Capital · United States

Popular AI leaderboard Arena nearly doubles valuation to $3.1B valuation in 10 months

Credited to TechCrunch · techcrunch.com · 14h ago

Arena, which originated in 2023 as a research project at UC Berkeley that crowdsourced rankings of AI models, has raised a $200 million Series B round at a $3.1 billion valuation, it said on Thursday.

This comes after the company said it reached $100 million in annualized run-rate revenue in June.

The round was led by Lightspeed Venture Partners and Khosla Ventures, with Salesforce Ventures, 01 Advisors, Dell Technologies Capital, Endeavor Catalyst, a16z, Felicis, and others joining in. Arena previously announced a $150 million Series A in January at a $1.7 billion post-money valuation. At the time, its annualized revenue was $30 million, it said. So that means its valuation has nearly doubled in about 10 months.

Arena provides a crowdsourced platform that is free for consumers to use. People enter prompts or request vibe-coded projects and then rate which model does it better. Arena claims it has tens of millions of monthly visitors.

In September of last year, it introduced its commercial product, AI Evaluations, a service that provides model labs and enterprises with detailed performance analytics based on its community feedback. The timing proved impeccable. This year, AI labs realized that their models were gaming benchmarking tests, finding ways to rack up good scores without truly earning them. At the same time, enterprises wanted help determining which model works best for their own internal needs rather than relying only on standardized benchmarks.

“AI is advancing faster than our ability to evaluate it, and static benchmarks break down once models recognize they’re being tested,” the company said in its funding announcement. “The world needs a neutral third party to measure how safe and aligned AI actually is once it’s in the hands of real people. Arena is stepping into that role today,” it added.

To that end, Arena has also added a new category to its leaderboard: alignment. This is where it ranks models based on issues like unauthorized action (taking actions it wasn’t asked to take); false attribution (wrongly crediting statements or facts to the wrong source); and what it calls “deceptive completion” (lying about completing tasks that it didn’t do).

Currently, a slate of OpenAI’s models are at the top of its preliminary alignment leaderboard, with Claude Opus 5.5 and Claude Fable in sixth and ninth place, respectively.

CapitalUnited States

Turba Labs Secures $52M to Optimize Existing AI Infrastructure

Credited to Converge Digest · convergedigest.com18h ago

Useful

Turba Labs has secured $52 million in funding led by Creandum and Cusp Capital to develop an AI performance platform designed to increase useful compute from existing infrastructure. The October 6 announcement outlines an approach that combines analytics, a calibrated digital twin and orchestration to optimize workloads across compute, memory and networking. The company’s ambition is to double useful compute without building additional data centers. The financing comprises combined seed and Series A funding, according to The Wall Street Journal.

The platform treats AI workloads and their underlying infrastructure as one execution system. Before deployment, it predicts how changes in models, demand or hardware will affect performance, reliability and cost. During operation, it adjusts routing, admission control, replica counts, placement, batching and parallelism within memory, service and power constraints. Turba targets neoclouds and enterprises operating AI infrastructure, emphasizing application output that meets service objectives rather than GPU utilization or raw tokens per second alone.

Turba reports a modeled mixed-inference example in which blended cost per token declined by a factor of 9.4—approximately 89%—across five workload profiles and six optimization measures while maintaining service objectives. The company explicitly identifies this as a scenario-specific modeled result, rather than a measured production outcome or universal performance guarantee. Its stated evaluation approach includes comparisons against well-tuned baselines, representative workloads and demand bursts, with latency, throughput, cost and energy reported separately.

• $52 million in funding, with Creandum and Cusp Capital named as lead investors.

• Connects analytics, a calibrated digital twin and orchestration across compute, memory and networking.

• Predicts the effects of model, demand and hardware changes before operators implement them.

• Coordinates routing, admission, replicas, placement, batching and parallelism within service and resource limits.

• Modeled example: approximately 89% lower blended cost per token; no universal or measured production improvement established by this example.

• Founded by Dr. Patrick Jahnke and Dr. Hans-Juergen Schmidtke, bringing experience from SAP and Meta.

“Useful work is the only metric that matters,” wrote founders Dr. Patrick Jahnke and Dr. Hans-Juergen Schmidtke in the announcement.

The founders’ backgrounds help explain Turba’s focus on dependencies between software workloads and physical infrastructure. Jahnke previously worked at SAP on AI systems and complex enterprise platforms; Turba’s biography identifies experience in predictive maintenance and utilization optimization. His academic work also connects directly to the platform’s proposed feedback loop: his 2021 doctoral thesis at Technical University of Darmstadt examined observing, predicting and enforcing properties of interactions in data centers. The connection between that research and Turba’s analytics, prediction and orchestration architecture is an editorial inference, rather than evidence that the commercial platform implements the thesis directly.

Schmidtke brings a complementary history in networking, optical communications and large-scale infrastructure. Turba says he recently led AI infrastructure systems engineering at Meta and executed large-scale deployments. Optica’s archived biography documents earlier responsibilities in Facebook’s Infrastructure Foundation team, followed by a career history that includes Juniper Networks’ CTO office, advanced technologies and converged-core architecture. Before Juniper, he led Nokia Siemens Networks’ North American fixed-network operator business. His physics education included the University of Düsseldorf and the Max Planck Institute of Quantum Optics, with a doctorate from the University of Würzburg. This experience provides relevant context for Turba’s decision to model network and memory constraints alongside accelerator performance.

Headquarters — Palo Alto, California, United States; additional office in Heidelberg, Germany.

Founders — Dr. Patrick Jahnke and Dr. Hans-Juergen Schmidtke.

Platform — AI performance software combining analytics, digital-twin modeling and orchestration.

Funding — $52 million announced October 6, 2026; lead investors Creandum and Cusp Capital.

Target Markets — Neoclouds and enterprises operating AI infrastructure.

Coverage — GPU optimization • Digital twins • AI inference • Training • Infrastructure orchestration

June 21, 2026 · Rack-Scale Infrastructure CoreWeave Details Rack Engineering for NVIDIA Vera Rubin Examines coordination of networking, cooling, scheduling and fleet management in rack-scale AI systems.

March 17, 2026 · Digital Twins and Power NVIDIA DSX Architecture Targets Token-Per-Watt Optimization Explores integrated simulation and optimization across AI factory compute, networking, power and cooling.

November 3, 2025 · GPU Orchestration Rafay and Aviz Join Forces to Streamline GPU Cloud Orchestration Connects GPU lifecycle management with fabric automation and observability for multi-tenant infrastructure.

July 29, 2025 · AI Memory Infrastructure Enfabrica Launches Elastic AI Memory Fabric Addresses memory bottlenecks and stranded compute through pooled memory accessible over Ethernet.

July 17, 2024 · AI Fabric Validation Juniper opens Ops4AI Lab in Silicon Valley Highlights workload validation, congestion control and network tuning aimed at improving GPU utilization and job completion times.

🌐 We’re tracking the latest developments in AI infrastructure. Follow our ongoing coverage at https://convergedigest.com/category/ai-infra/

Original · Converge Digest

Front Method

Capital · United States

Cal AI’s 19-year-old founder just raised $10M for his new AI startup

Credited to TechCrunch · techcrunch.com · 19h ago

Zach Yadegari, the teen co-founder of calorie tracking app Cal AI, has launched Persona, a new AI agent startup, and already raised $10 million. Vine Ventures led the round, with Z Fellows founder Cory Levy and Collective Global participating.

In March, MyFitnessPal acquired Cal AI for an undisclosed sum after it zoomed ahead of the older calorie app in the app store rankings. The deal was big enough to convince Yadegari and his co-founders to sell the company after it had pulled in over $30 million in annual revenue in under two years, MyFitnessPal told TechCrunch at the time.

Yadegari and his co-founders originally went to work for their new owner, continuing development on Cal AI, but he left the bigger company in June, he tells TechCrunch. He then went right to work on his next idea. (He is working with another co-founder who has yet to be announced.)

That idea is Persona, a personal AI assistant similar to Instinct or Meta’s Muse but with a hardware component that will soon be available: a $179 wearable band.

That makes Persona somewhat more like Bee, the wearable AI device that Amazon acquired in 2025, but Persona’s device differs in several ways, Yadegari tells TechCrunch. For one, it won’t be ambient AI, meaning it won’t always be listening and recording. The band will have a button that activates the assistant, or it can be set up to activate with the flick of one’s wrist.

Yadegari also says that Persona is designed with user privacy in mind. Persona doesn’t run its AI model or store user data on the device. Instead, the model runs in Persona’s cloud, and user data is sent there. The assistant protects privacy by encrypting data in transit and at rest. Users can also delete their data, the company says.

In this way, it is promising that user conversations stay private and cannot be sold to advertisers or data brokers.

The team is also baking in safeguards to protect the assistant against phishing and prompt-injection attempts, Yadegari says. These are two areas that can plague agents, say AI labs like OpenAI.

Plus, he promises that Persona’s agent can’t see a user’s credit-card details and that purchases will require the user’s approval, with payment handled through external providers such as Stripe Link. Link is Stripe’s secure wallet designed for use by agents.

Although user data won’t be sold to advertisers, the startup’s current business model does rely on ads. Yadegari says the company will show advertised wares during shopping research. But he says that the agent will present unbiased recommendations because the agent won’t know which of the items are advertised. For example, if someone is researching office chairs, he says, “the agent knows their preferences” and will show them chairs that match those preferences, some of them sponsored results.

The bands are expected to be available in December, and an app is in the works. In the meantime, Persona has launched as a free beta product in which users work with the assistant via iMessage text messages. It has attracted “a few thousand beta users,” Yadegari said. The band has also already generated “five figures in preorder revenue.”

Like other consumer AI assistants, he says, Persona can order food or rides, take notes, book travel, answer questions and emails, among other tasks.

“All of the utility apps that you use on your phone day-to-day, whether those are apps for shopping, whether it’s Uber, DoorDash, your email — these are all collapsing and will collapse into a single super app powered by an AI assistant,” Yadegari said.

Eventually, he hopes to make Persona even more proactive. “It can offer help and solve problems before you even know you have a problem, and overall be more seamless with your life, so that you don’t need to stare at a rectangle in your hand all day.”

Come see Zach Yadegari speak at TechCrunch Disrupt on “How to create viral growth and capitalize on it.” Disrupt takes place October 13-15 at the Moscone Center in San Francisco. Before Cal AI, Yadegari built his first business in the ninth grade and sold it for $100,000 when he was 16.