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

bitcoinistPublished on 2026-02-10Last updated on 2026-02-10

Abstract

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

Related Questions

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.

Related Reads

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

In recent months, the rapid growth of the AI industry has attracted significant talent from the crypto sector. A persistent question among researchers intersecting both fields is whether blockchain can become a foundational part of AI infrastructure. While many previous AI and Crypto projects focused on application layers (like AI Agents, on-chain reasoning, data markets, and compute rentals), few achieved viable commercial models. Gensyn differentiates itself by targeting the most critical and expensive layer of AI: model training. Gensyn aims to organize globally distributed GPU resources into an open AI training network. Developers can submit training tasks, nodes provide computational power, and the network verifies results while distributing incentives. The core issue addressed is not decentralization for its own sake, but the increasing centralization of compute power among tech giants. In the era of large models, access to GPUs (like the H100) has become a decisive bottleneck, dictating the pace of AI development. Major AI companies are heavily dependent on large cloud providers for compute resources. Gensyn's approach is significant for several reasons: 1) It operates at the core infrastructure layer (model training), the most resource-intensive and technically demanding part of the AI value chain. 2) It proposes a more open, collaborative model for compute, potentially increasing resource utilization by dynamically pooling idle GPUs, similar to early cloud computing logic. 3) Its technical moat lies in solving complex challenges like verifying training results, ensuring node honesty, and maintaining reliability in a distributed environment—making it more of a deep-tech infrastructure company. 4) It targets a validated, high-growth market with genuine demand, rather than pursuing blockchain integration without purpose. Ultimately, the boundaries between Crypto and AI are blurring. AI requires global resource coordination, incentive mechanisms, and collaborative systems—areas where crypto-native solutions excel. Gensyn represents a step toward making advanced training capabilities more accessible and collaborative, moving beyond a niche controlled by a few giants. If successful, it could evolve into a fundamental piece of AI infrastructure, where the most enduring value in the AI era is often created.

marsbit10h ago

Gensyn AI: Don't Let AI Repeat the Mistakes of the Internet

marsbit10h ago

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

A US researcher's visit to China's top AI labs reveals distinct cultural and organizational factors driving China's rapid AI development. While talent, data, and compute are similar to the West, Chinese labs excel through a pragmatic, execution-focused culture: less emphasis on individual stardom and conceptual debate, and more on teamwork, engineering optimization, and mastering the full tech stack. A key advantage is the integration of young students and researchers who approach model-building with fresh perspectives and low ego, prioritizing collective progress over personal credit. This contrasts with the US culture of self-promotion and "star scientist" narratives. Chinese labs also exhibit a strong "build, don't buy" mentality, preferring to develop core capabilities—like data pipelines and environments—in-house rather than relying on external services. The ecosystem feels more collaborative than tribal, with mutual respect among labs. While government support exists, its scale is unclear, and technical decisions appear driven by labs, not state mandates. Chinese companies across sectors, from platforms to consumer tech, are building their own foundational models to control their tech destiny, reflecting a broader cultural drive for technological sovereignty. Demand for AI is emerging, with spending patterns potentially mirroring cloud infrastructure more than traditional SaaS. Despite challenges like a less mature data industry and GPU shortages, Chinese labs are propelled by vast talent, rapid iteration, and deep integration with the open-source community. The competition is evolving beyond a pure model race into a contest of organizational execution, developer ecosystems, and industrial pragmatism.

marsbit12h ago

Why is China's AI Developing So Fast? The Answer Lies Inside the Labs

marsbit12h ago

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

Corning, a 175-year-old glass company, is experiencing a dramatic revival as a key player in AI infrastructure, driven by surging demand for high-performance optical fiber in data centers. AI data centers require vastly more fiber than traditional ones—5 to 10 times as much per rack—to handle high-speed data transmission between GPUs. This structural demand shift, coupled with supply constraints from the lengthy expansion cycle for fiber preforms, has created a significant supply-demand gap. Nvidia has invested in Corning, along with Lumentum and Coherent, in a $4.5 billion total commitment to secure the optical supply chain for AI. Corning's competitive edge lies in its expertise in producing ultra-low-loss, high-density, and bend-resistant specialty fiber, which is critical for 800G+ and future 1.6T data rates. Its deep involvement in co-packaged optics (CPO) with partners like Nvidia further solidifies its position. While not the largest fiber manufacturer globally, Corning's revenue from enterprise/data center clients now exceeds 40% of its optical communications sales, and it has secured multi-year supply agreements with major hyperscalers including Meta and Nvidia. Financially, Corning's optical communications revenue has surged, doubling from $1.3 billion in 2023 to over $3 billion in 2025. Its stock price has risen nearly 6-fold since late 2023. Key future catalysts include the rollout of Nvidia's CPO products and the scale of undisclosed customer agreements. However, risks include high current valuations and potential disruption from next-generation technologies like hollow-core fiber. The company's long-term bet on light over electricity, maintained even through the telecom bubble crash, is now being validated by the AI boom.

marsbit12h ago

3 Years, 5 Times: The Rebirth of a Century-Old Glass Factory

marsbit12h ago

Trading

Spot
Futures
活动图片