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Viewpoint: Don’t let America’s copyright crackdown hand China global AI leadership – Grand Forks Herald
China is racing to outpace the United States in artificial intelligence development and deployment, and it is making progress. To stay ahead, America needs a national innovation strategy that strengthens all the pillars of our leadership, from data and infrastructure to talent and adoption. Thankfully, political leaders on both sides of the aisle are waking up to the intensity and importance of this race, and many are implementing innovative policies to ensure America maintains its edge.
Submitted / Inside Sources
If the United States wants to lead in AI, it needs a national innovation strategy grounded in four parts:
- Policy clarity: Freeze patchwork state regulations and preserve pro-growth legal frameworks, including supporting antitrust law that fosters growth and maintaining the current “fair use” copyright law to help catalyze innovation.
- Infrastructure: Build the computer power, energy and data systems needed to train and deploy AI at scale.
- Talent: Invest in Science, Technology, Engineering, and Mathematics education, upskilling, and skilled trades to propel innovation.
- Adoption: Ensure our AI models are adopted at home and abroad so that American values, creativity and innovation continue to underpin the global tech infrastructure, rather than China’s authoritarian vision.
For 50 years, fair use has allowed creators, educators, researchers and entrepreneurs to use snippets of copyright material to produce transformative inventions, and not just market substitutes for the original. In the early days of the internet, fair use was the legal bedrock that enabled search engines to index the web, web archives to preserve knowledge, and platforms to build global communities. The law worked well to expand innovation and benefit the public, including by enabling researchers to use academic texts and developers to train early AI models on diverse, real-world content.
We didn’t strangle the internet in its infancy with impossible licensing requirements, and we shouldn’t make that mistake now with AI. Today’s large language models use vast public datasets to learn how language, code and medicine work, not to reproduce them but to understand and create ideas and solutions to our most pressing societal problems.
Access to this data is essential to modern AI
that allow small businesses to grow, doctors to discover treatments, and
through personalized content. That’s precisely the kind of innovation Congress intended to protect with fair use. And it’s the kind of innovation we can’t choke off if we want America and our values to keep leading the world.
While some are challenging this longstanding principle,
China is using every speck of data possible
to train its AI models and to get a leg up in our fierce competition. That includes public data, government surveillance feeds, and personal information.
China is pouring
into advanced technologies, including AI. It also uses hacking and spying to capture $500 billion in American tech and trade secrets annually, and is now using AI to export suppression, surveillance and political control globally.
The stakes are high:
recently exposed how China is training AI to manage simulated cities, tightly controlling speech, behavior and movement according to Communist Party values. If Chinese models become the global standard, we’ll see freedoms erode worldwide.
Some want to force every AI developer to get explicit permission — and pay licensing fees — for every piece of content used in training models. This would break from precedent, kneecap innovation, bankrupt startups, and could hand China a lasting edge in global tech leadership.
Today, there are
against AI companies alleging that using copyright material to train models constitutes willful infringement. Some plaintiffs are asking courts for
that would effectively shut down dozens of U.S. firms overnight, an outcome that China would welcome with open arms.
If these lawsuits succeed, or if Congress radically rewrites the law, it will become nearly impossible for startups, universities or mid-size firms to develop competitive AI tools. U.S. firms could move to more favorable foreign jurisdictions,
driving jobs, talent and capital
out of the United States. Such changes could also
damage America’s open-source AI movement
, one of our strongest checks against the global spread of Chinese technology.
America’s tech leadership is under threat from Beijing to Brussels: China seeks AI dominance while the
EU punishes American innovation
through overregulation. Meanwhile, our lawmakers have introduced more than
in state legislatures this year.
Maintaining our fair use framework supports American creativity and technological leadership. If we eliminate it, we will unintentionally hobble our innovators and allow China to surge ahead, risk more dependence on foreign technology, have less freedom in the digital space, and face a global playing field redefined by authoritarian actors. Fair use has been, and continues to be, a cornerstone of American innovation. If we want to win the AI future, we must ensure this critical component of copyright law remains intact.
Kent Conrad, a Democrat, represented North Dakota in the Senate from 1986 to 2013. Saxby Chambliss, a Republican, represented Georgia in the Senate from 2003 to 2015. They both are advisers to the American Edge Project and wrote this for
.
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The tech industry’s history is littered with cautionary tales of irrational exuberance: the dot-com boom, the crypto craze, and the AI winter of the 2010s. Today, Palantir Technologies (PLTR) stands at the intersection of hype and hubris, its stock up over 2,000% since 2023 and trading at a Price-to-Sales (P/S) ratio of 107x—a metric that dwarfs even the most speculative valuations of the late 1990s. This is not sustainable growth; it is a textbook bubble. With seven critical risks converging, investors are poised for a reckoning that could slash Palantir’s valuation by 60% by 2027.
The Illusion of Growth: Valuation at 107x Sales
Let’s start with the math. A P/S ratio of 107x means investors are betting that Palantir’s revenue will grow 107-fold to justify its current price. For context, during the dot-com bubble, Amazon’s peak P/S was 20x, and even Bitcoin’s 2017 mania never pushed its P/S analog to such extremes.
Seven Risks Fueling the Implosion
1. The AI Bubble Pop
Palantir’s valuation is tied to its AI product, Gotham, which promises to revolutionize data analytics. But history shows that AI’s promise has often exceeded its delivery. The AI winters of the 1970s and 1980s saw similar hype, only to crumble under overpromised outcomes. Today’s AI tools—despite their buzz—are still niche, and enterprise adoption remains fragmented. A cooling in AI enthusiasm could drain investor confidence, leaving Palantir’s inflated valuation stranded.
2. Gotham’s Limited Market
Gotham’s core clients are governments and large enterprises. While this niche offers stability, it also caps growth potential. Unlike cloud platforms or social media, Palantir’s market is neither scalable nor defensible against competitors. If governments shift spending priorities—or if AI’s ROI fails to materialize—the demand for Gotham’s services will evaporate.
3. Insider Selling: A Signal of Doubt
Insiders often sell shares when they anticipate a downturn. While specific data on Palantir’s insider transactions is scarce, the stock’s meteoric rise since 2023 has coincided with a surge in institutional selling. This behavior mirrors the final days of the dot-com bubble, when executives offloaded shares ahead of the crash.
4. Interest-Driven Profits, Not Revenue Growth
Palantir’s profits now rely partly on rising interest rates, which boost returns on its cash reserves. This financial engineering masks weak organic growth. When rates inevitably fall—or inflation subsides—this artificial profit driver will vanish, exposing the company’s fragile fundamentals.
5. Dilution via Equity Issuances
To fund its ambitions, Palantir has likely diluted shareholders through stock offerings. The historical data shows its adjusted stock prices account for splits and dividends, but no splits are noted. This silent dilution reduces equity value, a tactic common in bubble-stage companies desperate to fund unsustainable growth.
6. Trump’s Fiscal Uncertainty
Palantir’s government contracts depend on political stability. With a potential Trump administration’s fiscal policies uncertain—ranging from spending cuts to regulatory crackdowns—the company’s revenue streams face existential risks.
7. Valuation Precedents: The 2000 Dot-Com Crash Revisited
Valuation metrics matter. In 2000, the NASDAQ’s P/S ratio averaged 4.5x. Palantir’s 107x ratio is 23 times higher—a disconnect from reality. When the dot-com bubble burst, companies like Pets.com and Webvan, once darlings, lost 99% of their value. Palantir’s fate could mirror theirs.
The Inevitable Correction: 60% Downside by 2027
If Palantir’s valuation reverts to a more rational 10x P/S—a still aggressive multiple for its niche market—its stock would plummet to $12.73, a 60% drop from its July 2025 high. Even a 20x P/S, akin to Amazon’s peak, would price it at $25.46—a 75% drop. This is not a prediction of doom; it is arithmetic.
Investment Advice: Avoid the Sizzle, Seek the Steak
Investors should treat Palantir as a warning sign, not a buy signal. The stock’s rise has been fueled by sentiment, not fundamentals. Stick to companies with proven scalability, sustainable margins, and valuations grounded in reality. For Palantir? The only question is whether it will crash to $12 or $25—either way, the party is over.
In the annals of tech history, one truth endures: bubbles always pop. Palantir’s 2023–2025 surge is no exception. The only question is how many investors will still be dancing when the music stops.
Data sources: Historical stock price summaries (2023–2025), Palantir’s P/S ratio calculations, and fusion of market precedents.
Does artificial intelligence (AI) make working life easier or complicated? Experts suggest the answer depends on the context.
In a recent IDC-hosted interview, SIAC CEO Toni Townes-Whitley described AI as the ultimate weapon against system complexity, noting that her company is employing AI to reduce tech complexity in some of the most complex technology environments on the planet — within the US Department of Defense.
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Her team has been able to reduce mission planning and other operational functions at the department from “hours to minutes,” she said. AI can have the same impact in commercial businesses, “reducing time and toil for business development, proposal development, searching, and creating new documents and content.” On the developer side, AI has reduced the time spent on code generation.
These results are positive. However, other voices advised caution, as AI is just as capable of increasing as reducing complexity. The impact depends on where and how AI is applied, with the right kind of governance, of course.
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“The integration of AI into our technological landscape brings with it a host of new complexities,” said Supriya Bachal, program manager for R&D at Siemens.
“These complexities exist on multiple levels. Individual engineers and developers who are tasked with integrating AI into the tools we use must deal with new levels of complexity, as do the organizations that must manage these new AI systems.”
Skill requirements may complicate the situation. While AI might potentially reduce the need for headcount in many areas, particularly in coding and IT management, applying the technology requires expertise in AI-friendly programming languages and frameworks, machine learning, deep learning, natural language processing (NLP), analytics, math, statistics, algorithm design, and deductive reasoning.
“With AI-driven solutions across apps, APIs, and varied user endpoints, the IT landscape will become increasingly intricate,” said Amitha Pulijala, vice president at Vonage. “This will require more specialized expertise to manage these new tools.”
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At the same time, “AI has shifted the focus from foundational IT skills to use cases, implementation, and user experience,” said Dennis Perpetua, senior VP of digital workplace services at Kyndryl. “This change opens up opportunities for new talent to use AI tools to accelerate their careers in IT.”
It was suggested that open collaboration on AI initiatives is the key to overcoming skills challenges, an approach that brings together developers, data scientists, IT teams, and business stakeholders.
Alleviating workplace challenges
When it comes to operational complexity, AI offers a mixed bag of benefits, but with many ways to overcome the issues. “AI can automate routine tasks, streamline processes, and in some cases, directly manage the intricate webs of applications and services that comprise our modern IT architectures,” said Siemens’ Bachal.
“Tools like AI-driven observability platforms enable problem-solving in the digital space that would otherwise require a lot of human attention and cognitive load to simulate and synthesize.”
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Another plus of emerging technology is that “when platforms run into operational problems, AI doesn’t just offer infinite human problem-solving power; it can also do some problem-solving work of its own,” Bachal added.
It was suggested that AI will hold the ability to help alleviate workplace challenges. “The technology can help optimize workflows, automate simple app development, and provide insights into system performance, freeing up developers and IT teams to focus on higher-value tasks,” said Vonage’s Pulijala.
In short, while AI is making technology access more complex in some circumstances, AI is also helping to manage complexity. “Overall, while AI has increased the complexity in certain aspects of IT, it has also brought significant efficiencies, creativity, and productivity, making the challenges worthwhile,” said Kyndryl’s Perpetua.
For example, he pointed out, “tools like GitHub Copilot are increasing efficiency in coding tasks, and AI-based APIs are becoming more autonomous, reducing the time spent on creating and maintaining them.”
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Another consideration is NLP, considered the gateway to the AI world, capable of breaking down traditional integration barriers between APIs and simplifying sprawling infrastructures, said Loren Absher, a director who leads ISG’s AI advisory practice in the Americas. However, this NLP-enabled progress comes with challenges: “Machines interpreting human language must untangle ambiguous queries, maintain security, and ensure precision — all while scaling dynamically.”
He said that good governance is critical. “AI should be employed not just for automation — such as monitoring, issue detection, and optimization — but also as a mediator between traditional and NLP-enabled APIs,” said Absher. “Tools like middleware platforms and orchestration engines can facilitate seamless communication across diverse systems.”
Design AI systems “with transparency, adaptability, and robust security protocols,” Absher advised. “A strong governance framework and ongoing investment in training and tools will ensure teams can harness AI’s transformative power without losing control.”
Watch out for the agents
It was also suggested at the event that agentic AI could simplify rather than exacerbate complexity.
“Agents can streamline ecosystems by connecting legacy applications, APIs, and disparate data sources,” said Aaron Kesler, vice president at RozieAI, and formerly director of AI product management at SnapLogic.
“They can identify inefficiencies, flag bottlenecks, and automate streamlined workflows optimized for existing systems, without requiring custom code or extensive dev time.”
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For example, “fraud-detection agents can autonomously analyze transactions, flagging suspicious patterns while providing actionable insights to human analysts,” said Kesler.
“Similarly, research agents can scan the web to track mentions of specific products, aggregating data in real time to keep teams informed and proactive. All of this can now be built without heavy reliance on the data science team. Tasks can now be accomplished by one or two data engineers within the IT department.”
Still, it’s important to note that AI’s impact on complexity will vary on a case-by-case basis.
“For organizations with an already robust IT infrastructure and team, AI will probably just shift resources from one place to another,” said Brandon Andersen, technology consultant and co-founder of Ceralytics.
“Instead of a team working on maintaining legacy systems, they will now troubleshoot and periodically maintain the new AI systems — especially the litany of connections.”
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For smaller IT teams, the story may be very different. “Instead of being an escalation point for current SaaS systems, these teams will now be in charge of the various API connections and be the first line of defense when the system goes down,” Andersen said.
“IT teams will absorb a lot of responsibility for these systems because the connections are no longer owned by a third party.”
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