Category: 3. Business

The current education research-to-policy pipeline is too slow to keep pace with the urgent needs of districts and states. Researchers face steep barriers to accessing high-quality, multimodal data, while existing R&D infrastructures remain siloed and under-resourced. Without scalable, trusted, systems that enable timely and secure data use, the U.S. risks falling behind in generating actionable and evidence-based insights to guide policy and practice. In this memo, we discuss how privacy-preserving research models can be used to strengthen education R&D capacity. 

Challenge and Opportunity

Learning is a lifelong and multidimensional process, yet data about learning has historically been difficult to obtain. The shift to digital learning platforms (DLPs), accelerated by COVID-19, has created a wealth of data, but accessing it remains complex and slow – especially for researchers with fewer institutional resources.

Additionally, complex privacy laws, such as the Children’s Online Privacy Protection Act (COPPA) and Family Educational Rights and Privacy Act (FERPA), alongside state-specific regulations and institutional risk aversion, create substantial barriers. These laws were not designed to accommodate privacy scenarios within the current environment of pervasive data collection and rapidly advancing AI. 

As such, trusted mechanisms for safe data access that remove barriers to critical R&D, bolster global competitiveness, and leverage innovation to cultivate a skilled STEM workforce, are more important than ever.  Without trusted mechanisms to ensure privacy while enabling secure data access, essential R&D stalls, educational innovation stalls, and U.S. global competitiveness suffers.

Flipping the traditional research model

The landscape of educational research and development (R&D) is rapidly evolving as digital learning platforms (DLPs) capture increasingly rich streams of data about how students learn. These multimodal data streams provide unprecedented opportunities to accelerate insights into how learning happens, for whom, and in what contexts – as well as how these processes, in turn, affect learning outcomes, engagement, and persistence. Yet, despite this potential, access to platform-generated learning data remains highly constrained – particularly for early-career researchers with minimal institutional resources and organizations outside elite academic settings. 

Current challenges to accessing DLP data include privacy risks (e.g., data leaks), opaque legal environments, institutional risk aversion, and the lack of trusted third-party intermediaries to balance privacy with data utility. As a result, promising research is delayed and the research-to-policy pipeline is exacerbated – leaving decision-makers without timely evidence to address urgent needs such as learning recovery, responsible AI integration, or workforce readiness.

Privacy-preserving models offer transformative opportunities to address these barriers. Across sectors, the field is converging on trusted research environments that include secure enclaves that keep data in situ and move analysis to the data. SafeInsights, the U.S. Census’ Federal Statistical Research Data Center (FSRDC), and North Carolina Education Research Data Center (NCERDC) are examples of such systems complemented by privacy-preserving methods.

Privacy-preserving research models, such as SafeInsights, flip the traditional research model: instead of giving data to researchers, it brings researchers’ questions and analyses, encoded as software, to the data. At no point in the research process does the researcher have direct access to raw data, thereby minimizing concerns for data leaks. 

Researchers instead use sample or synthetic data to craft their analyses. Once the researchers’ analysis code is submitted to the owner of the data, it is reviewed by experts for approval. This model minimizes risk, reduces delays in the research-to-policy pipeline, and unlocks data that would otherwise remain inaccessible.

Think of it as a secure research zone: a trusted third-party intermediary where researchers can run analyses using specific tools and applications, but cannot access data directly, ensuring strict security.

Rather than extracting and sharing sensitive data with researchers, privacy-preserving research models bring researchers’ analytic tools to secure data enclaves – preserving privacy while enabling rigorous, scalable, inquiry of DLP data. Through secure enclaves, transparent governance, and standardized compliance frameworks, a durable large-scale infrastructure for research can be created.

Benefits of privacy-preserving research models

• Accelerate time to insight for policy and decision-makers who need rapid, evidence-based guidance. Standardized governance reduces delays arising from fragmented compliance and legal processes. For federal, state, and local level policy and decision-makers, this means actionable insights can be delivered in months rather than years, potentially informing legislative decisions and programs with greater speed.
• Safely join data across platforms, enabling richer analyses of student learning. Shared infrastructure maximizes critical research infrastructure return on investments and spreads costs across funders. Secure, trusted, interoperable research environments protect privacy while enabling cumulative evidence. This aligns with federal agency priorities to modernize research infrastructure and ensure taxpayer investments translate into impact.
• Democratize access and participation in complex research by lowering barriers for early-career researchers with minimal institutional resources and organizations outside elite academic settings. Lowering barriers to entry broadens the reach of federal R&D investments and supports state leaders and research organizations seeking to participate in research.

By securing cross-sector investment for embedding scalable privacy-preserving models into R&D ecosystems and infrastructures, we can expand access to high-value data while supporting long-term research scalability, security, and trust.

Such models can fill a critical gap in the R&D ecosystem by establishing a secure and sustainable research infrastructure that extends well beyond its initial NSF funding and is ideally suited to broker access between DLP developers, school districts, and researchers.

Plan of Action

Promote R&D Infrastructure Development and Sustainability

Privacy-preserving research models have the potential to offer researchers safer, faster, reliable, high-value, de-identified data analyses – while simultaneously saving DLPs and school districts time and resources on compliance reviews and privacy audits. It also creates opportunities for funders to support a sustainable research infrastructure that multiplies the impact of each dollar invested.

To move from promise to practice, interested stakeholders, including research institutions, school districts, and funders, should consider the following actions:

Recommendation 1. Lay the Foundation for Sustainable Large-Scale R&D Infrastructure

• Conduct policy landscape scans, including review of state student privacy laws, to identify commonalities, constraints, and pathways for district participation.
• Interview stakeholders, including district data leads, state education agencies, and platform providers, to understand pain points and demand for trusted intermediaries.
• Review existing research infrastructures and operational frameworks, including research data hub governance, fee structures, data-sharing agreements, IRB support services, and services, adapting effective practices to the privacy-preserving context.

Recommendation 2. Embed Infrastructure Costs into Research Contracts and Budgets

• Require researchers to include service fees for privacy-preserving infrastructure directly in grant applications, with templates to simplify proposal preparation.
• Embed privacy-preserving infrastructure costs in contracting and budgeting to support scalability, drive down the marginal cost of data access across the field, and make rigorous educational research more accessible and sustainable beyond single grants.

Recommendation 3. Catalyze Scaling through Foundation and Philanthropic Support

Recommendation 4. Develop Large Scale R&D Infrastructure across Sectors

• Extend privacy-preserving models across sectors, such as education, health, workforce, housing, and finance, to capture increasingly rich streams of data about how people live, learn, work, and access services.
• Enable secure, interoperable, cross-sector research on questions such as how early education experiences impacts long-term workforce outcomes or  how neighborhood-level educational access connects to public health disparities.
• Align with federal agency efforts, such as the Federal Data Strategy, to support the linking of data ecosystems across sectors.

Conclusion

Privacy-preserving research models offer standardized, secure, and privacy-conscious ways to analyze data – helping researchers at the local, state, and federal levels understand long-term educational trends, policy impacts, and demographic disparities with unprecedented clarity.

By accelerating time-to-insight, investing in critical R&D infrastructure, and expanding participation in complex research, privacy-preserving research models offer possibilities for delivering on urgent policy priorities – building towards a modern, responsive, trustworthy education R&D ecosystem.

Unlock the White House Watch newsletter for free

Your guide to what Trump’s second term means for Washington, business and the world

Shares in a US cryptocurrency miner backed by Donald Trump Jr and Eric Trump shed a third of its value on Tuesday as early investors cashed out en masse at the end of a lock-up period.

American Bitcoin was down 37 per cent by mid-afternoon in New York, wiping roughly $1bn from its market value. Trading volume in the stock was almost 40 times the daily average, according to Bloomberg data.

Eric Trump attributed the sell-off to investors in a $215mn private placement in June exercising their ability to “cash in on their profits for the first time”.

This is “why we will see volatility” in the company’s share price, the president’s son said on X. “I’m holding all my [American Bitcoin] shares — I’m 100% committed to leading the industry.”

American Bitcoin, which says its “ambition is to build the strongest and most efficient Bitcoin accumulation platform in the world”, went public in September through a reverse merger with Nasdaq-listed miner Gryphon Digital Mining.

Eric Trump is American Bitcoin’s co-founder and chief strategy officer. Donald Trump Jr was an early investor. Matt Prusak, American Bitcoin’s president, said the end of the share lock-up period “affects who can buy or sell, not the assets we operate or the work the team is doing every day”.

Donald Trump has loosened regulations on the crypto sector since returning as president this year and has vowed to make the US a digital currency “superpower”. He previously described the value of crypto as based on “thin air”.

The broader crypto market has tumbled in recent weeks as investors have pulled back from risky assets, with bitcoin down about 30 per cent since its latest peak in early October. Bitcoin steadied on Tuesday, however, up 5 per cent at about $90,000 per token.

American Bitcoin — which creates new bitcoin through the computational process known as mining and also maintains its own “strategic” bitcoin reserve — was originally called American Data Centers but rebranded in late March in a joint venture with Hut 8, another crypto miner.

As part of the deal, Hut 8 agreed to hand over all of its mining equipment in exchange for a majority interest in ADC. Hut 8’s shares were down 11 per cent on Tuesday.

American Bitcoin is among several crypto companies backed by the president’s sons, who also co-founded World Liberty Financial, which has issued billions of its own tokens and lists Donald Trump as co-founder emeritus.

The value of its WLF token has collapsed 86 per cent over the past year, according to CoinMarketCap data.

Trump Media & Technology Group, which runs the Truth Social app and is controlled by the president’s family, earlier this year said it planned to raise $1.5bn in fresh equity and another $1bn through convertible bonds to create a “bitcoin treasury”.

TMTG’s share price is down almost 70 per cent this year.

Shares in other so-called bitcoin treasury companies have tumbled in recent months amid the wider sell-off for hundreds of digital assets.

Michael Saylor’s Strategy — which pioneered the model and holds 650,000 bitcoin, equivalent to 3.1 per cent of the world’s total supply of the cryptocurrency — has fallen 40 per cent this year as investors have cooled on the company’s prolific issuance of shares, convertible debt and new preferred equity instruments to fund its bitcoin-buying spree.

Sam Altman has declared a “code red” at OpenAI to improve ChatGPT as the chatbot faces intense competition from rivals.

According to a report by tech news site the Information, the chief executive of the San Francisco-based startup told staff in an internal memo: “We are at a critical time for ChatGPT.”

OpenAI has been rattled by the success of Google’s latest AI model, Gemini 3, and is devoting more internal resources to improving ChatGPT.

Last month, Altman told employees that the launch of Gemini 3, which has outperformed rivals on various benchmarks, could create “temporary economic headwinds” for the company. He added: “I expect the vibes out there to be rough for a bit.”

OpenAI’s flagship product has 800 million weekly users but Google is also highly profitable due to its search business and has substantial data and financial resources to throw at its AI tools.

Marc Benioff, the chief executive of the $220bn (£166bn) software group Salesforce, wrote last month that he had switched allegiance to Gemini 3 and was “not going back” after trying Google’s latest AI release.

“I’ve used ChatGPT every day for 3 years. Just spent 2 hours on Gemini 3. I’m not going back. The leap is insane – reasoning, speed, images, video … everything is sharper and faster. It feels like the world just changed, again,” he wrote on X.

OpenAI is also delaying a foray into putting advertising in ChatGPT as it focuses on improving the chatbot, which celebrated its third birthday last month.

The head of ChatGPT, Nick Turley, marked the anniversary with a post on X pledging to break new ground with the product.

He wrote: “Our focus now is to keep making ChatGPT more capable, continue growing, and expand access around the world – while making it even more intuitive and personal. Thanks for an incredible three years. Lots more to do!”

Despite lacking the cash flow support enjoyed by rivals Google, Meta and Amazon, which is a big funder of competitor Anthropic, OpenAI has received substantial funding from the likes of the SoftBank investment group and Microsoft. In its latest valuation, OpenAI reached $500bn, up from $157bn last October.

OpenAI is loss-making and expects to end the year with annual revenues of more than $20bn, which Altman expects will grow to “hundreds of billion[s]” by 2030. The startup is committed to steep revenue growth after pledging to spend $1.4tn on datacentre costs to train and operate its AI systems over the next eight years.

“Based on the trends we are seeing of how people are using AI and how much of it they would like to use, we believe the risk of OpenAI of not having enough computing power is more significant and more likely than the risk of having too much,” said Altman last month.

Apple has also responded to increasingly intense competitive pressures in the sector by naming a new vice-president of AI. Amar Subramanya, a Microsoft executive, will replace John Giannandrea.

Apple has been slow to add AI features to its products in comparison with rivals such as Samsung, which have been quicker to refresh their devices with AI features.

Subramanya is joining Apple from Microsoft, where he most recently served as corporate vice-president of AI. Previously, Subramanya spent 16 years at Google, where his roles included the head of engineering for the Gemini assistant.

Earlier this year, Apple said AI improvements to its voice assistant Siri would be delayed until 2026.

Compute is the most valuable resource in the AI world we live in today. Nvidia. Google TPUs. Amazon Trainium. OpenAI and Broadcom’s partnership. Elon’s recent post about Tesla’s AI chips.

Designing the most performant chips for AI workloads sits at the heart of accelerating technological progress.

But major hurdles exist.

First, chip design is slow. It takes 12-24 months at mature nodes and 18-36 months at the leading edge for 5nm or 3nm.

Second, chip design is prohibitively expensive. It costs on average $200-250 million for 7nm, $450-500 million for 5nm, and $600-650 million for 3nm. Roughly 50-70% of that is human labor. Another 5-15% is Electronic Design Automation tooling spend in a market long dominated by Cadence and Synopsys, where each generates $5-6 billion in annual revenue and are worth approximately $90-100 billion in market cap.

AlphaChip caught my eye for these exact reasons. It gave us a peek at AI’s potential to transform the entire chip design process, showing we can cut the floorplanning step in physical design from months to hours.

What if we could extrapolate this and build AI to automate the entire flow, from architecture design to RTL to verification, all the way through physical design?

What if chip design took days, not two to three years? Every day is massively costly; some reports from August 2024 indicated that a multi-month Blackwell delay could result in more than $10 billion in lost revenue for 2025 alone. More importantly, imagine the revenue potential unlocked when new generations of chips are designed faster and shipped earlier.

What if each design didn’t cost hundreds of millions of dollars? What if chip companies didn’t need to operate large human teams on top of clunky EDA tooling?

And most exciting: what if we unlocked novel chip designs we might never have explored?

AlphaChip revealed an important human bias: in chip design, we tend to think in Manhattan grid-like structures. AlphaChip’s designs were different, more organic in shape, more like forms inspired by nature. So different, in fact, that humans wanted to reject them at first … Yet AlphaChip went on to shape four generations of the TPU.

We at Sequoia are so excited to partner with co-founders Anna Goldie and Azalia Mirhoseini, leading their very first round from the formation of Ricursive Intelligence. They pioneered AI for chip design by creating and leading the AlphaChip effort and are at the epicenter of this emerging AI for chip design ecosystem. They are visionaries with incredible clarity of thought, intensely ambitious, humble yet exceptionally accomplished, and real talent magnets who move, and inspire others to move, with urgency and velocity.

Anna and Azalia founded Ricursive Intelligence to build the frontier AI lab defining this category. In just the first weeks since company formation, they have assembled a team with the highest talent density you can imagine in the field.

Their core belief: chip design is the compute bottleneck, and progress in AI, hardware and infrastructure is capped by the speed and efficiency of silicon creation.

In their words: “If we get this right, it’s not just faster chip design cycles; it’s a fundamental expansion of what’s possible in hardware. Once chip design becomes fast and accessible, everyone will be able to customize. The automation here will unlock a flood of new hardware innovation.”

Anna and Azalia’s vision for Ricursive is to define a new movement, from “fabless” to “designless.” Fabless, meaning a company designing chips without owning expensive fabs, outsourcing production to foundries. Designless, meaning outsourcing not only manufacturing but the entire chip design process, taking an idea and converting it into a manufacturable design.

We envision a world where Ricursive helps any company design chips for its own workloads faster, more efficiently and more creatively than is possible today. In doing so, Ricursive can help revolutionize the most valuable resource in our era: compute. We could not be more excited to help build a true generational company in the making.

Sign up here.

Bobcat asked the court for an unspecified amount of monetary damages and the U.S. International Trade Commission for an order blocking imports of Caterpillar’s patent infringing equipment. It also filed related lawsuits against Caterpillar in German district court and at the European Union’s Unified Patent Court.

Spokespeople for Caterpillar did not immediately respond to a request for comment. Bobcat spokesperson Nadine Erckenbrack said the company seeks to “protect our patented technologies, defend fair competition, and safeguard the innovation and craftsmanship that have defined our company for more than 65 years.”

“CAT’s use of so many of Bobcat’s patented technologies is consistent with its pattern and practice of identifying and emulating the key features in its competitors’ products,” Bobcat said in the Texas complaint.

The lawsuits are Doosan Bobcat North America Inc v. Caterpillar Inc, U.S. District Court for the Eastern District of Texas, Nos. 2:25-cv-01184 and 2:25-cv-01185; and In the Matter of Certain Skid-Steer Loaders, Compact Track Loaders, Excavators, Wheel Loaders, Dozers and Components Thereof, U.S. International Trade Commission.

For Bobcat: Sean Pak, Iman Lordgooei, Nathan Hamstra, Marc Kaplan and James Pak of Quinn Emanuel Urquhart & Sullivan

For Caterpillar: attorney information not yet available

Our Standards: The Thomson Reuters Trust Principles., opens new tab

• Globalization is being rewired by geopolitics and policy shifts. The financial system will have to adapt to better serve the real-economy needs.

• Policy volatility is now a persistent challenge for trade, investment and development.

• Financial shocks spill over rapidly into the real economy, revealing gaps in the global economic architecture.

• Developing economies drive global growth but face the highest financing and climate risks.

• Coordinated reforms linking trade, finance, debt and climate action can restore stability and recentre development.

Global growth will slow to 2.6% in 2025, down from 2.9% in 2024, as global trade and investment face growing pressure from financial volatility and geopolitical uncertainty, according to UN Trade and Development’s new “Trade and Development Report 2025: On the Brink – Trade, finance and the reshaping of the global economy”. The report shows that shifts in financial markets move global trade almost as strongly as real economic activity, influencing development prospects worldwide.

UN Trade and Development (UNCTAD) Secretary-General Rebeca Grynspan said the findings show how financial conditions increasingly determine the direction of global trade: “Trade is not just a chain of suppliers. It is also a chain of credit lines, payment systems, currency markets and capital flows.”

Global trade rose by about 4% early in 2025, driven in part by firms accelerating imports ahead of tariff changes, but also by structural shifts: Services are expanding faster, supported by growth in the digital economy and artificial intelligence, and South–South trade is growing above average. Beneath these factors, underlying trade growth is estimated at between 2.5% and 3% and is expected to ease further as financial conditions influence production and investment decisions more strongly.

More than 90% of global trade depends on bank finance. Dollar liquidity and cross-border payment systems are also crucial for international trading activities. This deep reliance on financial channels makes trade closely linked to global financial and monetary conditions. A shift in interest rates or investor sentiment in a major financial centre can affect trade volumes worldwide. For developing countries, where access to affordable credit is limited, these financial pressures can undermine otherwise viable trade transactions.

The report also highlights the increasing role of financial factors of commodity markets, particularly in essential food systems.

For several major food trading companies, more than 75% of income now stems from financial operations rather than the physical movement of goods.

Developing economies face mounting pressures

Developing economies are forecast to grow by 4.3%, significantly faster than advanced economies. But they face higher financing costs, greater exposure to sudden shifts in capital flows and rising climate-related financial risks. These factors limit the fiscal and investment space needed to sustain growth.

The global South accounts for more than 40% of world output, nearly half of global merchandise trade and more than half of global investment inflows.

Yet its role in global financial markets remains limited. Excluding China, developing countries represent only about 12% of global equity market value and around 6% of global bond issuance.

Because their domestic financial markets are small, many developing economies rely on external borrowing at significantly higher cost. Borrowing rates of 7% to 11% are common, compared with 1% to 4% in major advanced economies. 

These elevated costs often reflect structural issues in the international financial architecture rather than economic fundamentals, reducing long-term investment and slowing growth.

Climate vulnerability adds to financial pressures. Countries repeatedly exposed to extreme weather now pay an estimated 20 billion dollars more each year in interest because lenders perceive them as riskier. Since 2006, these additional premiums have cost climate-vulnerable economies about 212 billion dollars – resources that could have supported social investment or climate adaptation.

Dollar dominance continues to anchor global finance

Despite gradual diversification of international reserves, the dollar remains central to global finance. Its share of international payments through SWIFT has risen from 39% to about 50% in five years. 

The United States also accounts for half of global equity market value and about 40% of global bond issuance. 

While this provides stability in uncertain periods, it also links developing economies to financial cycles over which they have limited influence.

Targeted reforms to restore stability and support development

UNCTAD outlines a set of practical reforms aimed at reducing financial vulnerability, improving predictability and supporting stronger alignment between trade, finance and development. The report calls for:

• Fix the multilateral trade dispute system so rules are enforced and uncertainty is reduced.
• Update trade rules for today’s economy; including services, digital trade, climate action and new industrial strategies.
• Close data gaps on trade and investment statistics to better inform and coordinate policies.
• Reform the international monetary system to limit harmful swings in currencies and capital flows.
• Strengthen regional and domestic capital markets so developing countries can raise affordable long-term finance.
• Use macroprudential tools (rules that reduce negative financial spillovers) to better protect trade and investment.
• Improve transparency in commodity trading and expand access to affordable trade finance, especially for small businesses.

Rebeca Grynspan said reconnecting trade and finance is essential for lasting stability: “What does genuine resilience require? Integrated policy frameworks that recognize links between trade, finance and sustainability.” She added that coordinated reforms can strengthen long-term development prospects: “Fundamentally, we cannot understand trade isolated from finance.”

Getty Images

How Trump accounts work