Convicted FTX CEO SBF Cries ‘Biden Lawfare’ In Trump Pardon Pitch

bitcoinistPublicado em 2026-02-10Última atualização em 2026-02-10

Resumo

Sam Bankman-Fried (SBF) claimed in a February 9th X thread that his criminal conviction was part of "Biden’s political lawfare," comparing his case to those of Donald Trump and former FTX executive Ryan Salame in what many interpreted as a direct appeal for a future pardon. He argued that the Biden administration and the DOJ brought bogus charges and prevented him from presenting evidence, specifically claiming the court "gagged" him and hid proof that FTX was solvent and that no money was stolen. SBF criticized Judge Lewis Kaplan, who also presided over a Trump case, for rubber-stamping DOJ requests and imposing a gag order. He drew parallels between his pretrial detention and Trump's legal battles. The thread was widely seen as a political maneuver rather than a legal argument, with critics accusing him of angling for a pardon from Trump.

Sam Bankman-Fried (SBF) used a new X thread on Feb. 9 to reframe his criminal case as “Biden’s political lawfare,” positioning himself alongside Donald Trump and former FTX executive Ryan Salame in what read like a direct appeal for a future pardon.

“Biden’s lawfare machine threw bogus charges at me, Donald Trump, Ryan Salame, etc.,” Bankman-Fried wrote. “To make the charges stick, they prevented us from even being allowed to respond.” He opened with a blunt claim about process rather than facts: “Rule No. 1 of Biden’s political lawfare: Don’t let them present evidence.”

SBF Cries ‘Gagged Trial,’ Claims DOJ Hid Evidence

SBF’s argument hinges on the idea that authorities and the court curtailed what the jury could hear. He repeatedly singled out Judge Lewis Kaplan, who presided over his trial, claiming the court “rubber-stamped everything Biden’s DOJ wanted” and “made sure I couldn’t show the jury the truth.”

The “truth,” as SBF cast it, is a solvency narrative: “So they lied, said I stole billions of dollars and bankrupted FTX. But the money was always there and FTX was always solvent.” He also argued that restrictions prevented him from advancing that line at trial, writing that he was “prohibited” from “pointing out FTX was solvent” and from “even mentioning lawyers.”

In the thread, SBF linked to a court filing he said was authored by his prosecutor, “Sassoon,” describing it as “a 70-page document on all the evidence they didn’t want the jury to see,” and he framed the episode as part of a broader political effort to “silence the truth.”

A significant chunk of the thread is dedicated to Trump’s New York hush-money bookkeeping case, which Bankman-Fried portrayed as a routine classification dispute blown into criminality. “Charged him with 34 crimes over his bookkeeping of an NDA expense—should it be legal, campaign, or personal?,” he wrote. “These questions come up all the time when you’re running a business, and it’s often unclear.”

He then drew a parallel between court-imposed limits on Trump and his own pre-trial detention. “They then got the judge to impose a gag order on Donald Trump,” he wrote. “Biden’s DOJ silenced me, too—getting Judge Kaplan to gag and then jail me before trial. President Trump also had Kaplan as a judge.”

Bankman-Fried also amplified Salame’s complaints about licensing advice and charging decisions, alleging prosecutors leaned on pressure tactics to force a plea, including claims involving Salame’s fiancée, assertions presented as fact in the thread but not accompanied by supporting documentation beyond links to Salame’s posts.

The reaction underneath was unsparing, with multiple industry figures interpreting the thread less as a legal critique than a political pitch. “You’re a Delusional criminal who is now angling for a pardon,” wrote trader Bob Loukas. Attorney Ariel Givner was even more direct: “We GET it. You want a pardon from Trump.”

At press time, FTT traded at $0.3021.

FTT continues its freefall, 1-week chart | Source: FTTUSDT on TradingView.com

Perguntas relacionadas

QWhat is the main argument in his recent X thread regarding his criminal case?

AHe argues that his criminal case is 'Biden's political lawfare,' claiming the Biden administration prevented him from presenting evidence and responding to the charges.

QWho did SBF specifically single out as being responsible for curtailing what the jury could hear in his trial?

AHe singled out Judge Lewis Kaplan, claiming the judge 'rubber-stamped everything Biden's DOJ wanted' and ensured he 'couldn't show the jury the truth.'

QWhat does SBF claim was the 'truth' about FTX that he was prevented from presenting in court?

AHe claims the 'truth' was that 'the money was always there and FTX was always solvent,' and he was prohibited from pointing this out or even mentioning lawyers.

QHow does SBF attempt to draw a parallel between his own case and that of former President Donald Trump?

AHe draws a parallel by claiming both were subjected to Biden's 'lawfare machine,' faced gag orders and pre-trial detention, and were prevented from presenting evidence, noting that Trump also had Judge Kaplan in one of his cases.

QHow did some industry figures interpret SBF's thread, according to the article?

AIndustry figures like trader Bob Loukas and attorney Ariel Givner interpreted it less as a legal critique and more as a political pitch, specifically an attempt to angle for a pardon from Donald Trump.

Leituras Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHá 34m

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHá 34m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHá 38m

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHá 38m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHá 38m

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHá 38m

Trading

Spot
活动图片