Revealed: the Alameda venture capital portfolio

Financial TimesPublished on 2022-12-07Last updated on 2022-12-08

Abstract

A look inside Sam and Caroline’s grab-bag of crypto, etc.

As well as running a crypto exchange that didn’t exchange crypto and owning a hedge fund that didn’t hedge, Sam Bankman-Fried had a venture capital fund that didn’t venture its own capital.

The VC division, in contrast to the rest of the FTX group, can now provide some insight into where some of the money went. Here’s where it went:

The screenshot above and all those below are taken from an Excel spreadsheet dated early November, when SBF was seeking rescue funding amid a run on FTX customer deposits. Mobile and app users are advised to click the magnifying glass below each image for more detail.

According to a person familiar with the rescue effort, the document shows an Alameda Research private equity portfolio — with some FTX bets mixed in — that was being offered as collateral in an attempt to secure a new credit line for the stricken group.

The disparate bundle of nearly 500 illiquid investments is split across 10 holding companies. The total investment value is given on the spreadsheet as in excess of $5.4bn.

As well as forming a central plank in efforts to maximise recoveries from FTX’s bankruptcy, the portfolio might offer regulators insight into whether the group’s trading and exchange businesses were ever operationally separate as claimed.

Bankman-Fried conceded in an interview with the Financial Times that he was involved in Alameda’s venture capital activities but has so far ducked questions around the misuse of FTX customer funds.

Going by the spreadsheet, boundaries between SBF’s companies were blurred. Two of Alameda’s biggest holdings, the crypto miner Genesis and the artificial intelligence research group Anthropic, are also listed on the draft FTX balance sheet published last month by FTAV. (Semafor subseqently reported that FTX had seized certain assets from Alameda after a margin call.)

As previously reported, the portfolio includes stakes in FTX backers Sequoia Capital and Anthony Scaramucci’s SkyBridge Capital, as well as in Elon Musk’s SpaceX and Boring Company projects through the investment in K5.

Of Alameda’s remaining investments, crypto and DeFi projects account for the majority. But the list also includes numerous start-up video game studios and betting platforms, online banks, publishers, a fertility clinic, a military drone maker and a vertical farming company,

Some entries have no clear link to an active business, suggesting they may be misspelt or mislabeled.

Note that FT Alphaville has excluded entries where an investment type is not given, which removes approximately a dozen names with a total stated investment of about $100mn. All other data are presented as they were shown to prospective FTX investors. The FT makes no claim as to the data’s accuracy or completeness.

When asked about why FTX used customer funds to prop up Alameda, SBF has repeatedly pleaded ignorance. The former FTX CEO said that to avoid conflicts of interest he chose not to get involved in Alameda’s trading and risk management, so before last month was not fully aware of its parlous state.

However, SBF told the FT that in early summer he had participated in conversations where Alameda’s financial health and borrowing were discussed. The venture capital investments Alameda had made were “effectively, some of them, on margin”, he added.

Alameda’s spreadsheet predates SBF’s current media blitz by a month, though it comes with all the same warnings about potentially selective recall and unreliable presentation, As the FT’s Joshua Oliver reports:

Bankman-Fried’s attempt to account for what went wrong was laced with caveats and references to his incomplete memory. He cited lack of “confidence” in his answers at least a dozen times, calling other responses “idle speculation” or “shitty answers”. At one point, he paused for half a minute with his head in his hands.

Caroline Ellison, former CEO of Alameda and SBF’s one-time romantic partner, could not be reached for comment.

Related Reads

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手1h ago

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手1h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbit2h ago

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbit2h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbit2h ago

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbit2h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbit4h ago

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbit4h ago

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

Cognition AI, the company behind the AI programmer "Devin," has raised over $1 billion in new funding at a valuation of $26 billion, just eight months after reaching a $10.2 billion valuation. The round was led by Lux Capital, General Catalyst, and 8VC. Founded by three young Chinese entrepreneurs with strong competitive programming backgrounds, Cognition initially gained fame with Devin, marketed as the world's first AI software engineer capable of handling tasks from start to finish. While its early demos were impressive, real-world usage revealed reliability and cost-effectiveness issues, leading to a significant price cut for Devin in 2025. A pivotal moment came when Cognition acquired the assets of AI IDE company Windsurf after a failed acquisition by OpenAI. This move gave Cognition a crucial developer-facing tool, allowing it to pursue a two-pronged strategy: Devin for autonomous task execution and Windsurf for integrated, collaborative coding within an IDE. This shift helped the company move away from the controversial "AI replacement" narrative towards a model of augmenting human engineers, particularly for repetitive or maintenance tasks. This strategic pivot is backed by strong commercial metrics. The company reports a 10x increase in enterprise usage this year, with an annual revenue run-rate of $492 million and a 50% month-over-month growth in enterprise Devin usage over the past six months. Its client list now includes major corporations like Goldman Sachs and Mercedes-Benz, as well as government agencies like NASA and the U.S. Army. Investors are betting on Cognition becoming a foundational piece of next-generation software engineering infrastructure, positioning it at the center of a hybrid future where AI agents and human developers work in tandem.

marsbit4h ago

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

marsbit4h ago

Trading

Spot
Futures
活动图片