AI Quantifies Lender Fortification Amidst Borrower Flexibility - Episode Hero Image

AI Quantifies Lender Fortification Amidst Borrower Flexibility

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TL;DR

  • Increased "flight to fortification" in deal terms, with lenders demanding more structural protections like anti-pest and anti-sort clauses, signals growing anxiety about potential distress and recovery in looming maturity walls.
  • The prevalence of "cost savings add backs" in EBITDA calculations has reached record highs, allowing borrowers to inflate current cash flow, potentially masking underlying financial weaknesses for lenders.
  • "Net short lender" terms are appearing in more deals, empowering borrowers to exclude lenders with short positions from voting, thereby mitigating risks from activist investors.
  • Complex, off-balance-sheet financing structures, particularly in AI data center deals, utilize high leverage and immature assets, creating interdependencies that obscure true financial risk for lenders.
  • The emergence of novel deal terms, such as "new outside date structure" for regulatory uncertainty and "tariff event of default," demonstrates continuous innovation in adapting to macro-economic and geopolitical shifts.
  • AI's ability to analyze semantic meaning in legal language enables the quantification of market terms, providing unprecedented insight into deal structures and market sentiment for transactional professionals.
  • The "Frank" acquisition by JPM highlights the critical need for robust indemnification clauses, as fast-moving markets can lead to significant financial exposure for acquirers when dealing with synthetic data or fraud.

Deep Dive

Emerging AI capabilities in analyzing financial deal documents reveal a significant shift towards increased structural protections for lenders, indicating heightened anxiety within credit markets. This "flight to fortification" is driven by looming maturity walls and a growing concern over potential distress events, compelling both lenders and borrowers to secure their positions, even as borrowers gain more economic flexibility.

The analysis of deal terms through AI, as pioneered by Noetica, quantifies market sentiment and risk allocation. Historically, deal terms functioned as the "plumbing" of transactions, dictating rules and preventing exploitation. However, recent market events, like the Revlon erroneous payment incident, have spurred the adoption of specific protective clauses. For instance, "anti-pests" and "J. Crew blockers" have seen dramatic increases, with "anti-pests" rising from 4% to 28% of deals and "J. Crew blockers" from 15% to 45% in Q3. Similarly, "anti-sort protections," which safeguard a lender's position in the payment waterfall during distress, have surged to 84% of deals, a significant jump from 61% in Q2 and a baseline of 39% in 2023. This trend suggests a proactive preparation by creditors for potential defaults and a focus on recovery leverage rather than solely preventing liability management exercises.

Concurrently, borrowers are securing greater economic flexibility. This is evident in the rise of EBITDA add-backs, particularly "cost savings add-backs," which allow companies to count anticipated future cost reductions as current cash flow. These add-backs have reached record highs, appearing in 64% of deals and exceeding 20% of EBITDA in 51% of them. Additionally, terms like "net short lender provisions," which exclude lenders shorting a company's debt from voting, are appearing in 13% of deals, reflecting a sophisticated response to market dynamics and activist investors. This dual fortification on both sides signals a complex risk allocation in response to macro-economic uncertainties and the sheer volume of debt taken on in 2020-2021, with significant maturities looming in 2028-2029.

The application of AI to complex financing structures, particularly in the burgeoning AI sector, highlights new forms of risk. Deals involving data center financing, such as Meta's joint venture with Blue Owl, exemplify this. These structures often employ high leverage (up to 90%) and keep significant debt off the primary company's balance sheet through special purpose vehicles. The underwriting of these deals relies on immature, rapidly evolving assets like AI compute power, whose long-term value and pricing are less defined than traditional assets like pizza. This creates a scenario where off-balance sheet financing with substantial leverage is applied to an unstable asset class, a risk amplified by the circular and incestuous nature of some AI-related financings.

Ultimately, the increasing sophistication and volume of deal terms, facilitated by AI's ability to parse complex language and identify trends, suggest a continuous cat-and-mouse game between market participants. While AI can quantify existing terms and identify new ones, its development also fuels the creation of even more intricate contractual language. This dynamic implies that while AI may automate aspects of legal and financial analysis, the need for human ingenuity in structuring and interpreting these agreements will persist, with lawyers continuously adapting to outsmart detection systems and navigate evolving market risks.

Action Items

  • Audit 10-15 recent deal documents for "erroneous payment" terms to assess market adoption post-Revlon incident.
  • Track prevalence of "anti-pest smart terms" and "J Crew blockers" across 50-75 new credit deals to quantify "flight to fortification."
  • Analyze 20-30 AI infrastructure financing deals for off-balance sheet structures and leverage ratios to identify systemic risks.
  • Measure the correlation between "net short lender terms" and deal volume in 10-15 distressed credit markets to understand lender risk mitigation.
  • Evaluate 5-10 complex AI financing structures for circularity and interdependencies to map potential contagion pathways.

Related Episodes

Key Quotes

"What we build at Nuetica is AI-powered software for benchmarking real-time data on what's market in credit M&A capital markets deal terms. So said another way, we help folks like transactional attorneys, credit managers, bankers. We help them figure out whether the terms of their transactional agreements, like think financing agreements, merger agreements, prospectuses, and really all other corporate transactions, are on or off market by benchmarking them to market comps."

Dan Wertman, co-founder of Nuetica, explains that his company provides AI software to benchmark deal terms in credit and M&A markets. This service helps legal and financial professionals determine if the terms in their agreements are standard or deviate from market norms. Wertman highlights that this offers a quantifiable way to assess deal terms, which was previously difficult to achieve.

"So when I say deal terms, what I mean is deal terms are really the underpinning of the entire transactional system, the rules of the road. You could think about them like speed limits, double yellow lines, street lights. They're kind of the plumbing that goes into the transactions."

Dan Wertman uses an analogy to explain the fundamental importance of deal terms in financial transactions. He likens them to traffic signals and road markings, emphasizing that these terms establish the operational framework and rules for complex deals. Wertman suggests that understanding these terms is crucial for navigating the transactional system effectively.

"Now, that was two and a half plus years ago. Now I left Wachtell to start Nuetica with a fairly simple idea, which is AI enables us to finally quantify what market agreement terms should look like in these markets. You know, now we work with almost all the top 20 law firms on the street. We're helping them advise their clients on these deals, and this year on track to do about a trillion dollars of transactions through our platform."

Dan Wertman describes the genesis of his company, Nuetica, stemming from his personal experience as a corporate lawyer. He realized the lack of a centralized, quantifiable database for understanding market deal terms. Wertman states that AI technology now allows for this quantification, and Nuetica is actively working with major law firms, processing a significant volume of transactions.

"So anti-pests, smart terms, these are protections that prevent guarantor releases when subsidiaries of the credit group become non-holded. In other words, it prevents value from being transferred away from the loan into some other structure which doesn't provide credit support."

Dan Wertman explains a specific type of deal term, "anti-pests smart terms," which are designed to protect lenders. He clarifies that these terms prevent the transfer of assets or value out of a credit group's subsidiaries in a way that would diminish the collateral supporting a loan. Wertman indicates this is a mechanism to ensure credit support remains intact.

"We're seeing massive increases in lenders getting structural protections in these deals, basically these are protections that help make sure their collateral is locked. Things like the anti-pests smart terms. In return, borrowers are getting the same fortification. In fact, they're getting more economic flexibility. And you can think about it as a way for them to weather the storm."

Dan Wertman describes a market trend he calls a "flight to fortification," where both lenders and borrowers are seeking increased protections. He notes that lenders are securing more structural protections to safeguard their collateral, while borrowers are gaining more economic flexibility to navigate market uncertainties. Wertman suggests this indicates a cautious market environment.

"The other thing we wanted to ask you about and again, we reference this in the intro is we are seeing these really complicated deals that I admittedly cannot keep track of in the AI market where, you know, one company is going to buy chips from this other company and then that company is going to borrow from whoever and use the chips funding to pay them back and then that money somehow goes into the company that is buying the stuff in the first place. It is all very circular, all very incestuous in many ways in my mind. Are you examining those types of deals or just putting on your credit expertise hat if you see something like that, what are you thinking?"

Tracy Alloway expresses her confusion and concern regarding complex, circular financing structures in the AI market. She questions Dan Wertman about whether his company analyzes these deals and what his perspective is on such intricate arrangements. Alloway highlights the interconnected and potentially incestuous nature of these financial dealings.

Resources

External Resources

Books

  • "Cockroaches in the Coal Mine" by Howard Marks - Mentioned as a letter discussing similar themes to the podcast's credit market analysis.

Articles & Papers

  • Oracle Credit Fear Gauge Hits Highest Since 2009 on AI Bubble Fears (Bloomberg) - Referenced as an article discussing AI bubble fears in credit markets.
  • Secretive $3 Trillion Fund Giant Makes Flashy Move Into Private Assets (Bloomberg) - Referenced as an article discussing private asset market movements.

People

  • Dan Wertman - Co-founder and CEO of Noetica, a startup using AI to scan deal documents and measure trends.
  • Jamie Dimon - Mentioned for speculating about "cockroaches" lurking in the credit industry.
  • Joel Wertheimer - Mentioned in relation to a previous podcast episode about deal text length.
  • Howard Marks - Mentioned for his letter "Cockroaches in the Coal Mine."

Organizations & Institutions

  • Noetica - A startup that uses AI to scan deal documents and measure linguistic and term trends.
  • Blackrock - Where Dan Wertman started his career, on a team responsible for developing new financial products.
  • Wachtell Lipton - Where Dan Wertman worked doing corporate transactions from 2017 to 2022.
  • Palantir - Mentioned for building AI that helps workers and unlocks their full potential.
  • Lenovo - Mentioned for gaming computers and exclusive deals on their website.
  • Adobe Acrobat Studio - Mentioned for its PDF capabilities and AI assistant.
  • Bloomberg - Mentioned as the source of the podcast and for its business news.
  • IBM - Mentioned for helping businesses scale and manage AI.
  • CVS - Mentioned for being part of the community and serving customers.
  • Experiencce Columbus - Mentioned for its podcast about Columbus, Ohio.
  • Revlon - Mentioned in relation to an erroneous payment incident by City Bank.
  • J. Crew - Mentioned in relation to "J Crew blockers" deal terms.
  • Windstream - Mentioned in relation to a case involving hedge funds shorting debt.
  • Superior Industries - Mentioned for a deal that included a tariff event of default.
  • Meta - Mentioned in relation to the Hyperion deal and AI infrastructure financing.
  • Blue Owl - Mentioned in relation to the Hyperion deal and AI infrastructure financing.
  • City Bank - Mentioned for an erroneous payment of $900 million to Revlon lenders.
  • JP Morgan - Mentioned for acquiring the company Frank.

Tools & Software

  • AI - Used by Noetica to scan deal documents and measure trends, and discussed as a paradigm shift.
  • ChatGPT - Mentioned as a tool that could potentially be used to analyze credit agreements.

Websites & Online Resources

  • lenovo.com - Mentioned for exclusive deals on gaming PCs.
  • bloomberg.com/subscriptions/oddlots - Mentioned for subscribing to the Odd Lots newsletter.
  • omnystudio.com/listener - Mentioned for privacy information.
  • bloomberg.com - Mentioned as a source for business news and unlimited site access for subscribers.

Other Resources

  • Deal terms - The core subject of the discussion, referring to the rules and structures within financial agreements.
  • Erroneous payment deal terms - A specific type of deal term that emerged after an incident with Revlon.
  • Anti pests smart terms - Protections preventing guarantor releases when subsidiaries become non-holded.
  • J Crew blockers - Protections preventing issuers from moving material intellectual property outside of the credit group.
  • Anti sort protections - Lean subordination protections that secure a place in line during distress.
  • EBITDA add backs - Allowances for borrowers to add back certain items to cash flow to flatter their balance sheet.
  • Cost savings add back - A specific type of EBITDA add back related to predicted future cost optimizations.
  • Net short lender terms - Terms allowing borrowers to exclude lenders who are shorting their debt from voting.
  • Regulatory uncertainty - A factor influencing deal structuring in M&A markets.
  • Tariffs - Mentioned as a factor in M&A deal structuring and a potential event of default.
  • Liability management - Discussed in the context of deal terms and borrower/lender strategies.
  • Tax uncertainty - Mentioned as a factor influencing deal structuring in M&A markets.
  • Outside date structure term - A new deal term allowing buyers to lock in financing longer in case of regulatory review delays.
  • Tariff event of default - A specific type of default clause related to tariffs.
  • M&A carve outs - Provisions within M&A deals related to tariffs.
  • Trailer of respect clauses - Clauses related to tariffs in M&A deals.
  • Long range dependencies - The complex nature of legal documents where information may be spread across multiple clauses.
  • Knowledge graph of deal terms - The database built by Noetica, containing comparable deal terms.
  • Precedent - The reliance on previous deal terms and legal cases in drafting new agreements.
  • Private credit markets - Discussed as a deep market enabling complex financing structures.
  • Receivables financing facilities - A type of financing used by First Brands, which was not fully disclosed.
  • Off balance sheet financing - Financing structures not reflected on a company's balance sheet.
  • Data center financing - Financing for data centers, particularly those optimized for AI.
  • Investment grade credit rating - A rating that allows for higher leverage in financing deals.
  • Residual value guarantee - A guarantee of a capped amount of cash flow, which can affect balance sheet reporting.
  • GPU performance - A key metric for data centers optimized for AI model training.
  • Paradigm shift - AI is described as a paradigm shift similar to the internet or the iPhone.
  • Synthetically made up data - Mentioned in relation to the acquisition of the company Frank.
  • Indemnification - A clause where one party agrees to cover the losses of another, mentioned in the J.P. Morgan/Frank deal.
  • Cove light - A term used to describe deals with fewer protections for investors.

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