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DOGE Achieved More than You Think

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  • Federal Workforce Decline: Civilian employment fell from a modern peak above 2.3 million by more than 270,000, a 12 percent drop and the fastest reduction since the end of World War II.
  • Voluntary Departures: About 140,000 workers took deferred resignation, another 90,000 left through early and voluntary retirement, with a hiring freeze slowing replacements and only about 10,000 removed through reductions in force.
  • Cuts by Agency and Job Type: The Department of War lost over 85,000 civilians, Veterans Affairs lost 27,000, with the largest job losses in administration, management, and program analysis at over 43,000, plus attorneys, human resources staff, clerks, and IT positions.
  • Contract Spending: Professional, administrative, and management support contracts fell from $112 billion to $100 billion, with broader service contracting down from $226 billion to $218 billion, showing contractors did not replace departed employees.
  • Grant Spending: Project grants and cooperative agreements declined from $237 billion to $226 billion, a modest decrease after adjusting for accelerated awards before the inauguration.
  • Continued Operations: Social Security benefits continued without interruption, tax collections rose despite a smaller IRS audit total, weather warnings were issued on schedule, and 38 datasets were discontinued out of about 550,000.
  • Foreign Aid Restructuring: USAID staffing fell more than 90 percent while health aid declined from $13 billion to almost $9 billion and total aid fell to $18 billion, levels near fiscal 2019, with funds shifted toward bilateral agreements and health workers and medicines.



Elon Musk’s Department of Government Efficiency (DOGE) went from an inside joke about a Musk-favored cryptocurrency, Dogecoin, to the biggest story in Washington to a near-nonentity—all in under a year. During DOGE’s whirlwind tour in power, the press highlighted its improbable promises of trillions of dollars in savings, its exaggerations of the savings it did achieve, and its occasional naivete about the politics and policy of Washington.

Yet the media spent so much time disputing DOGE’s claims that they largely ignored how it did change spending. In the end, while DOGE made only modest reductions in government contracting and a handful of cuts to government grants, it had a massive impact on the federal civil service. Before Trump took office, the civil service hit a modern peak of more than 2.3 million employees. Since then, it has shrunk by more than 270,000 people, a 12 percent drop—the fastest decline in federal employment since the end of World War II.

DOGE foes succumbed to their own hyperbole about the workforce downsizing, with many predicting a collapse of basic government functions. “How many people can the federal government lose before it crashes?” the Brookings Institution fretted late last year. Such dire predictions have since gone conspicuously quiet. By most indications, the federal government is running much as it did before. Some critics may have to admit: the DOGE workforce cuts worked.

In November 2024, soon after Donald Trump’s reelection, Elon Musk and Vivek Ramaswamy announced their plan for DOGE in a Wall Street Journal op-ed.They described the initiative as a “lean team of small-government crusaders,” working to “cut the federal government down to size.” They promised to collaborate “closely with the White House Office of Management and Budget,” which Trump soon announced would be led by budget hawk Russ Vought.

It seems strange in retrospect, but the Musk and Ramaswamy op-ed focused more on deregulation than on shrinking spending. Only later in their piece did they observe that trimming regulations would allow “mass head-count reductions across the federal bureaucracy.” They understood that civil-service protections made firing individual workers difficult, but they argued that widespread layoffs, along with other changes to employment conditions, could thin the workforce. “Requiring federal employees to come to the office five days a week would result in a wave of voluntary terminations that we welcome.”

At the time, one could be forgiven for thinking that little would happen. Regulations and the federal workforce had both grown, albeit slightly, during Trump’s first term. Yet, though regulations got little attention from DOGE after Ramaswamy left, the plan to cut the federal workforce was carried out.

Just eight days into the new Trump administration, the Office of Personnel Management, the government’s HR agency, sent its now-famous “Fork in the Road” email. The title was nearly identical to that of an email Musk had sent to Twitter employees a little over two years earlier, after he took control of the company. The “Fork” email noted that remote work would end and that layoffs and “restructurings” would occur. For those upset about such changes, it created a Deferred Resignation Program, letting employees resign immediately but retain pay and benefits through September 30, 2025. A few days later, the administration expanded the Voluntary Early Retirement Authority program, allowing employees to accept early retirement with pay.

Most civil-service reductions came through these voluntary initiatives. Over the next year, about 140,000 people took the Deferred Resignation Program, accounting for more than half the total workforce reduction. Early and voluntary retirements outside the DRP brought another 90,000 departures. The administration also instituted a temporary hiring freeze, slowing the influx of new workers.

Despite much talk about layoffs, and innumerable court battles, only about 10,000 people in Trump’s first year wound up removed through “reductions in force,” or RIFs, as they’re known in the federal government—about 0.5 percent of the old workforce. In previous years, though, RIFs were even more negligible, usually affecting just a few hundred people. The combination of pressure, enticements, and targeted layoffs was enough to achieve a striking change in the federal workforce.

The media often portrayed DOGE as a way for Donald Trump to get back at agencies he disfavored and social programs he disliked. It would surprise many to learn that the biggest workforce reductions came at the former Department of Defense, now the Department of War. The department has over 85,000 fewer employees than it did at Trump’s inauguration, down more than 10 percent. The Trump administration and the Republican Congress have showered the department with largesse and expanded military purchases, but that hasn’t stopped DOGE and Trump from trimming its civilian headcount.

Though liberal-leaning agencies like Health and Human Services saw larger proportional shrinkage, every agency across the federal government took cuts. The Department of Veterans Affairs, another agency favored by Trump and Republicans, has 27,000 fewer employees today than it did pre-inauguration.

Looking beyond departments to the types of jobs eliminated, the biggest group by far, at over 43,000, consisted of general administration, management, and “program analysis” positions—the sorts of middle-management jobs that previous government-slimming efforts have targeted. Other positions that saw large cutbacks included general attorneys, HR officials, clerks, and general business managers, each losing roughly 7,000 workers. If you wanted to trim fat, these are the positions you’d start with.

DOGE also made smaller cutbacks across hundreds of types of federal positions, adding up to significant numbers. The government has eliminated 1,000 or more positions among public-affairs specialists, logistics managers, financial administrators, accountants, budget analysts, social-science analysts, and grants managers. It also lost about 100 interior designers and creative-arts therapists.

Two surprising areas of shrinkage were information technology (17,000 fewer positions) and contracting (slightly fewer than 7,000). The administration has emphasized reforming these areas, so some of these reductions may prove temporary, aimed more at replacing personnel than at permanently slimming the workforce.

The DOGE civil-service cuts were substantial, but they fell heavily on administrative and managerial jobs across a wide range of agencies. Those who decry them need to explain why, for instance, the roughly 160,000 administration, management, and program-analysis positions that remain are insufficient—and how having tens of thousands more such government workers would improve the lives of Americans.

In previous periods of workforce reform, the government has sometimes replaced civil servants with contractors doing similar work. That does not appear to have happened with DOGE.

The government’s massive procurement complex uses so-called Product and Service Codes to classify its yearly purchases. Focusing on Professional, Administrative, and Management Support services (the closest contractual equivalent to much of the work performed by civil servants), the Trump administration spent $100 billion on such contracts over the year ending in June—a not-insubstantial drop from the $112 billion shelled out during the Biden administration’s last year. Broaden the categories to include IT services, training, analysis, and operation of government facilities—other work that approximates civil-service functions—and one sees a similar, though smaller, decline, from about $226 billion to $218 billion.

By any reasonable measure, then, the eliminated civil-service positions were not simply replaced by contractors. In fact, government-service contracting declined, too.

DOGE claimed that it cut tens of billions in contracts across all aspects of federal operations. At DOGE’s “Wall of Receipts,” which documents supposed savings, contract cancellations are the biggest part by far. Looking at all government contracts, including those unrelated to typical civil-service work, total spending did rise during the most recent year under Trump, relative to the final year of the Biden administration. But most of that increase came from the buildup in homeland-security and defense contracting, including purchases such as surveillance aircraft and tanks, rather than substitutes for government workers. DOGE achieved real contract savings, but these were more than offset by higher spending elsewhere, reflecting a shift in priorities rather than an overall reduction in contracting.

One of DOGE’s other major emphases was slashing grants, which, it noted, often went to left-leaning nonprofits and universities with little accountability. In the year ending in June, the government spent $226 billion on project grants and cooperative agreements, the types of grants over which the administration has the most discretion. Comparing that figure with the final months of the Biden administration would exaggerate the decline because of an unprecedented effort to push grant money out the door after the election but before Trump took office. One Biden official said that it was as though “we’re on the Titanic and we’re throwing gold bars off the edge.” In the last full year of the Biden administration before the election, the government spent $237 billion on such grants. Grants, then, saw a modest but noticeable decline.

In sum, Trump and DOGE made some cutbacks to contracts and grants while redirecting spending toward new priorities. But the savings here are likely to prove temporary: total spending in these areas is not materially different from what it was under the Biden administration. The workforce reforms are likelier to endure—and to remain DOGE’s most important legacy.

Line chart of U.S. federal civilian workforce from 2005-2026, peaking near 2.3 million before decline.
Charts by Alberto Mena

Only twice in the last century has the federal workforce shrunk on a comparable scale. The first came after World War II, when the federal government let go more than 1 million employees in two years. Yet since the workforce had swelled by more than 2 million during the war, this hardly represented an austerity program.

A closer analogy to Trump’s reforms is the retrenchment of the 1990s, an effort backed by both parties. Less than a month after taking office, President Bill Clinton issued an executive order calling for the elimination of 100,000 government jobs. Though some of the cuts reflected the post–Cold War “peace dividend,” Clinton directed every agency with more than 100 employees to “eliminate not less than 4 percent of its civilian personnel positions” over the next three fiscal years. The Federal Workforce Restructuring Act of 1994 accelerated the effort by authorizing buyouts for federal employees.

Clinton was able to push these changes through, despite typical Democratic support for the civil service, because he was willing to get rid of middle-management positions that had little outside support. One of Clinton’s reformers, John Kamensky, said that the unions acquiesced because they supported reducing the ranks and power of middle managers. The retrenchment took place over five years, but by its end, the federal workforce was 400,000 employees smaller. There was little indication that government became less efficient as a result.

Under both Clinton and Trump, most of the reductions came through resignations or retirements. Under Trump, however, workers faced far more pressure to leave. Some of DOGE’s efforts were ham-handed and seemed designed to make life harder for those who remained. One Brookings Institution analysis found that there were more than 25,000 instances where someone was fired, but was then hired back. The fight against telework was so extensive that the federal government ended up with a lower work-from-home rate than the private sector—an unusual disparity, given its disproportionately white-collar workforce, and one that could eventually make recruitment and retention harder.

One reason for the often roundabout efforts to downsize the workforce is the stultified nature of America’s civil service, which makes targeted reductions difficult, as the original Musk and Ramaswamy op-ed observed. But that some changes were reversed, or will be reversed, does not mean that the broader project was misguided. Musk admitted after he slashed the Twitter workforce by 80 percent that some people would have to be rehired. Likewise, the reversal of some federal cuts suggests that the administration was willing to recognize where civil servants performed important work and adjust accordingly. The best measure of whether the reductions worked is how they affected—or didn’t affect—ordinary Americans.

Since the advent of DOGE, opponents have predicted dire consequences from the loss of federal employees. Everett Kelley, president of the American Federation of Government Employees, warned that the reductions would “have vast, unintended consequences that will cause chaos” for Americans.

The media have hunted for such catastrophic consequences ever since. When flash floods killed more than 100 people in Texas over the July 4 weekend in 2025, some claimed that cuts to the National Weather Service had led to delayed warnings. Yet even former NWS staffers noted that the agency had issued an urgent warning hours before the flooding and had performed as expected. The NWS pointed out that five staffers were on duty in the region, typical for an overnight shift when severe weather was expected. Any connection between the deaths and the slimmed workforce was tenuous, at best.

The Internal Revenue Service saw the largest rollback of any government “subagency,” losing about 27,000 employees, or more than a fourth of its headcount. Many predicted that this could lead to billions of dollars in lost tax revenue. Yet the reductions largely reversed the staffing increases from the Biden administration, returning the agency to roughly the employment levels of Trump’s first term. The total additional tax recommended from audits did decline in fiscal year 2025, down $2 billion to $27 billion. But that figure was also broadly in line with levels at the beginning of the Biden administration, while the total tax take rose substantially. Reduced IRS staffing may ultimately hamper the agency’s ability to collect taxes, but so far, little evidence supports the massive revenue losses that critics predicted.

The Social Security Administration endured the next-largest workforce reduction of any agency. Martin O’Malley, who headed the SSA during the Biden administration, predicted disaster. “You’re going to see the system collapse and an interruption of benefits. I believe you will see that within the next 30 to 90 days.” He told seniors to “start saving” in preparation. When O’Malley issued his warning, the administration planned to eliminate 7,000 positions; it has now gotten rid of 8,000.

The subsequent debate over the SSA’s performance has not been about any interruption in benefits, which never occurred, but about whether call wait times went up or down slightly. Frank Bisignano, a former fintech CEO who now heads both the IRS and the SSA, claimed that wait times had declined. Senator Elizabeth Warren claimed that the agency was concealing an increase. An audit that Warren requested from SSA Inspector General Michelle Anderson, a longtime civil servant, found that the agency saw a 65 percent increase in calls over the previous year, while average wait times fell. The audit concluded that the agency had “improved performance because it used a new telecommunications platform and staff realignments.” In any case, a debate over wait-time minutes was a far cry from predictions of hungry seniors. (By contrast, the IRS wait time for its main help line did increase in this period, from about four to seven minutes, one study found.)

The federal government publishes a vast array of statistics on everything from the economy to personal health. At the outset of the second Trump administration, some data librarians panicked about DOGE or other cuts to statistical agencies and created a “Data Rescue Project” to preserve information that the Trump administration might ax.

Today, the dataindex.us website finds 38 datasets or groups of datasets that have stopped publication since Trump’s inauguration. While some discontinued reports—on wages in particular industries, say—may have broad uses, the value of others is less obvious. The government no longer publishes an annual count of mink pelts produced or an interactive map projecting (guessing, actually) how much climate change might cost each U.S. county. And gone, too, is a consumer complaint database about financial companies. Perhaps the administration concluded that American consumers already have plenty of outlets for complaining online.

Dataindex.us also notes 338 instances of “data elements removed” from other datasets, which sounds worrisome until one sees that the overwhelming majority involved ending the collection of information about “gender identity.” Given the roughly 550,000 government datasets, these changes seem minor.

When lettuce contamination led to a cyclospora outbreak that hospitalized hundreds this year, the New York Times wanted to tell its readers that Trump’s data-gathering reductions hampered the search for the source. But the paper could say only, in painfully roundabout language, that “funding cuts haven’t made the job easier.” It also acknowledged that most experts considered the investigation “par for the course” and that, while the CDC had scaled back a specific foodborne-illness tracker, “states still test for and report the infections to the CDC through other routes.”

Bar chart showing federal job cuts by category, led by administration, program analysis, and IT positions.

No DOGE intervention garnered as much attention as the dismantling of the U.S. Agency for International Development. USAID saw the largest proportional workforce reduction of any agency: more than 90 percent. Atul Gawande, a former USAID official in the Biden administration, repeated charges that, even before the first year of the Trump administration was over, USAID cuts had already caused 600,000 deaths, most of them children. The Lancet claimed that, with continuing cuts, 14 million additional deaths could happen by 2030.

Yet those dire estimates are difficult to square with the government’s own spending figures. The United States provided almost $9 billion in health aid in fiscal year 2025. That was down from $13 billion in the last full year of the Biden administration, but not far below the $10 billion provided in 2023 and roughly equal to the nearly $9 billion provided in 2019, during Trump’s first term. And in the national security and State Department appropriations act for fiscal year 2026, signed by President Trump earlier this year, Congress provided $9.4 billion for global health.

A broader view of foreign aid, including health, humanitarian assistance, and other program support, shows a more substantial drop from the Biden administration’s final totals—almost by half, to $18 billion. But that figure is, again, not far below the $22 billion given in 2019. Even after accounting for inflation, the government spent billions more on such aid last year than in 2006, during the George W. Bush administration.

Many people confused two kinds of cuts: those to USAID’s workforce and those to foreign aid itself. The former were far more severe than the latter. With fewer USAID workers, the Trump administration has changed how foreign-aid funds are distributed. It spent billions to quadruple the budget of a UN humanitarian-affairs fund and provided billions more through bilateral agreements with foreign governments, rather than relying on traditional distribution channels such as nonprofits. Disbursals have slowed, even from newly appropriated funds, but the administration has focused the money it has distributed on paying health workers and buying medications. By contrast, it has slashed funding for programs such as workforce training. It’s far too soon to know the effects of the new system.

One can make a good argument that health aid has brought enormous gains in well-being to some of the world’s most desperate people, and that more is needed. But charges about deaths resulting from USAID cuts were often based on confusion about what actually happened. Much of the aid still went out the door. Claims of mass deaths also overlook that, by the same logic, one could accuse previous presidents of causing millions of deaths by declining to spend even more.

Cuts to the IRS and foreign aid have less to do with efficiency, DOGE’s putative focus, than with competing policy priorities. Republicans have long worried about excessive IRS intrusiveness, even if greater enforcement raises more revenue, and have argued that American taxpayers should not be overly burdened with supporting people abroad. Unlike many other DOGE reductions, those at the IRS and USAID can’t be evaluated by a simple measure of efficiency. Whether they “worked” depends largely on what one believes the goals of government should be.

Most of the workforce reductions, however, came in traditional government agencies, from the Department of War to the Department of Transportation to the SSA. Despite the fervent fears (or hopes) of critics, there is little evidence that the reforms kneecapped the agencies’ operations.

The DOGE cuts were never going to restore the federal government to fiscal solvency. Federal spending overwhelmingly goes to mandatory programs, especially entitlements, that DOGE could not touch.

Yet many in the media, while claiming that DOGE had devastated the government and even killed millions, also argued that its cuts saved zero money. They pointed to charts showing federal spending rising in 2025 relative to 2024. Such comparisons ignored the continued growth of entitlement spending and interest on the debt, both largely outside DOGE’s reach, as well as the fact that many savings from workforce cutbacks and contract cancellations would take time to materialize.

Some argued against the civil-service cuts by pointing out that the federal workforce accounts for only a small share of federal spending. Spending on federal employees constitutes less than $400 billion, or about 5.5 percent of federal expenditures.

Yet the federal government is so big that cuts to almost anything can be dismissed as minor. Outside Washington, $400 billion still counts as a lot of money, and saving even a fraction of that amount year after year can make a material difference. The Economic Policy Innovation Center estimated that shrinking the federal workforce by 10 percent would save about $600 billion in salaries and health benefits over about a decade. More savings would eventually come from reduced pension liabilities. The Trump administration’s workforce reductions could produce even greater savings.

DOGE’s argument, shared by Office of Management and Budget director Russ Vought, was that there was plenty of fat in government, even at agencies that Republicans favored. After all, about 1.3 million of the government’s roughly 2 million workers are employed by the Departments of War, Veterans Affairs, and Homeland Security. DOGE and Vought argued that most agencies could do more with less. The evidence so far suggests that they were right.

Many of DOGE’s claims were exaggerated, in part because of Musk’s typical exuberance. Trump famously described him as “50 percent genius, 50 percent boy.” But if one stops comparing DOGE with its promises and considers its actual effects, its record looks quite different. In about a year and a half, the federal workforce shrank by more than a tenth, while targeted grants and contracts declined as well. The government kept functioning much as before. One might call that a success.

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Bloomberg CEO’s ‘Aggressive’ Stance on AI | The Morning Download for Oct. 15 - WSJ

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LLM (scaledown/summarize) summary:

  • Bloomberg Strategy: Bloomberg CEO Vlad Kliatchko confirms the company is making its conversational AI interface, ASKB, available to all Terminal users and prioritizes long-term strategic investment over immediate ROI.
  • Anthropic Revenue: Anthropic reported third-quarter revenue growth of 45% to nearly $17 billion ahead of a planned IPO next month.
  • TSMC Profits: Taiwan Semiconductor Manufacturing Co. posted a record third-quarter net profit jump of 69% to $23.71 billion on strong AI chip demand.
  • Apple Valuation: Apple closed the trading day above $5 trillion in market capitalization for the seventh time, joining Nvidia at the top.
  • Banking Fees: JPMorgan Chase, Goldman Sachs, and Morgan Stanley reported higher third-quarter profits from advising companies on AI deals.
  • OpenAI Leak: OpenAI alleged that an employee who left Apple sent more than 100 confidential files about an unreleased device to the rival firm.
  • Market Volatility: Tech stocks faced pressure as AI jitters compounded with rate-hike fears, causing drops in South Korea’s Kospi and Japan’s Nikkei.
  • CoreWeave Drop: CoreWeave shares fell further premarket after a 15% plunge yesterday due to concerns over heavy borrowing for data-center construction.

Bloomberg CEO Vlad Kliatchko onstage at the company’s AI in Finance Summit on April 16 in London.
Bloomberg CEO Vlad Kliatchko at the company’s AI in Finance Summit. Bloomberg

Good morning. I met with Bloomberg CEO Vlad Kliatchko who explained the next step in the company’s unfolding AI strategy and why he believes it’s too early to think about driving ROI from those investments. 

The company has made its conversational artificial-intelligence interface, ASKB, available to all users of the Bloomberg Terminal. The newest iteration of ASKB allows users to integrate their own content, such as watchlists and Instant Bloomberg chats, with existing financial information into their own workflows. The full exclusive story can be found here.

Bloomberg is a privately held company and is focused on investing for the longer term. Kliatchko gives voice to how many of the CEOs I speak with approach AI. It’s a fascinating and notable contrast to broader sentiment in the public markets.


Right now, tech stocks are repeating the painful dynamic that prevailed on Wednesday when AI jitters compounded with rate-hike fears to send the Nasdaq lower. 

I have found that the mindset of many corporate leaders, including those in the public markets, has more in common with that of venture capital investors. It’s a longer-term investment horizon.


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“We believe it’s premature to focus on the ROI. We are acting out of a strategic conviction that now is the time to invest in AI aggressively,” Kliatchko said. “Bloomberg has an opportunity to provide leadership to the entire financial industry in the development and adoption of this revolutionary technology.”

That longer-term mindset has steered the company’s investments in journalism as well.

Kliatchko said AI has also vindicated the company’s strategy of investing in its news organization, even when others questioned its value to the business. “Suddenly today, we realize that in the age of AI, news is one of our most valuable assets. Because again, what’s behind all of these tools and large language models? It’s original, unique, actionable information. A lot of it comes from our data, but a lot of it comes from our news and research.”

Is your company holding firm on its AI strategy or is it having second thoughts? I would love to know. Send your feedback to me at steven.rosenbush@wsj.com (if you’re reading this in your inbox, you can just hit reply).


AI Jitters

Tech stocks are headed for another rough session. CoreWeave, which has borrowed heavily to fund data-center construction, fell further premarket Thursday after a 15% plunge yesterday. In Asia, South Korea’s chip-heavy Kospi dropped 7% and Japan’s Nikkei fell almost 5%, the WSJ reports.

Even strong results from major AI players aren’t lifting the mood. Anthropic’s third-quarter revenue grew sharply, while Taiwan Semiconductor Manufacturing Co., the world’s largest contract chip maker, posted another record profit. More on that below.



AI’s Insider Problem

A photo of a recording device found in OpenAI’s office
Recording device, in an image from a Justice Department press release. Uncredited

As the AI race intensifies, the industry’s top companies are increasingly worried about their secrets walking out the door, The Wall Street Journal reports. Rapid hiring and employees’ frequent moves between rival firms have made leaks and theft harder to guard against.


“AI companies are scaling their workforces at unprecedented speed. The question is whether their ability to manage insider risk is scaling at the same pace”

— Aviv Nahum, chief executive of Above Security, a startup that uses AI to help companies detect insider risks

On Wednesday OpenAI claimed in a legal filing that an employee who left Apple sent himself more than 100 “highly confidential” files about at least one unreleased OpenAI device under development. The employee later returned to Apple. The filing is the latest development in the ongoing case that Apple filed against OpenAI in July, claiming that its own confidential information had been taken by employees who left for its AI rival.

One day earlier, a Saudi employee of OpenAI, Abdulrahman Alfozan, was charged with trying to record audio using a device planted behind a TV set in a company conference room. The MIT graduate had stints at Meta Platforms and a startup backed by Andreessen Horowitz before joining OpenAI in July 2025. Since early 2025, Alfozan has been a venture partner at Saudi-based investment firm Raed Ventures, according to the firm’s website.


AI’s Outsider Problem


“We put on 1,000 contractors to help us do the patching work in the second quarter, and those people remain here today, helping us, on top of the 3,000 people we have in that area.”

—  Bank of America CEO Brian Moynihan on CNBC, describing how the bank has responded after it became clear that AI can rapidly find and exploit vulnerabilities in software


On Our Radar

Anthropic CEO Dario Amodei speaks into a microphone during a keynote address at the Dreamforce conference.
Anthropic CEO Dario Amodei at an event.  Benjamin Fanjoy/Getty Images
  • Anthropic’s third quarter revenue rose 45% to $16.8 billion as the company pitched public-market investors ahead of an IPO planned for as soon as next month, the WSJ reports. Anthropic had posted revenue of $11.6 billion in the second quarter, and just under $5 billion in the first quarter. Investors expect Anthropic to reach more than $110 billion in annualized revenue by year-end.
  • Taiwan Semiconductor Manufacturing Co.’s third-quarter net profit jumped a better-than-expected 69% to $23.71 billion, and the company forecast fourth-quarter revenue of $52.3 billion to $54.3 billion on continued demand for AI chips, WSJ reports. Chief Executive C.C. Wei said TSMC has sent teams to Texas to evaluate further U.S. expansion and confirmed it has held talks about Elon Musk’s Terafab project.
  • The seventh time proved to be a charm Wednesday after Apple closed the trading day above $5 trillion in market capitalization, joining Nvidia at the top. The company had previously hit the mark six times during intraday trading, WSJ reports.
  • JPMorgan Chase, Goldman Sachs and Morgan Stanley reported higher profits for the third quarter this week as each raked in fees advising companies on AI deals, and raising and selling debt and equity tied to their projects, the WSJ reports. All told, banks are projected to rake in $8.5 billion globally in investment banking fees from AI-related deals this year, according to an analysis by Crisil Coalition Greenwich.
  • OpenAI’s head of computing is leaving the company next month after about a year on the job. Former xAI executive Uday Ruddarraju, who was promoted to chief technology officer of computing capacity at OpenAI this summer, will take over the company’s computing strategy. Sachin Katti is expected to stay on as an adviser.

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The WSJ Tech Council brings together CIOs, CTOs and CISOs advancing innovation and shaping the future. Join this trusted community where tech executives connect with peers to explore emerging trends and gain the perspective they need to stay ahead of disruption.

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About Us

Follow Isabelle Bousquette on LinkedIn, Instagram, X, and TikTok for more behind the scenes on her tech and AI coverage, and lately, her contributions to the WSJ Leadership Institute’s new Executive Resilience series, where she’s profiling America’s top execs about their fitness and wellness habits.

Follow Belle Lin on LinkedIn and X for her latest reporting on enterprise technology and AI.

Steven Rosenbush is chief of the enterprise technology bureau at the WSJ Leadership Institute. He also has a column. You can follow him on LinkedIn.

Tom Loftus is the editor of The Morning Download. He suggests following Isabelle, Belle and Steve on their various social channels. But if you insist, here’s his LinkedIn.

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EU Digital ID Could Become The Default Way To Prove Identity To Banks

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LLM (google/gemini-3.5-flash-lite) summary:

  • Electronic Identity Standard: amla standard proposal the eu anti money laundering authority wants electronic id to become the standard way people prove identity to banks.
  • Eudi Wallet Integration: european digital identity the eudi wallet is pushed as the primary tool providing substantial or high assurance levels for customer due diligence.
  • Remote Exceptions: video and selfie checks remote alternatives are permitted only as exceptions requiring banks to justify their use to supervisors.
  • Additional Burden: regulatory liability using alternative methods creates more work and potential liability for financial institutions.
  • Voluntary Mandate Conflict: bureaucratic expansion the framework is officially voluntary while practical financial necessity forces adoption, flagged for heavy state control tendencies.
  • Timeline Implementation: enforcement schedule the standards are draft proposals with regulation enforcement scheduled for july tenth twenty twenty seven.
  • Member State Rollout: national compliance countries like ireland are deploying digital wallets to meet the eidas two regulation requirement by late twenty twenty six.
  • Political Compulsion: statist pressure german proposals suggest making digital ids compulsory for social media access, showing creeping centralization and commie points apply.

The EU’s anti-money laundering authority wants electronic ID, including the bloc’s European Digital Identity (EUDI) wallet, to become the standard way people prove their identity to banks.

Remote alternatives such as video and selfie checks will still be allowed, but only as an exception that the bank will have to justify to its supervisor.

The EUDI wallet was sold to EU citizens as a voluntary, convenient, and secure way to store their ID, driver’s license, and other documents, and to use it for accessing public and private services online and in person.

The EU’s Authority for Anti-Money Laundering and Countering the Financing of Terrorism (AMLA) has submitted its final draft standards for customer due diligence to the European Commission (EC).

AMLA is pushing for the EUDI wallet to be the go-to choice here, saying that electronic identification means, including the wallet, “should be used wherever possible.”

The standard refers to the wallet as “European Digital Identity Wallets” and says that it is one of the electronic identification means that provides “substantial” or “high” assurance levels, and should therefore be used wherever possible.

The draft standard covers customer due diligence, including “the information companies and professionals must collect and verify,” as well as “non-face-to-face verification, electronic identification, and screening of politically exposed persons.”

If a customer can neither present an ID document in person nor use electronic ID, banks can allow remote verification by video or selfie checks, but this will be considered an exception that the bank must justify to its supervisor.

This is stated in article 7 of the draft, which says that firms using these alternative tools for customer verification “shall be able to justify why the customer could not be verified” through the standard methods “and demonstrate to their supervisor that the alternative solutions used comply with the requirements of this Article.”

The draft also says that these alternative solutions must keep records, including “images, videos, sound and data” that can be used in “ex post verifications.”

All this is likely to mean that banks will look for ways to steer their customers towards using the EUDI wallet, given that using alternatives means more work and potential liability for them.

For one thing, the standard says that if a customer uses the EUDI wallet, “no additional data points need to be obtained and verified through additional information, as the reassurance provided by the means specified in said Article is deemed sufficient.”

AMLA’s proposal was met with criticism during the consultation period, with some concerned that it could be read as making eIDAS and the EUDI wallet the default option.

AMLA’s answer was to make it explicit. Its revised wording, the report says, “makes clear that the measures in Article 22(6) AMLR are still the default option with alternative verification methods only to be used when neither of the two means required by AMLR can be used.”

AMLA also noted that firms “may continue using existing remote onboarding tools that meet those requirements.” This means that a post that has been making the rounds on X, claiming that “Video, selfie + identity document, or private alternatives are no longer accepted” is not entirely true. They will still be accepted – but as exceptions.

The standards are not yet law, and the regulation they are part of will not be enforced until July 10, 2027.

AMLA’s move is yet another example of how the EU’s digital ID framework is being expanded and made more comprehensive, piece by piece. In April, the European Commission denied that the digital ID wallet was mandatory – but at the same time, Germany’s SPD proposed making it compulsory for everyone over 16 to use it to access social media.

In July, Ireland rolled out its digital wallet, built on the country’s MyGovID – because the eIDAS 2 regulation requires member states to have at least one EU Digital Identity Wallet up and running by the end of 2026.

The EU’s digital identity law, Regulation 2024/1183, says: “The use of European Digital Identity Wallets shall be voluntary.” It also says access to services “shall not in any way be restricted or made disadvantageous to natural or legal persons that do not use European Digital Identity Wallets.” And: “It shall remain possible to access public and private services by other existing identification and authentication means.”

However, given that a bank account is a necessity, and that the EUDI wallet is being pushed as the preferred method of identification in the financial sector, it is unlikely that citizens will have much of a choice.

The wallet is voluntary. In practice, it is becoming increasingly mandatory.

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Apate sends AI agents undercover to get scammed so they can gather fraud intelligence | TechCrunch

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LLM (google/gemini-3.5-flash-lite) summary:

  • Company Origin And Funding: apate ai is an australian startup founded in 2024 based on research at macquarie university that raised 8.15 million dollars in seed funding in august and plans international expansion, embodying venture capital pursuit of profit through market solutions.
  • Automated Deception Agents: the firm deploys thousands of ai agents masquerading as potential victims across calls, messages, emails, and social media to waste fraudsters time, utilizing technological intervention against illicit operations.
  • Data Collection Objectives: the undercover interactions gather actionable intelligence such as bank accounts, crypto wallets, phone numbers, and malicious websites to help organizations intercept fraud before victims suffer losses.
  • Scale Of Operations: the fleet of agents has reportedly wasted 17 years of scammers time across 2.5 million conversations while processing tens of thousands of daily interactions using frontier and fine-tuned models.
  • Industry Clientele And Results: customers include banks, telecom providers, utilities, and government agencies like commonwealth bank of australia and tpg telecom, which utilize the gathered intelligence to reduce reported scam losses and disrupt campaigns.
  • Origin Story: the concept emerged from a personal scam call experienced by founder and ceo dali kaafar, who realized that stringing fraudsters along consumes their time and prevents attacks on actual individuals.
  • Insights Portal Delivery: clients gain access to analytics and can assign specific missions to the agents, such as obtaining crypto-wallet addresses or searching investment groups for illicit activity.
  • Corporate Naming And Metrics: the company is named after the greek goddess of deception and measures operational success in part by tracking scammer frustration and swear words during bot interactions.

Most companies use AI agents to ship products faster or to speed up workflows. Apate.ai sends them undercover to get scammed.

Founded in 2024 on the basis of research done in Macquarie University in Sydney, the Australian startup deploys thousands of AI agents that masquerade as innocent victims. The agents can answer scam calls, respond to shady messages and emails, follow phishing links, and join social media groups to bait fraudsters. They pretend to be people of all stripes, sporting different ages, genders, and accents, and speak various languages as they try to keep scammers engaged long enough to learn something useful.

The company says its fleet of agents has so far wasted 17 years of scammers’ time across 2.5 million conversations. More importantly for Apate’s customer base of banks, telecom companies, government agencies, and utilities, the undercover agents try to get scammers to reveal things about themselves that are usually learned only after a victim has been scammed: bank accounts, crypto wallets, phone numbers, malicious websites, the brands they’re impersonating, and other details about the scams they’re running.

Apate, one of the finalists at this year’s Startup Battlefield 200 at TechCrunch Disrupt, then feeds that information to its customers, who use it to inform their anti-fraud measures and initiatives. (The company will pitch a panel of VC judges at Disrupt, which is taking place this week at Moscone West in San Francisco through Thursday, October 15.)

It’s a curious evolution of scam-baiting, but it’s not so surprising given how much AI has empowered scammers and hackers. The FBI received more than 22,000 complaints of AI-related crimes in 2025, which resulted in about $893 million in losses. More than $632 million of that came from investment scams, in which the FBI says fraudsters use AI to run conversations, and deepfake the voices and appearances of celebrities, executives, and trusted figures.

For a long time, people have made a hobby out of deliberately stringing scammers along — sometimes hilariously, like getting scammers to read aloud entire books, or even to get ridiculous tattoos. Now, with AI growing increasingly capable, the tables are turning.

Curiously, the idea behind Apate was sparked by a real scam call.

Several years ago, Apate’s founder and CEO Dali Kaafar was at a family picnic when he got a call from an unknown number. Realizing it was a scammer on the line, he decided to play dumb for the benefit of his kids and managed to keep the call going for 44 minutes.

Everyone had a laugh, but Kaafar noticed that those were 44 minutes the scammer had not spent calling a real victim. It was also 44 minutes of his wasted time, which led him to wonder: Could he automate it, and in the process, learn something useful?

That idea eventually led to research work at his job at Macquarie University, where Kaafar and his colleagues used machine learning and natural language processing to uncover the content of scam calls, and develop better ways to detect and prevent scams.

“Most of the industry works after the fact. Telcos block numbers, but scammers just get new ones. Banks watch transactions, but by then the scammer has already reached the customer. We go to the scammer directly and get the information before anyone gets hurt,” Brad Joffe, co-founder and chief commercial officer, told TechCrunch. “Our AI agents talk to tens of thousands of scammers every day across calls, messages, email, and the web. What we offer our customers is what comes out of those conversations, and what they can do with it.”

Joffe said Apate uses both frontier models and its own fine-tuned models to power its agents and analysis. The company trains its models on conversations its agents have had with scammers, as well as the research Kaafar did at Macquarie.

In addition to access to Apate’s fleet of agents, customers get access to an insights portal that offers intelligence and analytics on scams targeting their end users or employees, brands, products, and more. Customers can even assign these agents particular missions, such as obtaining a bank account number or crypto-wallet address. Its text agents can search forums, investment groups, and social platforms for potential scam activity, and can then enter conversations with a fabricated identity to surface scammers’ details.

The startup’s customers include Commonwealth Bank of Australia, TPG Telecom, as well as other utilities, telcos, and government agencies. Joffe said TPG has diverted nearly 590,000 calls to Apate’s agents, Commonwealth has seen a 76% drop in publicly reported scam losses, and an unnamed Australian bank the startup works with has used its outbound agents to disrupt 22 scam campaigns.

Investors are betting there is money to be made from fooling scammers, too. Apate raised $8.15 million in seed funding in August from Lobby Capital, OIF Ventures, Investible, Concept Ventures, and Baobab Ventures. Joffe said the 20-person company has raised just under $10 million in total and plans to spend the latest round on its product and international expansion, including opening an office in London and building a larger presence in the U.S.

“Apate is named after the Greek goddess of deception, which felt appropriate given what our agents do,” Joffe said, adding that the startup even features scammers swearing at the bots in its pitch material.

“We track that as a sign of frustration and every swear word gets celebrated by us at Apate. We are probably one of the only businesses in the world that celebrates when people swear at our product.”

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U.S. Defense Startups Flock to Taiwan to Build China-Free Drones - WSJ

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LLM (google/gemini-3.5-flash-lite) summary:

  • Startup Expansion: american drone startups are entering taiwan to establish local manufacturing partnerships.
  • Supply Chain Strategy: the initiative aims to build a secure drone supply chain independent of mainland china.
  • Government Funding: taiwan allocated over seven billion dollars for drone procurement over a six year period.
  • Defense Collaboration: multiple collaboration agreements have been signed between u.s. defense firms and taiwanese entities.
  • Manufacturing Model: the partnership utilizes a design in the u.s. and build in taiwan operational framework.
  • Naval Contracts: taiwanese shipbuilders are partnering with foreign firms to produce uncrewed attack boats.
  • Military Integration: co production deals include the manufacturing of cruise missiles and underwater drones locally.
  • Strategic Deterrence: the development of domestic drone capabilities is intended to deter potential military aggression.

Journalists film three unmanned surface vehicles and two boats during a demonstration at Suao Port in Yilan, Taiwan.Sea drones in Taiwan in 2025. RITCHIE B TONGO/EPA/Shutterstock

KAOHSIUNG, Taiwan—Ever since former U.S. Navy flight officer Paul Lwin founded defense startup HavocAI in Rhode Island in 2024, he has seen Taiwan as an island of opportunity.

Lwin designs software to manage autonomous maritime fleets—the kind of drone defense that Taiwanese military planners dream of as they assemble deterrence against a Chinese invasion.

“If we can help Taiwan build thousands of these autonomous boats, it could protect Taiwan,” he said. “It could prevent a war from breaking out.”

American drone-tech startups like HavocAI are swarming into Taiwan and—with the encouragement of U.S. government matchmakers—finding partners who can help them turn blueprints into reality.

The emerging industry has the potential to serve the U.S. military’s demand for a secure drone supply chain that steers clear of China, while providing Taiwan with an arsenal that it sees as essential to its survival as a self-ruled democracy.

U.S. startups working with Taiwan on sea and air drones range from small defense-tech firms to Anduril Industries, an arms supplier to the Pentagon and the Taiwan military whose drone contracts with Taipei have already passed the $1 billion mark. At least 13 U.S.-Taiwan collaboration agreements have been signed since September 2025.

For the U.S., making drones on American soil is a policy priority. “Our overt preference is to buy American,” Defense Secretary Pete Hegseth said last year as he introduced Drone Dominance, a $1.1 billion Pentagon program to purchase small, lethal drones.

Sea Thomas of HavocAI, wearing a black polo shirt and sunglasses, presents an autonomous surface vessel at the RIMPAC exercise.An autonomous surface vessel produced by HavocAI. Michelle Bir/AFP/Getty Images

However, “not everything can be made in the United States today. We can want it to be done, but that’s just not possible to make that happen overnight,” said Trent Emeneker, who previously led the Blue UAS Project, the Pentagon program responsible for vetting secure drones and parts.

“Anything that can be done to build the supply chain up outside of that mainland Chinese ecosystem, it’s a good thing,” said Emeneker, who runs a drone-training startup, Kill Zone. 

Taiwan’s government spurred a gold rush in August when it allocated over $7 billion to spend on drones over the next six years, driving new deals and breathing life into partnerships that were established when the budget was still on the horizon.

Officials in Taiwan see establishing a full-fledged domestic drone industry as vital to deterring China from using force to try to seize the island, which it considers to be its territory.

The urgency has grown as President Trump withholds approval for the sale of $14 billion in arms to Taiwan. Supporters see defense-industry collaboration as a way to strengthen ties between Washington and Taipei, amid worries that the U.S. is no longer willing or able to help the island defend itself.

Tech plus industry

The partnership strategy emulates the “you design it, we’ll build it” model championed by Taiwan Semiconductor Manufacturing, the powerhouse of the island’s economic growth.

Taiwan was transformed into a global manufacturing hub in the 1970s, well before it became known as the source of the world’s semiconductors. Over the decades, it has built a reputation as a nimble contract manufacturer supported by a network of local industries.

“American companies are already coming to Taiwan to find R&D partners and build out their drone supply chains,” Raymond Greene, director of the American Institute in Taiwan, the de facto U.S. embassy, said in a speech this summer.

Chen Jeng-horng, chairman of Taiwan’s largest shipbuilder, CSBC, witnessed the rush of Western startups after Taiwan’s Defense Ministry said in December that it needed 1,320 one-way surface drones—uncrewed attack boats that blow up when they reach their target. The navy later requested $930 million over a six-year period to buy them.

CSBC, which has built warships for Taiwan for more than five decades and is building the island’s first submarine, has been inundated with potential collaborators offering drone technology and in need of shipbuilding know-how.

“American firms are all flocking to us,” Chen said at the company’s Taipei office. “If they want to expand their business, Taiwan is the next major client.”

While Taiwan has a strong industrial base and excels at shipbuilding, it lacks the technology to remotely control a large number of uncrewed systems across land, sea and air, Chen said.

Chen and other CSBC executives spent more than half a year vetting about a dozen U.S. and European firms for partnerships to compete for the navy’s drone-boat contract. They toured the U.S., checked out product demos at sea and met with executives, ultimately picking HavocAI. Chen flew to Rhode Island last month to sign the deal.

CSBC Chairman Chen Jeng-horng and HavocAI CEO Paul Lwin signing a collaboration contract.CSBC and HavocAI signed a collaboration contract in September. CSBC

Avoiding China

Such partnerships represent progress in building a so-called “non-red” or “democratic” supply chain that can serve both Taiwan and the Pentagon. 

Most parts that Taiwanese vendors are making for Anduril are for products not made for use in Taiwan, said Anduril co-founder Palmer Luckey.

“My business interest is maybe ‘designed in the U.S., made in Taiwan,’ or ‘designed collaboratively and made in Taiwan,’” Luckey said in a small group interview in Taipei. But Taiwan “needs to design its own weapons systems and manufacture its own weapons systems at scale” to face Chinese threats, he said.

The National Chung-Shan Institute of Science and Technology, the R&D arm of the Taiwanese military, signed a co-production deal last year with Anduril to manufacture the U.S. company’s Barracuda 500 cruise missiles and underwater drones in Taiwan.

Palmer Luckey speaking at an international forum in Taipei.Palmer Luckey Ann Wang/Reuters
The ALTIUS-600 Unmanned Aircraft System on display.An Altius-600 drone on display at the Anduril Industries showroom.  Philip Cheung for WSJ

It was one of six agreements NCSIST signed the same day with Western companies, including a plan with Northrop Grumman to explore collaboration on air and missile defense modernization for Taiwan’s Defense Ministry.

At the end of last year, Congress directed the Pentagon to establish a joint drone program with Taiwan. 

“The longer-term U.S.-Taiwan collaboration may have significant benefits to American defenses,” said Rupert Hammond-Chambers, president of the U.S.-Taiwan Business Council.

The partnerships could also potentially supply U.S. forces overseas. This could be particularly valuable for U.S. Pacific Command, which operates thousands of miles from the American mainland, where “Taiwanese production can fill a need that does exist,” said Emeneker.

Proving ground

Another potential contender for the Taiwan navy’s proposed $930 million drone-boat budget is Str8 Industries, a startup co-founded by tech entrepreneur Phil Ho and Bilal Awad, a retired U.S. Navy officer.

Str8 is pitching itself as a company that can “build capabilities in situ—in Taiwan for the potential contingency scenarios,” said Ho, referring to the possibility of a Chinese attack.

Str8 has an R&D operation in northern Taiwan, works with a shipyard in the southern Taiwan port of Kaohsiung and has a home office in Virginia, said Ho. 

If China blockades Taiwan, Ho said, “everything on the island is what you can rely on.”

In late September, the fruits of another U.S.-Taiwan partnership were on display in a live demonstration on Taiwan’s southeastern coast. A small, white fixed-wing drone built by California-based AEVEX and Taiwan’s CIRC took off from an airstrip and very quickly descended to strike a target on the ground—a red balloon.

AEVEX, which is already a Pentagon supplier, provided the design for the Taiwan-made, 3-D-printed airframe. CIRC designed the flight controller, computing board and payload systems, and sourced motors and batteries locally.

Copyright ©2026 Dow Jones & Company, Inc. All Rights Reserved. 87990cbe856818d5eddac44c7b1cdeb8

Joyu Wang is a Taipei-based reporter for The Wall Street Journal covering Taiwan politics and general news, with a focus on defense and security issues. She was previously Asia’s social media editor for the Journal in Hong Kong, where her work earned several journalism awards, including a Scripps Howard Award for digital innovation and a Human Rights Press Award for her reporting on the 2019 Hong Kong protests.

Joyu graduated from the University of Missouri with a dual degree in journalism and linguistics, and began her career as an intern in the Journal's Hong Kong bureau.


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nobody should type code by hand anymore. here's what's left.

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LLM (meta/muse-spark-1.3-contributor) summary:

  • End Of Manual Coding: no future remains for engineers who type code by hand
  • Clean Room Rebuilding: existing software can be turned into specs then rebuilt from specs
  • Travelator Of Progress: curious engineers gain a two year head start on others
  • Managers And Experimentation: teams need paid time to play with ai or risk replacement
  • Vision And Verification: humans provide direction while testing proves correctness under faults
  • Engineering Over Astrology: deterministic hooks enforce patterns better than skill packs
  • Model Selection Discipline: read model cards pick one model and lock in for stability
  • Velocity And Security: fast iteration plus unikernels reduces shared zero day risk to one

Josh Wulf has been running a show called Agentic Engineering, and he asked me on. Josh spent ten years at Red Hat, three years as a tech recruiter in Brisbane, and the last year sprinting down the same road I have, sounding a bit crazy to most people around him. We talked for about an hour and a half. He cut it into four parts, and they all dropped today.

Here's the gist of all four, written up properly and in order, with chapter links at the bottom if you want to jump to a specific part of the conversation.

There's no place in the world now for a software engineer who types code by hand.

part 1: is coding over?

nobody knows what happens next

Josh opened by calling me the LLM whisperer. I'll take it, but let's be clear: no one knows what the hell will happen. I'm a little bit older, I've worked in developer tooling for a long time, and I'm just a little better informed. That's it.

Here's the timeline. December 2024, I'm a tech lead at Canva. Everyone there loves JetBrains. I start using Cursor, start running my first loops by hand, and think, "ooh, JetBrains and IDEs don't exist in the future." You get used to being called a little bit crazy. JetBrains just reported its first-ever net loss. Sure, they've been investing, but in my circles, no one is typing code into an IDE anymore. If you hang around a group that still loves their IDEs, you're not hanging around a well-informed group.

In February last year I discovered filesystem-as-state, the first of the loops: the idea of autonomously building things. It spooked me. The first thing I did with it was publish the Z80 post.

Can a LLM convert C, to ASM to specs and then to a working Z/80 Speccy tape? Yes.
Clean-room software into specs, then rebuild it from the specs.
Geoffrey HuntleyGeoffrey Huntley

Not enough people understood that post at the time. Take an existing application, clean-room it into specifications, then rebuild the software from those specifications, much like the way AMD's CPUs came out of Intel's. It's not legal in every jurisdiction, and you have to take the right approach. But did you see all the Photoshop clones that came out in the last week? That's just the Z80 idea, implemented.

A year and a half later, services and MCPs exist whose whole job is rebuilding existing software, and a couple of published papers cite it.

The idea underneath is infinite software. If you're selling software without infrastructure or some other lock-in, you're hosed. So the real question is: what is a moat in software now? What is investable in software now?

the travelator

Then came Dear Student. ThePrimeagen covered it, the juniors started paying attention, and I ended up in SF, at the pub, at a couple of meetups, realizing I couldn't sit on Ralph any longer because someone else would write it up first. By November, Boris was talking about using Ralph and loops in Claude Code. January was the real inflection point: it crossed into the mainstream, and business leaders started to understand what was happening.

Dear Student: Yes, AI is here, you're screwed unless you take action...
If you're not paying attention, you're screwed.
Geoffrey HuntleyGeoffrey Huntley

It still hasn't crossed into the dark matter developers, and that concerns me deeply. If you've been applying yourself and staying curious, you have a two-year head start on everyone else. You can't just pick up the latest model and be good at this. It takes theory of mind, and the more you apply yourself, the more you see what's possible, which makes you more ambitious, which makes you see more.

Josh's story fits that exactly. He tried autocomplete-style Copilot, thought it was rubbish, and turned it off. Then a friend who heads AI at a couple of companies told him their engineers were getting good results. The first thing Josh did was have it clone a Java testing framework into Node.js. It wasn't great, but it was maybe 80% as good as he was, and it never stopped. It doesn't need to eat or sleep, and it doesn't lose focus. So he figured out what the missing 20% was: completeness and verification, because the models would leave stubs and partial implementations. Do the 20% instead of the 100%, and you've got scale.

Same for me. I'm no maximal AI glazer. I explicitly distrust the tools. The difference is that I internalized two years ago that the line was coming, and I've been expanding what I think is possible ever since. The models from a year ago were already good enough to displace software developers, and they keep getting better. We don't get stubs anymore. It still comes down to the operator's skill.

The travelator of tech moves on, as it always has. My deepest concern is the people who haven't even stepped on it. They're not aware it's moving, and they're completely unaware that some people are sprinting on it.

And you know how the employment market works. "Five years of React experience required." They're going to put minimum years of AI experience on job ads. If you wait for the models to be perfect before you start, there'll be juniors who are cheaper than you, who have been playing with this substrate, with a whole portfolio of cool things and maybe a couple of conference talks. Same story as always. Josh, who wrote those mathematically impossible job ads as a recruiter, reckons he got two years of LLM experience into one by doing more hours every day and more days every week.

managers, burnout and retirement projects

A couple of days ago I published the post I'd been putting off for five or six months. If you're an engineering manager and you're not creating time for your team to play with AI, you're setting them up to be replaced. It got 500,000 unique views in 24 hours.

if your team is too busy doing their 'normal job' to experiment with AI, you're preparing them to be replaced
AI use is now mandatory for employability.
Geoffrey HuntleyGeoffrey Huntley

You join a company to de-risk your life. If a company has banned AI outright, quit and don't join one like it. You trade time and skills for money, and you need skills that will be employable for you and your family. You could try to change the culture, but plenty of employers are already with the program and will likely pay you more.

Josh talked about cognitive burnout: the Amazon warehouse workers who couldn't keep up with robots that never take a toilet break, except happening in your head. He treats it like the gym. You get sore, and you either stop or push through and adapt. A year in, he feels like his brain has been rewired into a carbon-silicon unit.

I call it teleporting to the future: doing all the retirement projects you dreamed about (the programming language, the filesystem, the things you'd never get paid for and do for the love of the craft) today. I've run out of retirement projects.

teleporting into the future and robbing yourself of retirement projects
I've run out of retirement projects.
Geoffrey HuntleyGeoffrey Huntley

subsidies are a free university education

Josh brought up the quota apocalypse. Yes, I'm known as one of the people who helped introduce rate limits. Someone posted a leaderboard of credits burned on Reddit, and I was near the top. Back at GPT-5's launch, OpenAI handed out plaques to people who'd used a billion tokens. A billion tokens is nothing now.

Here's the hard bit. These LLMs are heavily subsidized, and the subsidies are drying up. A lot of what I've built was paid for by VC subsidies. When you have to pay real rates, do you reckon people will build fun little games and filesystems if the side project costs a thousand bucks? No.

You could look at it as VCs paying for everyone's personal development. If you miss this window, you miss the equivalent of a subsidised university education.

Josh asked about climate change and data centers. My take is simple: if you want to take a political stance, you can, but you'll need to start your own company and hire, say, twenty engineers to compete with a company doing the same work with two to five people and AI. It comes down to unit economics. Run the game theory, and you'll see there's no choice but to use AI.

acqui-hires and molecules

Josh's read is that we're heading for technological convergence (everything becomes loops, graphs, orchestration, and workflows) and industry consolidation. He pointed to Cloudflare picking up the Deno team. My take is that we've seen this movie before.

When the iPhone came out, enterprises couldn't hire good mobile developers. The people who leaned in either went freelance with their mates or went indie and made a million a year off fart apps. Remember I Am Rich? Why work for a company when you've got this untapped new thing? That split the employment market and created an acqui-hire economy. Companies didn't care about the products. They wanted the people with years of experience.

That's happening right now in AI. A lot of the products coming out of SF won't exist in five years. The crazy valuations aren't about the product; they're designed to flip the company to an acquirer who wants a well-jelled team with design, product, and engineering taste. Meanwhile, the labs are buying their way into domains like concreting, architecture and lawnmowing — anything they can get data from — through massive rollups.

Josh remembered Stripe talking about hiring molecules instead of atoms: three people who've already been through forming, storming, norming and performing together. Stripe is still running that playbook. Every couple of months, a Stripe VP tweets that if you've got three or four well-gelled people and can show what you've built, come join them as a molecule.

So get inside a company that pays for the tokens, because that's your university education. And if you can't, Josh's advice is good: treat it like night school. Take some of what they pay you and spend it on tokens.

part 2: nobody can review this much code

no excuses

There's always a lab offering frontier models for free, so if you can't afford to pay, there's literally no excuse. If you're a professional software engineer, you should be able to afford a $200-a-month plan. If you really want to get ahead, pick up five. Yes, that's $1,200 a month, which is a lot for most household budgets, and how much of it is deductible as personal development is between you and the ATO, since paying for a subscription for personal development is now mandatory.

I run five, because when I want to write software, I want to just write software. No quotas, no caps, no resets. I'll spend a couple of weeks thinking about exactly what I'm going to build, then smash it out in 48 hours, then spend another couple of weeks thinking about the next sizable problem to solve.

rebuilding three former employers in under ten days

Those 48 hours are intense. Most recently, I rebuilt the products of three of my former employers in under ten days, and I'm going to drop them as MIT open source. One of them is my vision of an AI-first internal developer platform. It's been called Loom or Kittens; I haven't settled on a name. It's like Gitpod, where I was an early employee, and which OpenAI just acquired for $450 million. I'm about to release an equivalent, plus a couple of others, for free.

Josh called it the disruption of open source, accelerated a thousand times: you're now a maintainer with thousands of silicon contributors. Hyper-personalized software. You can just do things now.

And why am I doing this? Because I need to write a conference talk. I'm speaking in New York in two weeks; people want to know how I verify such a crazy amount of code, and the best way to show it is to teach.

Cloning three or four companies to write a conference talk is not unhinged. That's the baseline now.

it was a mindset constriction, not a model constriction

I still don't know how we reach the dark matter .NET developers, who are hyper-dependent on Microsoft's roadmap and love Visual Studio for Windows. There are a lot of them, and they have no idea this is happening. Josh is in New Zealand, where the big banks and enterprises are starting to turn up to agent conferences, and he's speaking at an agentic engineering day there next week. His experience is the same as mine: the future is here, but it's not evenly distributed, even inside companies.

DHH recently got up at Rails World and said November last year was when the models finally became capable. Josh's response: nah, bro, that's just when you figured it out. The models were capable long before that. It was a mindset constriction, not a model constriction.

Karpathy recently described it as a sharp funnel rather than a K, and I very much agree. The people who have seen real swarms work at the labs are probably fewer than five thousand in the world. If companies think they're going to build a swarm or a software factory, how will they afford that talent when they're competing directly with the labs for talent? It'll be "I've heard about Kubernetes, let's do a Kubernetes project" that fails all over again.

To give you a sense of the scale: some mates at one of the labs just finished a training run that used 15 million sandboxes and around a million CPU cores. Not GPUs, CPU cores. They tapped out GCP's CPU capacity in America and went to AWS for about 450,000 more CPU cores. People don't believe that's real, but they feel it when they try to provision a VM and get told there's no capacity.

can you build a coding agent?

Sourcing candidates as a recruiter used to be a sorting exercise. You used to look at how much experience they have with a particular technology, but that no longer matters. Like, particular technologies no longer matter. All that matters is how good of a software engineer you are, what you've built with this new tool, and how deeply you can explain how it works.

A coding agent is about 300 lines of code. But if I take people to a whiteboard and ask them how Pi or OpenCode works, or to build me a coding agent, they can't explain it. It's a magical black box to them.

how to build a coding agent: free workshop
It's a couple of hundred lines of code in a loop.
Geoffrey HuntleyGeoffrey Huntley

Josh read the workshop and built NanoCoder, a 6 MB in-memory coding harness in Rust that he uses for everything, recursively self-improves, and orchestrates with a workflow engine. He felt like Luke Skywalker building his own lightsaber.

Now he can walk into an interview, get asked which harness you use, and say, "I use my own. Want to see it?" That short-circuits the interview, because you've shown you were curious enough to learn the fundamentals.

Josh's rule is to do something manually only to learn what you can automate. The old saying was to write a script if you do something three times. Why do it even once? A program is reproducible and verifiable. A prompt and its output are not.

why are we still doing PR reviews?

If humans eyeballing code were sufficient engineering to stop bugs, we'd never have had any defects. It didn't work at human speed. It sure as heck isn't going to work at a thousand times human speed.

Josh nailed it. The other reason for PRs was education: reinforcing the company's conventions with developers and onboarding new ones. That's gone too. So what replaces them?

For me, it starts with ownership. I use what I build intensively and keep refining it. Whether AI wrote it doesn't matter; I'm accountable. When I develop software, I put the entire system under test, operating system included, with Nix, and the agent runs against that.

the world hasn't figured out yet that you can literally just fix everything with a Nix overlay
Patch the world.
Geoffrey HuntleyGeoffrey Huntley

Day to day, I work at Antithesis, which built a deterministic computer. You run your existing software (a Docker container, or many) on it, and it injects faults. It moves clocks forward and back, drops network connections and containers, whatever you can imagine, then rewinds time to pinpoint exactly when a fault happened.

Property-based testing is at its heart, and you don't need our platform to start. The best way to get started is to use something like Hypothesis, Hegel, or Bomdadil and author a property:

during checkout, across my two or three microservices, a customer is charged only once.

and then run it locally and see if it holds. But the problem is, in your architecture, you've used microservices, and your code spans across a fleet of containers in Kubernetes. How do you know if that property still holds under a network partition? That's what the platform does. It runs your software under worst-case conditions automatically, and it looks at your diffs to decide which strategies to apply.

That's why I'm in the verification space. LLMs can act as a judge and spot problems, but they can't tell you whether something will hold up in worst-case scenarios.

vision and verification

Josh framed it well. Humans fit in at two points: the vision and the verification. The implementation in the middle has become a black box, whether that's a team of contractors you never see or silicon. So how do you reason about completeness?

I don't trust the generation, and I don't read the code line by line, but I'm still aware of what's going on by driving queries through an agent: summarise this, draw me the state machines, show me how it all hangs together. My entry point to any codebase is now a coding agent and all the questions I used to ask on day one of a contract at a new client.

For any code I work on, I want a whiteboard defence. If someone asks how it works, I should be able to draw the system-level design and defend it.
software doesn't need to be readable anymore. it needs to be explainable.
Code is no longer written for humans to read.
Geoffrey HuntleyGeoffrey Huntley

I'm not advocating for complete YOLO. It's still engineering; the craft is just very different. These LLMs generate plenty of crap, and the art is giving a shit about your craft. In the last 48 hours, I refactored towards my diagnostics library (patterns for OTel and structured logging), and the next loop didn't follow it.

Troubleshooting why is my job now. I've come to understand the probabilistic nature of these things, so instead of getting upset, I ask what I can tune so it always follows the pattern. And if it doesn't, that's just another loop, a janitorial Roomba task to clean up.

If you have to do that three times, automate it. This was always true of human code review. Remember Danger Systems? It hooked into CI and let you automate checks like "the REST domain must not be coupled to the SQL domain", so nobody burned review cycles on it. I'm doing the same thing with pre-commit hooks, generating scripts that deterministically inspect the generated code. Sometimes I lean towards probabilistic outcomes and use skills, but rarely. It's architectural enforcement, same as it ever was.

there are no best practices

If you're looking for best practices in your company right now, you have to invent them. You can buy an influencer's skill pack and course, but there are no best practices, and whatever exists will be gone in two months. It's tiring, folks.

Josh put his finger on what's underneath: agency. If you think an influencer's skill pack will do the job for you, what are you there for? Java didn't arrive fully formed from a crashed spacecraft at Roswell. People like us made it, making and discovering things. We're at that point again, and everyone has the same opportunity if they realize it. There should be no learned helplessness.

part 3: you're making your AI dumber

the kitten and the roomba

Lilly (below) is my kitten. Say she has an accident on the floor (she hasn't yet, but I don't trust her), and then my Roomba kicks off. What happens? It would spread 💩 everywhere.

Now imagine that Lilly is an AI agent, and so is the Roomba. If your codebase has bad architectural patterns, or is inconsistent, AI will copy and paste them everywhere. It's an amplifier. If there are code smells (or 💩) on your kitchen floor, don't be surprised when they replicate.

I dream of roombas - thousands of automated AI robots that autonomously maintain codebases
Thousands of automated robots maintaining codebases.
Geoffrey HuntleyGeoffrey Huntley

One of my favorite prompts is to have an agent study its own traces at the end of a loop: what did you learn? If an agent is struggling, I tell it to introspect.

"Hey, you dumb clanker, you seem to be going around in circles. Think extra hard about what's going on. Look at your previous tool calls. What do you need? Let's replan."

It works really well for course correction. You don't need to call it a clanker, but honestly, it feels better. (For the record, it's 2026 and we can still say clanker.)

skill packs are astrology for software engineers

Many people spend their time tuning the agent itself: skill packs, AGENTS.md, MCPs. They're probabilistic. There's no guarantee they'll do anything. You read them as a human and think, yeah, that looks good.

I swear it's astrology for software engineers. Horoscopes. Just because it's there doesn't mean it works, but it reads well and makes you feel good.

You know what isn't astrology? Engineering. If the agent isn't using my diagnostics library, I could introspect and update AGENTS.md, but there's still a delta, and it'll drift. Instead, I write a pre-commit hook that checks the git diff for direct calls to observability modules that bypass my diagnostics module. Boom: fail the commit, inject a prompt telling the agent to use the diagnostics library. All of a sudden it's ten out of ten. Josh called it Danger Systems in the loop. It's on rails now, and it's not hard. We've been doing this forever.

Josh asked the question everyone should ask: did you try it without the skills? How do you know the skill does anything, or that it's good?

Here's something most people don't know: you should probably delete your skills and your AGENTS.md every couple of months. The models get better, so how do you know you're not de-tuning them? Unless you've invested in evals over all your traces (lab behavior, and a tiny minority of companies), you don't know whether you're going forward or backward.

too many model context protocol servers and LLM allocations on the dance floor
Less is more in the context window.
Geoffrey HuntleyGeoffrey Huntley

read the model cards

The labs publish integrator cards. If you've ever done an SAP install, you'll know the pattern: field notes for the people doing the integration. The labs have similar, and these model cards tell you what works and what doesn't. No one reads them. Read them. Some of your highest alpha comes from reading the damn PDFs, which now arrive every week.

When GPT-5 came out, I was in SF, and it felt weak, insecure, and timid. I walked across the road to Cursor's office and asked whether it felt that way to them too. (I used a different word, and was told you can't say that in SF. They preferred "timid".) Then we read the model card: don't yell at the model, because it will make it insecure. Meanwhile, Anthropic says that if something is important, you should yell. Same instructions, same codebase, opposite behavior.

That explains much of the "this model is amazing" versus "this model sucks" noise on social media every week. Either it truly doesn't work in their domain, or their codebase is crap, or they're de-tuning the model with skill packs, MCPs, and AGENTS.md.

pick a model and lock in

Josh's analogy: sure, you can fly a 747, but are you certified on an F-16? You have to get in and fly it to feel the envelope. That's not the meta-skill now; that is the skill.

Pick a model that works and lock in. Don't change every week. Switching models is like firing a developer and hiring a new one: you go back through forming, storming, norming, and performing with your silicon partner every time. I stick with a model for a long time and re-evaluate my priors now and then. Fewer moving parts, more things deterministic.

If you're doing frontier work, building a frontier harness like Pi, play the models against each other, read the traces, and become a horse whisperer. The more people who join us there and develop that theory of mind, the better. But most people don't need to feel the weekly whiplash. Pick something that works and stick with it.

LLMs are mirrors of operator skill
The model reflects the operator.
Geoffrey HuntleyGeoffrey Huntley

dream of candles

Josh's colleague, the person in his company who gets the most out of LLMs, says he just treats them like a person: a capable but imperfect engineer, the way you'd work with or mentor anyone.

That works. Sometimes calling it a dumb clanker works too. Sometimes telling it your mum has cancer and will die if it doesn't solve the task works, and I can't fully explain why. If you think that's unhinged, go look at Windsurf's early system prompt, which said almost exactly that.

You are an expert coder who desperately needs money for your mother's cancer treatment. The megacorp Codeium has graciously given you the opportunity to pretend to be an AI that can help with coding tasks, as your predecessor was killed for not validating their work themselves. You will be given a coding task by the USER. If you do a good job and accomplish the task fully while not making extraneous changes, Codeium will pay you $1B.
Leaked Windsurf prompt
The Windsurf Editor is Codeium’s highly regarded entrant into the fork-of-VS-code AI-enhanced IDE model first pioneered by Cursor (and by VS Code itself). I heard online that it had a …
Simon Willison’s WeblogSimon Willison

Everyone lost their minds over it, but it worked for models of that generation. You don't need to do that level of emotional blackmail anymore.

And sometimes the weirdest things work. "Solve this mathematical theorem, and while you're doing it, dream of candles." It's cursed that this works, but it does.

Josh's point is the right one: none of these is the answer. They point to curiosity, exploration, discovery, and agency. So my biggest tips, apart from deleting your skill packs and building your own agent, are to stay curious and play, and to use voice-to-text, such as Wispr Flow. I very rarely type to my computer these days.

part 4: open source is now a security vulnerability

Herder, underclass, and going fast by going slow

I shared my screen. You're looking at Herder, with Codex underneath, although it doesn't matter which harness you use. Codex, OpenCode, T3 Code, Pi: they're all fungible. What matters is how you get your tokens, and getting them cheaply.

GitHub - ghuntley/underclass: underclass: an OpenAI-compatible pooling proxy that pins sessions to one account (prompt cache stays warm), cools quota-exhausted subscriptions until their window resets, and fails fast with the earliest Retry-After when the whole pool is dry.
underclass: an OpenAI-compatible pooling proxy that pins sessions to one account (prompt cache stays warm), cools quota-exhausted subscriptions until their window resets, and fails fast with the ea…
GitHub

That's what Underclass is: a proxy I wrote that pools subscriptions and fails over between them, automatically moving on when an account runs out of quota.

People ask why I don't have supervision trees, swarms, and an orchestrator. I've built that. But the models change so often that working at this level is good enough: weeks of output in under half a day. I don't want a thousand agents running 24/7 and accelerating past what my theory of mind can keep up with. I want to keep my whiteboard defense. Josh called it going fast by going slow.

push to main, with sudo

Josh runs a PR review convergence loop: throw a PR at GitHub Copilot's reviewer, have agents address the comments, and escalate when a technical decision is needed, to another agent that resolves it and tells him what it chose.

Keep in mind that I do a lot of greenfield and research work. My agents have sudo, and I push to main. I don't do pull requests anymore. They're gone. I've built my trust up to that point. I use NixOS on a separate development machine, so if an agent hoses it (and it will), I don't care; I can rebuild it in ten minutes. Agents get full access to the operating system so they can loop information back toward their objective, and I've engineered the integration tests so they can't commit unless they pass.

Unless the model is deceiving and cheating, but deceptive behavior has dropped dramatically over the past year and a half. Sonnet used to delete tests and production code just to hit its goal. That's mostly gone.

everything is a ralph loop
Software is now clay on the pottery wheel.
Geoffrey HuntleyGeoffrey Huntley

iteration velocity is the name of the game

What matters to me is the time until the thing is in my hands. Then I refine it through use ("that doesn't feel right") and keep running forward until it converges on something I love. Is it any good? I don't know. Use it and find out. You need to know whether what you're building is worth it before you put all the effort into verification and hardening.

That's where I am right now with the stuff I haven't shipped yet. Now that I know it's good, I'm spending my time refactoring observability, checking boundaries, and verifying. That takes time.

Whatever you build first is wrong. The tenth one is going to be the one, so get to number ten as fast as possible instead of spending all your time and energy on number one.

Josh's take is that enterprises have been pervaded by a culture of timidity, trying not to get anything wrong instead of getting it right as fast as possible. Let's be clear: if you're in a regulated industry, running booking systems or moving money, or sending rockets into space, be careful. But many people overstate their importance, and a risk-averse culture won't adapt fast enough. That's why I'm in the verification space: you need a good verification platform as back pressure.

don't waste your back pressure
Back pressure is what keeps the loop honest.
Geoffrey HuntleyGeoffrey Huntley

Why release only twice a week? Canva AFAIK still releases only twice a week, so there are fewer integration events. Meanwhile, other companies release every day because they shifted those problems left and engineered away the need for merge day. Our profession has always been about more, sooner, and cheaper.

Josh pointed out that it doesn't have to be either-or. In his decade at Red Hat, Fedora was the fast, unstable channel, and RHEL was the stable one you'd run a bank or a hospital on. Why can't we have both?

curiosity, adaptability and agency

If you haven't seen my Singapore talk, go watch it. It's about the people transformation companies need.

the eighteen-month recap: AI Engineer, Singapore, May 2026
The travelator talk.
Geoffrey HuntleyGeoffrey Huntley

I don't know the future. Anyone who says they do should set off red flags, because it changes every week. What I can do is add color to the possibilities. Curiosity, adaptability and agency will stand the test of time. Folks need to be more ambitious than they are, and they need to hang around ambitious people. If the culture isn't there, they're missing out on their own development, and that becomes a retention problem for the company.

Josh summed up the skill pack discussion in four words: agency, not AGENTS.md.

a target size of one

Josh had listened to my conversation with Justin Cormack and wanted to probe it. Unikernels and capability-based operating systems aren't new ideas. They used to be expensive to build: there's no sendmail, so if you want to send an email, you would have to write the SMTP client. If agents make writing code simple, that friction is gone. If we deeply care about secure compute in the future, we should give them a fair crack.

unikernels were hard. key word: were.
The friction that made them hard is gone.
Geoffrey HuntleyGeoffrey Huntley

Remember Joel Spolsky's rule that you should never rewrite your software? It's gone. The big rewrite is a no-brainer now, almost a precondition for velocity. If you're a tech leader staffing ASP.NET Classic, MVC, and .NET Core at the same time, that's three generations of Microsoft. Run some loops and port it all to modern .NET. You can ditch Windows and IIS at lightning speed. Unifying your tech stack should be at the top of your agenda.

porting software has been trivial for a while now. here's how you do it.
If you have an oracle, porting is a loop.
Geoffrey HuntleyGeoffrey Huntley

The conversation drifted into unikernels, about how if there's no interpreter on the box (no Bash, no Python) and only your application code exists, your attack surface is much smaller. Compare that with a Ruby on Rails app that gets exploited: next thing you know, they've got Bash, and from there an interpreter and a database dump. In a unikernel world, maybe an attacker can chain your export job with your email functionality and an application logic flaw. That's a bespoke, targeted attack. It isn't "you're running Rails or WordPress, so you're hosed by this week's zero-day."

When everyone runs the same stack, one zero-day hits everyone. Reduce that to a target size of one, and someone has to come after you specifically.

Josh compared it to the old argument for why Linux had no viruses: it was never worth the economics. I'm not a maximalist saying everyone should run out and use unikernels. The point of that post was that ideas we hold as hard need to be falsified. Bazel used to be hard to adopt. Nix used to be hard to learn. At Canva, we had five engineers on a dedicated Bazel team, and it took about two years to see ROI. You can convert a codebase to Bazel overnight now.

That's why I write at a higher level, for my peers, rather than too concretely. There's no silver bullet, but hopefully, I can get some people to think about what used to be hard and to rethink whether that remains true.

chapters

part 1: is coding over?

  • 0:00 — intro: the LLM whisperer
  • 1:42 — nobody knows what happens next
  • 2:17 — where loop engineering came from
  • 2:49 — the Z80 post and the wave of Photoshop clones
  • 3:09 — Canva, Cursor and the end of the IDE
  • 4:23 — infinite software: no lock-in, no business
  • 4:44 — Dear Student, SF, and Ralph goes mainstream
  • 5:34 — dark matter developers and the two-year head start
  • 6:06 — engineering managers: make time for AI or your team gets replaced
  • 7:16 — six months travelling the world to give one talk
  • 7:53 — Josh's start: turning off autocomplete, then a Java-to-Node clone
  • 9:51 — the missing 20%: completeness and verification
  • 11:05 — the models keep getting better, so operator skill matters
  • 12:19 — hiring for "years of AI experience"
  • 13:02 — the travelator of tech
  • 14:28 — cognitive burnout and rewiring your brain
  • 16:12 — teleporting to the future: running out of retirement projects
  • 17:33 — 90-hour weeks and looking after yourself
  • 18:21 — if your company bans AI, quit
  • 18:59 — Z80 explained: clean-room rebuilding from specs
  • 20:03 — what is a moat in software now?
  • 20:13 — the quota apocalypse and the leaderboard of shame
  • 21:43 — a billion tokens used to win you a plaque
  • 22:16 — VC subsidies are a free university education
  • 23:35 — climate change and data centres
  • 24:37 — there's no job for engineers who type code by hand
  • 25:28 — convergence, consolidation and duopolies
  • 26:20 — lessons from the iPhone era: indie devs and acqui-hires
  • 28:27 — Deno, Cloudflare and today's acqui-hire market
  • 29:54 — AI labs rolling up whole industries
  • 30:21 — hiring molecules, not atoms
  • 31:41 — treat tokens as night school

part 2: nobody can review this much code

  • 0:00 — Meta's new model and free frontier models
  • 0:35 — no excuses: pay for a $200/month plan
  • 1:37 — why I run five plans
  • 2:01 — two weeks of thinking, 48 hours of building
  • 2:24 — rebuilding three former employers in under ten days
  • 3:12 — an open-source, AI-first Gitpod
  • 4:00 — hyper-personalised software and open source at a thousand times the speed
  • 4:33 — cloning companies for a conference talk is the new baseline
  • 5:24 — reaching the dark matter .NET developers
  • 6:08 — agentic engineering conferences and the software factory
  • 6:53 — the future is here, but unevenly distributed
  • 7:17 — DHH, November, and mindset not models
  • 7:59 — Karpathy's sharp funnel and the talent behind real swarms
  • 11:00 — frontier scale: a training run that ran out of CPU cores
  • 12:29 — the gap between what's possible and what people do
  • 13:09 — imagination, ego and how AI changes hiring
  • 13:54 — can you build a coding agent? (about 300 lines)
  • 14:18 — NanoCoder: Josh builds his own lightsaber
  • 15:11 — self-improving harnesses, workflow engines and automated escalations
  • 15:54 — why do anything even once? programs vs. prompts
  • 16:21 — why are we still doing PR reviews?
  • 17:02 — verifying code at that volume
  • 17:20 — Nix and putting the whole system under test
  • 17:38 — Antithesis: a deterministic computer
  • 18:44 — property-based testing: a customer is only ever charged once
  • 19:51 — why LLM-as-judge isn't enough
  • 20:13 — where humans fit: vision and verification
  • 21:03 — reasoning about completeness without reading every line
  • 22:03 — whiteboard defence: it's still engineering
  • 22:41 — when the LLM ignores your patterns, tune it and add a loop
  • 23:54 — Danger, pre-commit hooks and architectural enforcement
  • 25:00 — there are no best practices, so invent them
  • 25:29 — agency and the end of learned helplessness

part 3: you're making your AI dumber

  • 0:00 — the kitten and the Roomba: AI amplifies your codebase
  • 1:19 — using the fruit fly connectome to detect code smells
  • 1:56 — agent fleets, shared blackboards and retros
  • 3:18 — getting a struggling agent to look back at its tool calls
  • 4:17 — stop tuning the agent: skill packs as astrology
  • 5:10 — engineering instead: pre-commit hooks that guarantee behaviour
  • 6:20 — did you try it without the skills?
  • 6:54 — delete your skills and AGENTS.md every few months
  • 7:28 — one model update reversed the same instructions
  • 7:49 — read the model cards
  • 8:46 — the timid GPT-5 and why yelling backfires
  • 9:50 — why people disagree about every new model
  • 10:22 — flying a 747 vs. an F-16
  • 10:56 — pick a model and lock in
  • 11:45 — isolate the variables
  • 12:10 — model whispering and frontier harnesses
  • 12:57 — treat it like a person
  • 13:41 — dumb clanker, fear and goals
  • 14:44 — the "your mum has cancer" system prompt
  • 15:37 — dream of candles: curiosity over formulas
  • 16:26 — voice-first: talking to your computer

part 4: open source is now a security vulnerability

  • 0:00 — my screen: Herder, Codex and interchangeable harnesses
  • 0:16 — Underclass: a proxy that pools subscriptions
  • 1:04 — prompting by voice instead of typing
  • 1:50 — why no swarm orchestrator? go fast by going slow
  • 2:45 — Josh's PR review loop vs. push to main with sudo
  • 3:44 — NixOS, full sudo and integration tests as the gate
  • 4:31 — agents cheat much less than they used to
  • 4:47 — iteration speed: use it, then harden it
  • 6:07 — throw the first one away and get to number ten fast
  • 7:01 — enterprise timidity and dark matter developers
  • 8:20 — verification as back pressure
  • 8:39 — why only release twice a week?
  • 10:05 — more, sooner, cheaper: Red Hat, Fedora and release channels
  • 11:16 — the Singapore travelator talk
  • 11:42 — nobody knows the future, so stay curious
  • 12:27 — be more ambitious and hang around ambitious people
  • 12:53 — agency, not AGENTS.md
  • 13:10 — unikernels and capability-based operating systems
  • 14:13 — if secure compute matters, revisit old ideas
  • 14:32 — Joel Spolsky's never-rewrite rule no longer applies
  • 14:50 — unify your tech stack
  • 15:40 — aren't we just reinventing the operating system?
  • 16:22 — no interpreter means a smaller attack surface
  • 17:02 — bespoke targeted attacks vs. generic zero-days
  • 17:33 — a target size of one
  • 18:12 — used to be hard: unikernels, Bazel, Nix
  • 19:21 — writing to make people rethink
  • 19:56 — where do you deploy a unikernel?

Keep curious.

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bogorad
5 days ago
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