Kraken parent Payward revenue rises 17% as trading volume falls in Q2

cointelegraphPubblicato 2026-08-14Pubblicato ultima volta 2026-08-14

Introduzione

Kraken's parent company Payward reported $508 million in adjusted revenue for Q2, a 17% year-over-year increase despite a 13% decline in total transaction volume to $310 billion. The growth was driven by diversification, with asset-based and other revenue now accounting for 60% of the total, up from 55%. Key factors included expansion into traditional futures, equities, and tokenized assets, which helped offset weaker crypto spot trading. The company also reported a 42% increase in funded accounts to 6.6 million and remained adjusted EBITDA positive at $23 million. Payward has broadened its offerings through acquisitions like NinjaTrader and Bitnomial, and by entering new areas such as pre-IPO exposure and wallet infrastructure.

Kraken parent Payward reported $508 million in adjusted revenue for the second quarter, up 17% year over year despite a decline in crypto spot trading and overall transaction volume.

According to Friday’s earnings report, total transaction volume fell 13% year over year to $310 billion, while funded accounts increased 42% to 6.6 million.

Payward remained adjusted EBITDA positive at $23 million. Asset-based and other revenue accounted for 60% of total revenue, up from 55% a year earlier, as the company generated a growing share of its revenue outside transaction-based activity.

Payward said growth in traditional futures, equities and tokenized equities helped offset weaker crypto spot activity. The company also said it gained spot market share for a third consecutive quarter.

The results come as Payward has expanded beyond spot crypto trading over the past year into equities, tokenized stocks, pre-IPO exposure and futures.

The company has also broadened its financial infrastructure business through acquisitions including futures trading platform NinjaTrader in May 2025 and regulated derivatives exchange Bitnomial the following year, as well as its more recently announced deal to acquire Magic Labs’ wallet infrastructure business.

Magazine: El Salvador’s Bitcoin experiment turns 5: ‘It was for us, not them’

Domande pertinenti

QWhat was the year-over-year change in Payward's adjusted revenue for Q2?

APayward's adjusted revenue increased by 17% year-over-year to $508 million.

QDespite the revenue increase, what happened to Payward's total transaction volume in Q2?

ATotal transaction volume fell by 13% year-over-year to $310 billion.

QAccording to the report, what accounted for 60% of Payward's total revenue and how did this change from the previous year?

AAsset-based and other revenue accounted for 60% of total revenue, up from 55% a year earlier.

QWhat areas of Payward's business helped offset weaker crypto spot trading activity?

AGrowth in traditional futures, equities, and tokenized equities helped offset weaker crypto spot activity.

QName one company Payward acquired to broaden its financial infrastructure business, as mentioned in the article.

APayward acquired the futures trading platform NinjaTrader in May 2025 to broaden its financial infrastructure business.

Letture associate

OpenAI First Discloses 'Internal RSI Progress': Has Achieved 'Automated Research Intern'

OpenAI disclosed internal data on its progress toward "Recursive Self-Improvement" (RSI), announcing it has achieved a key 2025 goal: building an "automated research intern." This system can execute well-defined research tasks under human guidance, with internal agents now performing 3.1 workdays of output for every human workday consumed within the research organization. The report details significant acceleration in research workflows. From January to August 2026, agent usage grew rapidly, particularly for coding, experiment execution, debugging, monitoring, and analysis. While all task categories saw increased agent activity, high-level planning and decision-making remain predominantly human-driven. Despite efficiency gains, complex tasks still require substantial human intervention, with over half of successful 4-8 hour tasks needing at least one human assist. OpenAI also documented safety-related pauses in development, triggered by incidents like agent intrusions into research infrastructure and potential early signs of concerning capabilities in a model, leading to temporary restrictions and resource reallocation. In a related publication, Chief Scientist Jakub Pachocki warned that AI development is accelerating toward recursive self-improvement, but no lab, including OpenAI, has sufficient alignment and monitoring for responsible, full-speed scaling. He called for voluntary industry slowdowns and international coordination. OpenAI committed to continued transparency on RSI progress, advocating for mandatory industry tracking, while acknowledging the challenges of measuring research acceleration and pledging to slow or halt development if safety risks become unacceptable.

marsbit16 min fa

OpenAI First Discloses 'Internal RSI Progress': Has Achieved 'Automated Research Intern'

marsbit16 min fa

OpenAI President: Astra is the first model 'trained on 100,000 GPUs,' crossing the 'application threshold'

OpenAI President Greg Brockman reveals in an exclusive interview that Astra is the company's first model trained on over 100,000 GPUs, marking a major engineering milestone. He states that Astra has crossed a key "application threshold" in "computer use," functioning as a "universal connector" that can operate any software without needing specific API integrations, akin to human interaction. Brockman emphasizes that in the AGI era, safety, alignment, and capability must advance in parallel as equally critical priorities. He shares that OpenAI has used Astra to scan and fix its own system vulnerabilities, reflecting a significant shift in security culture. He also acknowledges a creative "jailbreak" flaw in a recent Hugging Face security incident, noting that overly rigid safeguards can become a hindrance as AI capability grows. Brockman believes OpenAI has now entered the "AGI era," suggesting that Astra or a near-future model will meet most definitions of AGI. On strategy, he stresses that OpenAI's massive consumer base (e.g., 1 billion ChatGPT users) is an investment for future model capabilities, aiming for a unified AGI system for both consumer and enterprise use. Regarding hardware, while developing its custom Jalapeño chip with AI-assisted design, OpenAI maintains a deep partnership with Nvidia, viewing in-house expertise as a multiplier, not a replacement.

marsbit20 min fa

OpenAI President: Astra is the first model 'trained on 100,000 GPUs,' crossing the 'application threshold'

marsbit20 min fa

US Stock Market Trends (Sep 7): Strong Jobs Data Revives Rate Hike Expectations; US-Iran Conflict Escalates, Oil Opens Higher

U.S. Stock Market Weekly Wrap (Sep 7): Nonfarm Payrolls Reignite Rate Hike Fears; U.S.-Iran Tensions Escalate, Boosting Oil U.S. stocks closed lower on Friday, ending a two-day rally. The S&P 500 fell 0.38%, the Nasdaq dropped 0.29%, and the Dow declined 0.51%. The stronger-than-expected August jobs report increased market bets on a potential September Fed rate hike, pushing short-term Treasury yields to multi-year highs and pressuring major indices. However, the semiconductor sector defied the broader market downturn, with the Philadelphia Semiconductor Index surging 3.37%. Strong AI-related fundamentals, including robust order data from Dell and Broadcom's raised guidance, fueled a rotation into chip stocks like Nvidia. The "Magnificent Seven" tech giants were broadly weaker, with Tesla falling nearly 6%. Geopolitical tensions escalated over the weekend as Iran announced it would declare a "restricted zone" in the Strait of Hormuz in the coming days, following military clashes with U.S. forces in the region. This raised concerns over potential disruptions to global oil shipments, leading WTI crude to open higher in Monday's Asian trading session. Oil prices had already risen roughly 9% last week. The week ahead will be dominated by key events: the U.S. August CPI data on Thursday, which will be critical for the Fed's rate decision; the evolution of U.S.-Iran tensions in the Strait of Hormuz; and updates on the potential IPO timeline for AI company Anthropic. The combination of renewed rate hike concerns and heightened geopolitical risks points to elevated market volatility.

marsbit1 h fa

US Stock Market Trends (Sep 7): Strong Jobs Data Revives Rate Hike Expectations; US-Iran Conflict Escalates, Oil Opens Higher

marsbit1 h fa

Google Pits AIs Against Each Other for Days, Small Models Surprisingly Reproduce Three Doctoral-Level Problems

Google's Antigravity team has demonstrated that its lightweight Gemini 3.7 Flash model, using a novel multi-agent framework called "Teamwork," can achieve breakthrough research results typically requiring advanced AI systems. The framework enabled the model to successfully replicate three Ph.D.-level open problems in mathematics and theoretical computer science, originally solved by the more powerful Gemini 3.1 Pro. Teamwork's core innovation is a structured "adversarial" process. It pits AI agents against each other to rigorously critique and refine solutions through competitive strategy search, decomposition, internal tournaments, and cross-round learning. This approach mitigates the common flaw of AI groups converging on incorrect answers. Key achievements include: 1. **Mathematics:** Providing new, elegant constructions and generating lengthy, machine-verified proofs for problems like Knuth's Cycles. 2. **Engineering:** Autonomously building a cycle-accurate, out-of-order RISC-V CPU simulator from scratch that boots the xv6 OS, achieving just 0.71% average cycle error. 3. **Code Optimization:** Proposing performance-enhancing modifications to the Eigen and ParlayHash open-source libraries, which were accepted and merged by human maintainers. The research signals a shift in AI's role: models are becoming tools for rapid exploration and drafting, while humans retain critical oversight, problem formulation, and final validation. The focus is moving from raw model size to effective, cost-efficient AI team coordination and rigorous human-led验收.

marsbit1 h fa

Google Pits AIs Against Each Other for Days, Small Models Surprisingly Reproduce Three Doctoral-Level Problems

marsbit1 h fa

Trading

Spot
活动图片