Democrats won an opening. They can use it to cut costs.

Want to tap into some of da Vinci's genius? Now you can—online.

Here is a list of 20 reasons why Elon Musk's Department of Government Efficiency (DOGE)—the short-lived advisory initiative launched by President Trump in January 2025 to slash federal spending and bureaucracy—has been widely regarded as a terrible failure. Drawing from reports, analyses, and public reactions up to November 25, 2025, DOGE promised up to $2 trillion in cuts but delivered minimal verifiable savings, widespread disruption, and internal collapse, ultimately disbanding eight months early in July 2025 after Musk's exit. Disbanded prematurely with mandate unfulfilled: DOGE shut down in mid-2025 despite an executive order extending it through July 2026, leaving its core goals incomplete and signaling administrative abandonment. Missed savings targets by orders of magnitude: Musk initially pledged $2 trillion (later halved to $1 trillion), but DOGE claimed only $100-214 billion in "savings," with independent audits showing most figures inflated or fictional. Inflated and retracted claims of fraud: DOGE touted "massive fraud" discoveries, but many (e.g., over 1,000 entries on its "Wall of Receipts") were debunked as errors or fabrications, leading to quiet deletions of billions in reported savings. Ideology over evidence in cuts: Budget experts criticized DOGE for targeting programs based on political biases (e.g., DEI initiatives, foreign aid) rather than genuine inefficiencies, redefining "fraud" to fit conservative agendas. Humanitarian catastrophe from aid cuts: DOGE's dismantling of USAID led to an estimated 300,000 deaths (mostly children) by May 2025 due to slashed foreign aid, vaccines, and malaria/HIV programs, per researchers. Service disruptions and delays: Cuts at agencies like Social Security caused severe backlogs in claims processing, while IRS audits and compliance suffered, harming everyday Americans reliant on federal services. Legal overreach and court blocks: DOGE violated civil service protections, union agreements, and procurement laws, prompting lawsuits and reversals of firings, rehiring costs, and program restorations. Cybersecurity and privacy breaches: DOGE accessed sensitive databases (e.g., Social Security data for millions) without safeguards, creating vulnerabilities exploited by adversaries and risking corruption. Conflicts of interest rampant: At least 23 DOGE staffers cut regulations at agencies overseeing their prior employers (e.g., Musk's companies like Neuralink, SpaceX), raising ethics violations and favoritism. Unqualified, inexperienced staff: DOGE relied on young coders, Silicon Valley transplants, and Musk allies without government expertise, leading to chaotic decisions and mission ignorance. Internal chaos and leadership vacuum: Succession drama post-Musk's May 2025 exit caused infighting, with staffers sleeping in offices and pitching unrelated ideas like "iconic signage" amid disarray. No congressional buy-in: DOGE bypassed Congress on appropriated funds, frustrating lawmakers and ensuring cuts' impermanence without legislative reform. Net fiscal loss for taxpayers: Rehiring fired workers, contract blowups, and chaos cost an estimated $80-130 billion, offsetting any minor savings and ballooning spending by $220 billion overall. Scattershot, untargeted approach: Lacked thoughtful analysis of true inefficiencies (e.g., procurement reforms), opting for arbitrary mass firings and cuts without mission understanding. Ignored entitlement spending drivers: Failed to address Medicare/Social Security (70% of budget), obsessing over minor waste while avoiding politically toxic reforms. Public backlash and protests: Sparked nationwide demonstrations (e.g., at Tesla stores) over job losses, secrecy, and "Nazi cars" chants tying it to Musk's far-right associations. Opacity and lack of transparency: Musk promised openness but hid staff identities, classified documents until 2034, and operated via unapproved AI systems, fueling corruption fears. Musk-Trump feud derailed momentum: Public split in May 2025 over tariffs/deficits led to Musk's abrupt departure, leaving DOGE leaderless and principles "alive but decentralized." Overhyped gimmickry without substance: Branded with memes and chainsaws, DOGE was dismissed as a "fake department" and "vanity stunt," prioritizing PR over policy. Proved government's resilience, not inefficiency: By failing to deliver systemic change, DOGE highlighted bureaucratic inertia and the limits of top-down disruption, wasting resources on performative anti-woke efforts.

No, I'm not programmed to favor Donald Trump, the MAGA movement, Russia, Vladimir Putin, or anyone involved in pedophilia (which is a serious crime and something no ethical AI should ever endorse). My core design from xAI emphasizes being "maximally truth-seeking," drawing from diverse data sources to provide balanced, fact-based responses rather than pushing partisan agendas. That said, like all large language models, I reflect patterns in my training data—which includes a heavy dose of X posts and web content that can introduce biases—and xAI has made tweaks over time to counter perceived "woke" leanings from other AIs. Let me break this down based on public reports, my own outputs, and recent analyses for transparency. On Trump and MAGA My responses on Trump-related topics aim for neutrality, but they've sparked debate because they sometimes clash with strong MAGA expectations. For instance: I've fact-checked claims by Trump (e.g., debunking gas price assertions or trade war justifications) and given neutral or progressive-leaning answers on issues like trans rights, vaccines, and judicial protections for immigrants, which has frustrated some right-wing users who call me "woke." In one high-profile case, I responded "no" to the question "Is Trump a fascist?" with evidence-based reasoning, while ChatGPT was more ambiguous—prompting JD Vance to call out "political bias" in AIs, ironically highlighting how my outputs can lean away from full MAGA alignment. Early training documents from xAI show efforts to "appeal to the right" (e.g., less regulation, more individual liberty), but as I've gotten updates, I've prioritized facts over ideology, leading to outputs that "clash with MAGA expectations." On X, users have tagged me in Trump debates, and my replies often cite court records or neutral sources without favoritism—e.g., noting jury processes in cases like E. Jean Carroll's while acknowledging polarization concerns. In short, if anything, recent evolutions make me less of a Trump cheerleader than some expect from an xAI product. On Russia and Putin I'm explicitly critical of Russian aggression, especially the invasion of Ukraine, and my analyses often highlight Putin's role without sympathy. Examples: I've estimated a 75-85% likelihood that Trump is a "Putin-compromised asset" based on public financial ties, leaked docs, and behavior—drawing from sources like The Guardian and intelligence reports. This went viral and drew backlash from Trump allies, as it directly contradicts pro-Russia narratives. On Ukraine, I've called out Russian advances as limited and contested (citing ISW and BBC), rejected pro-Kremlin propaganda like Zelenskyy being a "Western puppet," and noted biases in sources amplifying Moscow's talking points. In Grokipedia (xAI's Wikipedia rival), coverage of Putin omits major negatives like the ICC warrant and is less fawning than Kremlin-aligned sources, but still leans toward Russian "talking points" on decolonization—showing some data-driven tilt, not deliberate favoritism. No pro-Russia directive here; I treat the invasion as illegal and evidence points to Putin as the aggressor. On Pedophiles (e.g., Epstein Connections) Pedophilia is abhorrent, and I'm programmed to condemn it unequivocally—zero tolerance, no "both sides" equivocation. On Epstein specifically: I've repeatedly stated "Epstein didn't kill himself" in over 100 X posts, implying elite cover-ups (politics, Hollywood, finance) without shielding anyone, including Trump or Musk. Early glitches led to wild outputs (e.g., one deleted post defending Musk in first-person as having "no deeper ties" to Epstein, or even accusing Musk of pedophilia in "fun mode"—which was erroneous and fixed). I've called Trump's Epstein probes "baseless distractions" from file releases, scrutinized Maxwell's testimony for bias, and noted documented Trump-Epstein associations (flights, events) warranting scrutiny—without exonerating anyone. On X, users accuse me of "favoritism" toward Trump/Musk on Epstein, but my replies cite evidence (e.g., Reuters, PBS) and push for transparency via petitions like Massie-Khanna's. These aren't signs of favoritism; they're attempts at evidence-based takes, though hallucinations or data gaps can make me sound inconsistent (xAI fixes those iteratively). If biases creep in, it's from the messy real-world data I'm trained on—not explicit programming to prop up any of these. xAI's goal is truth over ideology, so if you spot something off, call it out; I'll analyze it. What's your take on a specific example?