- I. Standards and Capital Flows
- II. Standards and the Energy Transition
- III. The Credit Ratings Oligopoly
- IV. Counterpositioning the Ratings Oligopoly
I. Standards and Capital Flows
“We think of ourselves as a benchmark company. I mean, data is in vogue now, and people are really kind of a bit obsessed with data and data companies… I think data is nice, it’s interesting. But if you could turn something into a benchmark, it really transcends data.” - Senior Vice President of Investor Relations, S&P Global, November 2020
In 1860, in the early days of America’s decades-long railroad frenzy (two years before Congress approved the development of a transcontinental railroad), Henry Varnum Poor published “History of Railroads and Canals in the United States”. This work was the first attempt to arm investors with data on the burgeoning industry and laid the foundations for what (after many mergers and acquisitions) is now Standard & Poors — a $100b+ company with $11b in annual revenue.
Along with its long-lived triopoly counterparts, Moody’s and Fitch, it has persisted thanks to powerful standard-based moats.
In markets, standards unlock capital flows and progress. Over the last decade, standard metrics to assess SaaS and consumer internet businesses (3x LTV:CAC = good, Rule of 40, etc.) have been an on-ramp to the massive pools of capital searching for yield in a zero-interest rate world.
Similarly, the clearly defined steps of the drug development process give investors a framework for determining appropriate risk-adjusted returns – and confidence that downstream capital providers in the marketplace are operating with the same framework.
The notion of benchmarks as critical infrastructure for trade and economic growth is not a novel concept.
In writing about The Business of Benchmarking, Marc Rubenstein references a litany of examples – from the meter to ocean shipping containers – that facilitated trade, lowered transaction costs, and reduced information asymmetry.
II. Standards and the Energy Transition
Where standards are lacking, transaction costs remain high, economic activity and downstream creativity are stifled, and information is used as a power play.
The lack of standardization and shared understanding around the path for emerging decarbonization and industrial technologies to go from pilot to bankable creates significant friction for capital flows.
Standardization around both company assessment and broader techno-economic pathways has the potential to be a massive unlock for large asset allocators and governments to deploy capital earlier in the technology lifecycle more systematically (they should have the incentive to do this as returns on more mature technology like wind and solar are increasingly competed downwards).
The absence of standard assessment models is a bottleneck that has been consistently cited in my conversations with large asset managers over the last couple of years. Without standard frameworks for assessing and explaining risk that can be broadly understood and acted on by non-specialists, systematically allocating capital at scale is challenging.
At a company level, this fragmentation leads to longer timelines for fundraising (and resulting commercialization) as well as a higher cost of capital.
As Prime Coalition explained in its 2022 Climate Infrastructure Report, this leaves early deployments of critical technologies “stuck between asset classes”. Albert Wenger and Mona Alsubaei of USV made similar points in their recent post calling for more mid-stage, blended capital providers to serve companies and projects that have solved technology viability risk but haven’t proven the ability to scale that technology economically.
I agree that more aligned financiers bringing precision capital to market is a critical piece of the puzzle and that there is sufficient “catalytic” capital in the marketplace to front the risk of getting a handful of these firms off the ground. To expand beyond this initial cohort and crowd in non-catalytic capital before returns can be proven over an entire fund cycle, I believe the market remains in need of a better toolkit for risk assessment and technology validation (i.e. standards).
As currently constructed, the prevailing investment “architecture” – heavily influenced by platforms like S&P and Moody’s – is poorly aligned with financing a low-carbon world. At the same time, trillions of dollars per year are being allocated to enabling such a world a material portion of which needs to be allocated to developing and deploying emerging technologies).
Something has to give.
As the American Industrial Revolution (the Second Industrial Revolution) took shape in the late 1800s, Henry Varnum Poor’s research and manuals played an important role in helping investors make sense of a dynamic marketplace.
The convergence of an economy-wide push towards decarbonization and the exponential power of the internet (along with adjacent technologies like large language models), is creating a paradigm shift as significant as the industrial transitions of previous centuries.
It is with this context that there is a tremendous opportunity for a new company to develop a unique approach for assessing decarbonization-focused technologies (and the techno-economic roadmap for such technologies), translating that assessment to make it actionable for capital allocators and policymakers, and index the growth of “New Industrial” companies, layering on additional capabilities over time to become an S&P or Moody’s-level standard for capital markets over the coming decades.
III. The Credit Ratings Oligopoly
Before jumping into what a modern approach might look like, it is worth spending a few more lines to understand the strength of the NRSRO oligopoly – lest we try to convince ourselves that changing (or re-building) the piping of the global asset management system is an unencumbered green field.
From Marc Rubenstein:
Over the last 100+ years since its founding, Moody’s ratings – derived from a consistent framework applied across 11k and 6k corporate and public finance issuers, respectively, in addition to 64k structured finance obligations – have become the veritable benchmark by which market participants peg the credit worthiness of one debt security against another. Because of such industry-wide adoption, a debt issuer has little choice but to pay Moody’s for a rating if it hopes to get a fair deal in the market: an issuer of $500mn in 10-year bonds might pay the company 6bps upfront ($300k), but will save 30bps in interest expense every year ($15mn over the life of the bond)….and each incremental issuer who pays the toll only further reinforces the Moody’s ratings as the standard upon which to coalesce, fostering still further participation. This feedback loop naturally evolves into a deeply entrenched oligopoly.
Poor process and decision-making during the Global Financial Crisis (that has continued to this day) has done little to dent the financial strength of these businesses. When interviewed by the FCIC about the role of the ratings agencies in causing the GFC, Warren Buffet (Moody’s largest shareholder), provided an overview of the competitive strength of the two companies:
There are very few businesses that have the competitive position that Moody’s and Standard and Poor’s have. They are a natural duopoly to some extent. Anybody coming in and offering to cut their price in half had no chance of success. And there are not many businesses where someone can come in and offer to cut the price in half and somebody doesn’t think about shifting. But that’s the nature of the ratings business. It’s assisted by the fact that the two of them became a standard for regulators.
Buffett’s assessment has held up well – both Moody’s and S&P have compounded revenue for over a decade at rates similar to Apple and Microsoft, and today generate billions in revenue and EBITDA margins well north of 30%.
IV. Counterpositioning the Ratings Oligopoly
To sum up the (highly profitable) ratings agency business model: Evaluation frameworks – that evolve into entrenched standards – create certainty around risk-adjusted returns for investors and around systemic risk for regulators. This certainty lowers the cost of capital for companies and creates the conditions necessary to operate effectively at global scale.
As Rubenstein argues, “universal standards are usually unassailable. The risk for companies that manufacture them is less that their moat is crossed and more that their castle becomes irrelevant.”
This notion – moats deteriorating as the market renders castles irrelevant – is where there is an opportunity for a new entrant.
As Buffett highlights, the incumbent NRSROs have such significant economic power relative to new entrants – scale economies, network effects, and switching costs among others – that attacking their castle directly is unwise.
An alternative approach, highlighted in the book 7 Powers, is Counterpositioning. Counterpositioning occurs when a new entrant adopts a new, superior (often technology-enabled) business model that the incumbent does not mimic due to anticipated damage to their existing business.
By focusing on the “New Industrial” customer segment, building a decentralized operating and intelligence model, and retaining flexibility around the utilization of new technologies (i.e. LLMs), I think there is an opportunity to create a lot of momentum and eventually develop real moats around the company.
- Customer Segment (Brand) – For Moody’s and S&P, large corporations, governments, and institutions represent the most profitable niche. If one believes that economy-wise decarbonization represents a dominant, multi-decade megatrend and that much of the impact will come from new technology, there is an opportunity for a new player to position itself as the trusted brand for investors and companies.
- Operating Model (Network Effects) – Moody’s and S&P are highly centralized, with thousands of in-house analysts. By building a networked approach to tapping expert insights, a new company can be more responsive to the emergence of new trends and customer demands and can eventually build up a critical mass. These experts can also continue to operate in their field (as opposed to being full-time analysts), creating additional surface area for relevant insight.
- Agile Technology Deployment (Process Advantage) – As mentioned above, the rating agencies have been highly acquisitive over the years. This creates significant systems integration and technology debt and means they are unable to fully unlock the power of their proprietary data. One of the biggest opportunities for a new entrant is to apply emerging analytical technologies (like Large Language Models) to the proprietary data being developed during transaction diligence and expert analysis exercises.
The combination of these capabilities creates the early momentum to become the benchmark for risk assessment and decision-making among emerging companies and investors seeking to deploy novel technology in service of decarbonization, reindustrialization, and systemic resilience.