I was quoted in At Brooklyn Navy Yard, Mamdani’s Jobs Agenda Collides with His Anti-ICE Politics. It reads, in part, 

… BNYDC’s leases generally do not give the corporation authority to govern a tenant’s customers or what those customers do with products they purchase.

Asked specifically whether Crye had come up in her conversations with Mamdani, Greene said it had not.

A City Hall source similarly said the Mayor’s Office does not get involved in individual Navy Yard leases and that evictions may only take place when tenants violate contractual requirements such as financial obligations, legal compliance or permitted uses.

However, Council Member Shekar Krishnan, chair of the Council’s Oversight and Investigations Committee, said public ownership should change the equation.

“Public land like the Navy Yard should not be doing business with companies that dress ICE officers and support ripping families apart,” Krishnan said, adding that he was exploring “all tools at our disposal to keep public land away from ICE operations.”

Asked what those tools could include, his office said it was still exploring its options.

Cornell Law School clinical professor David Reiss, a real estate law expert, who reviewed two other Navy Yard leases and BNYDC’s bylaws but not Crye’s private agreement, told amNewYork that the legal options may look very different depending on whether Crye is still in the middle of a lease or seeking another term.

“Based on other Navy Yard leases I have reviewed — not Crye’s own, which I have not seen — Brooklyn Navy Yard Development Corporation leases can only be terminated for specific things like nonpayment, misuse of the space, or sanctions-list status, not because of who a tenant’s customers are or how they use a tenant’s products,” Reiss said.

“Lease renewal is a different question,” he added. “Landlords generally have much broader discretion about whether to keep renting to a tenant once a lease has fully run its course.”

Reiss said BNYDC would likely have broad discretion not to renew Crye, unless the company has an unexercised option giving it a contractual right to another term. A nonrenewal could still draw a lawsuit.

“Crye might argue the decision was arbitrary and capricious or exceeded BNYDC’s corporate authority under state law,” Reiss said. Federal constitutional claims involving retaliation, viewpoint discrimination or equal protection, he added, “could face significant doctrinal hurdles on these facts.”

That makes the terms of Crye’s lease, and who sits on BNYDC’s board when a renewal decision arrives, central to what happens next.

BNYDC’s bylaws allow the mayor to appoint up to 29 members, including as many as 25 discretionary appointees who serve at the mayor’s pleasure. The mayor may also remove members with or without cause.

Reiss said that structure gives Mamdani “real leverage” over such decisions even though BNYDC itself is the landlord.

Dan Abrams

I was interviewed in Trump’s Mortgage Fraud Hypocrisy on The Dan Abrams Show (SiriusXM POTUS 124) (behind paywall). A recording is available on YouTube (no paywall). The auto-generated (cleaned up a bit) transcript of the relevant part reads,

AI Image created with ChatGPT

I have posted Who Controls the Block? How States Can Regulate Tokenized Residential Real Estate to SSRN (with Bizub & Peralta). The abstract reads,

In July 2025, the City of Detroit filed a major nuisance abatement action against RealT, a fintech that had sold blockchain-based fractional interests in more than four hundred Detroit rental properties to some 22,000 investors around the world. Within a year, a court had ordered the company’s rents into escrow, the company had conceded to its investors that its “model no longer works,” and it had announced the liquidation of its portfolio  —  leaving tenants without basic services and token holders facing steep losses.

This article uses the rise and collapse of RealT, together with case studies of the other leading real estate tokenization business models, to evaluate the claims made for tokenized real estate as a new asset class for individual investors. Measured against the publicly-traded REIT, tokenization offers only one advantage to investors —  a bespoke level of diversification  —  and it does so while shedding the investor protections that registration and exchange listing provide. More fundamentally, the leading business models rest on skirting the state and local legal infrastructure of real property: recording regimes, transfer and property taxation, and homeowner and tenant protections. Like the mortgage industry’s Mortgage Electronic Registration Systems, Inc. (“MERS”) a generation ago, tokenization externalizes the costs of that end-run onto the parties least able to bear them.

Real estate tokenization is still in its infancy, and state and local governments have a window of opportunity to shape how these fintechs operate within their borders. Some have begun to use it: Maine’s first-in-the-nation statute regulating shared appreciation agreements supplies one template, and Detroit’s enforcement campaign another. This article maps the gaps that remain  —  most notably in transfer taxation and tenant protection  —  and offers an agenda for closing them before tokenization scales.

Robert MacKenzie and I published a column in Corporate Compliance Insights based on a recent eCornell workshop we taught. It reads,

We have been teaching lawyers how to use generative AI in their actual work — the drafting, reviewing and decision-making that fills their days. But when we designed our workshop, “Generative AI for Business Transactions,” we built it for a broader range of professionals: the healthcare compliance officer who had never opened ChatGPT, the corporate counsel whose legal department had recently deployed Harvey, the financial analyst running queries through Gemini and the operations manager who had heard the buzz but didn’t know where to begin. What we found confirmed what we suspected: the gap between professionals experimenting with AI and those waiting on the sidelines is widening fast. The ones who will thrive are not those using AI most aggressively but those using it most deliberately.

AI is transforming professional work

Generative AI is reshaping professional workflows in every industry we have encountered. In our workshop, we organize its everyday applications into four areas: communication, such as turning bullet points into polished emails and summarizing meeting transcripts; ideas and content, such as brainstorming and adapting material for different audiences; people and careers, such as preparing for interviews and difficult conversations; and money and numbers, such as building budgets, comparing costs and translating dense financial or legal language into plain English.

The best use cases are for time-intensive tasks. A transactional lawyer compares indemnification clauses across a dozen precedent agreements. A healthcare administrator turns regulatory guidance into a compliance checklist. A finance team compares top holdings across multiple fund prospectuses. The common thread: AI tools excel at quickly doing first-pass, high-volume work that used to consume hours.

A practical framework for responsible use

Every industry carries confidentiality obligations. Privilege in law, HIPAA in healthcare, fiduciary duties in finance, trade-secret protections in business. AI introduces a new exposure vector for professionals who are not careful about which tools they use. A key distinction we identify is the level of control and protections granted by enterprise AI tools versus consumer or free-tier tools. Enterprise tools are provided under negotiated contracts that typically commit the vendor not to train on your inputs and to keep your data confidential Consumer or free-tier tools often are packaged with settings permitting the provider to train on whatever information you input into the tool, undercutting confidentiality obligations you may be subject to. Vendor policies and features change, so verify that your expected protections are in place rather than assume.

We summarize this verification discipline in three words: pause, read, protect. Pause before entering data and ask whether it is safe to share and whether your workplace policies or professional obligations permit use of the tool for the intended purpose. Read the tool’s terms, and your workplace policies or guidance regarding the tool, to understand how your information will be treated. Protect by changing default settings, anonymizing confidential details and ensuring your cybersecurity and IT teams are in the loop when seeking to use new tools or approving use of updated features.

For task-level decisions, we recommend users adopt a red/yellow/green triage system. Red tasks are high importance and high risk and never get delegated to AI (e.g., strategy, high-stakes judgment calls and final approvals). Yellow tasks are lower importance and lower risk and may be delegated because they benefit from AI’s speed, but require competent human oversight and verification (e.g., research, first drafts and issue analysis). Green tasks are low importance and low risk and may, and sometimes, should, be delegated to AI, with minimal required human oversight (e.g., document reformatting, routine correspondence preparation and generation of ideas). If you supervise a team, you should be thinking about how you triage and how you want your team to triage matters. A breakdown in expectations can produce a “garbage-in, garbage-out” cycle.

Evaluating AI outputs critically

Our key takeaway is that AI’s greatest value lies in refining professional judgment, not replacing it. Generative AI is probabilistic, not deterministic. This means that the same prompt can produce different outputs in the same tool across different sessions. Models predict the next likely word in a sequence; they do not understand your question or verify their own answers.

Our recommendation to be effective with this technology: tell the tool what you need and be dynamic in your approach to prompting and task execution. We teach a simple prompting framework that is easy to recall and apply: RCTF—role, context, task, format. R: assign the AI a role. C: provide relevant context. T: define the task precisely. F: specify the output format. We think of this framework in the same way as ordering at a drive-thru. You would not pull up, say “food,” and expect to get what you want. You need to say what you are ordering, how you want it and where to hand it to you.

Other effective strategies we recommend professionals are:

• • Chunking. Breaking tasks into smaller pieces to keep tools on task.
  • Few-shot prompting. Provide examples of good work products to the tool before commencing a task.
  • Iterative refining. Adopting a “the first answer is a first draft” mindset.
  • Flipping interactions. Ask the tool to guide you on how to use it for a particular task.
  • Perspective switching. Assign the tool competing perspectives to pressure-test your work.

Managing hallucinations & overreliance

AI tools are known to generate plausible-sounding outputs that contain errors and invented citations. They also misread sources and silently drop items from long documents. These “hallucinations” are not bugs that will be patched away; they are inherent to how large language models work.

A deeper risk for inexperienced users of AI tools is what Wharton researchers Steven D. Shaw and Gideon Nave call “cognitive surrender.” In their 2026 study spanning three experiments and more than 1,300 participants, they found that participants were highly susceptible to following incorrect advice from AI tools. Access to an AI chatbot during the experiments appeared to inflate participants’ confidence in their answers, even when the answers were wrong. Observations like these point to a broad human tendency towards cognitive surrender: When a fluent, confident-sounding tool delivers a coherent answer, the pull to accept it is powerful.

We want to draw a sharp distinction between cognitive surrender — letting AI do your deliberate thinking and accepting its output uncritically — and “cognitive offloading” — handing defined steps to AI while retaining control of the overall analysis. The first is a professional hazard. The second is a legitimate productivity strategy. After every substantive AI-assisted task, ask yourself: Have I thought this through as fully as I would have without the tool? If not, dig back in.

Building reusable templates & checklists

One of the highest-value applications of generative AI is converting complex source documents into workflows a team can reuse, such as checklists, trackers and comparison matrices. In our workshop, we demonstrate how to take a dense document and instruct AI tools to produce a structured checklist to capture desired variables, like task status, assigned parties, deadlines, source references and risk flags.

We also teach benchmarking: uploading a set of similar documents and directing the AI tool to create a comparison matrix of key terms among the documents. AI tools offer value in their continually improving (but imperfect) ability to accurately extract and categorize information from new documents based on historical templates. For professionals with high accuracy needs, this skill can offer considerable leverage by accelerating the manual steps in these types of workstreams (initial review, identification and extraction or summarization of terms).

The bottom line

Whether you work in law, healthcare, finance or any field built on complex documents and careful analysis, the starting point is the same: Develop your own judgment first, verify before you rely and triage every task before you hand it off.

I am teaching an online course with Robert MacKenzie on July 9th about Generative AI for Business Transactions: Practical Applications for Professionals through eCornell. The Workshop Overview reads,

Generative AI is transforming how business transactions are conducted, from drafting and reviewing contracts, to summarizing due diligence materials, to benchmarking contract terms across industries. Many professionals, however, lack a practical framework for integrating these tools responsibly into their workflows.

This Workshop offers a hands-on introduction to applying AI in real-world transactional work. Participants will draft and refine communications, review and benchmark contract terms, and build compliance checklists and workflow playbooks, all while comparing outputs across AI tools to understand strengths, limitations, and potential errors.

Throughout the session, we focus on ethical, legal, and practical considerations, helping participants use AI as a complement to professional judgment rather than a substitute. By the end, participants will leave with reusable workflows, practical experience, and a clear approach to integrating AI responsibly into business transaction processes.

The Key Workshop Takeaways include,

The Cornell Law Forum republished an article that I wrote with Robert MacKenzie, Law Schools Should Teach How to Integrate AI Tools Into Practice. It opens,

Now that artificial intelligence tools for lawyers are widely available, we decided to integrate them for a semester in our Entrepreneurship Clinic. We have some important takeaways for legal education in general and the transactional practice of law in particular.

First, employers and educators need to account for law students who already are using AI tools in their legal work and guide new lawyers about how to use such tools appropriately.

Second, different AI products lead to wildly different results. Just demonstrating this to law students is very valuable, as it dispels the notion that AI responses can replace their independent judgment.

Third, AI’s greatest value may be in refining legal judgment for lawyers in ways that can help new and experienced lawyers alike.

Excited to be newly affiliated with Cornell Law School’s Center for Law and AI. The Center “brings together researchers working at the intersection of law and artificial intelligence. The Center explores how AI is reshaping the legal profession, how legal education should evolve in response, how law and policy can effectively govern AI, and how AI tools can advance scholarship on legal and societal questions.”

The Center has four focus areas:

AI and Legal Education. Artificial intelligence is transforming legal practice, from document review to predictive analytics. At the Center for Law and AI, we examine how legal education must evolve to prepare students for this changing landscape. Our work explores curricular innovation, ethical training, and the integration of computational thinking and AI literacy into the legal classroom.

Eflon CC BY 2.0

I was interviewed in the Forum (Cornell Law School’s Alumni Magazine) about AI and Legal Education. It reads, in part:

When she first began her legal studies at Cornell, [Jessica Rosberger ’26] says many students didn’t want to disclose they were using AI (“it felt like academic dishonesty”) but now it is “explicitly discussed” and used as a tool in studies and research. “I feel confident now going into practice to understand how the technology is evolving. I don’t know where it’s going, but I want to keep up with it,” says Rosberger, who joins the Manhattan District Attorney’s office after graduation. “In my view, Cornell is maintaining its integrity as a top law school by integrating AI into coursework and clinics. We have a professional responsibility to keep up with the technology and the platforms available to us.”

This sense of responsibility is especially evident at Cornell Tech, which offers courses and programs—including a Master of Laws (LL.M.) in Law, Technology, and Entrepreneurship degree—bringing together experts in engineering, computer science, design, business, and law “to build the foundations for new digital technologies—especially AI.”

“Our faculty are thought leaders in artificial intelligence and machine learning,” says David Reiss, clinical professor of law and research director of the Blassberg-Rice Center for Entrepreneurship Law. “The Law School has always been committed to ensuring that our graduates are practice ready. The integration of AI into the curriculum provides them with better tools. It will make them better lawyers and give them a leg up in practice.”

Reiss co-authored an article in Bloomberg Law explaining why law schools should teach how to integrate AI into practice. “Different AI products lead to wildly different results. Just demonstrating this to law students is very valuable, as it dispels the notion that AI responses can replace their independent judgment,” writes Reiss. Simulations were designed in which students were asked to complete the same transactional tasks, like drafting a contract or creating a client email, using different AI tools. The results were different because each platform drew from different sources or used different algorithms to interpret language—some provided a helpful first draft, while others did not, or even hallucinated.

“Our goal is student learning. It was for this reason that we like to deploy the AI tools at the end of our exercises: You do the work and then interrogate it with the AI tools of your choice,” explains Reiss. Just recognizing the capacity for different platforms to produce different results is critical. “AI is not a replacement for lawyers. We want our students to understand how it can be an enhancement for lawyers. It can increase efficiency and help meet tighter deadlines. The lawyers who adapt to AI will succeed and those who put their heads in the sand will fall behind.”

    *     *     *

Some law students are already trying to improve AI for practical usage. When Biying Cheng ’25 was at Cornell Tech’s Entrepreneurship Clinic, she assisted a client who designed an AI chatbot to help tenants deal with landlord issues and housing court. “We devised questions to test what level of detail the chatbot could and should provide, considering legal liability and jurisdictional issues,” says Cheng, who was already comfortable with AI. “I’m not a native speaker, and ChatGPT helped me draft emails, outline memos, and find resources. I called it Professor G and asked it to explains words I was unfamiliar with.”

Cheng’s pursuit of a J.D. came after receiving a master’s in international finance from Columbia University and a B.A. from The Chinese University of Hong Kong. Her work with the AI chatbot was a full-circle moment for someone who had witnessed Chinese students in Hong Kong participating in protests because of a serious housing problem. She saw how the law and AI could help New York City tenants facing housing issues. Cheng also co-authored an article with Professor Reiss on the real world impact of crypto and blockchain on tenants and real estate investors.

“The legal industry tends to be pretty conservative,” says Cheng, who now clerks for the U.S. District Court, Eastern District [of New York]. “But lawyers should want to become fluent in AI as a way to understand how it can impact lives in better ways.”

“We’re going to look to our younger colleagues to move us forward,” says Reiss. He believes that AI can help “refine legal judgment” with the right kind of prompting and critical review. AI can help lawyers “stress test” their own reasoning , identify blind spots, and learn about novel issues.

By Claude-Étienne Armingaud – Claudé, CC BY 2.5

Cornell Law School is hiring! We are looking for a clinical professor of entrepreneurship law who will work with our Entrepreneurship Law Clinic and our Blassberg-Rice Center for Entrepreneurship Law. Our students work with clients with a diverse range of entrepreneurial efforts, and in the process gain valuable skills for their legal careers. If you are interested in helping to train the next generation of entrepreneurs and the lawyers who will serve them, please consider applying. Or if you know of other suitable candidates, please let them know of this great opportunity in Ithaca.

The full job posting is here.

AI image generated by Reve

Marketplace interviewed me in for this response to a reader’s question, Can Divorce Affect The Housing Market? The story reads,

How much does divorce affect the economy, especially housing prices? In Davis, California, where I live, at least four households on my block have kids who effectively have a second house somewhere else in town with their other parent.

We know the effects divorce can have on household finances — it can lead to a decline in income, especially for women. One study from researchers at the University of Michigan and Boston University found that women increased “their labor in the workforce” following a divorce. But when it comes to the housing market, there’s little economic research on this topic.

The evidence we do have indicates that divorce can lead to a decline in homeownership rates, said Anthony Orlando, an associate professor of finance, real estate and law at California State Polytechnic University, Pomona, pointing to one Denmark study from 2019 that used a model to predict the correlation between the two.

“When there’s a divorce, there’s usually a sharp drop in wealth or net worth, because they’re splitting assets and there are costs associated with the divorce, paying for lawyers, etc. and all those things tend to reduce homeownership,” Orlando said. “If you’re only relying on your income rather than also having somebody else’s, you’re less diversified, and you might have more difficulty making the mortgage payments.”

Orlando said he hasn’t seen good studies on how divorce affects housing prices, but if there’s a decline in homeownership demand, then prices could decline.

The inverse is also true – the state of the housing market affects divorce.

“If housing prices increase significantly, there’s some evidence suggesting that divorce rates among homeowners actually goes down, and the reason is because when housing prices increase, there’s less financial stress. The married couple now has a house that’s worth more money,” Orlando said.

Rising rental prices could also make couples more hesitant to take the leap toward divorce. When housing prices go up, so do prices in the rental market. If someone is considering dissolving their marriage and sees high apartment prices, they might decide it doesn’t make financial sense to divorce, Orlando said.

There’s also a correlation between the broader economy and divorce.

“When the economy is hot, people divorce at higher rates, and when the economy is weak, it’s in recession, they divorce at lower rates,” said David Reiss, a law professor at Cornell University who studies housing policy.

Their mortgage may be underwater and they could be financially strapped, making the prospect of divorce difficult, Reiss said.

“Most of us in our day-to-day lives think to ourselves that questions of love and hate and relationships are driven by us as people,” Reiss said. But when you take a 10,000-foot view, you see how much the economy can drive our decisions, Reiss said.