Gemini IPO Stumbles as Profitability Concerns Shadow the Exchange

TheCryptoTimesPublicado em 2025-10-07Última atualização em 2025-10-07

New York City-based crypto exchange Gemini Space Station Inc, saw its stock surging after initial public offering (IPO) in September but then it dropped quickly, which left a lot of investors cautious. At the time, the IPO raised $446.3 million, but the stock is now down more than 11% as competition and lack of profit is challenging the company.

Right now, Wall Street is split on how the exchange will recover with six analysts giving buy-equivalent ratings and five hold-equivalent ratings, according to a Bloomberg report.

Early Hype Fades Quickly

On the first trading day, Gemini’s stock jumped 14%, helped by the twins founders, Cameron and Tyler Winklevoss’ political connections and also the frenzy around other crypto companies like Circle Internet Group and Bullish which recently had successful stock launches.

However, this surge did not last long as the stock is now down more than 11%. Meanwhile, its competitors are doing better. Circle shares have soared over 370% since their IPO, and Bullish is up more than 75%. Analysts say Gemini is playing catch-up.

Citigroup’s Peter Christiansen, who maintains a neutral rating, said: “We believe investors can wait for more execution proof-points in rebuilding the user base, institutional partnerships, and providing a better line-of-sight towards profitability.”

Profit Challenges Since IPO Launch

So far, Gemini is not making a profit yet. In the first half of the year, the company lost $282.5 million while earning $68.6 million from trading fees. Most of its income comes from people trading on the platform, but it has spent a lot on marketing to attract more users.

Another problem was the Earn program, which gave customers interest in cryptocurrency deposits. After the 2023 crypto crash, the program’s partner, Genesis Global, went bankrupt. Gemini had to pay $37 million to the New York Department of Financial Services and $50 million to the New York Attorney General, though it did not admit any wrongdoing.

One bright spot is Gemini’s crypto rewards credit card, which offers users cashback in cryptocurrency. Truist Securities analyst Matthew Coad said the card helps the company earn more money and get more users. However, he warned that the stock could still be very unpredictable because the overall crypto market is unstable.

Also Read: Pineapple Financial Kicks-off $100M Asset Treasury, Buys $8.9M in INJ

Follow The Crypto Times on Google News to Stay Updated!

Leituras Relacionadas

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

The article, "Wang Chuan: How to Remain Unanxious After Neighbor Lao Wang's Thirty-Fold Gain on Storage Stocks (Part 6) - The Trap of Commoditized Goods," analyzes the cyclical and perilous nature of the data storage industry through historical and current case studies. It begins with the example of Iomega, whose Zip drives led to a stock surge of over 160x in the mid-1990s before collapsing over 97% from its peak due to competition from cheaper CD-R technology. This pattern is characteristic of storage, where products like DRAM are highly commoditized, leading to extreme price volatility. The sector has seen prices crash over 80% multiple times, with companies often facing bankruptcy. The core dynamic is "elastic demand facing heavy-asset, long-cycle, rigid supply." High prices attract new capacity, but the long lead time means supply eventually overshoots, causing sharp price corrections. The current AI-driven boom, exemplified by surging demand for High-Bandwidth Memory (HBM), has led to skyrocketing prices and profit margins for companies like SanDisk and Micron, despite relatively flat production volumes. However, the author warns this high-margin environment is self-defeating. The high profits are already triggering massive new capacity investments (hundreds of billions starting 2026), with supply expected to ramp up by late 2027. When supply catches up, total revenue and profits may fall even as more units are sold. Long-term supply agreements offer little protection, as buyers can find ways to renegotiate if market prices drop, similar to fragile political treaties. Key risks include economic downturns, cuts in AI spending, faster-than-expected capacity expansion (especially from Chinese firms), and innovations in chip/algorithm design that reduce memory needs. A critical trap is that at the cycle's peak, storage stocks often appear cheap with low P/E ratios, luring value investors just before an impending downturn where profits evaporate. The conclusion cautions that for commoditized goods like storage, high margins inevitably destroy themselves, and the current asymmetry favors downside risk over further upside. The neighbor's dream of easy wealth from storage stocks is portrayed as a precarious illusion.

链捕手Há 16m

Wang Chuan: When the neighbor Lao Wang earned thirty times from investing in memory storage stocks, how can you still avoid anxiety (6) - The trap of homogeneous products

链捕手Há 16m

AI PCs Are Here, Going Toe-to-Toe with 120B Models Locally! NVIDIA Redefines the "Personal AI Computer" Foundation with RTX Spark

NVIDIA has redefined the "AI PC" standard with the launch of the RTX Spark super chip at GTC 2026. Boasting 1 petaflop (1000 TOPS) of AI performance, it dwarfs the 45-50 TOPS NPUs in current AI PCs. The SoC features a Blackwell GPU, a 20-core Arm CPU co-designed with MediaTek, and crucially, up to 128GB of unified memory shared between CPU and GPU. This architectural shift enables local execution of 120-billion-parameter large language models with million-token context windows, a massive leap from the 9B-40B models typical on current consumer hardware. Beyond AI, use cases include 12K video editing and high-fps ray-traced gaming. Key to enterprise adoption is a security collaboration with Microsoft. Windows security is upgraded, and NVIDIA's OpenShell sandbox runtime is integrated to safely contain AI agent actions. Major software support comes from Adobe, which announced a deep,底层-level rewrite of Photoshop and Premiere to leverage the unified memory for up to 2x performance gains. Six OEMs, including Dell, HP, Lenovo, and Microsoft Surface, will release RTX Spark-based轻薄本 and compact desktops this fall. However, questions remain about real-world performance,功耗, thermal management in laptops, pricing, and the actual impact of the OpenShell sandbox. The RTX Spark represents a fundamental power shift in the PC industry, moving from an x86 CPU-centric model to a GPU-centric SoC platform, but its ultimate success hinges on the upcoming product rollouts and ecosystem validation.

marsbitHá 30m

AI PCs Are Here, Going Toe-to-Toe with 120B Models Locally! NVIDIA Redefines the "Personal AI Computer" Foundation with RTX Spark

marsbitHá 30m

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

NVIDIA CEO Jensen Huang delivered the keynote speech at GTC Taipei 2026, announcing several major product launches and strategic directions. The company's Vera Rubin architecture is now in full-scale production, with OpenAI, Anthropic, and SpaceX among the first customers. NVIDIA highlighted AI Agent as a key future focus, introducing the Vera CPU designed for AI agents and the Vera BlueField-4 STX for secure, chip-level AI storage processing. A significant move involves challenging Intel in the PC market. NVIDIA, in collaboration with MediaTek, is developing the RTX SPARK PC chip (manufactured by TSMC) for Windows systems, set to launch this fall for laptops and desktops. This signals NVIDIA's push into the next-generation AI PC arena, aiming to provide a vertically integrated core computing platform for the entire Windows ecosystem, similar to Apple's approach. Other announcements include the new Nemotron 3 Ultra AI model and the NVIDIA DSX platform, described as a complete "playbook" for building AI factories, allowing performance simulation and validation before physical deployment. In automotive, the DRIVE Hyperion platform was positioned as a global robotaxi platform, with major Chinese automakers like BYD, Geely, Zeekr, Xiaomi, and Pony.ai already adopting or developing autonomous driving solutions based on it. The Alpamayo 2 super open inference model for robotaxis was also introduced. For robotics, NVIDIA unveiled the Isaac GR00T humanoid robot reference platform for academic research and a large open-source agent tools and skills suite for Physical AI. The company plans to collaborate with global humanoid robot manufacturers, including China's Unitree, whose H2 Plus robot served as the reference hardware for the GR00T platform demonstration.

marsbitHá 58m

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

marsbitHá 58m

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

Interview with macro expert Raoul Pal discusses the approaching "economic singularity," where AI-driven growth outpaces traditional economic systems. He explains the unprecedented scale of the AI race between the US and China, arguing capital will continue flooding this sector as neither side can afford to stop. Pal sees this as a catalyst for cryptocurrency, as AI agents will operate on-chain, creating an "infinite TAM" for crypto networks. He views the recent crypto downturn as a normal bull market correction, advocating for a long-term "buy and hold" strategy over trying to time the market. Key investments include Layer 1 smart contract platforms like Ethereum, Solana, and Sui, which he believes will capture most value due to their foundational role. He also sees potential in privacy coins like Zcash and a future resurgence for high-end NFTs as digital trophy assets. Pal emphasizes that while AI stocks are hot, crypto offers superior asymmetric returns. He outlines a bullish case for 2026-2027 based on institutional adoption, pro-regulation, increased global liquidity, and crypto's current undervaluation relative to assets like the Nasdaq. His core advice: accumulate crypto assets during dips and hold for the long term, as the shift towards a digital, AI-agent-driven economy is inevitable.

链捕手Há 1h

Interview with Macro Master Raoul Pal: The Economic Singularity is Approaching, Don’t Get Off the Ride in the Next Four Years

链捕手Há 1h

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

Meta's MobileMoE, a mobile-optimized Mixture-of-Experts (MoE) language model architecture, enables efficient on-device large language model (LLM) inference for the first time on commercial smartphones. Designed for decoder-only Transformers, it replaces dense feed-forward layers with MoE layers. Key design choices include 8 experts with granularity g=8, top-4 routing, and a shared expert. The model undergoes a four-stage training process: pre-training, intermediate training, supervised fine-tuning, and quantization-aware training. Results show MobileMoE models, with similar memory footprint, achieve equal or higher average accuracy across 14 foundational benchmarks while using only 1/2 to 1/4 of the FLOPs compared to dense baselines. After INT4 quantization, they remain competitive. Notably, on an iPhone 16 Pro, MobileMoE-S demonstrates significant speedups: up to 3.8x faster in the prompt phase and 2.2-3.4x faster in per-token generation compared to a dense counterpart, with lower peak memory usage. While MobileMoE establishes a new Pareto frontier for on-device LLMs in accuracy-compute trade-offs, particularly excelling in code and math tasks, it currently lags behind models like Qwen3.5 2B in advanced instruction following and knowledge reasoning. Future work includes improving post-training techniques, exploring NPU deployment, and managing the runtime memory sensitivity of MoE models to varying inputs.

marsbitHá 1h

Running MoE on Mobile Phones? Meta Proposes MobileMoE, Speeding Up iPhone 16 Pro by 3.8x

marsbitHá 1h

Trading

Spot

Futuros