creative culture we want to inhabit.
Tim Green
UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0009-0002-0156-9795
Email: [email protected]
2025-12-12 10:41:04
In an era where AI models write code, generate art, and automate workflows, it’s easy to overlook the simplest things—like converting a PNG file into a PDF.
But in real-world work, these “small” steps matter.
Every week, thousands of developers, designers, students, and office workers need to turn screenshots, UI assets, invoices, or scanned documents into a shareable PDF. And while heavyweight tools like Photoshop or Acrobat can do the job, they are often overkill.
That’s why lightweight, browser-based conversion tools still have a place.
Recently, I built a small web tool that focuses on one thing: converting PNG, JPG, and WEBP images to PDF, and converting PDF back to image formats. No login, no installations, no pop-ups. Just drag, convert, done.
Why do these tools remain relevant?
Cross-device workflows
People switch between laptop → mobile → tablet constantly. Online tools remove installation friction.
Corporate & school restrictions
Many environments don’t allow software installation. Browser-based tools bypass this.
Batch processing needs
Dragging 50 images into a page and clicking “Convert” beats manual exporting in a heavy editor.
Privacy concerns
Users prefer tools that auto-delete files instead of storing them indefinitely.
My design approach
Instant drag-and-drop interaction
No registration or tracking
High-quality output (no compression loss)
Auto-delete files after one hour
Support PNG → PDF, JPG → PDF, WebP → PDF, PDF → PNG, PDF → JPG, PDF → WebP
Nothing fancy—just something reliable that works everywhere.
If you’re building developer-friendly web utilities, I’ve found that simplicity often wins. Not every tool must be “AI-powered.” Sometimes, a clean interface and predictable results are all a user needs.