U.S. Supreme Court Declines AI Copyright Case — Human Authorship Stands (For Now)

The Supreme Court of the United States has declined to hear the most prominent case yet on whether AI-generated artwork can be copyrighted. By refusing review, the Court let lower court rulings stand — reinforcing a foundational principle: only humans can be authors under U.S. copyright law.

This decision leaves one of the defining intellectual property questions of the AI era unresolved at the highest level — but clarified, for now, at the lower courts.


The Case: Stephen Thaler and DABUS

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The dispute centers on computer scientist Stephen Thaler, who developed an AI system called DABUS (Device for the Autonomous Bootstrapping of Unified Sentience).

In 2018, Thaler sought to register copyright for artwork generated entirely by DABUS. Importantly, he listed the AI — not himself — as the author.

The United States Copyright Office denied the application, stating that copyright protection requires human authorship.

A federal district court agreed in 2023, calling human authorship a “bedrock requirement” of copyright law. The United States Court of Appeals for the District of Columbia Circuit upheld that decision.

Even the Trump-era Department of Justice supported the Copyright Office’s position, arguing that U.S. copyright statutes were written with human creators in mind — not autonomous machines.

With the Supreme Court declining review, that interpretation now stands.


What the Appeals Court Subtly Suggested

Interestingly, the appeals court noted that Thaler could have claimed authorship himself rather than naming the AI as the sole creator.

That nuance matters.

The ruling does not close the door on AI-assisted works. Instead, it draws a line:

  • ❌ Fully autonomous AI = no copyright
  • ✅ Human-directed or AI-assisted creation = potentially copyrightable

The distinction will shape future filings, litigation, and creative strategy.


Why This Matters

This case is striking for two reasons:

1️⃣ AI Was Creating Art Years Ago

DABUS generated artwork well before generative AI tools became mainstream. What once seemed experimental is now routine across writing, design, film, music, and software development.

2️⃣ The Creative Economy Is Already AI-Infused

AI-generated and AI-assisted content is pouring into:

  • Publishing
  • Advertising
  • Film and television
  • Gaming
  • Marketing
  • Social media

The ruling feels, in some ways, awkward against today’s reality. AI systems are deeply embedded in creative workflows — yet the legal framework remains rooted in a strictly human conception of authorship.


What Happens Next?

This issue is far from settled.

The next wave of cases will likely involve:

  • Major studios using AI in production pipelines
  • Individual creators leveraging AI tools
  • Disputes over how much human input is “enough”
  • International inconsistencies in copyright treatment

Unlike Thaler’s case — where the AI was declared the sole author — future litigation will focus on hybrid creativity.

And those cases may carry significantly larger financial stakes.


The Bigger Question

The core tension remains:

If AI can generate creative works indistinguishable from human art, but the law only recognizes humans as authors — how should ownership be structured?

For now, U.S. copyright law remains human-centric.

But as AI becomes more autonomous, more embedded, and more commercially significant, this question will almost certainly return to the Supreme Court — backed by far bigger players.

The defining intellectual property battle of the AI era has only begun.

https://www.reuters.com/legal/government/us-supreme-court-declines-hear-dispute-over-copyrights-ai-generated-material-2026-03-02

Document Set vs. Corporate Record in SharePoint

When to use each, and why it matters

As I matured my SharePoint architecture for Tanolis, I experimented with two different approaches:

  1. Document Sets (container-based model)
  2. Corporate Record (content type–based model)

Both are powerful.
But they solve different problems.

Understanding the difference is what turns SharePoint from “file storage” into a structured operating system.


1️⃣ Document Set Approach

(Container-Based Model)

What is a Document Set?

A Document Set is like a smart folder.

It allows you to group multiple related documents together under one container and apply shared metadata to the entire group.

Think of it as:

A project folder with intelligence.


When Should You Use Document Sets?

Use Document Sets when you need:

  • A project container
  • A client container
  • A case file
  • A contract package
  • A proposal bundle
  • A structured execution space

In plain English:

Use Document Sets when you are managing work that produces many related documents.


Example

Project: “City of New Orleans – Data Modernization”

Inside the Document Set:

  • Contract
  • SOW
  • Architecture diagrams
  • Invoices
  • Meeting notes
  • Change requests

All grouped under one structured container.


Benefits of Document Sets

✅ 1. Everything stays together

You don’t lose related documents across folders.

✅ 2. Shared metadata

You set metadata once (Client, Project Code, Status) and it applies to everything inside.

✅ 3. Automatic folder provisioning

You can auto-create subfolders like:

  • 01 – Contract
  • 02 – Design
  • 03 – Execution

✅ 4. Great for operational work

Perfect for delivery environments.


What Document Sets Are NOT Good For

They are not ideal for:

  • Corporate governance
  • Certifications
  • Tax filings
  • Policies
  • Compliance tracking

Because those are individual records, not grouped work artifacts.


2️⃣ Corporate Record Approach

(Content Type–Based Model)

What is a Corporate Record?

A Corporate Record is a structured document type.

Instead of grouping documents into containers, each document is treated as a governed record with required metadata.

Think of it as:

A database entry that happens to be a document.


When Should You Use Corporate Record Content Types?

Use this model for:

  • Articles of Organization
  • Operating Agreement
  • SWaM Certification
  • SAM Registration
  • Insurance Certificates
  • Tax filings
  • Policies
  • Resolutions

In plain English:

Use Corporate Record when the document itself is the official record.


Example

SWaM Certification

Metadata:

  • Record Status = Active
  • Expiration Date = Dec 2, 2030
  • Authority = DSBSD
  • Responsible Owner = Shahzad

This is a single governed record.

It doesn’t need a folder.
It needs structure and tracking.


Benefits of Corporate Record Model

✅ 1. Clean governance

Every document has required metadata like:

  • Status
  • Expiration date
  • Owner

✅ 2. Easy compliance dashboard

You can filter:

  • Expiring in 90 days
  • Active certifications
  • Archived records

✅ 3. Audit-ready

When SBA, SWaM, or a prime contractor asks for documents:
You can filter and export instantly.

✅ 4. Cleaner architecture

No unnecessary containers.
Just structured records.


Side-by-Side Comparison

Use CaseDocument SetCorporate Record
Project delivery✅ Yes❌ No
Client engagement container✅ Yes❌ No
Governance documents❌ No✅ Yes
Compliance tracking❌ No✅ Yes
Tax filings❌ No✅ Yes
Proposal bundles✅ YesSometimes
Expiration monitoringNot idealExcellent

Simple Mental Model

If you are managing work, use a Document Set.
If you are managing official records, use Corporate Record.

Work = container
Record = structured document


Why Separation Matters

Mixing both models inside the same library causes:

  • Extra columns
  • Schema confusion
  • Broken views
  • Hard-to-maintain governance

Separating them gives you:

  • A Corporate Core layer
  • A Projects / Execution layer
  • A clean architecture

This mirrors real enterprise design.


My Final Architecture Pattern

Corporate Side (Governance Layer)

Uses:
Corporate Record base content type
Derived types:

  • Governance Record
  • Policy Document
  • Compliance Record
  • Tax Filing
  • Proposal Record

No Document Sets here.


Projects Side (Execution Layer)

Uses:
Document Set as Project Container

With:

  • Client
  • Project Status
  • Contract Value
  • Structured subfolders

Why This Matters Long-Term

This separation:

  • Prepares Tanolis for audits
  • Makes renewals impossible to miss
  • Keeps compliance structured
  • Creates reusable architecture for future clients
  • Mirrors SaaS-style domain separation

You’re essentially applying:

Clean architecture principles inside SharePoint.


Final Plain English Summary

Use Document Set when you need a smart project folder.

Use Corporate Record when you need a governed, trackable official document.

They are both powerful.

But they solve different problems.

Understanding that difference is what turns SharePoint into a platform instead of storage.

OpenAI, Anthropic, and the Pentagon: The AI Power Shift That Triggered a Consumer Backlash

OpenAI has signed a deal with the Pentagon only hours after President Trump ordered federal agencies to cut ties with Anthropic — a company that had refused to remove safeguards against mass surveillance and autonomous weapons.

OpenAI says its own contract contains similar ethical red lines, but the timing has sparked intense scrutiny — and an immediate reaction from consumers.


What Happened

Anthropic was the first major AI lab allowed on the Pentagon’s classified networks. But negotiations broke down after the company insisted on explicit restrictions preventing:

  • mass domestic surveillance
  • fully autonomous weapons
  • removal of safety checks

The Pentagon reportedly declined to formally guarantee those limits, arguing it needed unrestricted lawful access to AI capabilities.

Following the dispute, President Trump ordered agencies to stop using Anthropic technology, while Defense Secretary Pete Hegseth labeled the company a “supply-chain risk” — a designation normally associated with adversarial actors.


OpenAI Steps In

Within hours, OpenAI announced a new agreement with the Pentagon to deploy its models in classified environments.

CEO Sam Altman stated that the contract includes key red lines:

  • no mass domestic surveillance
  • no autonomous lethal weapons
  • human responsibility for use of force

Altman said these principles are reflected in both policy and contract terms.

At the same time, he acknowledged the situation looked “rushed” and publicly called the Anthropic ban a “very bad decision,” highlighting the awkward optics of replacing a competitor immediately after its removal.


The Gray Area: Do the Safeguards Actually Match?

This is where the real debate begins.

Reports suggest Anthropic pushed for stronger contractual language explicitly restricting large-scale data collection and surveillance, while OpenAI’s agreement may rely more heavily on existing law and broader policy frameworks.

In other words:

  • Anthropic wanted stricter guarantees written directly into contracts.
  • OpenAI appears to be relying more on layered safeguards and legal constraints.

Whether those differences are meaningful or mostly semantic is now the central question being watched by analysts and AI ethicists.


Consumer Reaction: Fast and Emotional

The public response was immediate.

  • Claude reportedly surged to the top of Apple’s App Store productivity rankings.
  • Social media saw a wave of “Cancel ChatGPT” posts and users sharing subscription cancellations.

Online discussions framed the moment as a values decision — with some users supporting Anthropic’s refusal to compromise and others arguing that national-security partnerships are inevitable for frontier AI labs.


The Bigger Strategic Picture

This moment reveals a deeper shift happening in the AI industry:

1️⃣ Government relationships are becoming strategic assets

Winning defense contracts may matter more long-term than short-term consumer sentiment.

2️⃣ Safety language is becoming competitive positioning

AI companies are now competing not only on performance, but on how they define ethical boundaries.

3️⃣ Consumer trust can swing fast

The rapid movement between apps shows how quickly narrative and perception can influence market dynamics — even when underlying policies are complex.


Why This Matters

The key question isn’t just who got the Pentagon contract.

It’s whether OpenAI’s safeguards truly mirror Anthropic’s — or simply look similar on paper.

If they are equivalent, the backlash may fade.
If they aren’t, this moment could reshape how consumers evaluate AI companies and their alignment with government power.

Either way, the AI landscape is entering a new phase where:

  • policy decisions move markets,
  • ethics become product strategy,
  • and public perception can shift overnight.

https://openai.com/index/our-agreement-with-the-department-of-war

Google Launches Nano Banana 2 — High-End Image Generation at Flash-Level Cost

Google has officially released Nano Banana 2, the upgraded version of its viral image generation model — delivering major improvements in quality, consistency, speed, and pricing while taking the top spot on text-to-image leaderboards.

What’s New

Nano Banana 2 now ranks #1 for text-to-image generation on leading benchmarks such as Artificial Analysis and LM Arena, outperforming both Nano Banana Pro and OpenAI’s GPT Image 1.5. The model also secured a strong position for image editing tasks, ranking third overall.

Key upgrades include:

  • 4K output resolution across multiple aspect ratios
  • Improved scene consistency, supporting up to five characters and fourteen objects while maintaining visual coherence
  • Significantly better text rendering, a historically difficult challenge for image models
  • Faster generation speeds approaching Gemini Flash-level performance

Pricing and Accessibility

One of the most notable changes is cost efficiency. At roughly $0.07 per image, Nano Banana 2 comes in at nearly half the price of competing premium models while delivering top-tier performance.

Google has already integrated the model as the default image generator across Gemini and its broader tool ecosystem, while Nano Banana Pro remains available for paid users who need advanced options.

Why It Matters

The original Nano Banana models pushed image generation forward when they launched last August, but Nano Banana 2 signals a bigger shift in the market.

Historically, users had to choose between:

  • High quality but expensive models
  • Faster, cheaper models with lower fidelity

Nano Banana 2 begins to blur that line. By combining state-of-the-art image quality with flash-level speed and aggressive pricing, Google is moving toward a future where quality and affordability are no longer mutually exclusive.

If this trajectory continues, the competitive landscape for AI image generation could shift from pure capability races toward efficiency, ecosystem integration, and scale — areas where large platform players hold a strong advantage.

https://blog.google/innovation-and-ai/technology/ai/nano-banana-2

Modernization Isn’t Dying — It’s Being Rewritten

Anthropic just announced that #Claude can analyze #COBOL code — potentially helping enterprises modernize legacy systems and migrate to newer platforms faster.

Markets reacted quickly: investors dumped IBM stock, triggering a sharp ~13% drop on Monday (Feb 23, 2026).

And honestly… the reaction isn’t entirely irrational.

IBM’s mainframe ecosystem and its multi-billion-dollar modernization consulting business rely heavily on the complexity of legacy systems. If AI meaningfully reduces the cost and risk of understanding decades-old COBOL, it could reshape how modernization projects are delivered.

From Anthropic’s own framing:

COBOL still powers an estimated 95% of ATM transactions in the U.S.
Hundreds of billions of lines run critical systems across finance, airlines, and government — while the number of engineers who truly understand it keeps shrinking.

That’s the real pressure point: institutional knowledge is disappearing faster than organizations can modernize.

The interesting question isn’t whether AI will replace modernization work — it’s how it changes the economics of it.

Here’s the irony I keep thinking about:

What if IBM itself adopts Claude (or similar AI) to accelerate COBOL analysis, reduce delivery costs, and preserve margins inside the very modernization wave investors fear will shrink?

AI may not eliminate modernization consulting — it may simply redefine who captures the value.

#AI #EnterpriseIT #Mainframe #Modernization #IBM #Anthropic #SoftwareEngineering #DigitalTransformation