What is Red Queen competition in private credit lending?

Red Queen competition, from Stanford professor William Barnett's research, describes how lenders race to optimize their operations only to find that competitors catch up and the advantage disappears. Like the Red Queen in Alice in Wonderland who must run constantly just to stay in place, lenders that focus on back-office efficiency alone never pull ahead — they only avoid falling behind.

Why doesn't back-office AI optimization create a durable competitive advantage for private lenders?

Barnett's research shows that battle-hardened operational efficiency is a temporary advantage, not a moat. Once competitors adopt the same tools and processes, the advantage evaporates. Barnett even found that the most operationally optimized banks suffered higher failure rates when expanding into new markets because they were locked into old playbooks. The same dynamic applies to private lenders investing heavily in back-office AI.

How many private lenders offer API or AI-agent access for borrowers?

In a Lightwell audit of 65 hard money and private lending websites, 0% offered API or AI connectivity for borrowers or brokers. 9% published loan criteria an AI can read, 5% had structured data for AI discoverability, and 62% advertised convenience and speed as their primary differentiator.

What is the durable competitive advantage for private lenders in the age of AI?

The durable advantage belongs to lenders who build borrower-facing AI connectivity first — portals, APIs, and machine-to-machine agent access (via protocols like MCP) — before competitors recognize it as a race. Barnett's research shows that early movers who colonize new terrain lock in customer relationships and routines that latecomers cannot replicate, even if they arrive with superior resources.

What is MCP and why should private lenders support it?

MCP (Model Context Protocol) is an application protocol that allows AI agents to communicate directly with external systems. For private lenders, supporting MCP means borrowers' AI agents can query loan criteria, check program eligibility, and initiate applications without a human intermediary. McKinsey research shows corporate CFOs and treasurers are actively seeking this kind of direct AI connectivity, and lenders who build it first will capture high-quality borrowers before the market catches up.

Red Queens In Private Credit

Beyond Back-Office Efficiency · September 2026 · 7 min read

"Well, in our country, you'd generally get to somewhere else — if you ran very fast for a long time, as we've been doing." "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!" — Alice and the Red Queen, Through the Looking Glass

This passage from Alice inspired a generation of business research, spearheaded by Stanford professor William Barnett, dubbed Red Queen Competition. Today, Barnett's research shines a light on which lenders will win in the age of AI.

Red Queen Lending: What It Means for Private Credit

Barnett borrowed from biologists already using the Red Queen to describe how predators or competing males would spend huge amounts of energy reshaping their bodies just to maintain the same competitive advantage. Think of male elk growing antlers to compete in combat for females. All the males adapt, but none win.

Barnett quantified how the same phenomenon happens in business. Alongside Professor Morten Hansen at U.C. Berkeley, he tracked the failure rates of 1,000 banks across Illinois offering the same product under the same regulations.

For nearly a century, Illinois ran something called "unit banking" which prevented banks from opening more than one brick and mortar location. This created a rich natural experiment of 'island markets' where each town had an isolated and unique banking environment. Darwin had the Galapagos and Barnett had Illinois.

Barnett's team measured the failure rates of every lender in each town against (1) how many competitors were in the town, (2) how battle-hardened the lender was, and (3) how battle-hardened the competitors were. He discovered two things: battle-hardened lenders were more likely to survive each year, until their competitors were also battle hardened. Strong operations weren't a moat — they were a relative advantage that vanished when competitors caught up.

Yet this is exactly where private lenders are putting all of their resources. The same Red Queen wearing a digital gown.

Red in Tooth and Claw: The Myth of the Back-Office Moat

Gartner found that over 80% of financial institution executives report that internal operational efficiency and risk management are the single primary objective of their AI investments. But in this race there is no prize for first place.

In fact first place is actually worse off. Banks that had undergone the most battle-testing in their home markets suffered significantly higher failure rates in new territories. The "first place" optimizers had simply locked themselves into old playbooks.

Looking back at Barnett's data, the winning entrants were younger banks that moved into the new towns before the veterans showed up. These early movers had locked in domain-specific routines and customer relationships that, by the time the latecomers arrived, was too late to replicate. Being faster in a race yields a temporary extra profit, but starting the race before anybody else yields a permanent advantage, even if the others show up later.

Running Twice As Fast: Building Machine-to-Machine Borrower Access

Today, the "new town" isn't an internal back-office workflow. It is the ability to interface with borrowers in the way that is the most convenient for them. Sophisticated borrowers are using AI to craft their own software. Over time they will want their software to connect to their lenders software.

McKinsey research reveals unmet demand. As measured by actual buying choices, Corporate CFOs and treasurers strongly prefer embedded financial services and API connectivity. And as hard money borrowers obtain their own software, they will want the same convenience and efficiency.

This is the industry blind spot. Customer acquisition costs are high; the most sophisticated borrowers give the most repeat business and have the lowest default rates. And fintech surveys don't even track borrower integration as an AI investment category because lenders are so focused on the back office.

Lightwell audited 65 hard money and private lending websites: 62% advertise convenience and speed as their primary differentiator, 9% publish loan criteria an AI can read, 5% have structured data for AI discoverability, and 0% offer API or AI connectivity for borrowers or brokers.

Many lenders think, "Nobody is asking for APIs or agent connections, so why build them?" That is the exact trap Red Queen competitors fall into. They mistake an uncontested market for an absent market.

Banks made the same argument against Rocket Mortgage in 2015. Consumers weren't asking for electronic mortgages, and industry consensus insisted that mortgages were too complex to originate through a screen. So banks doubled down on retail branch networks and manual processing teams until Rocket dethroned them to become the #1 mortgage lender in the country.

It will be the same with AI. McKinsey already shows us that institutional demand exists. As that demand trickles down to private borrowers that are already using AI in their everyday business, Barnett shows us that the lenders who are prepared will create a deep and resilient moat.

What lenders can do today

Standardize loan data. Convert unstructured credit boxes into standardized formats and terminology. Once data is structured machines and AI can do the work.
Originate for borrowers using agents. Allow MCP and API connections. MCP stands for model context protocol and is a technique that allows AI agents to speak directly to each other. Many high quality borrowers are using AI to build in-house software to manage their workflows. Allowing them to apply in a low-friction way will help win these high quality borrowers over time.
Train loan officers for AI-native borrowers. Loan officers must understand how modern borrowers use custom software and AI to run their operations. Know your customers well and your best customers best.

It is better to be too early than too late.