LLM (google/gemini-3.1-flash-lite-20260507) summary:

• Political Maneuvering: mayor mamdani introduced a limited rent increase policy as a performative gesture rather than a structural solution to the city's housing shortage.
• Limited Scope: the program targets a negligible fraction of the city's fifty thousand vacant rent-stabilized units, leaving the vast majority of uninhabitable apartments untouched.
• Regulatory Constraints: current state law restricts allowable renovation cost recoupment to levels far below the actual capital investment required to restore distressed units.
• Capital Inefficiency: systemic rent caps combined with high rehabilitation costs render private ownership of older buildings economically unviable, incentivizing long-term vacancy.
• Administrative Discretion: the policy shifts power to the mayoral housing agency, allowing officials to exercise case-by-case control over property owners instead of implementing market-wide reforms.
• Subsidy Reliance: the proposal lacks a clear path for legal rent increases, necessitating potential reliance on existing federal or state subsidy mechanisms rather than market adjustments.
• Perverse Incentives: rent freezes and discretionary control may intensify owner distress, encouraging the further withdrawal of housing supply from the market.
• Ideological Obstruction: the administration avoids necessary revisions to the housing stability and tenant protection act to satisfy political allies, prioritizing optics over housing availability.

Courtesy Andreas M/Unsplash

On Tuesday, New York City Mayor Zohran Mamdani announced that owners of vacant regulated apartments would be eligible for a “one-time” rent increase if their units are part of city-financed, city-regulated affordable housing. The announcement was billed as relief for distressed rent-stabilized landlords whose vacant units can’t be economically repaired at current rents.

In reality, the measure is something closer to a political maneuver. It will affect a minuscule share of the city’s long-term vacant apartments, counterproductively create incentives to keep more units off the market, and place owners at the mercy of a mayoral housing agency. The real culprit behind the city’s vacancy crisis is a 2019 state law that created it in the first place—and that Mamdani has no intention of fixing.

Of the city’s roughly 1 million rent-stabilized apartments, an estimated 50,000 units sit vacant because their rents cannot be raised enough to justify costly renovations. (Precise figures aren’t available because the state’s Division of Housing and Community Renewal, which oversees rent stabilization, does not release the data—even though it collects it.) Readers may wonder: How is it possible that there are any vacancies in a city with a severe housing shortage?

To answer that, go back to 2019, when Albany passed the Housing Stability and Tenant Protection Act (HSTPA). Before HSTPA, when a tenant moved out, rent-stabilized landlords were allowed a “vacancy bonus”—a larger rent increase—to compensate for years of below-market raises. Once a unit’s legal rent reached $2,774.76, it could be entirely deregulated upon vacancy.

Tenant activists argued that arrangement created an incentive for landlords to harass tenants into leaving in order to capture the bonus. In response to these allegations, the 2019 law severely restricted owners’ ability to raise rents.

Today, landlords can no longer increase rents between tenancies. And when an apartment requires renovation, they can recoup at most up to $50,000 over 12 years—a maximum monthly rent increase of $347.

Rehabbing an apartment in New York City frequently costs double that or more. According to a 2018 analysis by the city’s Independent Budget Office, bringing highly distressed public-housing buildings in Brooklyn into good repair would cost an average of $260,000 per unit—roughly $325,000 in today’s dollars. Similar math constrains private owners. As a result, tens of thousands of apartments are uninhabitable and economically unviable to renovate.

How does Mamdani’s plan change this equation? By the administration’s description in Tuesday’s press conference, it seems like a longstanding HPD program would expand to a broader set of distressed properties. The mayor’s proposal would allow buildings subject to HPD financing and regulatory agreements to receive a rent bonus on vacant apartments on a case-by-case basis.

It remains unclear how city regulatory agreements can supersede the HSTPA’s restrictions on rent increases. Deputy Mayor Leila Bozorg suggested that federal Section 8 housing vouchers might be used to cover the higher rent.

Another answer may lie in Section 610 of the Private Housing Finance Law, signed by Governor Kathy Hochul in December 2022. Similar to Section 8, it allows owners of affordable housing projects with rental assistance to collect the full subsidy amount even if it exceeds the legal stabilized rent, without affecting what tenants pay out of pocket. While the law requires owners to execute an amendment to their existing regulatory agreement, Section 610 is explicitly a subsidy mechanism, not an authority to raise rents above HSTPA’s limits.

Whatever the mechanism, the Wall Street Journal reports that City Hall “projects that hundreds of apartments could use the rent increase”—a drop in the vacancy bucket. The proposal would do nothing to address the vacancies that are in buildings not subject to HPD financing or regulatory agreements, which make up the vast majority of the city’s 50,000 apartment vacancies. The mayor hasn’t called for overhauling the HSTPA to allow for vacancy bonuses, which would solve the problem for all owners as-of-right, without needing a discretionary review by a city housing authority.

But case-by-case control is politically useful for a mayor who built a campaign on the premise that landlords of rent-stabilized units were profiting too much. It lets him claim credit for returning vacant apartments to use while avoiding any concession that the HSTPA itself is broken. Such a solution will also give reason for far-left housing advocates to push for more public intervention in the housing market.

What’s more, in Tuesday’s press conference, Mamdani said that no tenant would see rents rise beyond what the Rent Guidelines Board determines, even if their apartment is otherwise eligible for a reset under the new program. Current tenants in affected buildings would remain subject to whatever the Rent Guidelines Board sets—including a freeze, if one is enacted in June.

A rent freeze, however, would only accelerate distress in stabilized buildings and force more units into vacancy. Owners might remove units from the market in the hopes of obtaining a vacancy bonus, potentially restricting supply further. Mamdani’s program thus provides an illusion that something is being done to address vacancies, which allows him to delay the reality that stabilized tenants will eventually need to pay more to keep their buildings afloat.

If the mayor were serious about filling vacancies, he would provide political cover for his leftist allies in Albany to amend the HSTPA and allow re-introduced vacancy bonuses. Instead, his program is a fig leaf—a chance to claim credit for “solving” a minuscule slice of a crisis that will only deepen if he achieves his campaign’s rent freeze.

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LLM (google/gemini-3.1-flash-lite-20260507) summary:

• Predictive Markets: online platforms allow gamblers and insiders to speculate on global events under the guise of market innovation.
• Public Vigilantism: internet sleuths now perform the basic investigative labor once reserved for actual oversight agencies.
• Corporate Malfeasance: google employees allegedly exploit their access to internal company data to manipulate betting outcomes for personal profit.
• Tracing Transgressions: the inherent transparency of blockchain technology serves as a digital leash for those attempting to hide illicit windfalls.
• Naive Obfuscation: even self-described technical experts fail to conceal their criminal financial trails when interacting with regulated banking infrastructure.
• Regulatory Vacuum: offshore betting hubs operate with minimal scrutiny until federal authorities finally catch up to the obvious evidence left behind.
• State Enforcement: justice department officials are forced to clean up the messes left by unregulated betting sites after amateur crowds verify the crimes online.
• Illusion Of Anonymity: these high-tech prediction schemes ultimately collapse when they collide with the reality of traditional identification and centralized financial systems.

By

Alexander Osipovich

4

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A blockchain engineer wrote that ‘a Google insider’ was ‘milking Polymarket.’ Pascal Mora/Bloomberg News

Late last year, Haeju Jeong noticed that a mystery trader on Polymarket had just earned more than $1 million with a series of uncannily accurate bets on the top Google search results of 2025. He shared his suspicions with the internet. 

“He’s a Google insider milking Polymarket for quick money,” Jeong, a blockchain engineer, wrote on X. “It’s one of the wildest things I’ve seen on the platform.”

Now, the government says he was right. In a criminal complaint this week, federal prosecutors alleged that Michele Spagnuolo, a longtime software engineer at Google, used his access to internal company data to place the lucrative bets, using a Polymarket account called “AlphaRaccoon.” 

It was the same account that Jeong identified in his Dec. 4 tweet, which went viral and drew more than 6 million views. It was the latest case of amateur sleuths identifying suspicious activity on Polymarket in nearly real time, before law enforcement gets involved.

Spagnuolo, an Italian citizen who was charged with fraud and money laundering, didn’t respond to a request for comment. His prosecution follows the April arrest of Gannon Ken Van Dyke, a U.S. special-forces soldier accused of using his role in the military operation in Venezuela to earn more than $400,000 in betting profits. Polymarket watchers shined a social-media spotlight on his successful bets in early January, within hours of the capture of Nicolás Maduro.

Michele Spagnuolo

Israeli authorities arrested two people earlier this year for using classified information to make profitable bets on military operations. Those wagers also drew attention online months before the arrests.

Sharp-eyed observers like Jeong are proving to be an unexpected ally of law enforcement as it tackles the emerging challenge of insider trading on prediction markets.

Platforms such as Polymarket and Kalshi let users bet on future events in areas such as politics, war, and popular culture. That can create tempting opportunities for people with nonpublic knowledge to make profitable bets at the expense of less-informed traders.

Many of the armchair sleuths hunting insiders are prediction-market traders themselves. They typically focus on Polymarket, whose offshore, crypto-based platform isn’t overseen by U.S. regulators and has drawn repeated allegations of insider trading and market manipulation.

Polymarket doesn’t require users of its offshore platform to submit proof of identity, making it largely anonymous. But its blockchain-based technology makes it possible for anyone to monitor the bets, profits and losses of individual accounts. 

That allows sleuths to spot suspicious activity and highlight it online, even if they can’t identify the actual traders. These sleuths are often anonymous themselves, such as an X user who goes by Andrey_10gwei and tracked potential insider bets on Middle East wars.

“Anyone with the right skills and knowledge can access all of this information,” Andrey_10gwei told The Wall Street Journal in a direct message via X. He declined to give his name.

Jeong, who flagged the alleged “Google insider” on social media, is an engineer at Meta Platforms. He didn’t respond to requests for comment, but on Thursday he posted on X again. “Bro got caught and is now getting federally charged. I called this out 6 months ago lol,” Jeong wrote. 

Polymarket said it cooperated with the investigations into Spagnuolo and Van Dyke and hailed their arrests as vindication for its blockchain-based model. “Blockchain trading is transparent, traceable, and bad actors leave footprints,” a spokesperson for the New York-based company said.

Polymarket has a data partnership with Dow Jones, the publisher of the Journal. 

To catch a Polymarket insider trader, authorities need to trace the movement of digital funds to another platform that has recorded the trader’s real-life identity. 

Van Dyke, the special-forces soldier, funded his Polymarket bets with transfers from Coinbase Global, a regulated cryptocurrency exchange that tracks customers’ identities and cooperates with law enforcement. That made it relatively straightforward for authorities to identify him, blockchain analysts said.

Spagnuolo—a network-security specialist with “extensive technical experience in blockchains,” according to his LinkedIn profile—used more sophisticated techniques to attempt to mask his identity, according to the Justice Department. 

When cashing in his winnings from the Google bets in December, Spagnuolo used multiple steps to obfuscate the digital money trail, including the use of a crypto-transfer service with special privacy protection, according to the DOJ’s criminal complaint.

But the FBI identified Spagnuolo because of an earlier withdrawal, in which he didn’t try as hard to cover his tracks. 

In November, he moved $149,980 off Polymarket onto a crypto “swapping service,” the complaint said. Soon afterward, an identical quantity of funds left the service for a payment processor, where they were received in an account in Spagnuolo’s name. His Italian government ID had been used to open that account.

Blockchain data show that the payment processor he used was Nexo, a crypto trading and lending platform whose offices are largely in Europe, while the swapping service he used was FixedFloat, which says on its website that it offers users “anonymity and security,” according to Bubblemaps, a blockchain analytics firm.

A Nexo spokeswoman said the platform cooperates with law-enforcement request, but couldn’t comment publicly on individual clients. FixedFloat didn’t respond to emailed questions. Bubblemaps identified the two platforms at the request of the Journal, based on details disclosed in the DOJ’s complaint.

The fact that Spagnuolo got caught shows how even sophisticated operators have trouble staying hidden, said Nicolas Vaiman, co-founder and chief executive of Bubblemaps, a Paris-based startup that has enjoyed a burst of publicity in recent months by identifying suspicious Polymarket bets tied to the war with Iran.

“It’s very easy to make a mistake with this blockchain betting,” Vaiman said. Bettors who want to convert their crypto winning into dollars or other traditional currencies eventually need to use banks or regulated crypto exchanges, making it possible for the authorities to identify them, according to Vaiman.

“If you’re smart enough, you’re going to create a trail of wallets to obscure the funding source,” he said. “But at the end of the day, it’s all going to be visible on-chain.”

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

Alexander Osipovich is a London-based business, finance and economics reporter for The Wall Street Journal. He previously covered exchanges and cryptocurrencies. Before joining The Wall Street Journal in 2016, he worked for The Moscow Times, Agence France-Presse and <a href="http://Risk.net" rel="nofollow">Risk.net</a>, a trade publication focusing on derivatives.

Alexander has completed a Knight-Bagehot fellowship in business journalism at Columbia University. He won a SABEW Best in Business award in 2011 for a profile of hedge-fund manager turned anti-Putin activist Bill Browder, and he contributed to the Journal's SABEW award-winning coverage of the 2022 collapse of FTX.

Earlier in his career, Alexander worked as a software engineer in Silicon Valley. He has a bachelor's degree in history and a master's degree in computer science, both from Stanford University.

Governor Gavin Newsom has dismissed fossil fuels as “alternative energy,” and wants to power California with, among other things, the sun. Through extensive mandates and extra energy costs for non-solar consumers, the Newsom administration has directed billions to building solar energy capacity.

The centerpiece of this initiative is the Solar on Multifamily Affordable Housing (SOMAH) program. SOMAH began under Governor Jerry Brown, who signed legislation requiring a state commission to apportion up to $100 million a year from California’s cap-and-trade program to pay for the installation of solar panels on apartment buildings in poor areas. Since then, California has devoted nearly $900 million to SOMAH, which the state hoped would create 300 megawatts of power by 2030 and advocates envisioned would create a million solar-using renters.

The results have been disastrous. Since 2015, the program has installed or reserved only 129 megawatts of solar power for approximately 65,600 residents—nowhere close to the target of 1 million “solar renters.”

What happened? First, incompetence. For a decade, businesses and utilities have been forced to buy emissions credits, and while the state has lavished nearly $900 million on SOMAH, administrators designed the program so poorly that they have paid out only $131 million for solar installation.

As the program’s largest contractor admitted in a draft audit, potential customers were turned off by the paperwork, bureaucracy, and red tape. “Initially, we found housing owners excited about the program,” the contractor said, “but after a long and laborious process, they are much less enthusiastic.”

That is an understatement. From the beginning, the SOMAH program has been plagued by delays and cancellations. More than 400 applications have wound up cancelled or withdrawn, or about a third of the total. On average, projects take three and a half years to make it through the program’s gauntlet of paperwork and inspections.

Some projects have been fully installed—only to sit idle for a year or more waiting for permission to begin operating. As a result, more than $700 million of the program’s budget remains unspent. In other words, California can’t even give away a heavily subsidized, and sometimes free, product.

These failures do not mean, however, that no one is profiting. The managers of the SOMAH program have spent about $60 million on overhead, including salaries, conferences, website development, and more. And, as part of that budget, they have devoted at least $5.5 million to “community-based organizations” (CBOs), most of which are left-wing nonprofits that, in one case, labeled giving solar panels to low-income housing residents a way to fight “racial injustice.”

Under the guise of marketing and outreach, SOMAH paid more than $163,000 to the Asian Pacific Environmental Network (APEN). Vivian Yi Huang, the group’s co-director, extolled the need to “fight against the systems of white supremacy, patriarchy, and capitalism of the extractive economy.” The organization called for Richmond, California to “Defund the Police and Invest in Black Lives” and is a member of the Defund Police Coalition in Oakland, California.

California Environmental Justice Alliance (CEJA) told supporters that it “led in the creation” of SOMAH. Officials awarded it $230,000. The nonprofit wants the “democratization” of “land, labor, and resources” to “reverse the long course of environmental racism, the climate crisis, and colonialism.”

CEJA uses aggressive language to support its apparent goal of banning oil. “What’s at stake is our very survival,” its then-executive director said in 2021. “We must make the transition to 100 percent clean energy and bring all communities along to avoid a devastating climate apartheid.”

The group’s political arm, CEJA Action, endorses candidates and publishes voter guides as it “builds the political power of communities of color to advance environmentally and socially just policies.”

While APEN and CEJA no longer partner with the state, one of the active CBOs, Communities for a Better Environment, is no less radical. Last year, the group demanded the immediate release of everyone who was detained in immigration raids in California. “[T]here is no environmental justice without migrant justice,” the group wrote. “[W]e stand in solidarity with migrant, low-income, queer and trans, and people of color.”

Have these groups delivered results? No.

In 2023, a state-contracted auditor interviewed some CBOs. It found that they were only “responsible for a handful of submitted applications.” This year’s draft audit noted an “inverse relationship” between new applications and spending on these groups.

The other main beneficiary of the SOMAH program is a San Francisco-based corporation called Sunrun Inc., which bills itself as the country’s largest solar provider and has been the contractor for 78 percent of all SOMAH projects. The company has donated hundreds of thousands of dollars to political candidates—including $50,000 to Gavin Newsom’s campaigns—and has employed an army of lobbyists in Sacramento.

Newsom, in turn, has packed his administration with former Sunrun employees. The governor appointed the company’s public policy manager to the California Energy Commission and appointed its former chief policy officer to a regional water quality control board. In recent years, Sunrun representatives have met with government regulators’ offices to discuss SOMAH, including last December, when the company supported efforts to expand the program.

SOMAH and Sunrun did not respond to requests for comment.

California’s infrastructure projects seem always to fall short. SOMAH shows why. Liberal nonprofits help pass sweeping climate laws and receive money from the programs those laws create. In turn, those groups use their expanded influence to push for still more mandates and spending. The laws finance the activists, the activists demand more laws, and the cycle feeds itself—and helps well-connected firms, like Sunrun, collect new contracts.

Meantime, Californians increasingly think solar isn’t worth the headache. As one property owner told auditors, it’s “hard to justify poking a whole bunch of holes in your roof all over the place, just for a small amount of benefit.”

LLM (google/gemini-3.1-flash-lite-20260507) summary:

• Systemic Obsolescence: terraform exists merely due to professional inertia rather than genuine technical utility.
• Abstract Fallacy: hashicorp configuration language fails to mirror the mental models engineers utilize during design.
• Translation Overhead: the process of converting whiteboard sketches into static code creates unnecessary and inefficient labor.
• Fragmented Architecture: modern workflows suffer from disconnected representations of intent, application logic, and security policy.
• Illusion Of Sync: maintaining separate infrastructure and logic layers inevitably leads to configuration drift and administrative errors.
• Artificial Intelligence Disruption: automated systems eliminate the need for manual translation layers by directly interpreting natural language and design documentation.
• Intent Based Orchestration: future infrastructure management replaces restrictive languages with iterative refinement and explicit constraint definitions.
• Tooling Irrelevance: terraform functions as a redundant abstraction that adds complexity while failing to integrate seamlessly with programmatic execution.

LLM (google/gemini-3.1-flash-lite-20260507) summary:

• Legal Dispute: the department of justice is fighting to reinstate sanctions against francesca albanese after a federal judge intervened to pause them.
• Sanction Basis: the measures were initially applied due to albanese’s active efforts to facilitate legal action against united states and israeli nationals.
• Constitutional Claim: judge leon bizarrely granted a foreign national living abroad the protection of the us constitution despite clearly established contradictory case law.
• Absurd Precedent: the ruling ludicrously claims that simply owning domestic property or having a child born in the country grants deep constitutional rights to foreign, state-aligned activists.
• Dangerous Implications: this judicial overreach threatens to undermine national security by effectively bestowing legal immunity upon countless foreign figures holding real estate assets.
• Emergency Appeal: the justice department is demanding a stay to stop this dangerous precedent which ignores the clear lack of substantial ties or legal merit in the rapporteur’s case.
• Taxpayer Funding: american citizens are currently forced to subsidize twenty-two percent of the expenses for a political activist who actively works against national interests.
• Accountability Demand: there is an urgent need to leverage withheld united nations dues to secure the removal of a figure widely condemned for documented bias and lack of independence.

The sanctions imposed by the United States against Francesca Albanese, the UN Special Rapporteur for “human rights in the Palestinian Territory occupied since 1967,” are the focus of a legal battle in Washington.

The Justice Department has filed an emergency motion seeking to set aside a May 13 ruling by federal district court judge Richard Leon pausing U.S. sanctions on Albanese, an Italian citizen residing in Tunisia. The U.S. had sanctioned Albanese, whose antisemitic comments and biased conduct have been condemned by numerous countries, in July 2025 for having “directly engaged with the International Criminal Court in efforts to investigate, arrest, detain, or prosecute nationals of the United States or Israel, without the consent of those two countries.”

Judge Asserts That Albanese Possesses Rights Under U.S. Constitution

Judge Leon ruled that the sanctions imposed on Albanese “violate[d] the First Amendment” because they “unnecessarily circumscribe[d]” her “protected” speech. 

This was surprising given that a Supreme Court decision in 2020 held that “foreign citizens outside U.S. territory do not possess rights under the U.S. Constitution.” Albanese is “a foreign national who lives abroad, has not lived in the United States for more than ten years, and . . . engaged in all relevant expression abroad,” as the government’s filing noted.

However, Judge Leon cited an earlier Supreme Court case for the proposition that foreign nationals located abroad do possess Constitutional rights if they can demonstrate “substantial connections” with the U.S. He determined that Albanese meets the substantial connections test and is therefore eligible for First Amendment protections, principally because “she bought – and she still owns – property in the United States,” and also because her daughter was born in the U.S. and is therefore a U.S. citizen.

Ruling Sets Worrying Precedent

Judge Leon’s reasoning would significantly hinder U.S. military and law enforcement actions overseas by creating a large class of foreign persons overseas who enjoy Constitutional protections. In the 12 months prior to March 2025 alone, foreign buyers who lived abroad purchased 34,400 homes in the U.S.

Several of the world’s most corrupt foreign officials and oligarchs have owned real estate in the United States. In addition, the daughter of Russian Foreign Minister Sergey Lavrov was born in New York while he served at the United Nations.

In Albanese’s case, the Justice Department filed an emergency motion on May 21 for an administrative stay and stay pending appeal in the U.S. Court of Appeals for the District of Columbia. The motion calls for the court to set aside Judge Leon’s preliminary injunction, thereby reinstating sanctions on Albanese while the government undertakes a full appeal of his ruling. 

The emergency motion provided two major grounds for setting aside Judge Leon’s ruling. The first ground is that “substantial connections” to the United States do not qualify a non-citizen residing and speaking abroad for First Amendment protection — yet even if they did, Albanese lacks such connections. The second ground is that Judge Leon erred in enjoining the sanctions in their entirety, even though the only plaintiffs were Albanese’s husband and child, whose complaints could easily be resolved by exempting them from the sanctions while retaining them against Albanese herself.

The U.S. Should Broaden Its Efforts To Counter Albanese

Albanese and other UN special rapporteurs do not receive UN salaries. However, the United Nations pays for rapporteurs’ official expenses including support staff, security, and travel. The United States is billed for 22 percent of the United Nations’s regular budget, meaning that U.S. taxpayers effectively fund 22 percent of Albanese’s official expenses.

Albanese clearly violated UN rules requiring impartiality. French Foreign Minister Jean-Noel Barrot said that Albanese “presents herself as a UN independent expert, yet she is neither an expert nor independent — she is a political activist who stirs up hate.” The United Kingdom’s Foreign Office has separately urged that Albanese be “urgently investigated” for violating the code of conduct for her post.

The administration currently possesses leverage by withholding over $4 billion in UN dues. The United States has three times previously used budgetary leverage to extract significant UN reforms. Ensuring that the United Nations undertakes reforms to hold Albanese accountable should be a top priority for the United States.

Orde F. Kittrie is a senior fellow at the Foundation for Defense of Democracies (FDD) and a law professor at Arizona State University. He previously served for over a decade in legal and policy positions at the U.S. State Department. For more analysis from Orde and FDD, please subscribe HERE. Follow FDD on X @FDD. Follow Orde on X @ordefk. FDD is a Washington, DC-based, nonpartisan research institute focused on national security and foreign policy.

LLM (google/gemini-3.1-flash-lite-20260507) summary:

• Automation Risks: reliance on artificial intelligence for coding can lead to a erosion of personal technical comprehension.
• Skill Degradation: passive task completion using generative tools often results in lower retention and reduced critical thinking capabilities.
• Research Evidence: multiple scientific studies demonstrate that heavy reliance on external assistance diminishes human cognitive engagement during complex work.
• Workflow Defaults: current software tools prioritize rapid output over pedagogical engagement which discourages deep understanding.
• Strategic Delegation: tasks involving routine boilerplate code may be delegated whereas structural architectural knowledge remains essential for long term effectiveness.
• Labor Market Impacts: software developers unable to function without ai support face significant risks regarding professional relevance and employability.
• Active Learning: integrating intentional methodologies such as socratic questioning or self initiated problem testing can mitigate cognitive decline.
• Dual Objectives: maintaining professional growth requires balancing immediate productivity targets with independent individual learning milestones.

Right now, it’s too easy to let AI write the code while you skip the learning. The bug gets fixed. Your mental model doesn’t move. We are silently trading future capability for present-day speed, and the tools won’t force us to do otherwise. That part has to come from you.

There’s a default loop most of us have settled into. You paste in a spec or error message. The model hands you a fix. The symptom vanishes. You ship. Somewhere in that loop, the messy struggle between problem and solution stops happening at all.

I’ve written before about cognitive surrender, the moment an AI reviewer’s verdict quietly replaces your own. This is the solo version of that same loop. It’s just you and the model. The model is faster, so you stop trying to compete on comprehension. Across thousands of these small interactions, what you can actually build without an AI looking over your shoulder gets a little weaker every week. None of these moments feel like a problem on the day they happen.

I’m not anti-AI. I use these tools daily and have shipped more with them in the last year than in the five years before it. But the default way we use them is optimized for one thing: closing tasks. That is a completely different goal from staying sharp enough to steer them over a career that spans decades.

The studies are converging on the same point

Several pieces of research over the last year have landed in roughly the same place.

Anthropic ran a randomized trial in early 2026 where engineers learned a new Python library, half with AI assistance and half without. Both groups finished the tasks at the same speed. But the AI group bombed the follow-up comprehension quiz: 50% versus 67% for the manual group, with the gap widening on debugging. The interesting cut was inside the AI group itself. Engineers who used AI to ask conceptual questions scored above 65%. Engineers who copy-pasted the generated code scored under 40%. The tool didn’t determine the outcome. The posture did.

MIT’s Your Brain on ChatGPT study compared essay writing across LLM, search-engine, and brain-only groups. EEG measurements showed brain connectivity scaling down with every layer of external support. The LLM group showed the weakest coupling. After writing the essay, 83% of LLM users couldn’t quote a single line of what they had just produced. The researchers called this cognitive debt: saving mental effort today, paying for it in critical thinking tomorrow.

A CHI 2026 study added a related finding. When people had LLM access at the start of a task, the LLM framed the entire problem. Even when the human did the rest of the work themselves, that initial anchoring produced measurably worse decisions. The order of operations mattered more than the total amount of AI used.

Different methodologies, same conclusion. Using AI without an active intent to learn quietly degrades the skill you’re being paid for.

The tools default to shipping, not teaching

If you fire up a coding agent and stick to the defaults, everything is tuned for one metric: getting the task done. The model writes the code. You accept it. The loop repeats. At no point does the tool pause and ask “what do you think the problem is?” or “try writing the first five lines yourself.”

That isn’t a conspiracy. It’s UX gravity. Product teams get rewarded for merged changes and shorter cycle times, not for making you a sharper engineer. We all want fewer keystrokes, so the tools have sanded the friction away. The trouble is that friction was where the learning lived.

A few companies have started pushing back. Anthropic shipped Learning Mode for Claude, which uses Socratic questioning and stops to ask you to write code before continuing. OpenAI and Google have shipped similar features. Almost nobody uses them for real production work. We’ve quietly filed them under “for students” and that’s a mistake. The same feature that helps a sophomore learn React works for a senior engineer learning Rust. You just have to be willing to feel like a beginner again.

“If the AI can do it, why do I need to understand it?”

A fair question. For some work, the answer is: you don’t. If it’s boilerplate, glue code, or a throwaway CI script you’ll never look at again, delegate it. The opportunity cost of memorizing YAML syntax is too high.

For real software, pure delegation breaks down in a few specific places.

When something breaks. AI-generated code crashes the same way human code does. “The agent wrote it” doesn’t help you debug problems. Somebody on the team has to understand the architecture.

When it’s confidently wrong. LLMs hallucinate. The only defense against a plausible-looking incorrect answer is enough expertise to spot it.

When the foundation changes. Code is temporary; systems are permanent. When frameworks update or a security review flags a structural issue, you can’t re-prompt your way out. You need engineers who understand the system well enough to migrate it.

When you leave the median. AI is brilliant at problems that have been solved a million times on GitHub. The further you stray from the median, the worse it gets. The hard, undocumented problems, the ones that justify a senior engineer’s salary, still require deep understanding.

When the market adjusts. That 20% drop in junior developer employment since 2022 isn’t a fluke. Engineers who can only ship with AI, and not without it, are entering a labor pool that is already re-pricing what expertise is worth.

If you use AI to skip learning, you’re trading future relevance for a slightly easier Tuesday.

The fix is in how you prompt, not whether you do

The good news is that the same tools that produce cognitive debt can produce sharper engineers. The difference is in what you ask of them.

Form a hypothesis before you ask. Before requesting a fix, write down two or three sentences on what you think the problem is. Use the model’s answer to test your theory, not to replace it.

Ask for the explanation before the code. In unfamiliar territory, your first prompt should be something like “explain how this works, what the alternatives are, and what the tradeoffs are.” Ask for the code only after you’ve grasped the concepts.

Turn on Learning Mode when you’re out of your depth. Claude has it. ChatGPT has Study Mode. Gemini has Guided Learning. Yes, it feels slower. That’s the point.

Treat AI output like a PR from a junior engineer. Read it. Critique it. Push back on it. Would you merge it just because the tests passed? If not, don’t merge it here either.

Re-derive things by hand once in a while. Take a piece of code the model wrote for you and try to recreate it from scratch. It’s the calibration check that tells you how much you’ve quietly lost.

Ask the model to teach you what it just did. After it writes a clever function, ask what concepts it used and what you’d need to read to understand the design choice. One extra prompt changes what you take away from the session.

None of these are dramatic. They’re small posture shifts inside the same tools you’re already using.

Two metrics, not one

I’ve started ending coding sessions with a simple question: did I learn anything today, or did I just close tickets?

Sometimes the honest answer is “I just closed issues” and that’s fine. If it becomes the answer for months in a row, cognitive debt is accumulating in the background.

Ship and learn are two separate metrics. Your manager and your customers will only ever ask about the first one. The second is on you.

I’d rather ship 80% of what I could have and learn 100% of what I needed to, than the reverse. Over years, those two strategies produce very different engineers.

You don’t have to choose between using AI and learning. You do have to choose a workflow that does both, because the defaults won’t choose it for you. The tools are ready whenever you are. The next boring task you were about to delegate is a good place to start.

Further reading: Anthropic’s skill-formation study, MIT’s Your Brain on ChatGPT (arXiv 2506.08872), the CHI 2026 paper on LLM use under time constraints, Stack Overflow’s AI vs Gen Z report, and my earlier posts on comprehension debt and cognitive surrender.