Nice piece in the Economist about how Costco’s model of operational simplicity leads to a unique position in modern retail: beloved by customers, investors, AND workers:
sell fewer things ➡️
get better prices from suppliers & less inventory needed ➡️
lower costs for customers ➡️
more customers & more willing to pay recurring membership fee ➡️
strong, recurring profits ➡️
ability to pay well and promote from within 📈💪🏻
Customers are not the only fans of Costco, as the outpouring of affection from Wall Street analysts after Mr Galanti announced his retirement on February 6th made clear. The firm’s share price is 430 times what it was when he took the job nearly four decades ago, compared with 25 times for the s&p 500 index of large companies. It has continued to outperform the market in recent years.
Once upon a time, the hottest thing in chip design was “system-on-a-chip” (SOC). The idea is that you’d get the best cost and performance out of a chip by combining more parts into one piece of silicon. This would result in smaller area (less silicon = less cost) and faster performance (closer parts = faster communication) and resulted in more and more chips integrating more and more things.
While the laws of physics haven’t reversed any of the above, the cost of designing chips that integrate more and more components has gone up sharply. Worse, different types of parts (like on-chip memory and physical/analog componentry) don’t scale down as well as pure logic transistors, making it very difficult to design chips that combine all these pieces.
The rise of new types of packaging technologies, like Intel’s Foveros, Intel’s EMIB, TSMC’s InFO, new ways of separating power delivery from data delivery (backside power delivery), and more, has also made it so that you can more tightly integrate different pieces of silicon and improve their performance and size/cost.
The result is now that many of the most advanced silicon today is built as packages of chiplets rather than as massive SOC projects: a change that has happened over a fairly short period of time.
This interview with IMEC (a semiconductor industry research center)’s head of logic technologies breaks this out…
What we’re doing in CMOS 2.0 is pushing that idea further, with much finer-grained disintegration of functions and stacking of many more dies. A first sign of CMOS 2.0 is the imminent arrival of backside-power-delivery networks. On chips today, all interconnects—both those carrying data and those delivering power—are on the front side of the silicon [above the transistors]. Those two types of interconnect have different functions and different requirements, but they have had to exist in a compromise until now. Backside power moves the power-delivery interconnects to beneath the silicon, essentially turning the die into an active transistor layer which is sandwiched between two interconnect stacks, each stack having a different functionality.
Thermal energy storage has been a difficult place for climatetech in years past. The low cost of fossil fuels (the source for vast majority of high temperature industrial heat to date) and the failure of large scale solar thermal power plants to compete with the rapidly scaling solar photovoltaic industry made thermal storage feel like, at best, a market reserved for niche applications with unique fossil fuel price dynamics. This is despite some incredibly cool (dad-joke intended 🔥🥵🤓) technological ingenuity in the space.
But, in a classic case of how cheap universal inputs change market dynamics, the plummeting cost and soaring availability of renewable electricity and the growing desire for industrial companies to get “clean” sources of industrial heat has resulted in almost a renaissance for the space as this Canary Media article (with a very nice table of thermal energy startups) points out.
With cheap renewables (especially if the price varies), companies can buy electricity at low (sometimes near-zero if in the middle of a sunny and windy day) prices, convert that to high-temperature heat with an electric furnace, and store it for use later.
While the devil’s in the details, in particular the round trip energy efficiency (how much energy you can get out versus what you put in), the delivered heat temperature range and rate (how hot and how much power), and, of course, the cost of the system, technologies like this could represent a key technology to green sectors of the economy that would otherwise be extremely difficult to lower carbon output for.
Rocks and hotness have existed for billions of years, but it’s only now that the two can be used to help the world decarbonize — and it’s all because the insanely low cost of solar and wind power has made thermal storage economically possible.
An oldie but a goodie — the story of how the YouTube team, post-Google acquisition, put up a “we won’t support Internet Explorer 6 in the future” message without any permission from anyone. (HT: Eric S)
IE6 had been the bane of our web development team’s existence. At least one to two weeks every major sprint cycle had to be dedicated to fixing new UI that was breaking in IE6. Despite this pain, we were told we had to continue supporting IE6 because our users might be unable to upgrade or might be working at companies that were locked in. IE6 users represented around 18% of our user base at that point. We understood that we could not just drop support for it. However, sitting in that cafeteria, having only slept about a few hours each in the previous days, our compassion for these users had completely eroded away. We began collectively fantasizing about how we could exact our revenge on IE6. One idea rose to the surface that quickly captured everyone’s attention. Instead of outright dropping IE6 support, what if we just threatened to? How would users react? Would they revolt against YouTube? Would they mail death threats to our team like had happened in the past? Or would they suddenly become loud advocates of modern browsers?
Very cool that we’re still finding new things we can control that can be applied to making the lives of people better.
I found that a small, middle-ear muscle called the tensor tympani can be tensed voluntarily, otherwise known as “ear rumbling.”
In studies funded by the National Institute for Health and Care Research (NIHR), we found that about 55% of people could tense the muscle in isolation, and about 80% could do so when yawning.
We believe that some people with neurological disabilities retain control over the tensor tympani, even when they’ve lost almost all other functionality. The muscle also moves physiologically as the eyes move, a fact that presents a further opportunity to track people’s intent or augment how we communicate non-verbally.
The ear isn’t just an auditory input device; it also has the potential to be a complex input and output tool.
Intel has been interested in entering the foundry (semiconductor contract manufacturing) space for a long time. For years, Intel proudly boasted of being at the forefront of semiconductor technology — being first to market with the FinFET and smaller and smaller process geometries.
So it’s interesting how, with the exception of the RibbonFET (the successor to the FinFET), almost all of Intel’s manufacturing technology announcements (see whitepaper) in it’s whitepaper to appeal to prospective foundry customers, all of it’s announcements pertain to packaging / “back end” technologies.
I think it’s both a recognition that they are no longer the furthest ahead in that race, as well as recognition that Moore’s Law scaling has diminishing returns for many applications. Now, a major cost and performance driver is technology that was once considered easily outsourced to low cost assemblers in Asia is now front and center.
IN AN EXCLUSIVE INTERVIEW ahead of an invite-only event today in San Jose, Intel outlined new chip technologies it will offer its foundry customers by sharing a glimpse into its future data-center processors. The advances include more dense logic and a 16-fold increase in the connectivity within 3D-stacked chips, and they will be among the first top-end technologies the company has ever shared with chip architects from other companies.
Immune cell therapy — the use of modified immune cells directly to control cancer and autoimmune disease — has shown incredible results in liquid tumors (cancers of the blood and bone marrow like lymphoma, leukemia, etc), but has stumbled in addressing solid tumors.
Iovance, which recently had its drug lifileucel approved by the FDA to treat advanced melanoma, has demonstrated an interesting spin on the cellular path which may prove to be effective in solid tumors. They extract Tumor-Infiltrating Lymphocytes (TILs), immune cells that are already “trying” to attack a solid tumor directly. Iovance then treats those TILs with their own proprietary process to expand the number of those cells and “further activate” them (to resist a tumor’s efforts to inactivate immune cells that may come after them) before reintroducing them to the patient.
This is logistically very challenging (not dissimilar to what patients awaiting other cell therapies or Vertex’s new sickle cell treatment need to go through) as it also requires chemotherapy for lymphocyte depletion in the patient prior to reintroduction of the activated TILs. But, the upshot is that you now have an expanded population of cells known to be predisposed to attacking a solid tumor that can now resist the tumor’s immune suppression efforts.
To me, the beauty of this method is that it can work across tumor types. Iovance’s process (from what I’ve gleamed from their posters & presentations) works by getting more and more activated immune cells. Because they’re derived from the patient, these cells are already predisposed to attack the particular molecular targets of their tumor.
This is contrast to most other immune cell therapy approaches (like CAR-T) where the process is inherently target-specific (i.e. get cells that go after this particular marker on this particular tumor) and each new target / tumor requires R&D work to validate. Couple this with the fact that TILs are already the body’s first line of defense against solid tumors and you may have an interesting platform for immune cell therapy in solid tumors.
The devil’s in the details and requires more clinical study on more cancer types, but suffice to say, I think this is incredibly exciting!
Its clearance is the “culmination of scientific and clinical research efforts,” said Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research, in a statement.
Van Brummelen suggests that Bianchini’s schooling in economics might have been key to his invention, because he wasn’t embedded in sexagesimal numbers from early in his career, as other astronomers were. But his approach was perhaps too revolutionary to catch on at first. “In order to understand what Bianchini was doing, you had to learn a completely new system of arithmetic,” he says.
A century and a half later, however, “decimal notation was in the air”. Astronomers working with smaller and smaller subdivisions were inventing different systems, desperate for ways to simplify complex calculations. Clavius’s work influenced later popularizers of decimal fractions, such as Flemish mathematician Simon Stevin, as well as Scottish astronomer and inventor of logarithms John Napier, who adopted the decimal point. Chabás argues that historians should reassess Bianchini’s importance. Although he has been “eclipsed” by other figures, there’s clearly “a path of ideas”, he says, leading back to Bianchini.
This one chart (published in Canary Media) illustrates both the case for optimism for our ability to deal with climate change as well as a clear case of how geopolitical pressures can dramatically impact energy choices: the rapid increase in use of renewable energy (mainly at the expense of fossil fuels) as source of electricity in the EU.
The cleanest sources of electricity could soon make up the largest share of electricity generation in the European Union. Wind and solar made huge strides last year, producing more than one-quarter of the EU’s electricity for the first time, while fossil fuel generation plummeted. Power-sector emissions fell by a record 19 percent in the region last year.
While much attention is (rightly) focused on the role of TSMC (and its rivals Samsung and Intel) in “leading edge” semiconductor technology, the opportunity at the so-called “lagging edge” — older semiconductor process technologies which continue to be used — is oftentimes completely ignored.
The reality of the foundry model is that fab capacity is expensive to build and so the bulk of the profit made on a given process technology investment is when it’s years old. This is a natural consequence of three things:
Very few semiconductor designers have the R&D budget or the need to be early adopters of the most advanced technologies. (That is primarily relegated to the sexiest advanced CPUs, FPGAs, and GPUs, but ignores the huge bulk of the rest of the semiconductor market)
Because only a small handful of foundries can supply “leading edge” technologies and because new technologies have a “yield ramp” (where the technology goes from low yield to higher as the foundry gets more experience), new process technologies are meaningfully more expensive.
Some products have extremely long lives and need to be supported for decade-plus (i.e. automotive, industrial, and military immediately come to mind)
As a result, it was very rational for GlobalFoundries (formerly AMD’s in-house fab) to abandon producing advanced semiconductor technologies in 2018 to focus on building a profitable business at the lagging edge. Foundries like UMC and SMIC have largely made the same choice.
This means giving up on some opportunities (those that require newer technologies) — as GlobalFoundries is finding recently in areas like communications and data center — but provided you have the service capability and capacity, can still lead to not only a profitable outcome, but one which is still incredibly important to the increasingly strategic semiconductor space.
When GlobalFoundries abandoned development of its 7 nm-class process technology in 2018 and refocused on specialty process technologies, it ceased pathfinding, research, and development of all technologies related to bleeding-edge sub-10nm nodes. At the time, this was the correct (and arguably only) move for the company, which was bleeding money and trailing behind both TSMC and Samsung in the bleeding-edge node race. But in the competitive fab market, that trade-off for reduced investment was going to eventually have consequences further down the road, and it looks like those consequences are finally starting to impact the company. In a recent earnings call, GlobalFoundries disclosed that some of the company’s clients are leaving for other foundries, as they adopt sub-10nm technologies faster than GlobalFoundries expected.
Every standard products company (like NVIDIA) eventually gets lured by the prospect of gaining large volumes and high margins of a custom products business.
And every custom products business wishes they could get into standard products to cut their dependency on a small handful of customers and pursue larger volumes.
Given the above and the fact that NVIDIA did used to effectively build custom products (i.e. for game consoles and for some of its dedicated autonomous vehicle and media streamer projects) and the efforts by cloud vendors like Amazon and Microsoft to build their own Artificial Intelligence silicon it shouldn’t be a surprise to anyone that they’re pursuing this.
Or that they may eventually leave this market behind as well.
While using NVIDIA’s A100 and H100 processors for AI and high-performance computing (HPC) instances, major cloud service providers (CSPs) like Amazon Web Services, Google, and Microsoft are also advancing their custom processors to meet specific AI and general computing needs. This strategy enables them to cut costs as well as tailor capabilities and power consumption of their hardware to their particular needs. As a result, while NVIDIA’s AI and HPC GPUs remain indispensable for many applications, an increasing portion of workloads now run on custom-designed silicon, which means lost business opportunities for NVIDIA. This shift towards bespoke silicon solutions is widespread and the market is expanding quickly. Essentially, instead of fighting custom silicon trend, NVIDIA wants to join it.
Fascinating data from the BLS on which jobs have the greatest share of a particular gender or race. The following two charts are from the WSJ article I linked. I never would have guessed that speech-language pathologists (women), property appraisers (white), postal service workers (black), or medical scientists (Asian) would have such a preponderance of a particular group.
The Bureau of Labor Statistics each year publishes data looking at the gender and racial composition of hundreds of occupations, offering a snapshot of how workers sort themselves into many of the most important jobs in the country.
There are sociology textbooks’ worth of explanations for these numbers. One clear conclusion: Many occupations skew heavily toward one gender or race, leading to a workforce where 96.7% of preschool and kindergarten teachers are women, two-thirds of manicurists and pedicurists are Asian, and 92.4% of pilots and flight engineers are white.
Commercial real estate (and, by extension, community banks) are in a world of hurt as hybrid/remote work, higher interest rates, and property bubbles deflating/popping collide…
Many banks still prefer to work out deals with existing landlords, such as offering loan extensions in return for capital reinvestments toward building upgrades. Still, that approach may not be viable in many cases; big companies from Blackstone to a unit of Pacific Investment Management Co. have walked away from or defaulted on properties they don’t want to pour more money into. In some cases, buildings may be worth even less today than the land they sit on.
“When people hand back keys, that’s not the end of it — the equity is wiped but the debt is also massively impaired,” said Dan Zwirn, CEO of asset manager Arena Investors, which invests in real estate debt. “You’re talking about getting close to land value. In certain cases people are going to start demolishing things.”
One of the core assumptions of modern financial planning and finance is that stocks have better returns over the long-run than bonds.
The reason “seems” obvious: stocks are riskier. There is, after all, a greater chance of going to zero since bond investors come before stock investors in a legal line to get paid out after a company fails. Furthermore, stocks let an investor participate in the upside (if a company grows rapidly) whereas bonds limits your upside to the interest payments.
A fascinating article by Santa Clara University Professor Edward McQuarrie published in late 2023 in Financial Analysts Journal puts that entire foundation into doubt. McQuarrie collects a tremendous amount of data to compute total US stock and bond returns going back to 1792 using newly available historical records and data from periodicals from that timeframe. The result is a lot more data including:
coverage of bonds and stocks traded outside of New York
coverage of companies which failed (such as The Second Bank of the United States which, at one point, was ~30% of total US market capitalization and unceremoniously failed after its charter was not renewed)
includes data on dividends (which were omitted in many prior studies)
calculates results on a capitalization-weighted basis (as opposed to price-weighted / equal-weighted which is easier to do but less accurately conveys returns investors actually see)
The data is fascinating, as it shows that, contrary to the opinion of most “financial experts” today, it is not true that stocks always beat bonds in the long-run. In fact, much better performance for stocks in the US seems to be mainly a 1940s-1980s phenomena (see Figure 1 from the paper below)
Stock and bond performance (normalized to $1 in 1792, and renormalized in 1982) on a logarithmic scale Source: Figure 1, McQuarrie et al
Put another way, if you had looked at stocks vs bonds in 1862, the sensible thing to tell someone was “well, some years stocks do better, some years bonds do better, but over the long haul, it seems bonds do better (see Table 1 from the paper below).
The exact opposite of what you would tell them today / having only looked at the post-War world.
This problem is compounded if you look at non-US stock returns where, even after excluding select stock market performance periods due to war (i.e. Germany and Japan following World War II), focusing even on the last 5 decades shows comparable performance for non-US stocks as non-US government bonds.
Even assumptions viewed as sacred, like how stocks and bonds can balance each other out because their returns are poorly correlated, shows huge variation over history — with the two assets being highly correlated pre-Great Depression, but much less so (and swinging wildly) afterwards (see Figure 6 below)
Stock and Bond Correlation over Time Source: Figure 6, McQuarrie et al
Now neither I nor the paper’s author are suggesting you change your fundamental investment strategy as you plan for the long-term (I, for one, intend to continue allocating a significant fraction of my family’s assets to stocks for now).
But, beyond some wild theorizing on why these changes have occurred throughout history, what this has reminded me is that the future can be wildly unknowable. Things can work one way and then suddenly stop. As McQuarrie pointed out recently in a response to a Morningstar commenter, “The rate of death from disease and epidemics stayed at a relatively high and constant level from 1793 to 1920. Then advances in modern medicine fundamentally and permanently altered the trajectory … or so it seemed until COVID-19 hit in February 2020.”
If stocks are risky, investors will demand a premium to invest. But if stocks cease to be risky once held for a long enough period—if stocks are certain to have strong returns after 20 years and certain to outperform bonds—then investors have no reason to expect a premium over these longer periods, given that no shortfall risk had to be assumed. The expanded historical record shows that stocks can perform poorly in absolute terms and underperform bonds, whether the holding period is 20, 30, 50, or 100 years. That documentation of risk resolves the conundrum.
While much of the commentary has been about Figma’s rapid rise and InVision’s inability to respond, I saw this post on Twitter/X from one of InVision’s founders Clark Valberg about what happened. The screenshotted message he left is well-worth a read. It is a great (if slightly self-serving / biased) retrospective.
As someone who was a mere bystander during the events (as a newly minted Product Manager working with designers), it felt very true to the moment.
I remember being blown away by how the entire product design community moved to Sketch (from largely Adobe-based solutions) and then, seemingly overnight, from Sketch to Figma.
While it’s fair to criticize the leadership for not seeing web-based design as a place to invest, I think the piece just highlights how because it wasn’t a direct competitor to InDesign (but to Sketch & Adobe XD) and because the idea of web-based wasn’t on anyone’s radar at the time, it became a lethal blind spot for the company. It’s Tech Strategy 101 and perfectly highlights Andy Grove’s old saying: “(in technology,) only the paranoid survive”.
Hey Jason…
“Clark from InVision” here…
I’ve been somewhat removed from the InVision business since transitioning out ~2 years ago, and this is the first time I’ve reacted to the latest news publicly. I’m choosing to do so here because in many ways your post is a full-circle moment for me. MANY (perhaps most) of the underlying philosophies that drove InVision from the very beginning were inspired by my co-founder @BenNadel and I reading and re-reading Getting Real. It was our early-stage hymnal.
Apologies for steam of consciousness rant and admitted inherent bias — I’m a founder after all 🙂
We have a Nissan Ariya and currently DON’T have a home charger (yet — waiting on solar which is another boondoggle for another post). As we live in a town with abundant EVGo chargers (and the Ariya came with 1 yr of free EVGo charging), we thought we could manage.
When it works, its amazing. But it doesn’t … a frustrating proportion of the time. And, as a result, we’ve become oddly superstitious about which chargers we go to and when.
I’m glad the charging companies are aware and are trying to address the problem. As someone who’s had to ship and support product, I also recognize that creating charging infrastructure in all kinds of settings which need to handle all kinds of electric vehicles is not trivial.
But, it’s damn frustrating to not be able to count on these (rest assured, we will be installing our own home charger soon), so I do hope that future Federal monies will have strict uptime requirements and penalties. Absent this, vehicle electrification becomes incredibly difficult outside of the surburban homeowner market.
J.D. Power reported in August that 20 percent of all non-Tesla EV drivers in its most recent study said they visited a charger but did not charge their vehicle, whether because the charger was inoperable or because of long wait times to use it, up from 15 percent in the first quarter of 2021.
Fear of inadequate public charging has now overtaken “range anxiety” as the chief concern about EVs among the car-buying public, according to J.D. Power. “Although the majority of EV charging occurs at home” — about 80 percent of it, according to industry data — “public charging needs to provide a much better experience across the board, not just for the users of today, but also to alleviate the concerns of skeptical future customers,” said Brent Gruber, executive director of J.D. Power’s global automotive practice.
The collapse of China’s massive property bubble is under way and it is wreaking havoc as significant amounts of the debt raised by Chinese property builders is from offshore investors.
Because of (well-founded) concerns on how Chinese Mainland courts would treat foreign concerns, most of these agreements have historically been conducted under Hong Kong law. As a result, foreign creditors have (understandably) hauled their deadbeat Chinese property builder debtors to court there.
While the judgements (especially from Linda Chan, the subject of this Bloomberg article) are unsurprisingly against the Chinese property builders (who have been slow to release credible debt restructuring plans), the big question remains whether the Mainland Chinese government will actually enforce these rulings. It certainly would make life harder on (at least until recently very well-connected) Chinese property builders at a moment of weakness in the sector.
But, failure to do so would also hurt the Chinese government’s goal of encouraging more foreign investment: after all, why would you invest in a country where you can’t trust the legal paper?
Never before has there been such a wave of Chinese corporate defaults on bonds sold to foreign investors. And never in recent memory has a bankruptcy judge in Hong Kong, the de-facto home for such cases, earned a reputation for holding deadbeat companies to account quite like Chan.
Chan, 54, has displayed an unwavering determination to give creditors a fair shot at recouping as much of their money as they can. One morning in early May, she shocked the packed courtroom by suddenly ordering the liquidation of Jiayuan. She had peppered the company’s lawyers that day as they tried, unsuccessfully, to explain why they needed more time to iron out their debt restructuring proposal.
And then, late last month, Chan put lawyers for Evergrande, the most indebted developer of them all, on notice: Either turn over a concrete restructuring proposal in five weeks or face the same fate as Jiayuan.
It’s both unsurprising but also astonishing at the same time.
Amazon.com has grabbed the crown of biggest delivery business in the U.S., surpassing both UPS and FedEx in parcel volumes.
The Seattle e-commerce giant delivered more packages to U.S. homes in 2022 than UPS, after eclipsing FedEx in 2020, and it is on track to widen the gap this year, according to internal Amazon data and people familiar with the matter. The U.S. Postal Service is still the biggest parcel service by volume; it handles hundreds of millions of packages for all three companies.
Market phase transitions have a tendency to be incredibly disruptive to market participants. A company or market segment used to be the “alpha wolf” can suddenly find themselves an outsider in a short time. Look at how quickly Research in Motion (makers of the Blackberry) went from industry darling to laggard after Apple’s iPhone transformed the phone market.
Something similar is happening in the high performance computing (HPC) world (colloquially known as supercomputers). Built to do the highly complex calculations needed to simulate complex physical phenomena, HPC was, for years, the “Formula One” of the computing world. New memory, networking, and processor technologies oftentimes got their start in HPC, as it was the application that was most in need of pushing the edge (and had the cash to spend on exotic new hardware to do it).
The use of GPUs (graphical processing units) outside of games, for example, was a HPC calling card. NVIDIA’s CUDA framework which has helped give it such a lead in the AI semiconductor race was originally built to accelerate the types of computations that HPC could benefit from.
The success of Deep Learning as the chosen approach for AI benefited greatly from this initial work in HPC, as the math required to make deep learning worked was similar enough that existing GPUs and programming frameworks could be adapted. And, as a result, HPC benefited as well, as more interest and investment flowed into the space.
But, we’re now seeing a market transition. Unlike with HPC which performs mathematical operations requiring every last iota of precision on mostly dense matrices, AI inference works on sparse matrices and does not require much precision at all. This has resulted in a shift in industry away from software and hardware that works for both HPC and AI and towards the much larger AI market specifically.
The HPC community is used to being first, and we always considered ourselves as the F1 racing team of computing. We invent the turbochargers and fuel injection and the carbon fiber and then we put that into more general purpose vehicles, to use an analogy. I worry that the HPC community has sort of taken the backseat when it comes to AI and is not leading the charge. Like you, I’m seeing a lot of this AI stuff being led out of the hyperscalers and clouds. And we’ve got to find a way to take that back and carve our own use cases. There are a lot more HPC sites around the world than there are cloud sites, and we have got access to all a lot of data.
I’m over two months late to seeing this study, but a brilliant study design (use insurance data to measure rate of bodily injury and property damage) and strong, noteworthy conclusion (doesn’t matter how you cut it, Waymo’s autonomous vehicle service resulted in fewer injuries per mile and less property damage per mile than human drivers in the same area) make this worthwhile to return to! Short and sweet paper from researchers from Waymo, Swiss Re (the re-insurer), and Stanford that is well worth the 10 minute read!
When TO and RO datasets were combined, totaling 39,096,826 miles, there was a significant reduction in bodily injury claims frequency by 93% (0.08 vs 1.09 claims per million miles), TO+ROBI 95% CI [0.02, 0.22], Baseline 95% CI [1.08, 1.09]. Property damage claims frequency was significantly reduced by 93% (0.23 vs 3.17 claims per million miles), TO+ROPDL 95% CI [0.11, 0.44], Baseline 95% CI [3.16, 3.18].
