• Strategic_Vision: Newsom portrayed as remarkably adept politically despite California’s struggles with affordability, homelessness, energy, regulation, and public trust.
• Performance_Matters: Current politics reward perceived strength, dominance, and media savvy more than policy records, aligning with Newsom’s style.
• Profile_Themes: Major profiles emphasize his deep network, digital fluency, and ideological ambiguity as assets for winning modern primaries.
• Trump_Parallel: Similar to Trump, Newsom recasts failures as pragmatism, leverages masculinity and spectacle, and controls narratives via short-form media.
• Online_Appeal: Some right-wing internet subcultures view him as a confident “chad,” enhancing his cultural power despite ideological differences.
• Democratic_Demand: Resistance liberals now seek fighters willing to absorb scandal, so attacking Newsom’s record may strengthen rather than weaken him.
• Opponent_Contrast: Governors like Shapiro or figures like Emanuel appear competent but lack Newsom’s dominance and willingness to generate conflict.
• Attention_Economy: Continuous podcasting, controversy courting, targeted books, and tech-friendly positioning boost familiarity, making charisma a payoff over governance.

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Courtesy Michael M. Santiago/Getty.

To those of us who dislike him, it is irritating that Gavin Newsom keeps making us concede that he might be the most strategically adept Democrat in American politics.

By all accounts, he should be flailing. As Republican political consultant Luke Thompson explained in City Journal a few years ago, Newsom is not a misunderstood technocrat or a victim of circumstance. He is the product, and now the steward, of a political machine that has grown self-satisfied and increasingly detached from the demands of everyday governance.

This machine and its leader are failing their constituents. By most conventional measures—housing affordability, homelessness, energy reliability, regulatory competence, public trust, lawlessness, and disorder—Newsom’s governorship is a case study in liberal dysfunction.

If politics still worked the way it was supposed to work, this would be disqualifying. And yet the uncomfortable truth—one that has become harder to ignore amid whispers about 2028—is that Newsom may be better positioned than any other Democrat to take the White House.

This is true not despite his record, but because that record doesn’t matter in the way it once did. American politics is drifting away from accountability, becoming more performative and aesthetic. Voters increasingly judge politicians by what they seem to represent: strength or weakness, dominance or deference. That’s an environment for which Newsom was made, and on which he has relentlessly capitalized.

This is the insight leaking through in the recent spate of Newsom profiles. Jonathan Martin’s Politico treatment argued not that Newsom was beloved, or even broadly trusted, but that his combination of deep political network, digital fluency, and deliberate ideological ambiguity is what’s needed to win the nomination today. Helen Lewis’s long essay in the Atlantic, though more skeptical, shows Newsom as a politician who has internalized an old Clinton-era lesson Democrats once understood instinctively and later forgot: voters tend to prefer strong and wrong to weak and right. Both pieces imply that Newsom’s disastrous governing record just doesn’t matter against these facts.

The parallel here is obvious, but worth stating carefully. Donald Trump did not win the presidency in 2016 because voters concluded that he had an impeccable business record, but because they saw his business dealings as evidence that he understood how to fight, and how to dominate attention. His inconsistencies were not hidden; they were reframed as authenticity and street smarts. Even his long habit of donating to both political parties became evidence that he knew how the system functioned and how to bend it to his advantage.

Newsom is attempting something similar on the left, trying to retroactively recast California’s recent history into a success story. When confronted with failures, he does not concede error. Instead he reinterprets his time in office as evidence of pragmatism, learning, and responsiveness to changing conditions. He changes the story, a strategy uniquely suited to a media environment saturated with short clips, viral moments, and perpetual conflict.

Newsom mirrors Trump too in his relationship to masculinity, media, and cultural power. For years, Democrats have struggled to articulate a positive vision of masculinity, and in the process, alienated voters—particularly young men. But Newsom offers a solution to their problem. His approach is neither the feminized style favored by progressive nonprofits and HR departments, nor the trad-Catholic identity increasingly in vogue in right-wing corners of Washington. Instead, Newsom, who describes himself as more spiritual than religious, projects a masculinity that is aesthetic, performative, and unapologetic.

The strategy is working, even among some of the internet’s more unsavory subcultures. Within corners of the online right populated by “groypers” and “looksmaxxing” obsessives—many of whom harbor open hostility toward liberalism writ large—Newsom is seen as superior to figures like JD Vance. To them, Newsom comes across as confident, dominant, and unembarrassed by power. In the strange vernacular of that world, he is a “chad” who “mogs” Vance and others.

In post-Trump politics, masculinity could work for the left, too. Rather than a “return to normalcy,” many Democrats now want a Trump-like figure: someone willing to break rules, absorb scandal, and bare-knuckle brawl. As a result, attacking Newsom’s past controversies or California’s problems—as some of his intra-party critics have already begun to do—may well backfire, hardening his appeal instead of weakening it.

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By contrast, candidates with more sterling records often feel oddly inert. Pennsylvania governor Josh Shapiro is more competent than Newsom and oversees a state Democrats need to win in 2028. But Shapiro lacks what Newsom has: dominance, confidence, and a willingness to pick fights. He folds when confronted by progressive activists on school choice or energy policy, and strains to maintain cordial relations with the antisemites in his party who will never truly accept him.

The dynamic recalls the 2024 Republican presidential primary. Ron DeSantis was disciplined, competent, and nerdy. Trump was unpredictable, brazen, and singularly attuned to the psychology of his base. Trump won, because of the vibes. Newsom may well do the same to Shapiro, and candidates like him.

Or take another likely 2028 contender, Rahm Emanuel, who has framed the Democratic primary as a contest between resistance theatrics (Newsom) and policy pragmatism (himself). Emanuel’s recent forays into early-primary states, framed heavily around education reform and a willingness to challenge Democratic orthodoxies, are designed with that narrative in mind.

To anyone who isn’t a Marxist, it is an appealing story. But 2028 is unlikely to be a two-pole contest between center-left competence and liberal spectacle. Instead, we may see a strange inversion of the 2020 primary.

Then, most candidates raced left, while Joe Biden benefited from standing still. This time, as Democrats compete on pseudo-moderation and “electability,” someone like Alexandria Ocasio-Cortez could benefit by refusing to budge.

Newsom, though, is positioned to survive both dynamics at once. He is neither a stylized centrist nor a militant progressive. He’s something more slippery.

Resistance liberals see a fighter eager to antagonize Trump. Tech-aligned moderates see an executive skeptical of heavy-handed regulation. Labor sees selective concessions. Woke culture warriors see symbolic validation. And Newsom, more aesthetic than substance, can’t be pinned down to any one group.

Part of the reason is that Newsom has gotten good at knowing when to play the moderate. His aggressive push for partisan redistricting was framed not as escalation but as proportional response—a necessary counter to Republican hardball elsewhere. He has also broken with Democrats on destructive proposals like a wealth tax, flirted with Abundance-style housing reforms, and leaned into relatively tech-friendly positioning on AI and autonomous vehicles.

None of this means a Newsom nomination is inevitable. Other figures—including the other Californian heavyweight who just ran on the Democratic national ticket in 2024—are being written off far too early. But it does suggest that the center of gravity in Democratic politics has shifted toward candidates in the new, Newsom mold.

At the same time, Newsom’s unabashed celebrity politics could outpace him. Unlike some potential dark horses—figures like Jon Stewart, Mark Cuban, or Stephen A. Smith—Newsom does not possess deep, long-running fame that extends beyond political notoriety. As Trump demonstrated, you can win with a more intimate form of familiarity that TV stars often command.

By the time he ran, voters had spent years with Trump, watching him judge, argue, bluster, and perform. That intimacy breeds a strange kind of trust—or at least tolerance—even among people who do not particularly like you.

Trump achieved that intimacy through The Apprentice. For the generation below the one that came to know Trump that way, similar bonds exist with Stewart from The Daily Show, Cuban from Shark Tank, or Smith from ESPN.

These are not universally beloved figures. In fact, they are actively polarizing. But they are known. In a country run on the attention economy, that kind of visibility confers a form of credibility that political insiders often underestimate.

Newsom, acutely aware of this dynamic, has been working to narrow that gap. His constant podcasting, his willingness to court controversy, and his decision to write a book aimed explicitly at young men are all of a piece.

As resistance politics becomes less woke and more masculine, Republicans should take notice and prepare accordingly. It is no longer sufficient to dismiss Gavin Newsom as a smug governor with a bad record. That critique, while accurate, risks misunderstanding the moment.

American politics increasingly rewards those who command attention, project confidence, and refuse to be pinned down by their own history. In that environment, the ability to narrate and perform often matters more than the ability to govern well—and Newsom has shown an unusually clear-eyed understanding of that reality.

You do not have to admire Gavin Newsom. You do not have to forgive his record. But you may, sooner than you would like, have to hand it to him. Because everyone is talking about him. And nowadays, that is half the battle.

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• Dual Identity: The Ralph Wiggum plugin for Claude Code is simultaneously described as a meme and an AGI-adjacent tool that automates economically valuable work via relentless iteration.
• Origin: Geoffrey Huntley, frustrated by human-in-the-loop bottlenecks, created a humble Bash loop on his Australian goat farm to force Claude to confront its own failures until success.
• Philosophy: The methodology hinges on context engineering, piping raw outputs—including errors—back into Claude so the model repeatedly faces its mistakes and seeks a correct solution.
• Sanitization Shift: Anthropic’s official plugin, formalized by Boris Cherny, reframes the hack with the mantra “Failures Are Data,” adding structured feedback and safety mechanisms.
• Stop Hook: Claude receives a completion promise and the hook blocks exits lacking it, feeding failures back into the session to create self-referential loops that demand passing tests or linters.
• Night Shift Gains: Users report massive efficiency wins—from $297 API contracts to overnight multi-repo generation and autonomous long maintenance runs—by letting Ralph run while they sleep.
• Caveats: Infinite loops can burn valid budgets, so experts recommend `--max-iterations` escape hatches, sandboxed environments, and cautious use of `--dangerously-skip-permissions`.
• Availability: Claude Code users can invoke the official plugin via `/plugin ralph` while community forks and Huntley’s original scripts remain on GitHub.

In the fast-moving world of AI development, it is rare for a tool to be described as both "a meme" and AGI, artificial generalized intelligence, the "holy grail" of a model or system that can reliably outperform humans on economically valuable work.

Yet, that is exactly where the Ralph Wiggum plugin for Claude Code now sits.

Named after the infamously high-pitched, hapless yet persistent character on The Simpsons, this newish tool (released in summer 2025) — and the philosophy behind it — has set the developer community on X (formerly Twitter) into a tizzy of excitement over the last few weeks.

For power users of Anthropic’s hit agentic, quasi-autonomous coding platform Claude Code, Wiggum represents a shift from "chatting" with AI to managing autonomous "night shifts."

It is a crude but effective step toward agentic coding, transforming the AI from a pair programmer into a relentless worker that doesn’t stop until the job is done.

Origin Story: A Tale of Two Ralphs

To understand the "Ralph" tool is to understand a new approach toward improving autonomous AI coding performance — one that relies on brute force, failure, and repetition as much as it does on raw intelligence and reasoning.

Because Ralph Wiggum is not merely a Simpsons character anymore; it is a methodology born on a goat farm and refined in a San Francisco research lab, a divergence best documented in the conversations between its creator and the broader developer community.

The story begins in roughly May 2025 with Geoffrey Huntley, a longtime open source software developer who pivoted to raising goats in rural Australia.

Huntley was frustrated by a fundamental limitation in the agentic coding workflow: the "human-in-the-loop" bottleneck.

He realized that while models were capable, they were hamstrung by the user’s need to manually review and re-prompt every error.

Huntley’s solution was elegantly brutish. He wrote a 5-line Bash script that he jokingly named after Ralph Wiggum, the dim-witted but relentlessly optimistic and undeterred character from The Simpsons.

As Huntley explained in his initial release blog post "Ralph Wiggum as a 'software engineer,'" the idea relied on Context Engineering.

By piping the model’s entire output—failures, stack traces, and hallucinations—back into its own input stream for the next iteration, Huntley created a "contextual pressure cooker."

This philosophy was further dissected in a recent conversation with Dexter Horthy, co-founder and CEO of the enterprise AI engineering firm HumanLayer, posted on YouTube.

Horthy and Huntley argue that the power of the original Ralph wasn't just in the looping, but in its "naive persistence" — the unsanitized feedback, in which the LLM isn't protected from its own mess; it is forced to confront it.

It embodies the philosophy that if you press the model hard enough against its own failures without a safety net, it will eventually "dream" a correct solution just to escape the loop.

By late 2025, Boris Cherny, Anthropic's Head of Claude Code* formalized the hack into the official ralph-wiggum plugin.

However, as noted by critics in the Horthy/Huntley discussion, the official release marked a shift in philosophy—a "sterilization" of the original chaotic concept.

While Huntley’s script was about brute force, the official Anthropic plugin was designed around the principle that "Failures Are Data."

In the official documentation, the distinction is clear. The Anthropic implementation utilizes a specialized "Stop Hook"—a mechanism that intercepts the AI's attempt to exit the CLI.

1. Intercept the Exit: When Claude thinks it is done, the plugin pauses execution.

2. Verify Promise: It checks for a specific "Completion Promise" (e.g., "All tests passed").

3. Feedback Injection: If the promise isn't met, the failure is formatted as a structured data object.

The "Tale of Two Ralphs" offers a critical choice for modern power users:

• The "Huntley Ralph" (Bash Script/Community Forks): Best for chaotic, creative exploration where you want the AI to solve problems through sheer, unbridled persistence.

• The "Official Ralph" (Anthropic Plugin): The standard for enterprise workflows, strictly bound by token limits and safety hooks, designed to fix broken builds reliably without the risk of an infinite hallucination loop.

In short: Huntley proved the loop was possible; Anthropic proved it could be safe.

What It Offers: The Night Shift for Coders

The documentation is clear on where Ralph shines: new projects and tasks with automatic verification (like tests or linters).

But for the "boring stuff," the efficiency gains are becoming the stuff of legend. According to the official plugin documentation on GitHub, the technique has already logged some eye-watering wins.

In one case, a developer reportedly completed a $50,000 contract for just $297 in API costs—essentially arbitraging the difference between an expensive human lawyer/coder and a relentless AI loop.

The repository also highlights a Y Combinator hackathon stress test where the tool "successfully generated 6 repositories overnight," effectively allowing a single developer to output a small team's worth of boilerplate while asleep.

Meanwhile, on X, community members like ynkzlk have shared screenshots of Ralph handling the kind of maintenance work engineers dread, such as a 14-hour autonomous session that upgraded a stale codebase from React v16 to v19 entirely without human input.

To make this work safely, power users rely on a specific architecture. Matt Pocock, a prominent developer and educator who posted a recent YouTube video overview of why Ralph Wiggum is so powerful.

As he states: "One of the dreams of coding agents is that you can wake up in the morning to working code, that your coding agent has worked through your backlog and has just spit out a whole bunch of code for you to review and it works."

In Pocock's view, Wiggum (the plugin) is about as close as you can come to this dream. It's "a vast improvement over any other AI coding orchestration setup I've ever tried and allows you to actually ship working stuff with longrunning coding agents," he states.

He advises using strong feedback loops like TypeScript and unit tests.

If the code compiles and passes tests, the AI emits the completion promise; if not, the Stop Hook forces it to try again.

The Core Innovation: The Stop Hook

At its heart, the Ralph Wiggum technique is deceptively simple. As Huntley put it: "Ralph is a Bash loop."

However, the official plugin implements this in a clever, technically distinct way. Instead of just running a script on the outside, the plugin installs a "Stop Hook" inside your Claude session.

1. You give Claude a task and a "completion promise" (e.g., <promise>COMPLETE</promise>).

2. Claude works on the task and tries to exit when it thinks it's done.

3. The hook blocks the exit if the promise isn't found, feeding the same prompt back into the system.

4. This forces a "self-referential feedback loop" where Claude sees its previous work, reads the error logs or git history, and tries again.

Pocock describes this as a shift from "Waterfall" planning to true "Agile" for AI. Instead of forcing the AI to follow a brittle, multi-step plan, Ralph allows the agent to simply "grab a ticket off the board," finish it, and look for the next one.

Community Reactions: 'The Closest Thing to AGI'

The reception among the AI builder and developer community on social media has been effusive.

Dennison Bertram, CEO and founder of custom cryptocurrency and blockchain token creation platform Tally, posted on X on December 15:

"No joke, this might be the closest thing I've seen to AGI: This prompt is an absolute beast with Claude."

Arvid Kahl, founder and CEO of automated podcast business intelligence extraction and brand detection tool Podscan, persuasively covered the benefits of Ralph's persistent approach in his own X post yesterday:

And as Chicago entrepreneur Hunter Hammonds put it:

Opus 4.5 + Ralph Wiggum with XcodeBuild and playwright is going to mint millionaires. Mark my words. You’re not ready

In a meta-twist characteristic of the 2025 AI scene, the "Ralph" phenomenon didn't just generate code—it generated a market.

And earlier this week, someone — not Huntley, he says — launched a new $RALPH cryptocurrency token on the Solana blockchain to capitalize on the hype surrounding the plugin.

The Catch: Costs and Safety

The excitement comes with significant caveats. Software firm Better Stack warned users on X about the economic reality of infinite loops:

"The Ralph Wiggum plugin runs Claude Code in autonomous loops... But will those nonstop API calls break your token budget?"

Because the loop runs until success, the documentation advises using "Escape Hatches."

Users should always set a --max-iterations flag (e.g., 20 or 50) to prevent the AI from burning through cash on an impossible task.There is also a security dimension.

To work effectively, Ralph often requires the --dangerously-skip-permissions flag, granting the AI full control over the terminal.

Security experts strictly advise running Ralph sessions in sandboxed environments (like disposable cloud VMs) to prevent the AI from accidentally deleting local files.

Availability

The Ralph Wiggum technique is available now for Claude Code users:

• Official Plugin: Accessible inside Claude Code via /plugin ralph.

• Original Method: The "OG" bash scripts and community forks are available on GitHub.

As 2026 begins, Ralph Wiggum has evolved from a Simpsons joke into a defining archetype for software development: Iteration > Perfection.

*Correction: This article mistakenly characterized Boris Cherney's title. The article has since been updated and corrected, and we regret the error.

• Data source: Analysis pooled responses from 56,892 individuals across three large online datasets to examine sexual attraction patterns.
• Methodology: Combined self-reported attraction ratings, fantasy frequency, implicit association tests, and questionnaire-based IATs for a multilayered approach.
• Gender specificity: Men reported high attraction to preferred gender and low attraction to non-preferred gender, creating a wide specificity gap.
• Female profile: Women showed somewhat lower attraction to their preferred gender but higher attraction to their non-preferred gender, indicating broader potential attractions.
• Straight group nuance: Straight women still preferred men but with less exclusivity; straight men displayed uniquely strong gender specificity.
• Sexual orientation variation: Gay and lesbian participants often exhibited smaller, absent, or reversed specificity gaps, with lesbians sometimes matching gay men.
• Social and cultural factors: Results challenge sex-drive explanations, suggesting societal norms and sexual objectification influence specificity, especially toward women.
• Orientation granularity: “Mostly” straight or gay identities, more common among women, drove much of the flexibility, while men favored “exclusive” categories.

A new analysis of data from over 50,000 individuals indicates that men exhibit a more exclusive pattern of sexual attraction than women do. The research shows that while men strongly prefer one gender over the other, women tend to display a wider range of potential attractions. These results appear in The Journal of Sex Research.

For decades, researchers have attempted to map the differences in how men and women experience sexual desire. Older investigations often relied on measuring physical signs of arousal in a laboratory setting. Those experiments frequently suggested that men are “gender-specific.” This means men typically show physical arousal only when viewing the gender they prefer.

In contrast, those same historical studies often found that straight women displayed physical arousal when viewing images of both men and women. This led to a prevailing theory that female sexuality is inherently less specific than male sexuality. However, it remained unclear if this pattern applied to psychological feelings of attraction or fantasies.

Sapir Keinan-Bar, Yoav Bar-Anan, and Daphna Joel conducted the current investigation to answer this question. They are researchers affiliated with the School of Psychological Sciences and the Sagol School of Neuroscience at Tel-Aviv University. They sought to determine if the gender gap in specificity exists when measuring self-reported feelings and subconscious associations. They also aimed to see how these patterns manifest across different sexual orientations.

The team aggregated data from three separate large-scale online datasets. The total pool of participants included 56,892 individuals. The datasets contained information from volunteers who had visited research websites or utilized paid survey platforms.

The researchers analyzed responses to direct questions regarding sexual identity. Participants rated their level of attraction to men and women on numerical scales. They also reported the frequency of their erotic fantasies involving men or women. This allowed the authors to compare conscious reports of desire.

In addition to direct questions, the study utilized indirect measures of attraction. One primary tool used was the Implicit Association Test (IAT). This computerized task measures the strength of mental links between concepts.

During an IAT, a participant might sort words or images into categories like “Men” or “Women” and “I am sexually attracted” or “I am not sexually attracted.” The speed at which a participant sorts these items reveals their automatic associations. A faster response time suggests a stronger underlying mental connection.

The researchers also used a variation called the Questionnaire-Based Implicit Association Test (qIAT). This version uses statements rather than single words or images. It assesses attraction to men and women separately rather than comparing them directly.

The analysis of this massive dataset revealed a consistent pattern. Men generally exhibited greater gender-specificity than women. This trend appeared across self-reported attraction, fantasy frequency, and the indirect association measures.

The data provided a detailed look at why this gap exists. Men reported very high levels of attraction toward their preferred gender. At the same time, they reported very low levels of attraction toward their non-preferred gender. This created a large statistical gap between their likes and dislikes.

Women showed a different profile. They reported slightly lower levels of attraction to their preferred gender compared to men. More importantly, they reported higher levels of attraction to their non-preferred gender than men did. This finding suggests that women are psychologically more open to their non-preferred gender.

The study clarified the nature of attraction among heterosexual women. Contrary to some interpretations of older physiological studies, straight women were not completely non-specific. They clearly preferred men over women in both self-reports and indirect measures.

However, the intensity of this preference was not as exclusive as the preference straight men held for women. Straight women demonstrated a distinct preference, but the separation was less extreme. The researchers noted that this pattern was robust across the different samples.

The study also examined individuals who identified as gay or lesbian. The researchers found that the gender gap in specificity was different in these groups. The large difference seen between straight men and women was often smaller, absent, or reversed among gay and lesbian participants.

For example, lesbian women showed levels of specificity that were sometimes similar to, or even higher than, gay men. This suggests that the high degree of exclusivity observed in straight men might be a unique characteristic of that specific group. It may not be a universal trait of male sexuality.

The analysis of sexual fantasies reinforced the findings regarding attraction. Men reported fantasies almost exclusively about their preferred gender. Women reported fantasies primarily about their preferred gender, but with more frequent exceptions than men.

The authors evaluated several theoretical explanations for these results. One common theory posits that men simply have a higher sex drive than women. The data presented a challenge to this idea.

If men simply had a higher sex drive, they should report higher attraction to everyone. Instead, women reported higher attraction to their non-preferred gender than men did. This indicates that the difference is not just about the total amount of sexual desire.

Another theory considers the impact of social norms. Society often imposes strict expectations on heterosexual masculinity. Men face social penalties for showing interest in other men.

This social pressure might encourage men to report extreme attraction to women and deny any attraction to men. This would create the highly specific pattern observed in the data. Women generally face less social stigma for expressing flexibility in their attractions.

The authors also discussed the theory of sexual objectification. Western culture frequently portrays women as sexual objects. This cultural conditioning might cause individuals of all genders to develop some degree of attraction toward women.

The results offered some support for this objectification hypothesis. Across the board, attraction to the non-preferred gender was higher when that gender was female. For instance, straight women reported more attraction to women than straight men reported to men.

The researchers pointed out the benefits of using detailed categories for sexual orientation. The study allowed participants to identify as “mostly straight” or “mostly gay” rather than just using three rigid categories. This nuance revealed that people in the “mostly” categories drove much of the flexibility seen in the data.

Women were more likely than men to identify with these “mostly” categories. Men were more likely to identify as “exclusively” straight or gay. This difference in self-identification aligns with the finding that men are more gender-specific in their attractions.

There are limitations to this research. The data came from online samples, which may not perfectly represent the general population. The participants were primarily English speakers and likely skewed younger and more liberal.

The measures relied on honesty in self-reporting and the assumption that reaction times reflect attraction. These are proxies for real-world experience. The study did not measure physiological arousal, so it cannot be directly compared to the older laboratory studies on that metric.

Future research could explore these patterns in different cultures. Examining societies with different gender norms could help separate biological tendencies from social conditioning. It would be useful to see if the high specificity of straight men persists in cultures with different concepts of masculinity.

The study, “Gender-Specificity in Sexual Attraction and Fantasies: Evidence from Self-Report and Indirect Measures,” was authored by Sapir Keinan-Bar, Yoav Bar-Anan, and Daphna Joel.

• Competition goal: South Korea launched a contest to develop an independent AI model using Korean technology for national self-reliance.
• Foreign code use: Three of five finalists were found to incorporate open-source code from foreign, including Chinese, AI models, prompting scrutiny.
• Expert perspectives: Some experts argue that excluding open-source software is impractical and wasteful, while others cite security risks and national ownership concerns.
• Sovereign AI push: The government seeks two winners by 2027 achieving at least 95% parity with top models, offering funding, talent access, and government-procured chips.
• Upstage controversy: Rivals alleged Upstage’s model mirrored Chinese Zhipu AI and retained copyright markers; Upstage shared logs showing scratch training but acknowledged open-source inference code usage.
• Naver and SK Telecom scrutiny: Naver admitted to using external encoders similar to Alibaba/OpenAI, and SK Telecom faced claims of inference code resembling China’s DeepSeek, while both stress their core engines are independently developed.
• Rules unclear: Competition guidelines didn’t ban foreign open-source use, the science ministry has issued no new rules, and it plans to cut one finalist while the minister welcomes debate.
• Core training stance: Seoul National University’s AI Institute director affirmed that finalists trained their models from scratch without relying on foreign tools for internal numerical tuning.

By

Jiyoung Sohn

Jan. 13, 2026 11:00 pm ET

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The SK Telecom pavilion at an information-technology show in Seoul. Jeon Heon-Kyun/Shutterstock

SEOUL—Last June, the South Korean government launched a competition to create a new independent AI model developed with Korean technology. A homegrown tool like that was critical to ensuring Korea’s technological self-reliance in a world already dominated by U.S. and Chinese artificial intelligence.

It is proving to be easier said than done.

Of five finalist companies in the three-year competition, three have been found to use at least some open-source codes from foreign AI models, including Chinese ones.

The companies and AI experts argue it makes little sense to shun existing AI models and attempt to build everything from scratch. But others say that any use of foreign tools creates potential security risks and undercuts hopes of cultivating an AI model undeniably a nation’s own.

It isn’t realistic to require every single piece of code be written entirely in-house when pursuing AI-model development, said Gu-Yeon Wei, an electrical-engineering professor at Harvard University, who is familiar with the Korean competition but not directly involved with any of the competitors.

“To forgo open-source software,” Wei said, “you’re leaving on the table this huge amount of benefit.”

Countries worldwide are increasingly looking to reduce foreign reliance and hone their own capabilities in a technology that could profoundly affect their economic competitiveness and national security.

A chip from Nvidia’s Quantum-X platform. Bridget Bennett/Bloomberg News

South Korea, with a bevy of chip giants, software firms and political backing, represents one of the most aggressive proponents of so-called sovereign AI.

The race seeks to identify two homegrown winners by 2027 able to achieve 95% or higher parity in performance with leading AI models from the likes of OpenAI or Google. Winners get access to state funding for data and hiring talent, as well as access to government-procured chips essential for AI computing.

Controversy over one of the finalists, Upstage, erupted in recent days. Some components of its AI model bore resemblance to an open-source model from China-based Zhipu AI, according to the chief executive of Sionic AI, a local rival. In addition, Zhipu AI copyright markers were left within some of Upstage’s code, he claimed.

“It’s deeply regrettable that a model suspected to be a fine-tuned copy of a Chinese model was submitted to a project funded by taxpayers’ money,” Ko Suk-hyun, the Sionic CEO, wrote on LinkedIn. Sionic had also entered the South Korean competition, though failed to make the finalist list.

In response, Upstage held a livestreamed verification session that shared its development logs to prove its model was developed and trained from a blank state using its own methods. But the inference code used to make the model run had used open-source elements that originated from Zhipu AI, which is widely used globally. Sionic’s CEO apologized.

The scrutiny prompted closer looks of the other finalists. Naver’s AI model was accused of bearing similarities to offerings from China’s Alibaba and OpenAI in its visual and audio encoders that translate images and sounds into a format that a machine can understand.

Naver’s headquarters in Seongnam, south of Seoul. Yonhap News/ZUMA Press

SK Telecom faced criticism that the inference codes for running its AI model bore similarities with those of China’s DeepSeek.

Naver admitted to using external encoders, but said it was a strategic decision to use a standardized technology. It stressed that the model’s core engine—which determines how it learns and is trained—were developed entirely by the company. SK Telecom made a similar argument, stressing the independence of its model’s core.

The competition’s rules didn’t explicitly state whether open-source code from foreign firms could be used or not. South Korea’s science ministry, which is overseeing the competition, hasn’t given out any new guidelines since the controversy. The country’s science minister, Bae Kyung-hoon, welcomed the robust debate.

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Do the benefits of using open-source code from Chinese AI models outweigh the potential security risks? Join the conversation below.

“As I watched the technological debates currently stirring our AI industry, I actually saw a bright future for South Korean AI,” Bae wrote in a social-media post earlier this month.

The ministry declined to comment when asked by The Wall Street Journal. It plans to eliminate one of the five finalists from the competition this week as scheduled.

AI models are developed by setting and fine-tuning internal numerical values to get an output, and those core tasks don’t appear to have relied on foreign tools in the models of the finalists that have faced questions, said Jae W. Lee, director of Seoul National University’s AI Institute.

“They trained from scratch,” he said.

Write to Jiyoung Sohn at jiyoung.sohn@wsj.com

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Videos

• Data centers as target: Environmental nonprofits now oppose data center development using tactics similar to past campaigns against oil, gas, nuclear, and chemical industries.
• Call for realism: America needs bipartisan energy and electricity policies that prioritize abundance, cost, and reliability amid accelerating AI demand.
• Climate movement critique: After Paris Accords, net-zero rhetoric led to trillions spent on green schemes without practical returns.
• Energy infrastructure losses: Zero-emission nuclear sites closed prematurely while coal reduction outpaced replacement, with blocked gas pipelines despite fracking benefits.
• CEO failures noted: Many CEOs failed to oppose unworkable energy policies, allowing climate activists to dominate decisions.
• Data centers equated to essential industry: Described as factories of the digital economy whose restriction raises compute costs and offshores technology.
• National competitiveness stake: AI, cloud computing, finance, healthcare, manufacturing, and defense all hinge on abundant domestic compute capacity.
• Leadership demand: Executives should publicly oppose data center moratoriums and educate on true energy requirements.

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Data center moratoriums are the new fracking bans. Environmental nonprofits are deploying the same playbook against data centers that they have used against oil, gas, nuclear and chemical companies over the past decade, and many business leaders are again tempted to stay silent.

But America is at a crucial moment that demands bipartisan realism on energy and electricity, the lifeblood of economic prosperity and global power. As artificial intelligence accelerates, abundance, cost and reliability must take precedence. Anything less will weaken national security, stifle growth and fuel inflation.

After the 2015 Paris Accords, the environmental movement initiated its attack on the carbon molecules underpinning electricity, fuel and pharmaceuticals. “Net zero,” “decarbonization” and “energy transition” became organizing principles for governments, profit centers for consultants, and buzzwords for corporate pledges rooted in financial engineering over reality. Trillions in taxpayer dollars have since been squandered on “Green New Deal” schemes in the U.S. and Europe.

We were told that eliminating carbon emissions was a moral necessity to deter an “existential” threat to humanity. Yet zero-emission nuclear plants were shut down before replacements were ready. Coal generation was dismantled faster than it could be replaced with reliable alternatives. Gas pipelines were blocked even as hydraulic fracturing unleashed energy abundance and affordability and strengthened America’s global position. In the past decade, the U.S. has removed more than 100 gigawatts of reliable coal and nuclear generation. Europe deindustrialized with even greater zeal, producing higher costs, poorer citizens and populist backlash.

Looking back, too many CEOs failed to challenge energy policies they knew were unworkable. Some supported them outright, either because they misunderstood physics or because political pressure made dissent risky. Climate activists were handed the reins as targets replaced trade-offs and aspirations replaced economic sense.

The arsonists are donning the firefighter helmets again. Trying to stop data center construction while expecting continued economic growth is no different from trying to stop oil, gas and nuclear production while expecting reliable, affordable electricity.

Data centers aren’t a luxury. They are the factories of the 21st-century economy, converting energy into processing capacity the way refineries convert oil into gasoline and chemical plants convert minerals into batteries. Treating them as something society can simply “pause” doesn’t reduce demand for digital services; it constrains the supply of compute, raising costs and outsourcing technological progress elsewhere.

The U.S. can’t afford to repeat its energy mistakes. Artificial intelligence, cloud computing, finance, healthcare, manufacturing and defense all require abundant, affordable compute power built at home. CEOs need not speculate about whether data center moratoriums will hurt their businesses—they already know the answer.

Leadership now means speaking up, telling the truth about what energy and electricity actually require and saying no to data center moratoriums.

Mr. Huntsman is chairman and CEO of Huntsman Corp., a petrochemical manufacturer.

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