Tag Archives: fiduciary duties

Offloading, not Surrendering, to AI

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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.

Move Fast and Break the Mortgage Market

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Bill Pulte, FHFA Director and Chair of Fannie Mae & Freddie Mac

I was quoted in the American Prospect’s story, Move Fast and Break the Mortgage Market. It reads, in part,

This week, the Donald Trump–appointed chief regulator for the two quasi-governmental companies that own or control about half of the residential housing market anointed himself the board chair of both those companies. This maneuver could signal a host of shenanigans: the culmination of a 17-year hedge fund get-rich-quick scheme, a balance-sheet fiction to justify tax cuts, a new favor factory for apartment developers with ties to the president, a data transfer so Elon Musk’s everything app can learn how to sell mortgages, or something equally problematic.

But what gives former board members, market observers, and officials at the regulator greater concern is the distinct possibility that mucking around with the $7.7 trillion secondary mortgage market could lead to breaking it.

If that happens, homebuyers may not be able to get mortgages, homebuilders may be reluctant to break ground, and uncertainty would abound in a market that has brought down the economy on more than one occasion in U.S. history, most recently in 2008. “It could freeze sales, freeze refinances, stop people from forming households, cause people to be afraid of moving, freeze up developers of housing and the secondary market,” said David Reiss, a professor at Cornell Law School.

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Multifamily Glad-Handing

The GSEs have a pretty sober business on the single-family side, and since the housing collapse really originated there, a lot of work was done to clean up that part of the business. But Fannie and Freddie also make loans in the multifamily market to support building of apartments and condos. A former official with one of the GSEs told me that business is a little looser, with ways to enhance those loans.

This president, of course, is a multifamily real estate developer himself, who has friends in multifamily real estate development. Hamara, one of the new board members, is a vice president at Tri Pointe Homes, a major homebuilder. You could imagine these relationships leading to the GSEs pushing risk limits, loosening credit standards, or raising loan-to-value ratios for favored borrowers. There is a secret mortgage blacklist at Fannie Mae for condos without enough property insurance or in need of repairs; controlling the board could make that blacklist go away, at least for certain developers.

This kind of setup resembles the opportunity zones that were a feature of the 2017 Trump tax cuts. They gave significant tax breaks to investors in certain communities deemed in need of development. Trump administration officials credit opportunity zones with increasing housing construction, but critics argue that the investments were rife with corruption and favor-trading.

That could also be the case here: New criteria guiding the new boards might lead to more multifamily housing, but with uneven results, favors to friends, and idiosyncratic deals that would be more about boosting allies than building housing. And as Calabria has pointed out, Fannie and Freddie are likely under Trump to cancel affordable-housing initiatives, meaning that sweetheart deals might only extend to the developers, rather than the public. Plus, there is the potential for dramatic losses if lending standards erode.

Reiss, of Cornell, agreed that this was all a possibility. “If someone gets to one of the directors, and they are there not acting as a fiduciary for the company, it opens the door to political favoritism,” he said.

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What If It Breaks

Pulte is expected to force job cuts at the GSEs, which employ roughly 15,000 people. He has already been making familiar noises about DEI and remote work. One possibility on the table at the GSEs is merging Fannie and Freddie; you don’t usually have the same person chair the boards of two direct competitors. The regulatory agency is also likely to see cuts; already at FHFA, according to one source, fair lending and consumer protection groups have been put on administrative leave, along with employees at the Division of Research and Statistics.

Controlling the boards would limit dissent about these actions. But cuts in the name of efficiency could strain or even rupture the numerous functions the GSEs carry out, with consequences for the entire housing market.

Due to the conservatorship, the GSEs are limited in what they can pay their employees, which has led to a talent drain. Some systems have not been integrated, and others are not up to industry standards. Fannie and Freddie have a cautious internal culture that doesn’t move quickly. Hacking away at their already weakened structure could easily create operational harm.

But Reiss explained that nothing has to overtly break to lose the confidence of the markets; even a lack of workforce to move the paper around could create that impression, and disrupt the flow of credit. “If there is any kind of uncertainty, the spread between Fannie and Freddie securities and Treasury bonds will increase,” he said. “Investors will ask if the government will make good on Fannie and Freddie bonds. This uncertainty and direction could increase costs over time for all borrowers.”