August 17, 2026
President Trump’s Two Residential Mortgages
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,
Dan Abrams: This effort to get rid of Lisa Cook as one of the governors at the Fed has heated up. Remember, the Supreme Court basically said Trump couldn’t do it without due process, and it seems now he’s beginning the process of quote-unquote doing the due process, telling her that they want to get rid of her. It made me remember an article that came out in December, which I think is very important in this context — it’s from ProPublica, and the headline is “Trump’s Own Mortgages Match His Description of Mortgage Fraud, Records Reveal.” It talks about how in 1993, Trump signed a mortgage for a home in Palm Beach, pledging it would be his principal residence. Seven weeks later, he got another mortgage for a seven-bedroom, marble-floored neighboring property, saying it too would be his principal residence. And in reality, ProPublica reports, Trump — then a New Yorker — does not appear to have lived in either home, let alone used them as a principal residence. That seems to me precisely the issue the administration is focusing on with regard to Lisa Cook. And if you’re interested in the article, it’s got all the documents Trump signed, with images of them.
Remember, in October federal prosecutors charged Letitia James, and a central claim in that case was that she purchased a house in Virginia pledging to her lender that it would serve as her second home, and then used it as an investment property and rented it out. [The source transcript is garbled here — something to the effect that Trump’s mortgage agreements were arguably a more significant misrepresentation, since his said the properties would be his primary residence, not merely a second home as in the James case; recommend checking the audio for the exact wording before publishing.] But Trump, when he was declaring that he was going to fire Lisa Cook, specifically noted that she had signed two primary-residence mortgages within weeks of each other — exactly as the records show he did in Florida. Here’s the quote they sent her: “You signed one document attesting that a property in Michigan would be your primary residence for the next year. Two weeks later, you signed another document for a property in Georgia stating it would be your primary residence for the next year. It is inconceivable that you were not aware of your first commitment when making the second. At a minimum, the conduct at issue exhibits the sort of gross negligence in financial transactions that calls into question your competence and trustworthiness.” The Trump administration has made similar claims regarding Adam Schiff and Eric Swalwell as well.
So when Trump administration officials are confronted about this, they do the usual, which is talk about it from a law enforcement perspective, talk about how important this is. This is Bill Pulte — remember, this guy’s the worst person in this administration, as far as I’m concerned, the most politicized. He’s the one who was acting director of National Intelligence; he couldn’t get confirmed, I don’t think, for any position, and yet they keep bouncing him around — but he’s been overseeing housing, and that’s given him access to all these mortgage records. So here is Bill Pulte, speaking in June:
“This is not political from my perspective. I’m in charge of making sure that we have a mortgage market that is safe and sound. It doesn’t matter whether you’re Republican or Democrat or a Fed governor — if you commit mortgage fraud, we’re going to refer it. And that’s what we did in the Lisa Cook case. I do believe that eventually she’ll be indicted. And let’s say the Supreme Court rules against the people who are saying there’s cause, or there’s ability, to fire her — I do expect her to eventually be indicted. That’s just my own opinion; I’d refer you to the DOJ for specifics. But if she is indicted, obviously that would give the ability to fire her for cause, even more so than we believe — I’ll just speak for myself — already exists.”
Let me bring in David Reiss. He’s a clinical professor of law at Cornell Tech and Cornell Law School, an expert in the real estate sector. Professor, thanks very much for coming on — appreciate it.
David Reiss: My pleasure.
ABRAMS: From a legal perspective, these are the kinds of cases that are almost never prosecuted — is that right?
REISS: That’s correct. There was a lot of this kind of behavior before the Great Financial Crisis in the early 2000s, but it was very rarely prosecuted.
ABRAMS: So is there a difference between what ProPublica seems to have been able to show that Donald Trump did and what Lisa Cook is accused of?
REISS: I don’t think so. I think it’s the same, or in some ways an even worse set of facts. There’s the statement by the broker who said these were going to be rentals from the beginning. This is exactly the kind of behavior that Pulte says is unacceptable — the kind he’s identified with opponents of the Trump administration.
ABRAMS: So could she use that — meaning, let’s assume for a moment that she is indicted — is that really just something for the court of public opinion, or is that something she could potentially introduce as a defense?
REISS: That’s an interesting question. On a straight legal answer, I’d say selective enforcement — arguing that I’m being prosecuted but somebody else isn’t — is a very high standard to meet, especially for a political case like we’re seeing with Cook and some of the others. But I do think judges have been choosing not to give a [presumption of regularity] to the Trump DOJ, so judges may use their discretion to look at this with some sense that it’s just a political hit job.
ABRAMS: Right — because the Trump allegations, the Trump information, is outside the statute of limitations. So there’s no way that could be prosecuted. Correct?
REISS: That is correct. Even if it violated the law, it’s past the statute of limitations. There’s no way to bring it back.
ABRAMS: Right. Now, in response to questions, a White House spokesperson told ProPublica: “President Trump’s two mortgages you’re referencing are from the same lender. There was no defraud[ing]. It is illogical to believe that the same lender would agree to defraud itself.” [As transcribed — worth checking this quote against the ProPublica article’s exact wording before publishing.] What do you make of that?
REISS: Well, it’s interesting, because that’s not the standard that applies. It’s a federal standard — a section of federal law, 1014. It’s really about whether, at the time you signed it, you knew it was false. It’s not a fraud standard — it doesn’t have all the elements of fraud, such as materiality. So that’s a bit of a misdirection, suggesting that the lender knew about this or went along with it. That’s not the standard for the criminal law here.
ABRAMS: Putting aside the criminal law for a minute — does what they’re saying make sense? I’m trying to figure out what their point even is. “President Trump’s two mortgages you’re referencing are from the same lender … it’s illogical to believe that the same lender would agree to defraud itself.” It seems to be suggesting the lender wouldn’t have done it a second time — but if there was fraud in the first case, maybe they just didn’t realize it. I don’t know — this isn’t my area of expertise, but as I think about it, maybe they didn’t realize, when they made the first mortgage, that the information was false, and so they just used the same information for the second one.
REISS: Interpreting the statement from the administration in the best possible light, they’re saying perhaps he intended the first property as his primary residence, and that was true at the time — and the lender knew about the first one and knew about the second one. If you think about the statute requiring knowledge of falsehood at the time of signing, you can construct a story where that’s the case. That would be the argument they’d make at trial, if this weren’t past the statute of limitations and if Pulte had referred it to DOJ and DOJ chose to pursue it.
ABRAMS: It is amazing to me — and again, I don’t know if you’re going to want to answer this, you don’t have to — but it feels like the double standard the president often applies to others versus himself is astonishing. This is such an apples-to-apples comparison. We often say, well, it’s not really apples to apples — but this really is apples to apples, isn’t it?
REISS: It is. I’m going to say a few things in response to that. One: this is genuine hypocrisy, but unless it enrages his base — unless they say, “yes, our leader is applying two standards, and that’s unfair, and we want to punish him for that and not vote for him or for his slate” — it doesn’t really matter. Second — and this doesn’t excuse his behavior in the slightest, or Pulte’s behavior in the slightest — hypocrisy is a real bipartisan issue. You have Spitzer prosecuting johns, you have Hastert and Gingrich bringing the impeachment against President Clinton. There’s a lot of hypocrisy by politicians, and this, I think, is just part of something massive—
ABRAMS: I guess what makes this different to me is that with these cases, you can make the argument that none of them should be brought, or you can make the argument that they’re really important to be brought. I don’t think Eliot Spitzer — who suffered, who lost his job, there were real consequences for him — was out there in public saying, “these johns, they’re a real problem.” And that’s what Trump is doing. He’s going out there criticizing Lisa Cook as if she’s a criminal. I think that’s what makes this different.
REISS: I agree. It’s more extreme, but it’s really part and parcel of his approach to politics, which is attack, attack, attack, and deny, deny, deny, if anything comes close to touching your behavior or your team’s behavior. And it’s not just Trump — there are members of the administration who have similar mortgage issues, and allegedly Letitia James, Cook, and Schiff have that issue too. It’s part and parcel of behavior on the left and on the right, but he’s only going after Democrats. And that’s obviously true.
ABRAMS: Yep — and again, [the source transcript is garbled here: “only going after Democrats is sort of part and parcel of this administration going after Democrats for doing exactly what he did to me, is a step further” — recommend checking against the audio for the exact wording before publishing]. David Reiss, thank you so much for coming on the program. Really appreciate it.
August 17, 2026 | Permalink | No Comments
August 7, 2026
Who Controls the Block? How States Can Regulate Tokenized Residential Real Estate
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.
August 7, 2026 | Permalink | No Comments
August 4, 2026
Offloading, not Surrendering, to AI
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.
August 4, 2026 | Permalink | No Comments
June 24, 2026
Generative AI for Business Transactions
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,
- Apply AI to core transactional workflows, including contract drafting, precedent comparison, and due diligence summaries
- Evaluate outputs across multiple AI tools to understand their strengths, limitations, and differences
- Manage key risks of AI use, such as hallucinations, confidentiality exposure, and overreliance, while integrating AI responsibly
- Build workflow templates and checklists to structure AI-assisted tasks for consistent, reliable outcomes
June 24, 2026 | Permalink | No Comments
Law Schools Should Teach How to Integrate AI Tools Into Practice
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.
June 24, 2026 | Permalink | No Comments
June 23, 2026
Center for Law and AI
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.
AI and Legal Practice. Artificial intelligence is reshaping how legal services are delivered—from contract analysis and legal research to client counseling and dispute resolution. At the Center for Law and AI, we study how these technologies are changing the roles of lawyers, the structure of legal work, and access to justice. We also examine the ethical, professional, and institutional challenges that accompany the integration of AI into legal practice.
AI and Scholarship. Artificial intelligence opens new frontiers for legal research and analysis—and raises new questions. At the Center for Law and AI, we explore how AI can assist in discovering patterns in legal texts, generating and testing legal theories, and expanding the empirical study of law. We also study how AI interacts with legal institutions and society to generate new legal and societal challenges and opportunities.
Regulating AI. The rise of artificial intelligence poses urgent questions for law and policy. At the Center for Law and AI, we examine how legal frameworks—domestic and international—can be designed to govern AI systems responsibly. Our work explores regulatory design, institutional capacity, democratic accountability, and the evolving role of law in shaping the development and deployment of AI technologies.
A list of other affiliated faculty members, led by Center Director Jed Stiglitz, can be found here.
June 23, 2026 | Permalink | No Comments
June 16, 2026
AI and Legal Education
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.
June 16, 2026 | Permalink | No Comments
