@treiner5
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Partner at Altimeter Capital, curious on all things software, gaming, travel and leisure. Views are my own, no investment advice. Amateur astronomer
Half Moon Bay, CA
Joined January 2009
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Stock-based compensation is one of the most underappreciated drags on shareholder returns. Most investors either ignore it or have no easy way to compare it across companies. So I built something to fix that.
Introducing Platform Aeronaut SBC & Dilution with quarterly dilution analytics, a live leaderboard, and source-auditable methodology across 80+ public companies.
The core idea: every company gets measured on trailing dilution, forward dilution, SBC dilution, and a blended view, then ranked against both cohort and sector peers. No cherry-picked company framing. One consistent lens.
I built an AV fleet tracker. Every robotaxi argument hits the same wall: it needs a fleet number, and every source gives a different one. So I went bottoms up. TX DMV VIN registry + CA CPUC filings + every press release and news story I could crawl, cross-referenced into one number.
🤖 5,574 autonomous vehicles operating or testing in the U.S. right now.
@Waymo is 4,252 of them. That's 76% of every robotaxi in the country. $TSLA is second at 621, @Avrideai third at 344. Everyone else is a rounding error, for now.
Texas is the real story. California still leads at 2,153, but Texas is right behind at 1,896, and it's the only state with a VIN-level public registry. Under SB 2807 every car has to be listed on a TxDMV authorization. The tracker crawls it daily, so when Tesla adds Cybercabs they show up immediately.
Also on the site: every Waymo CPUC filing for California in one place through Q2 2026 (trips, passengers, miles, deadhead %), a searchable AV news feed that links straight to the sources, and a map of fleet per 100k people. Early days on that last one, but give it 12 to 24 months. We don't have formal numbers on @nuro but should in the CPUC Q3 update.
Caveat: the numbers aren't perfect. They include test cars, safety-monitor cars, and cars registered but not yet deployed. It's my best attempt at ground truth, if you see something wrong, let me know.
📈 Tracker: avrideshare.platformaeronaut…
🖥️ Post:
platformaeronaut.com/p/av-fl…
A bit different from what I usually post here. 🌻 My wife wrote a children's book Sunflower and the Superpower, a story about self-compassion, inner strength, and the unique gifts inside every kid. Ages 3–7. Couldn't be prouder of her.
Out now here 👇 shop.sunflowerandthesuperpow…
I added a Benchmarks page to Platform Aeronaut Dilution that takes the full coverage universe of quarter-level blended dilution data and groups it by company scale, IPO maturity, and revenue growth. The result is a set of reference curves that give you an empirical baseline for what dilution looks like across different company profiles.
1⃣ Dilution by Market Cap:
The first chart groups dilution by market cap at the time of each quarterly observation. Smaller companies dilute more. A lot more. Companies under $5B are running blended dilution in the range of 3.5–5%. By the $25–50B range, that’s dropped to roughly 2.5%. At $100B+ it flattens out around 1–1.5%, and the trillion-dollar cohort sits near 0.8%.
The shape of this curve matters. The steepest drop happens between <$5B and $25B. That’s the zone where companies are scaling past the initial post-IPO grant overhang, buyback programs are starting to kick in, and comp committees are getting more serious about managing dilution. Once a company crosses into mega-cap territory, dilution converges toward a narrow band. The market basically forces convergence.
I've added $LYFT $UBER $DASH $CART illustratively
Stock-based compensation is one of the most underappreciated drags on shareholder returns. Most investors either ignore it or have no easy way to compare it across companies. So I built something to fix that.
Introducing Platform Aeronaut SBC & Dilution with quarterly dilution analytics, a live leaderboard, and source-auditable methodology across 80+ public companies.
The core idea: every company gets measured on trailing dilution, forward dilution, SBC dilution, and a blended view, then ranked against both cohort and sector peers. No cherry-picked company framing. One consistent lens.
2⃣ Dilution By Age Since IPO:
This chart groups dilution by how many years a company has been public. The pattern here is different from market cap, and more interesting in some ways.
Dilution increases slightly in the first year post-IPO, peaking around 3.5% at roughly the one-year mark as the initial wave of IPO-related grants vest. After that peak it enters a long, gradual decline. This decline is a lot shallower than I would’ve expected but this is likely reinforced by poor ZIRP behavior as companies could behave almost like children for longer.
The confidence band is widest around years 8–11, which suggests that’s the period where company outcomes diverge the most. Some companies are actively managing dilution down by that point. Others are still granting at elevated rates, often because they haven’t achieved the stock price appreciation that would let them reduce grant volumes without cutting comp.
3⃣ Dilution By Revenue Growth:
This is the chart that surprises people the most, and it’s the one I think is most underappreciated.
Dilution increases with revenue growth. Companies with declining or low-single-digit revenue growth cluster around 0.5–1% dilution. By 20–25% revenue growth, dilution is around 2%. And the fastest growers, 40%+, are running at 2.5% or higher. This makes sense as faster-growing companies are hiring aggressively, competing for expensive engineering talent, and using equity as a primary compensation tool.
But the revenue growth dimension adds something the other charts don’t: it lets you evaluate whether the growth justifies the dilution.
Look at $LYFT and $CART. Both are growing revenue in the 5–15% range, but diluting at 4%+. The benchmark for that level of growth is somewhere around 1–1.5%. They’re diluting at 3–4x the rate you’d expect for their growth profile. This is the chart that makes the sharpest case: you’re getting modest growth and elevated dilution, which is the worst combination for shareholders.
Interesting commentary on $TSLA robotaxi status in CA here:
Tesla's regulatory status is even weaker than most people realize. Tesla isn't just "early stage", they're literally not in the AV program at all. They're not subject to any of the CPUC's AV reporting requirements, meaning no trip data, no stoppage events, no passenger safety metrics are being collected. They're operating under the same permit structure as a limo company.
Tesla is classified as SAE Level 2, not Level 3 meaning California does not consider them an autonomous vehicle service at all. Level 3 requires the onboard AI to navigate designated road conditions independently within an operational design domain. Tesla doesn't meet that bar.
Tesla holds a charter party carrier (TCP) permit from the CPUC, the exact same permit type as a limousine company. That's it.
Tesla does not have a permit with the CPUC's autonomous vehicle program, and Pat's understanding is they don't have one with the DMV either.
The person in the driver's seat of a Tesla Robotaxi is legally "the driver," not a "safety driver." The CPUC draws a hard distinction here. Pat explicitly compared it to someone using Full Self-Driving Supervised on the Uber platform, same category.
To actually get into the AV program, Tesla would need to first obtain a DMV deployment permit, then operate for at least 30 days, then submit a full application including a passenger safety plan, then have CPUC staff review it for reasonableness, then have five commissioners vote to approve it. Pat expressed skepticism that 30 days alone would be sufficient, she said her staff would be "asking a lot of follow-up questions."
The CPUC's new "stoppage events" metric is interesting. They're now collecting data on any time a vehicle stops for more than 2 minutes or requires remote/towing intervention. This is a smart way to capture the non-collision disruptions (blocking traffic, blocking first responders) that have been generating negative headlines for Waymo and others. That 2-minute threshold was specifically debated and chosen.
Data transparency is still being fought over. The CPUC used to publish vehicle counts and trips per day (utilization), but companies pushed back on confidentiality grounds. Those decisions are apparently still being contested through multiple rounds of appeals. Universities and researchers are actively pushing for more disclosure.
🚨🎙️ The latest episode of @driverlessguy_ podcast is live. I sat down with Shao Pat Tsen, Deputy Executive Director for Consumer Policy, Transportation, and Enforcement at the @californiapuc (CPUC). We discussed:
🏛️📋 The CPUC’s role, what they regulate, and how it differs from the DMV
🚕📄 Steps to launching a robotaxi service in California—and the different types of permits
⚡🤖 Why Tesla’s robotaxi isn’t considered an autonomous vehicle service in California
📑✅ What it takes to get an AV permit from the CPUC
📊📬 What robotaxi companies currently have to report to the CPUC
🧠🔧 How the CPUC handles new edge cases and teleoperations
Quick update on best positioned for US AV Rideshare by end fo 2027: $UBER clear winner in terms of reader opinion. Waymo a strong second with $TSLA behind. Nobody viewed $LYFT as having a chance at being well positioned.
Uber Is Quietly Winning the AV Rideshare Setup
If 2025 was the proof point that consumers will actually take autonomous rides at scale, 2026 is starting to look like the year the strategic map gets redrawn. For the last few years the AV debate has mostly been framed around who has the best self-driving technology. That still matters of course. But increasingly that is the wrong question for investors.
The more important question now is: who is best positioned to turn AV supply into a scaled rideshare network? That is a different question entirely.
To level set: this is no longer just about the best AV stack
@Waymo is the only player that has really crossed from demo to scaled commercial reality.
The company said in February it was already doing more than 400,000 paid rides per week across its operating markets, and it raised another $1.6 billion while laying groundwork for expansion into more cities. Its new Arizona manufacturing facility with $MGA is designed to produce “tens of thousands” of autonomous vehicles per year at full capacity. That is the most real robotaxi business in the U.S. by a mile.
But the leap from “best AV operator today” to “winner of AV rideshare economics” is not automatic.
Because scaled rideshare is not just a software problem. It is a supply problem, a dispatch problem, a maintenance problem, a financing problem, and maybe most importantly a utilization problem. That is where $Uber's setup starts to look much more interesting than the market gives it credit for.
Uber is not trying to win autonomy. It is trying to win the network.
Uber’s strategy now looks pretty clear: let others build the autonomous brain, while Uber becomes the default marketplace, demand layer, and utilization optimizer. That may end up being the smarter economic position.
A lot of commentary around AV tends to sloppily bundle “partnerships” together as if they are equal. They are not, some partnerships are real supply, some are geographic options, but Uber increasingly has both.
Uber Is Quietly Winning the AV Rideshare Setup
If 2025 was the proof point that consumers will actually take autonomous rides at scale, 2026 is starting to look like the year the strategic map gets redrawn. For the last few years the AV debate has mostly been framed around who has the best self-driving technology. That still matters of course. But increasingly that is the wrong question for investors.
The more important question now is: who is best positioned to turn AV supply into a scaled rideshare network? That is a different question entirely.
To level set: this is no longer just about the best AV stack
@Waymo is the only player that has really crossed from demo to scaled commercial reality.
The company said in February it was already doing more than 400,000 paid rides per week across its operating markets, and it raised another $1.6 billion while laying groundwork for expansion into more cities. Its new Arizona manufacturing facility with $MGA is designed to produce “tens of thousands” of autonomous vehicles per year at full capacity. That is the most real robotaxi business in the U.S. by a mile.
But the leap from “best AV operator today” to “winner of AV rideshare economics” is not automatic.
Because scaled rideshare is not just a software problem. It is a supply problem, a dispatch problem, a maintenance problem, a financing problem, and maybe most importantly a utilization problem. That is where $Uber's setup starts to look much more interesting than the market gives it credit for.
Uber is not trying to win autonomy. It is trying to win the network.
Uber’s strategy now looks pretty clear: let others build the autonomous brain, while Uber becomes the default marketplace, demand layer, and utilization optimizer. That may end up being the smarter economic position.
A lot of commentary around AV tends to sloppily bundle “partnerships” together as if they are equal. They are not, some partnerships are real supply, some are geographic options, but Uber increasingly has both.
Tesla is still more narrative than scaled reality
$TSLA remains the company with the most upside in the bull case and the least patience left in the bear case. Yes, Tesla has now started robotaxi rides in Austin without safety monitors in the car. That is real progress, but the broader picture still looks messy. @ethanmckanna
Reuters reported in late February that Tesla logged zero autonomous test miles in California in 2025, the sixth straight year with no such miles reported there, and that the company has not taken the regulatory steps necessary for a commercial robotaxi launch in California. By contrast, Waymo logged over 13 million autonomous test miles before securing its full commercial permissions in the state. That is not a trivial difference. That is the difference between a live commercial system and a story still trying to become one.
At the same time, Tesla’s core auto business is wobbling. Reuters reported this week that analysts increasingly expect Tesla to post a third straight year of declining deliveries in 2026, while capex ramps sharply and concerns about future cash burn rise.
So yes, Tesla still has the biggest theoretical prize if it can crack low-cost, vision-based autonomy at scale. But sitting here today, Tesla looks far less like a clean AV rideshare winner and far more like a company still trying to bridge the gap between product narrative and real commercial deployment.
That is what I mean by floundering. Not dead. Not irrelevant. Just still nowhere near as concrete as the stock and the discourse often imply.
Lyft deserves some credit for at least making sure it has a seat at the table.
It announced partnerships with @May_Mobility and $MBLY in late 2024, launched its first May fleet in Atlanta in 2025, partnered with @Waymo for Nashville in 2026, and even struck a $BIDU partnership for Europe. But compared to $UBER, $LYFT still looks stuck between identities.
It is not the AV technology leader like Waymo.
It is not the moonshot vertical integration story like Tesla.
And it does not have the same magnitude of disclosed AV pipeline as Uber.
The May Mobility rollout was important, but it did not come with the type of hard fleet scale disclosures that Uber’s @nuro / Te tie-up did. The Waymo Nashville deal is meaningful, but it is also incremental rather than market-defining. The Baidu deal is outside the U.S. and does little to change the domestic competitive picture.
So Lyft is not absent. But it is hard to look at the current setup and conclude that Lyft is driving the market structure. It looks much more like a company trying to stay attached to whichever AV suppliers will have it. That is a very different strategic posture than Uber’s.
By the end of 2027, the U.S. AV rideshare map may look completely different
If you zoom out, the likely shape of the market by the end of 2027 is becoming much easier to imagine.
- Waymo will probably still be the premium gold standard in actual AV operations.
- Tesla may still matter enormously, but it still has to prove it can move from small controlled pilots and repeated promises into scaled, repeatable, regulatorily durable commercial service.
- Lyft will likely still participate, but right now it looks more like a follower than a shaper.
- Uber is the one increasingly assembling the broadest demand and supply clearinghouse.
That matters because the eventual winner in AV rideshare may not be the company with the single best robotaxi. It may be the company that can best aggregate robotaxis, finance them, route them, fill them, and keep them busy at higher utilization than anyone else.
Today that company looks increasingly like Uber.
And if that is right, then by the end of 2027 the U.S. rideshare market may look much less like Uber vs Lyft and much more like Uber sitting at the center of a hybrid human-plus-AV network, Waymo supplying part of the premium autonomous layer, Tesla still trying to force its own vertically integrated path, and Lyft trying not to get marginalized.
That is a very different market than the one investors were underwriting even a year ago. And in that world, Uber may not need to build the best AV stack. It may just need to become the best place for every AV stack to work.
The obvious risk to this thesis is that if Tesla or Waymo drive vehicle costs low enough, Uber’s utilization advantage matters less than I think.
By year-end 2027, who will be best positioned in U.S. AV rideshare?