Latest news with #dataLabeling
Bloomberg
02-07-2025
- Business
- Bloomberg
Scale AI CEO Stresses Startup's Independence After Meta Deal
Scale AI's new leader said the data-labeling startup remains independent from Meta Platforms Inc. despite the social media giant taking a 49% stake just weeks ago, and is focused on expanding its business. Interim Chief Executive Officer Jason Droege said Meta, a customer since 2019, won't receive special treatment even after its $14.3 billion investment.
Reuters
01-07-2025
- Business
- Reuters
Exclusive: Scale AI's bigger rival Surge AI seeks up to $1 billion capital raise, sources say
July 1 (Reuters) - Surge AI, a data-labeling firm that competes with Scale AI, has hired advisors to raise as much as $1 billion in the first capital raising in the firm's history, sources told Reuters, as it seeks to capitalize on growing user demand amid Scale AI's recent customer exodus. The company, founded by former Google and Meta engineer Edwin Chen, is targeting a valuation of over $15 billion, sources said, cautioning that the talks are still in early stages and the final number could be higher. The funding would be a mix of primary and secondary capital that provides liquidity for the employees. Surge AI, which has been profitable and bootstrapped by Chen, has raked in over $1 billion in revenue last year, bigger than its better-known competitor Scale AI, which reported $870 million in revenue over the same period of time. In comparison, Scale AI was valued at $14 billion in a funding round last year, and was mostly recently valued at nearly $29 billion when Meta invested for a 49% stake in the company and poached its CEO Alexandr Wang to be its chief AI officer to lead its new Superintelligence Labs. Surge AI declined to comment. Like other Scale AI competitors, Surge AI is benefiting from Scale AI's customer losses following Meta's investment. This includes OpenAI and Scale's largest customer, Google, who are now planning to move away from the platform over concerns that doing business with Scale could expose their research priorities to Meta. Scale has said its business remains strong, and it is committed to protecting customer data. Surge AI's quiet yet meteoric rise has positioned it as one of the largest players in the crowded data labeling industry, defying the typical Silicon Valley playbook of raising massive rounds of venture capital to fuel growth. Founded in 2020, the San Francisco-based company has largely operated under the radar, known for its premium, high-end data labeling services used by top AI labs, including Google, OpenAI and Anthropic. As reinforcement learning from human feedback (RLHF) has become more important in training advanced AI systems, the demand for meticulously labeled, nuanced datasets has grown. Surge AI has capitalized on this trend by appealing to a network of highly skilled contractors instead of large pools of low-wage labor. The outsized funding of Surge would be a test of investor interest in the data labeling sector. Some investors view data labeling as an ongoing necessity for AI development, predicting a continued demand from leading AI labs. Others express concern that the industry's low margins and reliance on human labor could make it vulnerable to automation, as AI technology advances and the need for manual annotation diminishes.
CNA
01-07-2025
- Business
- CNA
Exclusive-Scale AI's bigger rival Surge AI seeks up to $1 billion capital raise, sources say
Surge AI, a data-labeling firm that competes with Scale AI, has hired advisors to raise as much as $1 billion in the first capital raising in the firm's history, sources told Reuters, as it seeks to capitalize on growing user demand amid Scale AI's recent customer exodus. The company, founded by former Google and Meta engineer Edwin Chen, is targeting a valuation of over $15 billion, sources said, cautioning that the talks are still in early stages and the final number could be higher. The funding would be a mix of primary and secondary capital that provides liquidity for the employees. Surge AI, which has been profitable and bootstrapped by Chen, has raked in over $1 billion in revenue last year, bigger than its better-known competitor Scale AI, which reported $870 million in revenue over the same period of time. In comparison, Scale AI was valued at $14 billion in a funding round last year, and was mostly recently valued at nearly $29 billion when Meta invested for a 49 per cent stake in the company and poached its CEO Alexandr Wang to be its chief AI officer to lead its new Superintelligence Labs. Surge AI declined to comment. Like other Scale AI competitors, Surge AI is benefiting from Scale AI's customer losses following Meta's investment. This includes OpenAI and Scale's largest customer, Google, who are now planning to move away from the platform over concerns that doing business with Scale could expose their research priorities to Meta. Scale has said its business remains strong, and it is committed to protecting customer data. Surge AI's quiet yet meteoric rise has positioned it as one of the largest players in the crowded data labeling industry, defying the typical Silicon Valley playbook of raising massive rounds of venture capital to fuel growth. Founded in 2020, the San Francisco-based company has largely operated under the radar, known for its premium, high-end data labeling services used by top AI labs, including Google, OpenAI and Anthropic. As reinforcement learning from human feedback (RLHF) has become more important in training advanced AI systems, the demand for meticulously labeled, nuanced datasets has grown. Surge AI has capitalized on this trend by appealing to a network of highly skilled contractors instead of large pools of low-wage labor. The outsized funding of Surge would be a test of investor interest in the data labeling sector. Some investors view data labeling as an ongoing necessity for AI development, predicting a continued demand from leading AI labs. Others express concern that the industry's low margins and reliance on human labor could make it vulnerable to automation, as AI technology advances and the need for manual annotation diminishes.
Yahoo
01-07-2025
- Business
- Yahoo
Exclusive-Scale AI's bigger rival Surge AI seeks up to $1 billion capital raise, sources say
By Milana Vinn and Krystal Hu (Reuters) -Surge AI, a data-labeling firm that competes with Scale AI, has hired advisors to raise as much as $1 billion in the first capital raising in the firm's history, sources told Reuters, as it seeks to capitalize on growing user demand amid Scale AI's recent customer exodus. The company, founded by former Google and Meta engineer Edwin Chen, is targeting a valuation of over $15 billion, sources said, cautioning that the talks are still in early stages and the final number could be higher. The funding would be a mix of primary and secondary capital that provides liquidity for the employees. Surge AI, which has been profitable and bootstrapped by Chen, has raked in over $1 billion in revenue last year, bigger than its better-known competitor Scale AI, which reported $870 million in revenue over the same period of time. In comparison, Scale AI was valued at $14 billion in a funding round last year, and was mostly recently valued at nearly $29 billion when Meta invested for a 49% stake in the company and poached its CEO Alexandr Wang to be its chief AI officer to lead its new Superintelligence Labs. Surge AI declined to comment. Like other Scale AI competitors, Surge AI is benefiting from Scale AI's customer losses following Meta's investment. This includes OpenAI and Scale's largest customer, Google, who are now planning to move away from the platform over concerns that doing business with Scale could expose their research priorities to Meta. Scale has said its business remains strong, and it is committed to protecting customer data. Surge AI's quiet yet meteoric rise has positioned it as one of the largest players in the crowded data labeling industry, defying the typical Silicon Valley playbook of raising massive rounds of venture capital to fuel growth. Founded in 2020, the San Francisco-based company has largely operated under the radar, known for its premium, high-end data labeling services used by top AI labs, including Google, OpenAI and Anthropic. As reinforcement learning from human feedback (RLHF) has become more important in training advanced AI systems, the demand for meticulously labeled, nuanced datasets has grown. Surge AI has capitalized on this trend by appealing to a network of highly skilled contractors instead of large pools of low-wage labor. The outsized funding of Surge would be a test of investor interest in the data labeling sector. Some investors view data labeling as an ongoing necessity for AI development, predicting a continued demand from leading AI labs. Others express concern that the industry's low margins and reliance on human labor could make it vulnerable to automation, as AI technology advances and the need for manual annotation diminishes. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Associated Press
23-06-2025
- Business
- Associated Press
The Training Data Project Wins Prestigious ICEAA 2025 Best Paper Award for Work on AI Data Labeling and Risk Reduction
06/23/2025, Washington, D.C // PRODIGY: Feature Story // David Cook, Co-Founder of The Training Data Project (Source: The Training Data Project) The Training Data Project, a company focused on quantifying AI value and pioneering data labeling standards, has been awarded the 2025 Best Paper honor by the International Cost Estimating and Analysis Association (ICEAA) in the Management, EVM, Software & Agile category. The winning paper, 'Enabling Measurable Success in DoD AI Programs from Acquisition to Operations,' highlights the central role that training data and data labeling play in AI performance, accountability, and long-term program value. The paper, co-authored by The Training Data Project co-founder David Cook, was selected from a competitive field of government and industry contributors. It outlines a practical methodology for quantifying the value and risk associated with AI systems in Department of Defense programs, beginning not at deployment, but at the foundation: the training data pipeline. 'It's an incredible honor to be recognized by the ICEAA, especially at a conference of cost estimators, a community I've never formally belonged to,' said Cook. 'But that's also the point. As AI continues to expand, its financial and operational value depends on something often overlooked: the integrity of the data we feed into it.' At the core of The Training Data Project's mission is the belief that nothing moves in AI without quality data. Data labeling, the process of annotating and identifying data points to 'teach' AI models what to pay attention to, is the bridge between raw inputs and intelligent outcomes. When done incorrectly, the results can be not just ineffective, but dangerous. 'Bad training data is worse than no training data,' Cook added. 'Mistakes made early in the labeling process don't just vanish. They cascade. They replicate through the system like compound interest, and by the time you spot the failure, the only option might be to start over.' To train an AI to recognize a stop sign, for example, it's not enough to feed it thousands of perfectly clear images. The model must also be exposed to a wide range of real-world variations including poor lighting, partial obstructions, weather damage, unusual angles, and visual interference. The more representative and well-labeled the training data, the better the AI can generalize and respond accurately in unpredictable, real-life conditions. 'Training data is not optional, it is foundational,' said Cook. 'Its importance spans all forms of AI. For Large Language Models, which depend on scale, diversity, and structure to function, it is absolutely crucial. Without standards and measurable quality in training data, organizations invite unquantifiable risk across the entire AI pipeline. Value in AI begins with value in the data.' Founded in 2023 by Cook and CEO Noami DeVore, The Training Data Project helps government and enterprise organizations navigate the complex intersection of data labeling, AI governance, and risk reduction. The company's mission is structured around a framework it calls TRUST: Transparent, Reachable, Unbiased, Standards-based, and Traceable data practices. Its work spans three primary pillars: defining quality and standards for training data, sharing best practices for cost-effective curation, and developing open source tools that support responsible AI deployment. From military applications to commercial AI systems, The Training Data Project offers a clear warning and a hopeful path forward. If organizations commit to data quality at the outset, they can unlock both innovation and measurable value while avoiding costly downstream failures. Media Contact: Name - Noami DeVore Email - [email protected]
