• Remote Assistance Roles: Human workers provide data and advice to autonomous vehicle software when the systems encounter perplexing or complex driving situations.
• Safety Critical Impact: Remote operators are essential for navigating hazards like power outages and ensuring compliance with traffic laws to prevent accidents.
• Control Mechanisms Clarified: Waymo specifies that its agents provide situational advice rather than directly steering or remote-controlling the vehicles.
• Workforce Scalability Data: Approximately 70 agents monitor a fleet of 3,000 robotaxis, suggesting the automated system manages the majority of driving tasks independently.
• Global Labor Distribution: Fifty percent of Waymo’s remote assistance staff are based in the Philippines, while complex incidents are managed by US-based teams.
• Staffing Compliance Standards: Personnel undergo drug testing, background checks, and training on specific road rules to maintain operational safety standards.
• Tesla Operational Differences: Tesla utilizes domestic remote operators based in Texas and California, moving away from in-vehicle safety monitors toward chase cars and remote centers.
• Transparency And Oversight: Industry experts emphasize that understanding human intervention frequency and methods is vital for evaluating the overall safety of autonomous transport.

really just big, remote-controlled cars, with nameless and faceless people in far-off call centers piloting the things from behind consoles? As the vehicles and their science-fiction-like software expand to more cities, the conspiracy theory has rocketed around group chats and TikToks. It’s been powered, in part, by the reluctance of self-driving car companies to talk in specifics about the humans who help make their robots go.

But this month, in government documents submitted by Alphabet subsidiary Waymo and electric-auto maker Tesla, the companies have revealed more details about the people and programs that help the vehicles when their software gets confused.

The details of these companies' “remote assistance” programs are important because the humans supporting the robots are critical in ensuring the cars are driving safely on public roads, industry experts say. Even robotaxis that run smoothly most of the time get into situations that their self-driving systems find perplexing. See, for example, a December power outage in San Francisco that killed stop lights around the city, stranding confused Waymos in several intersections. Or the ongoing government probes into several instances of these cars illegally blowing past stopped school buses unloading students in Austin, Texas. (The latter led Waymo to issue a software recall.) When this happens, humans get the cars out of the jam by directing or “advising” them from afar.

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These jobs are important because if people do them wrong, they can be the difference between, say, a car stopping for or running a red light. “For the foreseeable future, there will be people who play a role in the vehicles’ behavior, and therefore have a safety role to play,” says Philip Koopman, an autonomous-vehicle software and safety researcher at Carnegie Mellon University. One of the hardest safety problems associated with self-driving, he says, is building software that knows when to ask for human help.

In other words: If you care about robot safety, pay attention to the people.

The People of Waymo

Waymo operates a paid robotaxi service in six metros—Atlanta, Austin, Los Angeles, Phoenix, and the San Francisco Bay Area—and has plans to launch in at least 10 more, including London, this year. Now, in a blog post and letter submitted to US senator Ed Markey this week, the company made public more aspects of what it calls its “remote assistance” (RA) program, which uses remote workers to respond to requests from Waymo’s vehicle software when it determines it needs help. These humans give data or advice to the systems, writes Ryan McNamara, Waymo's vice president and global head of operations. The system can use or reject the information that humans provide.

“Waymo’s RA agents provide advice and support to the Waymo Driver but do not directly control, steer, or drive the vehicle,” McNamara writes—denying, implicitly, the charge that Waymos are simply remote-controlled cars. About 70 assistants are on duty at any given time to monitor some 3,000 robotaxis, the company says. The low ratio indicates the cars are doing much of the heavy lifting.

Waymo also confirmed in its letter what an executive told Congress in a hearing earlier this month: Half of these remote assistance workers are contractors overseas, in the Philippines. (The company says it has two other remote assistance offices in Arizona and Michigan.) These workers are licensed to drive in the Philippines, McNamara writes, but are trained on US road rules. All remote assistance workers are drug- and alcohol-tested when they are hired, the company says, and 45 percent are drug-tested every three months as part of Waymo’s random testing program.

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The company says a highly trained US-based team handles the most complex remote interactions, including collisions, contacts with law enforcement and riders, and interactions with regulatory agencies. The company declined to comment beyond the details in its letter.

Tesla’s Human Babysitters

Tesla has operated a small robotaxi service in Austin, Texas, since last June. The service started with human safety monitors sitting in the vehicles’ front passenger seats, ready to intervene if the tech went wrong. Last month, CEO Elon Musk said the company had started to take these monitors out of the front seats. He acknowledged that while the company did use “chase cars” to monitor and intervene with the software, it had started to operate some cars without that more direct human intervention. (A larger but still limited Tesla ride-hailing service in the Bay Area operates with human drivers behind the wheel.) But the company has not revealed much about the people who help its vehicles out of jams, or how they do the job.

Now, in a filing submitted to the California Public Utilities Commission this week, Tesla AI technical program manager Dzuy Cao writes that Tesla runs two offices of “remote operators,” based in Austin and the Bay Area. (In a seeming dig at Waymo’s Philippines revelations, Cao emphasizes that it “requires that its remote operators be located domestically.”) The company says these operators undergo “extensive” background checks and drug and alcohol testing, and have valid US driver’s licenses.

But Tesla still hasn’t revealed how often these operators intervene with its self-driving technology, and exactly how they do it. Tesla didn’t respond to WIRED’s request for comment.

The details of these remote programs could determine whether self-driving cars actually keep others on the road out of harm’s way. “If there’s a person who can make a mistake that can result in or contribute to a crash, then you have a safety issue you have to deal with,” Koopman says.

• Company Valuation: Code Metal reached a 1.25 billion dollar valuation following a 125 million dollar Series B funding round in February 2026.
• Core Technology: The platform utilizes artificial intelligence to translate legacy defense software into modern programming languages like Rust, VHDL, and CUDA.
• Formal Verification: The system employs mathematical formal verification to prove that translated code behaves identically to the original, aiming for zero-error compliance with flight safety standards.
• Market Demand: The Department of Defense faces a 66 billion dollar annual IT challenge, with 60 to 70 percent of software budgets dedicated to maintaining aging legacy systems.
• Labor Shortage: Military infrastructure relies on outdated languages such as Ada, Fortran, and COBOL, while the original programmers are reaching retirement age without available replacements.
• Strategic Leadership: The firm is led by veterans of BAE Systems and MIT Lincoln Laboratory and has recruited executive talent from Salesforce and the National Security Council.
• Client Portfolio: Major defense contractors and entities, including L3Harris, RTX, and the U.S. Air Force, have engaged the company to modernize mission-critical systems.
• Scalability Risks: While effective in pilot programs, the mathematical verification method faces unproven technical hurdles when applied to undocumented, million-line codebases.

A decade ago, Peter Morales sat inside a BAE Systems office trying to solve a problem that would later make him a billionaire on paper. Engineers on the F-35 program had written machine-learning algorithms in MATLAB, MathWorks's programming environment, to recognize and jam enemy radar. The algorithms worked fine in the lab. Getting them to run on the fighter's actual onboard chips was another matter entirely.

"They developed some cool machine-learning algorithms and they needed help getting this running in real time," Morales told the Boston Globe in 2024. The gap between writing code that works in a lab and deploying it on hardware that flies at Mach 1.6 consumed months of painstaking manual translation. Every line rewritten by hand introduced risk, and every risk required testing. Months of it. Time that defense procurement officials did not have.

That frustration became Code Metal, a Boston startup now valued at $1.25 billion after closing a $125 million Series B in February 2026. The company's pitch is deceptively simple: use AI to translate code between programming languages, then mathematically prove the translation is correct. A wrong line of code in a commercial app means a crash and a patch. In defense software, it can mean a grounded fleet or a breached satellite link.

The central question for Code Metal is not whether verified code translation has value. The Pentagon has already answered that. What nobody knows yet is whether Code Metal's method holds up when you throw it at the sprawling, decades-old codebases that actually run American military infrastructure. Pilot programs are one thing. Production is something else.

The Breakdown

• Code Metal raised $125M Series B at $1.25B valuation, claims profitability with L3Harris, RTX, and Air Force as customers
• AI translates legacy defense code (Ada, COBOL, Fortran) into modern languages, then uses formal verification to prove correctness
• Pentagon spends $66B yearly on IT, with 60-70% going to maintain legacy systems written in languages losing their last programmers
• Key risk: formal verification has never been proven at scale on million-line, decades-old, undocumented codebases

A trillion dollars of technical debt

How bad is it? The F-35 alone runs on somewhere between 8 and 24 million lines of code, depending on who's counting and whether you include the ground-based logistics suite. C, C++, and Ada, that last one a language the DoD mandated in the 1980s because it seemed like a good idea at the time. Congressional testimony put the F-35's software bill at roughly $16.4 billion between fiscal years 2018 and 2024.

That is one weapons system. The Pentagon's total IT budget request for fiscal 2026 hit $66 billion, up $1.8 billion from the year before. Its cyber budget alone hit $15 billion. Some estimates put military software maintenance at 60 to 70 percent of total software budgets. Sixty to seventy cents of every dollar, spent keeping old systems breathing rather than building new capability. It is an embarrassing ratio for an organization that bills itself as the most technologically advanced fighting force on earth.

The human problem is worse than the financial one, and the Pentagon knows it. Ada, Fortran, COBOL. The people who wrote those languages into Pentagon systems three and four decades ago are heading for retirement. Their knowledge walks out with them, and nobody is lining up to replace them. Try posting a job listing for a COBOL specialist willing to maintain satellite communications protocols. See who applies.

Washington gets this, at least on paper. The DoD's Software Modernization Implementation Plan for 2025-2026 calls for software factories, cloud migration, legacy overhaul. The Army wants a whole new budget line, BA-8, built around software rather than the hardware-first model the Pentagon has run on for decades. Plans exist. A fast, safe way to actually convert millions of lines of legacy code does not.

How the machine works

Code Metal builds a translation engine. You feed it code in Python, Julia, MATLAB, or C++. Out the other end comes Rust, VHDL, Nvidia's CUDA, whatever the target hardware demands. The translation runs in phases. The system first analyzes the source codebase to identify what each component does. It generates a translation plan. Then a combination of large language models and traditional code-processing methods rewrites the software in the target language.

That description fits dozens of AI coding tools on the market. What Code Metal sells as its difference is the verification layer.

At each translation step, the platform generates test harnesses, automated containers of data and evaluation tools, that check whether the new code behaves identically to the original. The company uses formal verification, a mathematical method that maps every possible state of a program to prove correctness rather than merely testing for common failure modes. Code Metal claims compliance with MC/DC, the Modified Condition/Decision Coverage standard used to evaluate flight control software.

"There's no way to generate an error," Morales told WIRED. "The software will just say, 'There's no solution for this' if we can't complete the translation."

That is a strong claim. It means Code Metal would rather refuse a job than produce flawed output, a posture that maps well onto defense procurement culture, where the cost of a wrong answer dwarfs the cost of a slow one.

B Capital partner Yan-David Erlich, an investor in the Series B, put it without polish. The code that controls satellites and communications infrastructure "is old, it's crufty, it's written in programming languages that people might not use anymore. It needs to be modernized. But in the course of translation, you might be inserting bugs, which is catastrophically problematic."

The investor thesis boils down to timing. AI can now generate code fast enough to matter, and the defense industry is desperate enough to buy. B Capital has a phrase for this, "verified intelligence," and the logic is straightforward. AI is embedding itself deeper into infrastructure where mistakes kill people. The bar has to move from plausible output to provable correctness. There is no middle ground.

From seven employees to unicorn in twenty months

The funding trajectory does not look like a normal startup. It looks like a defense contractor sprinting toward a program of record.

They incorporated in 2023 with seven people and a $16.5 million seed from J2 Ventures and Shield Capital by July 2024. Accel came in at $36.5 million for the Series A that November, valuing the company at $250 million after hearing about eight-figure revenue. Three months later Salesforce Ventures wrote the $125 million Series B check. Valuation jumped to $1.25 billion. Fivefold in ninety days. That is not a normal trajectory.

Code Metal says it is already profitable and cash-flow positive. No audited financials to back that up, but if true, the company is a genuine outlier in an AI startup world that mostly burns cash and worries about margins later. The company has not disclosed specific revenue figures, but the phrase "eight-figure revenue" at the Series A stage and profitability at the Series B stage suggest a business model tied to large government and industrial contracts rather than high-volume, low-margin SaaS.

The customer list tells you where the money comes from. L3Harris, RTX (formerly Raytheon), the Air Force, Toshiba, Robert Bosch. Code Metal says some of those clients went from months-long deployment timelines to days. It is also in talks with a "large chip company" for code portability work across processor platforms, though it declined to name the firm.

The operators tell the story

Code Metal's recent appointments point in two directions at once.

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Ryan Aytay joined as president and chief operating officer. He spent 19 years at Salesforce, rising to chief business officer under CEO Marc Benioff before running Tableau as CEO from 2023 until his departure in early 2026. Aytay's background is enterprise sales and partnerships at massive scale. He managed Salesforce's strategic relationships with Amazon, Google, Apple, and IBM. You don't hire a former Tableau CEO to run a seven-person Boston startup unless you're building a sales operation that can handle decade-long procurement cycles and the relationship-heavy politics of defense contracting.

Then there's Laura Shen, executive vice president of growth, who used to run the China desk at the U.S. National Security Council. That hire says something about where Code Metal sees its next contracts coming from. Not just the Pentagon.

The founding team carries its own weight. Morales built AI reasoning systems for the F-35. He spent years at MIT Lincoln Laboratory working on counter-drone defense for the U.S. Capitol, and later moved to Microsoft to develop computer vision for HoloLens. CTO Alex Showalter-Bucher, also a Lincoln Lab alumnus, spent a decade across the Navy, Army, and Department of Homeland Security. The engineering bench draws from Intel, NASA, MathWorks, Lightmatter, and OpenAI.

Nobody on this team learned about defense software from a pitch deck. They were in the rooms where legacy code caused real problems, watching engineers spend months on translation work that produced more anxiety than confidence. Talk to Morales long enough and you hear that frustration in every answer. It is not a talking point. It is the reason the company exists.

The gap between proof of concept and proof at scale

Formal verification is not new. If you have been anywhere near defense procurement, you have seen it. Aerospace engineers and nuclear weapons designers have used it for decades to certify safety-critical software. Old technique. Code Metal's bet is that AI can bring the cost down far enough to make it practical outside those narrow, well-funded niches.

The catch is obvious. Formal verification works when the program is small and well-defined. Hand it a million-line legacy codebase, three decades of accumulated decisions by rotating teams working in multiple languages, half of it never documented? That is a different animal entirely. Nobody has proved formal verification scales to that kind of mess.

Code Metal won't say much about how it actually works. WIRED called the company "skittish about sharing too many details," and that tracks. Competitive secrecy makes sense when your moat is methodology, but it also means outsiders can't verify the claims. Zero errors in current pipelines? Maybe. For the controlled translation tasks the company has run so far, that could well be true. The question is what happens when the codebases get bigger, older, and tangled in ways nobody documented at the time.

Look at the broader AI coding market for context. The best code generation models in 2025 hit somewhere between 70 and 82 percent accuracy on common programming languages. Performance remains, as one AI safety report put it, "jagged," with leading systems still failing at some tasks that appear straightforward. Code Metal's proposition is that its neuro-symbolic approach, combining LLMs with formal methods, overcomes these limitations. The formal verification layer acts as a filter: if the AI hallucinates, the math catches it.

That architecture sounds compelling. But the company's own pricing model hints at the difficulty. Morales told WIRED that Code Metal negotiates pricing individually with each customer based on time to develop a kernel, lines of code translated, or development time saved. He acknowledged the process "can get murky." When the vendor acknowledges murkiness, the product is still finding its shape.

What Code Metal reveals about defense procurement

Defense technology meant hardware for decades. Jets, ships, missiles. Software? A cost center bolted onto the side of weapons programs. The F-35's budget categories still treat it that way, which is exactly why the Army wants BA-8, a funding line that treats software like what it actually is rather than an afterthought stapled to airframes.

Code Metal lives in the gap between those two worlds. Its customers are defense contractors and military branches that know their software needs modernizing but cannot do the work themselves fast enough. The pitch is speed without recklessness. Anduril and Palantir already proved that venture-backed companies could win Pentagon contracts. Code Metal is chasing something less visible but possibly bigger. The plumbing.

Autonomous drones get the headlines. Nobody writes breathless profiles about translating a thirty-year-old satellite protocol from Ada to Rust. But keeping legacy infrastructure alive while slowly dragging it into modern standards, that boring work might be worth more in total addressable market than any single new weapons system.

Rob Keith at Salesforce Ventures, who led the Series B, put it in procurement language. "AI code generation has hit an inflection point," he said. "Mission-critical industries cannot deploy what they cannot verify." Venture-capital polish aside, he's right about the core problem. The bottleneck in defense AI is not generation. It is trust.

The test

Code Metal started because getting working algorithms onto actual hardware took too long and broke too often. Whether the company can repeat that fix at orders of magnitude greater scale is the whole bet.

The pilots are done. L3Harris, RTX, and the Air Force are named customers. Morales says every pilot deployment has advanced to the next phase. The Series B gives Code Metal the capital to staff up, with Aytay running operations and Shen working growth channels that extend into national security policy.

Now comes the hard part. Converting pilot contracts into programs of record, the multiyear, multimillion-dollar deals that actually sustain defense technology companies. Not demos. Not proofs of concept. Production contracts that survive procurement cycles measured in decades.

That conversion will stress-test everything. Can formal verification hold at scale? Can the pricing model withstand government auditors? And can a startup founded in 2023 earn the kind of institutional trust that defense procurement demands, the kind that usually takes a decade to build?

The F-35's codebase took decades to accumulate. Modernizing it will take years no matter who does the work. Code Metal's $1.25 billion valuation says the market thinks the company can compress that timeline dramatically. The next twelve months will tell. Either the early contracts expand into production, or they stall in review. The math does not care about the valuation.

Frequently Asked Questions

What does Code Metal actually do?

Code Metal uses AI to translate software from one programming language to another, then applies formal verification to mathematically prove the translated code behaves identically to the original. Its primary customers are defense contractors and military branches modernizing legacy systems.

What is formal verification and why does defense need it?

Formal verification is a mathematical method that checks every possible state of a program to prove correctness, rather than just testing for common failures. Aerospace and nuclear engineers have used it for decades. For military software, a single bug can ground a fleet or breach a satellite link, making proof-based testing worth the cost.

How did Code Metal reach a $1.25 billion valuation so fast?

The company incorporated in 2023 and raised a $16.5 million seed by mid-2024. Accel led a $36.5 million Series A in November 2025 at $250 million. Salesforce Ventures then wrote the $125 million Series B check three months later. The company claims eight-figure revenue and profitability, though it has not released audited financials.

Who are Code Metal's main competitors?

Dozens of AI coding tools exist, but most focus on code generation without verified correctness. Code Metal's closest competitive space includes general AI code translation tools, but its formal verification layer targets a different buyer: organizations where incorrect code has catastrophic consequences. Defense primes like L3Harris and RTX are customers, not competitors.

What is the biggest risk to Code Metal's business?

Scaling formal verification to massive legacy codebases. The technique works well on small, well-defined programs. Pentagon systems involve millions of lines written over decades by rotating teams in multiple languages, much of it undocumented. Nobody has proven formal verification works reliably at that scale. The company also maintains secrecy about its methods, making independent evaluation difficult.

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• TRIMODAL-MARKET: The European software engineering pay landscape consists of three distinct tiers based on company benchmarking behaviors and business models.
• TIER-1-CHARACTERISTICS: Local companies and non-tech businesses treat engineering as a cost center, offering senior compensation between €50,000 and €75,000.
• TIER-2-CHARACTERISTICS: Regional competitors and high-growth startups benchmark against all local industries, typically providing senior total compensation from €75,000 to €125,000.
• TIER-3-CHARACTERISTICS: Big Tech firms and global trading companies benchmark internationally, with senior total compensation ranging from €125,000 to over €250,000.
• COMPENSATION-COMPONENTS: Higher tiers rely heavily on cash bonuses and equity, such as RSUs or options, which can fluctuate in value based on market performance.
• PERFORMANCE-INCENTIVES: Top-tier companies utilize outsized financial rewards and aggressive performance management to attract and retain high-leverage engineering talent.
• SILICON-VALLEY-IMPACT: US-based companies expanding into Europe and the shift toward remote work have significantly increased upward pressure on regional salary ceilings.
• HIRING-DYNAMICS: Achieving higher compensation requires navigating specialized technical interviews and accepting different levels of professional risk and work-life balance.

This article is part of a 3-part series on trimodal compensation:

• Part 1: The trimodal nature of software engineering salaries in the Netherlands and Europe (2021, this article)
• Part 2: The trimodal nature of tech compensation revisited (2024, this article)
• Part 3: The trimodal nature of tech compensation in the US, UK and India (2025)

Update: dozens of hiring managers confirmed this trimodal model applies to all global markets: from the US, through Asia to Latin America as well. Also see TechPays.com for data recorded for a growing number of countries in the three tiers.

(Watch this article as video narrated by me, with additional context)

I've been a hiring manager at Uber, in Amsterdam, for over 4 years. The market - and compensation - for software engineers have moved upwards at an incredible pace over during this time.

Interesting enough, many engineers did not notice any meaningful salary changes these years. The 2019 Honeypot Amsterdam developer survey says, "the most experienced developers earn an average of €55,000 high as over €70,000". The 2021 Talent.io salary report puts the most experienced software engineering salaries in Amsterdam at €60,000/year.

Meanwhile, I've observed the average senior total compensation figures at Uber nearly double from €110,000 in 2015 to €170,000-€230,000/year by 2020. It's not just Uber: <a href="http://Booking.com" rel="nofollow">Booking.com</a> senior total packages have gone up by 50% from around €100,000 in 2016 to €150,000+, as part of the EU salary research I've been running (I'll share the survey reports in-detail in later blog posts - subscribe here to not miss it).

Where is the disconnect?

Tiers of Companies

I'm seeing the software engineering compensation market becoming trimodal - split into three distinct groups that "spike" and that have little overlaps. Most engineers are not aware of this third, Big Tech pillar and the compensation ranges it introduces, assuming compensation can not go beyond what is offered at the second pillar:

There is no longer an "average" salary for software engineers in Europe / the Netherlands: just an average salary per one of the three, distinct categories. Which category does your workplace belong to?

You'll find little to no compensation data on this third pillar on likes of Glassdoor, Payscale, Honeypot, Talent.io, Stack Overflow Jobs and other public job or salary portals. For the US, the site Levels.fyi surfaces top tier companies; for Europe, TechPays.com – a site I'm building – showcases all three tiers, including this highest tier. This article also lists top tier companies across various European cities.

The range for #3 is almost entirely missing from most public salary data.

Here is the split of the three groups of companies, based on their compensation philosophy:

#1: Companies only benchmarking against their local competition and non-tech companies, competing with their local competitors. Most of these places call engineering as IT, and often view it as a cost center. Technology is not treated - or compensated - as a core competency at these companies. Examples would include IT teams for local supermarkets, or e-commerce sites and similar. They'd aim to pay right around or slightly above what the other local supermarkets, e-commerce sites and similar businesses pay.

Startups with little capital and bootstrapped companies might fall in this category. In the Netherlands, these companies would pay €50,000-75,000/year for a senior engineering role in the Netherlands, everything included (apart from the hard-to-value early-stage startup equity). For an entry-level role, this number would be €25,000-40,000.

Most of these companies will pay a salary only: a fraction of them will have any bonus scheme in place. Those that have will typically cap bonuses at 10% of the annual salary, and tie it to company performance. Engineers don't receive any equity - save for, perhaps, lead or principal positions in rare cases, and very little.

#2: Companies benchmarking against all local companies, even if they are not direct competitors. In the Netherlands, examples of these companies would be eBay classifieds, Adyen, Nike, Disney Streaming, and well-funded, high-growth startups like Catawiki, FindHotel, Miro, MessageBird, TripActions would also be in this category.

These companies would typically pay €75,000-125,000 total compensation (base salary + bonus + equity) for a senior engineering role in the Netherlands. For an entry-level role, this number would be €40,000-65,000.

Most of these companies will have a bonus scheme in place that pays up to 20% of the base salary in cash, and many offer equity to more senior engineers. Only a smaller portion of this category offer meaningful equity to all engineers, though.

#3: Big Tech: companies benchmarking against all regional or global companies. In Europe, this means competing against all other major EU companies, and often recruiting people from London, Berlin, Barcelona, and outside the EU. Examples of companies in this space are Uber, <a href="http://Booking.com" rel="nofollow">Booking.com</a>, Databricks, Amazon, Flexport, Plaid, Redis Labs, Stripe, Elastic, GitLab, GitHub, Datadog, Apple, or Netflix. Trading companies like IMC Financial, Optiver, Flow Traders or Jump Trading are in this group as well. Brexit should bring a lot more positions to the Netherlands for trading firms.

For a longer list of top-of-the-market companies and finding these, see my subscriber-only article Finding the Next Company to Work At, and in Compensation at publicly traded tech companies.

As an interesting data point, <a href="http://Booking.com" rel="nofollow">Booking.com</a> is the only non-US tech company in this group who has been keeping up with the market changes compensation-wise, competing with Silicon Valley companies from their Amsterdam HQ. Other European companies targeting the global markets would be wise to take note - and hold on to their key people.

These companies would typically pay €125,000-250,000+ total compensation for a senior engineering role in the Netherlands (meaning base salary + bonus + equity value: either liquid or paper-value: this would be up to around $300K/year in dollar value). All companies would offer a cash bonus and equity scheme for all engineers - even entry-level ones -: cash bonuses often going to 40-50% of the base salary for top performers, and equity sometimes accounting for more than the base salary for senior folks and high-performers. The exception to this is trading companies: they will not offer equity, but year-end cash bonuses will be 50-150% of the base salary.

For example, at Uber a typical senior engineer offer was around €175K/year total compensation: €115K salary, €20K bonus target and about €50K/year equity vesting - around 4,800 RSUs for vesting over 4 years, with a 1-year cliff). With equity appreciation, the total compensation value could go higher, as would with outsized rewards for top performers.

For an entry-level role, this number would be €65,000-100,000. For example, at Uber, a standard new grad offer was around €87,000 in 2020 (€70,000 salary + €7,000 target bonus + €10,000/year in stock). An engineer with a few years experience hired at the entry level could make above €100,000 in their first year, though. For example, at Uber, one of the Eng1/L3 engineers made €103,000 in their first year (€77,000 salary + €11,000 actual bonus + €15,000 in stock for their first year). Obviously, all of these Uber numbers are outdated: as the market changes, so will those figures.

Most engineers making outsized compensation in Europe have done so by taking a moderate risk on a high-growth, road-to-public company, joining before the IPO, and negotiating for equity. I know engineers at Uber, Datadog, Airbnb and Doordash who have seen great rewards for joining before going public was a "done deal". As time progresses, people working at companies that award large stock packages, and with stocks that see strong stock growth are also often in these positions.

What this means is both a new grad and a senior software engineer could be making up to 4x the annual compensation, depending on what company they are working at. Of course, there are overall fewer openings at Big Tech, and the competition and expectations are more fierce. Still, putting in the time to prepare for these interviews might make sense, knowing the difference in compensation.

Most companies tend to assume they are a tier higher than they actually are - because they have little data points that prove otherwise. Candidates keep accepting their current offers, that have not changed significantly the past years, and attrition remains as usual. However, many of these companies will be in for a surprise the coming years as the #3 category of companies hires the best talent away from #2. In turn, this will push #2 to increase compensation and equity allocation, and hire the best people from #2 and #1. And companies in #2 who do not offer meaningful equity for software engineers on top of a competitive salary will struggle to recruit anyone from #3.

If you're responsible for compensation planning, read more on the 🔒 reality of the 2021-2022 tech hiring market in this subscriber-only article that goes into what companies are doing who are hiring in this heated market. Also, follow my 🔒 The Scoop series which covers market changes and shares advice for hiring managers.

The Silicon Valley-Effect Pulling the Top of The Market

The top of the market has accelerated rapidly in the Netherlands, the past 5 years. In 2015, <a href="http://Booking.com" rel="nofollow">Booking.com</a> was the highest-paying employer across Amsterdam - and the Netherlands. Then, Uber opened an office. At first, Uber paid similar to <a href="http://Booking.com" rel="nofollow">Booking.com</a> - though more generous on options, and later with double-trigger RSUs. However, Uber soon stopped competing "just" locally.

Between 2016-2018, Uber re-benchmarked its compensation model to go head-on-head with the highest-paying tech companies in Europe: Facebook in London, Google in Zurich, Twitter in Dublin, and similar organizations.

The updated Uber compensation packages in 2018 were pretty much identical to that of Facebook London. This was no mistake: during these times, many candidates had offers both from us, and from Facebook or Google. I'd like to take credit for convincing people with Facebook/Google offers to choose my team: but without a compensation package that was competitive, all my efforts would have been worth nothing.

Silicon Valley companies opening EU offices have been a huge source of moving the market upwards - and swinging the balance back a little bit towards Europe / The Netherlands. The total compensation packages in the US have been shockingly high from the perspective of EU software engineers: and many engineers from the EU have packed up and left, seeing the wide gap.

An example is a Dutch engineer I know leaving their €80,000/year ($94,000) job in the Netherlands for an initially $350,000/year position at Lyft. Following promotions, they are now making more than $450,000/year (€378,000/year). Yes, healthcare and the cost of living are more expensive in the US: but this person has told me their only regret is they had to leave home to get paid their worth.

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Imaginary Money that Turns Into Real Money

Many engineers are hesitant to join companies that give out equity like double-trigger RSUs, options, SARs or others, but are not public.

However, imaginary money usually comes at a discount, and offered in larger amounts to offset the risk of people joining earlier. I benefitted from taking a risk at Uber. I joined in 2016, and I only had imaginary money up to the 2019 IPO. Then, all that imaginary money converted to a large amount of real money. People joining after the IPO got half or less the stock units than people joining beforehand: there was no risk to be compensated for, any more.

Databricks, Flexport, Miro, Messagebird and other fast-growth companies are all similar stories. There is a chance these companies won't IPO: just like there was the chance that Uber would not. They also offer stock more generously then they would, after a successful IPO. Look into understanding the money markets, the current and past IPO trends and make your own decisions. I was cautiously certain that Uber would have an IPO within a few years of joining given the investor pressure building up: some of my friends thought I was being reckless in joining a company that offered imaginary money at large.

There are also early-stage, high-growth startups that are promising, and employee equity they award can turn into big gains on an exit years down the line. Craft, Fonoa, Linear, Payaut, FindHotel and others are examples of these places. Yes, there are are cautionary examples of how things can go wrong as well. Use your own judgement, risk-assesment and how much you believe in the company's mission and trajectory.

After a company goes public, equity compensation tends to drop, while competition to get into those places becomes more intense. A person who works at Opendoor shared how right after their IPO, the number and quality of applicants have skyrocketed - which is an all too typical story. These companies still pay well, but will not pay any premium for a pre-IPO risk, and if you apply, you'll have far more competition to stand out from and you'll need to prepare more for the coding and systems design interviews.

Equity, IPOs and The Impact on Compensation

Much of the high total compensation numbers are linked to equity that software engineers receive. Publicly traded companies that compete globally issue high equity packages. Unicorns and decacorns competing for the same engineers often issue large private equity packages for employees.

This equity can become very valuable on a successful IPO - but is not guaranteed to do so thanks to the nature of equity:

The value of private equity for software engineers. Following a successful IPO, the total compensation of engineers can jump significantly. It all depends how much stock you have, if, when and how the IPO goes, and how the stock does afterwards.

Databricks, Flexport, Miro, MessageBird, and other companies with large Amsterdam offices are all ones that are expected to go public in the next few years. An engineer who joined Databricks in late 2018 has $6M worth of stock - on paper, that is. Joining rapidly growing companies that offer strong equity packages can increase your compensation significantly.

Risk, rewards, and luck often go hand in hand with engineering compensation on the high end. The higher the ration of equity in your compensation, the higher gains - or losses - you can see with the equity price going up or down.

For example, Uber was not always considered as the top of the market for salaries in the Netherlands. In 2015, Uber paid in-line with <a href="http://Booking.com" rel="nofollow">Booking.com</a>, and it was uncertain if there would be an IPO in the foreseeable future. In 2017-2018, Uber started to issue larger packages, and the IPO timeline became more certain:

However, most Uber engineers would describe the IPO in 2019 as disappointing. Many engineers had RSUs issued at a $48 preferred price, Uber IPO'd at $45, and the stock traded at $30 when the lockup expired, and people could sell:

By the time Uber employees could sell equity in 2019, the stock was down from a $48 preferred RSU price to $30. The stock would trade as low as $21 in 2020, before bouncing back.

The total compensation number of anyone with Uber stocks would go down at this point. However, anyone lucky enough to join late 2019 or early 2020 was issued stock at a price that is less than half of where Uber is trading now - and they could now have a much higher total compensation than most employees in a similar role.

The highest compensation packages will also typically be the most volatile ones, value-wise. This is also why it's hard to answer how much an engineer working at the likes of Uber is earning. The base salary and bonus: those are easy to quantify. But the equity that can make up a major portion of your salary? It is much harder to predict.

Performance on the Job and Compensation

The Silicon-Valley mentality of paying high-leverage engineers very well does not stop at salaries. It also includes how bonuses, and equity refreshes are awarded.

At some companies, the top performers can be paid outsized bonuses and stock, in ways that push their compensation well above the next level. While working at Uber, I was lucky to have engineers who were perceived as top 1% or top 3% of engineers. Here's the type of rewards some people got at Uber Amsterdam:

• A €190,000 bonus at the end of the year: €50,000 in cash paid on the spot, and €140,000 in stock, vesting over 4 years, on a monthly basis. The target end of year bonus for this role would have been around €62,000 (€22,000 in cash and €40,000 in stock vesting over 4 years - this person got 2.5x the cash, and over 3x the equity target).
• A $80,000 spot bonus to be paid out in 4 instalments over 2 years ($20,000 every 6 months). This was on top of the "normal" bonuses.
• A $220,000 stock refresh award, vesting over 4 years to bring a person up to the range they should be, based on their performance. The usual stock refreshes for this role would have been around $50,000 every year.
• A €40,000 spot bonus vesting 12 months later, serving as a retention grant.
• 24% salary increase on promotion, taking performance into account. The usual increases were 10-15%, and depended on where the salary band of the person was.

At Uber, I've seen people who were a level below me, but still making more per year than I did, thanks to their outstanding performance, impact, and the bonus that followed. Some of these people were on my team - and I loved that the system allowed this difference. Because we could reward high performers disproportionately, these people did not even think of leaving. Why would they?

Thanks to this policy, as a manager, I was able to work with some of the best engineers throughout my career - for an extended period of time. Did I mention that over 4 years, no person who reported to me left Uber - up to the summer 2020 layoffs, that is? I'm sure it was not just compensation: but it sure played some part in this.

At companies who pay top of the market, people who are seen as key engineers often see outsized rewards, on top of the already great salary. And even the normal bonuses are what would be considered very large at #2 type of companies.

Outsized compensation and rewards also attracts really good talent - who you get to work with, if you also make it into the same company. I was lucky enough to work with - and learn from - engineers who have built the original PayPal payments system while working on Uber's payments system, read RFC comments on whether to use React Native from one of the first React Native core engineers who joined from Facebook, or get feedback on how not to mess up a big project from a manager who saw Google Plus fail first-hand.

COVID Tilting the Balance to the EU

COVID has also been a major boost for high-paying senior jobs. A growing number of EU software engineers are considering or have moved back to Europe. And more US companies realize they can hire cheap, but world-class software engineers in Europe.

A Dutch software engineer with 15 years of experience has joined a pre-IPO US company, working remotely to build large-scale distributed systems. They said:

I live in The Netherlands and in software development since 2000. I freelanced from 2008 to 2018. In 2018, I was recruited by {US startup acquired by a Big Tech company}, and boy, did a whole new world open for me. I'm moving into a new role at {Pre-IPO US company} Monday and my salary is roughly double (sometimes even triple) of what I could earn at the average enterprise or consultancy. And that's not even counting equity. At {US startup acquired by a Big Tech company}, I was a bit too late, but here, I'm in a good position. I should make enough to get rid of my mortgage, assuming a conservative IPO.

I learned more at three years of {US startup acquired by a Big Tech company} than in the 15 years before that. And the people I got to work with and are in my network now are invaluable.

COVID helped a lot too, IMO. Companies realised full remote is an option and you can increase your talent pool to include the entire planet.

I talked with the head of mobile engineering at a pre-IPO decacorn, who told me he's amazed he can hire staff-level engineers for $180,000/year (€150,000/year), who work extremely well, and on par with their Bay Area engineers who cost 2-3x this much, at $400-600,000/year. This organization is deciding which EU countries to open an office, the head of mobile already getting budget approval for a major hiring spree. The Netherlands is a front-runner for them.

Stripe and Spotify have both started to hire for permanent remote positions in Europe as well, expanding their hiring pool to all of the EU. Both companies are competing across Europe, and not with the local market. They join companies like GoDaddy, GitLab, GitHub, and others who have been doing this for years.

Getting Into Big Tech

The majority of companies in the Netherlands will fall into the #1 and #2 categories of companies aiming to compete only on the local market. Getting an offer from companies in the #1 category is usually the easiest, while #2 can be more challenging. However, the Big Tech companies in the #3 category are by far the most competitive ones to get into.

While #3 pays the most, it's also the most challenging to get into. I would know: I was a hiring manager here for years.

For any given position, there are usually so many applicants that you need to have a good and tailored resume to get through the resume screen - my first book helps with clearing this CV stage. The best advice? Get a referral if you can, and tailor your resume to the position you apply to.

For entry-level roles, you'll have to really prepare for the coding interview. Data structures, algorithms: the works. Like it or not, this a hoop that these companies will want all successful applicants to jump.

For senior and above positions, it gets trickier. You'll need to get good at large-scale systems design and have relevant experience for the type of work you'd do to get an offer at the senior engineering level. You'd have to have worked on similar systems before, often working at either another #3 company, or one of the better #2 ones.

For above senior positions, you'll only have a shot if you've solved similar-scale challenges to what these companies are doing, and have worked at comparable environments. You also need to be up-to-date with where the industry is at: may this be backend, web, mobile, or ML systems. Above senior positions are rare, and are almost looking for people who can have organization-wide impact on complex and impactful problems that is measured in the tens or hundreds of millions in users or revenue.

Many of the companies in #3 represent exciting professional challenges that companies in #1 or #2 do not have to offer. Even if it was not for the compensation, they might be worth exploring at some point during your professional career.

A resource that has helped people get into, and perform at expectations in Big Tech is my newsletter, The Pragmatic Engineer Newsletter. I cover topics relevant to software engineers and engineering managers at Big Tech and high-growth startups. Here are examples of what I write about. You can subscribe here for free.

Differences Between #1, #2 and #3 Outside Money

There is far more to what makes a good job than compensation. Places that offer high compensation usually mean higher competition to get in and stay on the job. It usually means unpaid oncall, often working with various timezones, and it can mean poor work-life balance compared to other places.

Work-life balance tends to be drastically better in the #1 category of local-only companies, compared to #2 and #3. In the Netherlands, the #1 companies places where it's common for the majority of the people to work less than 5 days a week, work to be steady and easy to plan, and work not starting betfore 9, or running beyond 5.

There tends to be little difference between #2 and #3 in terms of pace: they are both fast-moving, and often set high expectations for those who would like to perform well. A major difference is how many #2 places are in one timezone, and people can often disconnect in the evening hours. At global companies in #3, this is not always the case: at Uber, I would often have calls at 6pm or 7pm with teams in SF - which was in the morning their time. In turn, when working with US companies, people at #3 would sometimes start their day later.

Being warned, then let go when not performing at your expected level is by far the most likely to happen at #3. These places pay well not just because they can, but because they want high-performing teams - and people. If someone is not up to expectations, and does not improve despite direct feedback, they won't think too much about letting this person go, and hiring someone else who will do so. Compensation is also structured in a way to pay for performance - for example, all employees performing at expectations already get large cash and equity bonuses, while those who are below this bar can see their bonus hit zero. It goes the other way as well: people seen as top performers will get rewards and retainer bonuses unheard of outside the group of #3 companies.

Netflix is well-known for their "keeper test" and while working there comes with more cash than any other place will pay, getting fired is never far from anyone's mind. On the flip side, when working at a place like Netflix, you don't have to put up with people who are deliberately slacking or are pulling the team down: they won't be for the company for very long.

It's Not Just About Comp

Compensation is just one data point - though one that tends to have little transparency in Europe. I'll be publishing more detailed data points on this blog on the Netherlands, UK, Germany, and other EU countries' software engineering salary data that people have shared- and you can share as well.

Just make sure you're weighing not just the salary but other important things like professional growth, autonomy, mentorship, working directly on a product, work-life-balance and the things that matter to you:

Inspired by this visualization from @Lizandmollie

I'll follow up with more detailed compensation analysis for the Netherlands and Europe with numbers and company names - subscribe to my newsletter or follow me on Twitter to not miss it. If you work in tech, in the EU, please consider anonymously sharing salary data you are aware of via this form.

If you're responsible for compensation planning in the Netherlands, you might also want to read my article 🔒 The Perfect Storm Causing an Insane Tech Hiring Market.

Interested in more details? I made a video, sharing more of my thoughts on why a comparable software engineering position can pay very different.

Other models: in 2016 – 5 years before this article was published – software engineer Dan Luu published the article Is dev compensation bimodal? and came to the conclusion:

"if I had to guess, I'd say that while the dispersion in programmer compensation is increasing, it's not bimodal, but I don't really have the right data set to conclusively say anything."

Subscribe to my weekly newsletter to get articles like this in your inbox. It's a pretty good read - and the #1 tech newsletter on Substack.

Migration from California has helped change Colorado from a libertarian-inflected reddish state into a solid-blue one. And now blue Colorado is starting to turn into California.

The state’s remarkable demographic reversal provides the clearest evidence of this transformation. Recent Census numbers show Colorado losing more than 12,000 residents to other states last year, while its total population growth is anemic. The metro area of Denver, once a city with buzz as hot as Austin or Nashville, is now growing more slowly than Midwestern cities like Indianapolis and Columbus. The state’s labor force has also started shrinking—something the Denver Post notes has “never happened outside a severe recession or economic shock like the COVID-19 pandemic.”

Not so long ago, Colorado was one of America’s booming destinations. During the 1990s, its population grew by over 30 percent, adding more than 1 million residents. Between just 1990 and 1997, Colorado attracted nearly 110,000 migrants from California, about six times the number from any other state. The state also grew in the 2000s and 2010s.

One reason for this demographic decline may be another way in which Colorado is converging with California: high housing prices. Redfin ranks the state as the fourth-most expensive in which to buy a home. In the Denver market, the average family needs to spend more than six times its annual income to buy the average home. (A rule of thumb for affordability is to spend no more than three times your annual income.)

Weak demographics, a stagnant or declining labor force, and sky-high housing prices bode ill for the state’s ability to attract business. Colorado is not like New York City or San Francisco, elite global cities where workers will pay any price for access to agglomerations of high-value industries or the feeling of being at the center of the universe.

Instead, Colorado is shedding companies. TIAA is closing its large Denver office and moving 1,000 employees to Frisco, Texas. Call-center company TTEC has moved its headquarters from Colorado to Austin. Several other firms have implemented layoffs or chosen to expand elsewhere. The problem is so bad that the Colorado Chamber of Commerce now maintains a relocation tracker.

It’s hard to identify a single decision that caused these trends, but broadly speaking, California-style blue governance is the culprit.

Take infrastructure. Driven in part by a law mandating a 90 percent reduction in greenhouse-gas emissions, the state has effectively abandoned building or expanding highways. Instead, it has poured cash into light rail and other forms of public transit. Unlike New York or Chicago, where public transit has historically played a major role, there’s little hope that light rail will serve the needs of a sprawling city like Denver. These rail investments are also a huge maintenance liability. Riders have already begun to experience the periodic slowdowns familiar to train riders in other cities with deferred maintenance issues.

Other California-style measures include an anti-gun law last year and a law limiting cooperation with federal immigration enforcement—passed after the state had made national news with Venezuelan illegals engaging in gang activity. The legislature also tried to make it easier for unions to require compulsory representation payments from non-members. Governor Jared Polis vetoed it, but proponents plan to keep pushing for it.

Colorado has also started targeting politically unpopular people and businesses. In 2024, Denver considered a ballot initiative to force the closure of the city’s lone meatpacking plant. The measure failed, but only after opponents spent $2.4 million to defeat it. Targeting specific businesses for elimination speaks poorly of the city’s business climate, as does the now-infamous litigation against Masterpiece Cakeshop for refusing to create cakes with messages that violated the conscience of its owner.

With its top-quality natural amenities and highly educated workforce, Colorado won’t collapse anytime soon. And it hasn’t reached California’s level of crazy—yet.

But Colorado has become a cautionary tale of what happens when Democrats and Golden State refugees capture a state’s politics. The high housing prices, demographic stagnation, and business weakness in California are not accidents—they are the consequences of a political culture that produces similar results wherever it takes hold.

Photo by Justin Edmonds/Getty Images

• Judicial Tension: The Supreme Court recently issued a decision that invalidated the legal rationale for the current administration's tariff policies.
• Historical Precedent: Republican leadership attributes their 2018 Senate gains to voter backlash against the contentious confirmation process of Justice Brett Kavanaugh.
• Retirement Speculation: Justice Samuel Alito, currently 75 years old, is the subject of rumors regarding a potential departure from the bench after twenty years of service.
• Diminishing Influence: Internal court data indicates Alito authored the fewest leading opinions this term, suggesting a possible decline in his relative standing among the conservative bloc.
• External Pressures: Public scrutiny regarding ethics and personal political expressions by the Alito family has increased the perceived likelihood of a voluntary resignation.
• Strategic Timing: Analysts point to the October 2026 release date of Alito’s upcoming book as a potential indicator of his plan to leave the court before the next term.
• Succession Planning: Conservatives may seek a retirement while they hold a Senate majority to ensure the seat is filled by a younger jurist with similar ideological views.
• Political Impact: A Supreme Court vacancy during a midterm year could serve as a primary mobilization tool for both parties, regardless of the ultimate electoral outcome.

 ![](https://pyxis.nymag.com/v1/imgs/712/68f/c34a86c6841c9801212c5e55f5fb495b8c-alito-age.rsquare.w400.jpg)

Photo: Chip Somodevilla//Getty Images

This week, there’s been a lot of attention focused on the U.S. Supreme Court, thanks to its stunning decision blowing up the rationale for Donald Trump’s tariff agenda. In his bitter remarks about the decision, the president went out of his way to praise dissenters Clarence Thomas, Samuel Alito, and Brett Kavanaugh.

It’s Alito who could make some additional political news later this year. To understand why, you must step back to 2018, when Trump faced his first midterm election as president and the dynamics looked grim. He had lost the popular vote in 2016. His job-approval ratings had been underwater from the second week of his term in office. One of his two big first-term initiatives, legislation to repeal and replace Obamacare, had ended in dismal failure. And unsurprisingly, his party wound up losing 40 net U.S. House seats and control of that chamber.

But at the same time, Republicans actually posted a net gain of two U.S. Senate seats and increased their majority from a fragile 51-to-49 margin to a more robust 53 to 47. Why? Well, according to many GOP spin-meisters, it was to a significant degree owing to “Kavanaugh’s revenge,” as CNBC reported at the time:

Sens. Mitch McConnell, R-Ky., and Lindsey Graham, R-S.C., both credited the so-called Kavanaugh effect for Republican victories in key Senate races against red-state Democrats.

Graham, in a thread of tweets Wednesday morning, said that the constituents of those Democratic incumbents who voted against Kavanaugh “held them responsible for being part of a despicable smear campaign orchestrated by the left.”

The ”#KavanaughEffect,” Graham said, should be renamed ”#KavanaughsRevenge” …

Republicans in critical states for the party were “highly offended” by the Democrats’ conduct during the confirmation proceedings, McConnell said, and the fallout from the process acted “like an adrenaline shot” for GOP turnout.

Graham, as you may recall from his feral attacks on Senate Democrats during the Supreme Court confirmation hearings for Brett Kavanaugh, chaired the Judiciary Committee during that confirmation fight and contended that accusations of sexual assault against the soon-to-be Justice were blatantly unfair — nay, villainous. So it was natural for him to claim the hearings enraged both Republicans and swing voters and saved the Senate (an interpretation that also inflated his own importance, as it happens).

It was a dubious interpretation of the midterms at the time, but the important thing is that many Republicans believed it. And that could feed a parallel development going into the 2026 midterms: a possible retirement by Kavanaugh’s senior and very right-wing colleague Samuel Alito.

Alito has been on retirement watch for a while now. He’s 75 years old (and will turn 76 on April 1) and recently celebrated 20 years on the Supreme Court. And as the intrepid Court watcher Joan Biskupic noted in 2024 after he twice lost an initial majority on a case, Alito’s influence within the Court has been slipping, leaving him visibly frustrated:

Alito has long given off an air of vexation, even as he is regularly in the majority with his conservative ideology. But the frustration of the 74-year-old justice has grown increasingly palpable in the courtroom. He has seldom faced this level of internal opposition.

Overall, Alito wrote the fewest leading opinions for the court this term, only four, while other justices close to his 18-year seniority had been assigned (and kept majorities for) seven opinions each.

His unique year in chambers was matched by the extraordinary public scrutiny for his off-bench activities, including lingering ethics controversies and a newly reported episode regarding an upside-down flag that had flown at this home in January 2021, after the pro–Donald Trump attack on the US Capitol. 

There is also evidence that Alito’s wife, Martha-Ann, would like him to step down from the bench so that both of them can openly express their political opinions.

Thus, there’s been speculation, mostly from the political left, that an Alito retirement could happen before or immediately after the current Supreme Court term. The Nation’s legal expert Elie Mystal, then Slate’s Dahlia Lithwick and Michael Joseph Stern, drew attention to the odd timing of a new Alito book. Here’s the clue on which Mystal focused:

[T]he book is scheduled to be released October 6, 2026. That’s a curious date. The Supreme Court starts its 2026–27 term on October 5, the first Monday of October. Alito’s book is set to drop the next day.

It sure feels like Alito doesn’t plan on having a real job the Tuesday his book launches and instead thinks he’ll be free to run around the country promoting it.

There’s also a political reason Alito might want to step down at this particular moment. He clearly cares about his legacy on the Court and wants to solidify the conservative majority for which he and Justice Clarence Thomas have served as the point of an ideological spear. Trump is leaving office in 2029, and it’s possible Republicans will lose their Senate majority in November. Confirmation of anyone remotely like Alito would be impossible with a Democratic Senate and difficult with a smaller majority than Republicans currently enjoy.

Add in the “Kavanaugh’s revenge” theory of 2018, and you can see why Republicans might really want to press for an Alito retirement and then a good, savage Senate confirmation fight over a controversial nominee to succeed him, possibly 40-somethings like Andrew Oldham or Emil Bove, both Trump-nominated Circuit Court judges. If Alito was to retire at the end of the current term (perhaps announcing the retirement earlier), then the shape of the future Supreme Court could become a base-mobilizing issue for the GOP, all right — but potentially also one for Democrats.

That leads us back to the idea that poor Kavanaugh’s persecution by Democrats “saved the Senate” in 2018. The alternative explanation is that Republicans had an insanely favorable Senate landscape that year in which three Democrats who lost (Joe Donnelly of Indiana, Heidi Heitkamp of North Dakota, and Claire McCaskill of Missouri) were doomed from the get-go by the rapidly rightward trends of their states, and a fourth, Florida’s Bill Nelson, lost by an eyelash in another red-trending state after being massively outspent by then-Governor Rick Scott.

Even if you believe the Kavanaugh fight provided Republicans with a net benefit in 2018, there’s no reason to assume the same thing will happen in 2026, a year in which the Senate landscape is far less favorable to the GOP than it was in 2018 (according to the Cook Political Report, four of the seven competitive Senate races this year are on GOP turf). We also don’t know how the confirmation hearings for an Alito successor will turn out.

But between Alito’s motives for retiring, the GOP’s fear that it could lose control of the confirmation process, and the “Kavanaugh’s revenge” mythology about 2018, don’t be surprised if there’s a Supreme Court fight this summer or fall. Democrats would be happy to bid farewell to the author of the infamous decision reversing Roe v. Wade. Even if it hurts rather than helps their midterm prospects, Alito’s right-wing fans will be happy to welcome a younger version of the cranky conservative onto a life-time seat on the Court.

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Tags:

• politics
• early and often
• u.s. supreme court
• samuel alito
• donald trump
• brett kavanaugh
• 2018 midterms
• 2016 midterms
• u.s. senate
• More

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How Justice Alito’s Retirement Might Upend the Midterms Your product is saved! You’ll receive emails when your saved products go on sale. Manage preferences.

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