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Finding Compounders
Michael Price Lecture Notes
Price was Seth Klarman’s mentor , so I’m sure investors can gain a lot by studying him. https://t.co/vIqHlGQehm
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Michael Price Lecture Notes
Price was Seth Klarman’s mentor , so I’m sure investors can gain a lot by studying him. https://t.co/vIqHlGQehm
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Quiver Quantitative
UPDATE: We recently launched a strategy on Autopilot that buys stock in companies that are ramping up lobbying spending.
It has now risen 20% since the start of the year. https://t.co/1itW8ACLHk
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UPDATE: We recently launched a strategy on Autopilot that buys stock in companies that are ramping up lobbying spending.
It has now risen 20% since the start of the year. https://t.co/1itW8ACLHk
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EndGame Macro
A Slow Burn Crisis Is Finally Showing Up in the Data
655 large U.S. companies have gone bankrupt through October, more than any full year since 2010, and the year isn’t even over yet. And these aren’t corner stores or tiny startups. They’re big, asset heavy firms: industrials, consumer discretionary names, healthcare operators. When the top of the corporate stack starts cracking, it tells you the stress is broadening.
The pattern is straightforward: stimulus and zero rates held everything together in 2020–2021, things looked deceptively calm in 2022, and then the pressure started building in 2023 and 2024. Now the adjustment is here. Companies that refinanced at 3% money back then are staring at 7–10% today. Some can handle that shift. A lot can’t.
What’s Driving the Spike
A few forces hit at once. Higher rates are the obvious one: the refinancing window that looked miles away in 2021 is now on top of companies with weaker balance sheets. Costs never really rolled back either…wages, inputs, insurance, transportation all of it stayed sticky. And demand isn’t as strong as the headline numbers suggest. Lower income consumers are stretched, inventory cycles are uneven, and certain sectors just don’t have the pricing power they used to.
Credit hasn’t disappeared, but it’s gotten selective. Good credits can refinance. Everyone else pays up or ends up in court. And that’s what you’re seeing here: restructuring, not liquidation. It’s a sign of a stressed system.
How This Fits Into the Bigger Macro Picture
At the same time this wave of bankruptcies is building, the Fed is quietly shifting gears. QT ends on December 1. All maturing Treasuries will be rolled over. MBS runoff gets steered into T-bills. So the front end of the system is getting easier while the long tail of corporate debt is still adjusting to a higher rate world. Liquidity is improving where markets feel it first…repo, bills, cash instruments but that doesn’t erase the damage already baked into corporate balance sheets.
This is why you can have smoother funding markets on one hand and the highest bankruptcy count in 15 years on the other. One reflects what the Fed can influence today. The other reflects decisions made five or ten years ago.
My Take
This is the credit cycle playing out…the slow, grinding kind where weak companies restructure, strong companies survive, and the market pretends everything is fine until suddenly it isn’t. Defaults aren’t a surprise; they’re the cleanup phase. And that’s exactly where we are.
So the way I’d frame this chart is simple.
The adjustment didn’t happen when rates went up…it’s happening now, as those old debts finally come due. The Fed can smooth the plumbing, but it can’t rewrite the math.
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A Slow Burn Crisis Is Finally Showing Up in the Data
655 large U.S. companies have gone bankrupt through October, more than any full year since 2010, and the year isn’t even over yet. And these aren’t corner stores or tiny startups. They’re big, asset heavy firms: industrials, consumer discretionary names, healthcare operators. When the top of the corporate stack starts cracking, it tells you the stress is broadening.
The pattern is straightforward: stimulus and zero rates held everything together in 2020–2021, things looked deceptively calm in 2022, and then the pressure started building in 2023 and 2024. Now the adjustment is here. Companies that refinanced at 3% money back then are staring at 7–10% today. Some can handle that shift. A lot can’t.
What’s Driving the Spike
A few forces hit at once. Higher rates are the obvious one: the refinancing window that looked miles away in 2021 is now on top of companies with weaker balance sheets. Costs never really rolled back either…wages, inputs, insurance, transportation all of it stayed sticky. And demand isn’t as strong as the headline numbers suggest. Lower income consumers are stretched, inventory cycles are uneven, and certain sectors just don’t have the pricing power they used to.
Credit hasn’t disappeared, but it’s gotten selective. Good credits can refinance. Everyone else pays up or ends up in court. And that’s what you’re seeing here: restructuring, not liquidation. It’s a sign of a stressed system.
How This Fits Into the Bigger Macro Picture
At the same time this wave of bankruptcies is building, the Fed is quietly shifting gears. QT ends on December 1. All maturing Treasuries will be rolled over. MBS runoff gets steered into T-bills. So the front end of the system is getting easier while the long tail of corporate debt is still adjusting to a higher rate world. Liquidity is improving where markets feel it first…repo, bills, cash instruments but that doesn’t erase the damage already baked into corporate balance sheets.
This is why you can have smoother funding markets on one hand and the highest bankruptcy count in 15 years on the other. One reflects what the Fed can influence today. The other reflects decisions made five or ten years ago.
My Take
This is the credit cycle playing out…the slow, grinding kind where weak companies restructure, strong companies survive, and the market pretends everything is fine until suddenly it isn’t. Defaults aren’t a surprise; they’re the cleanup phase. And that’s exactly where we are.
So the way I’d frame this chart is simple.
The adjustment didn’t happen when rates went up…it’s happening now, as those old debts finally come due. The Fed can smooth the plumbing, but it can’t rewrite the math.
BREAKING: 655 US large companies have gone bankrupt year-to-date, the highest number in 15 years.
This has already surpassed all previous full-year totals since 2011, except for 2024.
Since 2022, bankruptcies have risen nearly +100%.
This comes as 68 companies filed in October, 66 in September, and 76 in August, the highest monthly reading in at least 6 years.
Industrials have seen the highest number of bankruptcies in 2025, at 98, followed by consumer discretionary and healthcare, at 80 and 45, respectively.
Corporate bankruptcies are running at a crisis pace. tweet
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AkhenOsiris
Here are the results of the survey in order of how they were shared onstage:
What will be OpenAI’s annualized revenue be at the end of 2026?
Median answer: $30 billion.
What will Nvidia be worth at the end of 2026?
Median answer: $6 trillion.
What year will an independent committee of experts, as dictated by the Microsoft-OpenAI agreement, declare that we have reached AGI?
Top answer: 2030
Which venture capital firm’s AI portfolio are you the most jealous of?
The top three most voted for, from first to last: Andreessen Horowitz, Khosla Ventures, and Sequoia.
If you could put money in any private technology companies today, what would they be?
Top five companies in order from first to last: Anthropic, OpenAI, Cursor, Anduril, SpaceX, and OpenEvidence.
What global company’s model will top the LMArena web development leaderboard at the end of 2026?
In order from first to last: OpenAI, Anthropic, Gemini, Grok, Qwen.
If you could short a $1 billion-plus valuation startup, which would it be?
First place was Perplexity. Second place went to OpenAI. Other names shown onstage: Cursor, Figure, Harvey, Mercor, Mistral, and Thinking Machines.
What stood out to me from these results (Newcomer has also published the full slides for his paying subscribers):
A softening on OpenAI: Given that Sam Altman has said OpenAI plans to end this year with $20 billion of annualized revenue, this group of AI insiders doesn’t expect next year to be as exponential for the business as the leap from 2024 to 2025. The prediction that AGI won’t be declared until 2030 suggests a lack of faith in model progress meaningfully improving in the near term, although that answer could also be clouded by the complexity of how OpenAI and Microsoft must settle on how it’s decided. (I’m still waiting for either company to share information on who its “independent committee of experts” will be and how they’ll decide.) It was also notable that more attendees wanted to buy Anthropic stock than OpenAI’s, despite the consensus being that OpenAI would lead LMArena next year.
From Newcomer's Summit, as posted by Heath at Sources:
https://t.co/oPNe9NFAI3
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Here are the results of the survey in order of how they were shared onstage:
What will be OpenAI’s annualized revenue be at the end of 2026?
Median answer: $30 billion.
What will Nvidia be worth at the end of 2026?
Median answer: $6 trillion.
What year will an independent committee of experts, as dictated by the Microsoft-OpenAI agreement, declare that we have reached AGI?
Top answer: 2030
Which venture capital firm’s AI portfolio are you the most jealous of?
The top three most voted for, from first to last: Andreessen Horowitz, Khosla Ventures, and Sequoia.
If you could put money in any private technology companies today, what would they be?
Top five companies in order from first to last: Anthropic, OpenAI, Cursor, Anduril, SpaceX, and OpenEvidence.
What global company’s model will top the LMArena web development leaderboard at the end of 2026?
In order from first to last: OpenAI, Anthropic, Gemini, Grok, Qwen.
If you could short a $1 billion-plus valuation startup, which would it be?
First place was Perplexity. Second place went to OpenAI. Other names shown onstage: Cursor, Figure, Harvey, Mercor, Mistral, and Thinking Machines.
What stood out to me from these results (Newcomer has also published the full slides for his paying subscribers):
A softening on OpenAI: Given that Sam Altman has said OpenAI plans to end this year with $20 billion of annualized revenue, this group of AI insiders doesn’t expect next year to be as exponential for the business as the leap from 2024 to 2025. The prediction that AGI won’t be declared until 2030 suggests a lack of faith in model progress meaningfully improving in the near term, although that answer could also be clouded by the complexity of how OpenAI and Microsoft must settle on how it’s decided. (I’m still waiting for either company to share information on who its “independent committee of experts” will be and how they’ll decide.) It was also notable that more attendees wanted to buy Anthropic stock than OpenAI’s, despite the consensus being that OpenAI would lead LMArena next year.
From Newcomer's Summit, as posted by Heath at Sources:
https://t.co/oPNe9NFAI3
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AkhenOsiris
$DASH
Deductive's system builds what the company calls a "knowledge graph" that maps relationships across codebases, telemetry data, engineering discussions, and internal documentation. When an incident occurs, multiple AI agents work together to form hypotheses, test them against live system evidence, and converge on a root cause — mimicking the investigative workflow of experienced site reliability engineers, but completing the process in minutes rather than hours.
The technology has already shown measurable impact at some of the world's most demanding production environments. DoorDash's advertising platform, which runs real-time auctions that must complete in under 100 milliseconds, has integrated Deductive into its incident response workflow. The company has set an ambitious 2026 goal of resolving production incidents within 10 minutes.
"Our Ads Platform operates at a pace where manual, slow-moving investigations are no longer viable. Every minute of downtime directly affects company revenue," said Shahrooz Ansari, Senior Director of Engineering at DoorDash, in an interview with VentureBeat. "Deductive has become a critical extension of our team, rapidly synthesizing signals across dozens of services and surfacing the insights that matter—within minutes."
Deductive has root-caused approximately 100 production incidents at DoorDash over the past few months, with its accuracy improving with each investigation. For an organization of DoorDash's size, the company estimates this will translate to more than 1,000 hours of annual engineering productivity savings, with an estimated full revenue impact "in millions of dollars," according to Ansari. At location intelligence company Foursquare, Deductive reduced the time to diagnose Apache Spark job failures by 90% —t urning a process that previously took hours or days into one that completes in under 10 minutes — while generating over $275,000 in annual savings.
https://t.co/D0CWM25NT0
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$DASH
Deductive's system builds what the company calls a "knowledge graph" that maps relationships across codebases, telemetry data, engineering discussions, and internal documentation. When an incident occurs, multiple AI agents work together to form hypotheses, test them against live system evidence, and converge on a root cause — mimicking the investigative workflow of experienced site reliability engineers, but completing the process in minutes rather than hours.
The technology has already shown measurable impact at some of the world's most demanding production environments. DoorDash's advertising platform, which runs real-time auctions that must complete in under 100 milliseconds, has integrated Deductive into its incident response workflow. The company has set an ambitious 2026 goal of resolving production incidents within 10 minutes.
"Our Ads Platform operates at a pace where manual, slow-moving investigations are no longer viable. Every minute of downtime directly affects company revenue," said Shahrooz Ansari, Senior Director of Engineering at DoorDash, in an interview with VentureBeat. "Deductive has become a critical extension of our team, rapidly synthesizing signals across dozens of services and surfacing the insights that matter—within minutes."
Deductive has root-caused approximately 100 production incidents at DoorDash over the past few months, with its accuracy improving with each investigation. For an organization of DoorDash's size, the company estimates this will translate to more than 1,000 hours of annual engineering productivity savings, with an estimated full revenue impact "in millions of dollars," according to Ansari. At location intelligence company Foursquare, Deductive reduced the time to diagnose Apache Spark job failures by 90% —t urning a process that previously took hours or days into one that completes in under 10 minutes — while generating over $275,000 in annual savings.
https://t.co/D0CWM25NT0
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Dimitry Nakhla | Babylon Capital®
Berkshire Hathaway adds one new position:
$GOOGL $GOOG
Small allocation, still nice to see https://t.co/PUEGoyhN0a
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Berkshire Hathaway adds one new position:
$GOOGL $GOOG
Small allocation, still nice to see https://t.co/PUEGoyhN0a
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Quiver Quantitative
BREAKING: Warren Buffet's Berkshire Hathaway just filed a portfolio update.
They opened a new $4.3B position in Google, $GOOG.
Full holdings up on Quiver, link below. https://t.co/RoJTmS5xhJ
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BREAKING: Warren Buffet's Berkshire Hathaway just filed a portfolio update.
They opened a new $4.3B position in Google, $GOOG.
Full holdings up on Quiver, link below. https://t.co/RoJTmS5xhJ
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