Latest news with #Databricks
Yahoo
6 days ago
- Business
- Yahoo
Databricks, Perplexity co-founder pledges $100M on new fund for AI researchers
Andy Konwinski, computer scientist and co-founder of Databricks and Perpelexity, announced on Monday that his personal company, Laude, is forming a new AI research institute backed with a $100 million pledge of his own money. Laude Institute is less an AI research lab and more like a fund looking to make investments structured similar to grants. In addition to Konwinski, the institute's board includes UC Berkeley professor Dave Patterson (known for a string of award-winning research), Jeff Dean (known as Google's chief scientist), and Joelle Pineau (Meta's vice president of AI Research). Konwinski announced the institute's first and 'flagship' grant of $3 million a year for five years, and it will anchor the new AI Systems Lab at UC Berkeley. This is a new lab led by one of Berkeley's famed, Ion Stoica, current director of the Sky Computing Lab. Stoica is also a co-founder of startup Anyscale (an AI and python platform) and AI big data company Databricks, both from tech developed in Berkeley's lab system. The new AI Systems Lab is set to open in 2027 and, in addition to Stoica, will include a number of other well-known researchers. In his blog post announcing the institute, Konwinski described its mission as 'built by and for computer science researchers … We exist to catalyze work that doesn't just push the field forward but guides it towards more beneficial outcomes.' That's not necessarily a direct dig at OpenAI, which started out as an AI research facility and is now, arguably, consumed by its enormous commercial side. But other researchers have fallen prey to the lure of money as well. For instance, popular AI researcher Epoch faced controversy when it revealed that OpenAI supported the creation of one of its AI benchmarks that was then used to unveil its new o3 model. Epoch's founder also launched a startup with a controversial mission to replace all human workers everywhere with AI agents. Like other AI research organizations with commercial ambitions, Konwinski has structured his institute across boundaries: as a nonprofit with a public benefit corporation operating arm. He's dividing his research investments into two buckets that he calls 'Slingshots and Moonshots.' Slingshots are for early-stage research that can benefit from grants and hands-on help. Moonshots are, as the name implies, for 'long-horizon labs tackling species-level challenges like AI for scientific discovery, civic discourse, healthcare, and workforce reskilling.' His lab has, for instance, collaborated with 'terminal-bench,' a Stanford-led benchmark for how well AI agents handle tasks, used by Anthropic. One thing to note, Konwinski's company Laude isn't solely a grant-writing research institute. He also co-founded a for-profit venture fund launched in 2024. The fund's co-founder is former NEA VC Pete Sonsini. As TechCrunch previously reported, Laude led a $12 million investment in AI agent infrastructure startup Arcade. It has quietly backed other startups, too. A Laude spokesperson tells us that while Konwinski has pledged $100 million, he's also looking for, and open to, investment from other successful technologists. As to how Konwinski amassed a fortune enough to guarantee $100 million for this new endeavor: Databricks closed a $15.3 billion funding round in January that valued the company at $62 billion. Perplexity last month secured a $14 billion valuation, too. Does the world really need yet another AI 'good for humanity' research or with a murky nonprofit/commercial structure? No, and yes. AI research has become increasingly muddled. For instance, AI benchmarks designed to prove that a particular vendor's model works best have become plentiful these days. (Even Salesforce has its own LLM benchmark for CRMs.) An alliance that includes the likes of Konwinski, Dean, and Stoica supporting truly independent research that could one day turn into independent and human-helpful commerce could be an attractive alternative.
TechCrunch
6 days ago
- Business
- TechCrunch
Databricks, Perplexity co-founder pledges $100M on new fund for AI researchers
Andy Konwinski, computer scientist and co-founder of Databricks and Perpelexity, announced on Monday that his personal company, Laude, is forming a new AI research institute backed with a $100 million pledge of his own money. Laude Institute is less an AI research lab and more like a fund looking to make investments structured similar to grants. In addition to Konwinski, the institute's board includes UC Berkeley professor Dave Patterson (known for a string of award-winning research), Jeff Dean (known as Google's chief scientist), and Joelle Pineau (Meta's vice president of AI Research). Konwinski announced the institute's first and 'flagship' grant of $3 million a year for five years, and it will anchor the new AI Systems Lab at UC Berkeley. This is a new lab led by one of Berkeley's famed, Ion Stoica, current director of the Sky Computing Lab. Stoica is also a co-founder of startup Anyscale (an AI and python platform) and AI big data company Databricks, both from tech developed in Berkeley's lab system. The new AI Systems Lab is set to open in 2027 and, in addition to Stoica, will include a number of other well-known researchers. In his blog post announcing the institute, Konwinski described its mission as 'built by and for computer science researchers … We exist to catalyze work that doesn't just push the field forward but guides it towards more beneficial outcomes.' That's not necessarily a direct dig at OpenAI, which started out as an AI research facility and is now, arguably, consumed by its enormous commercial side. But other researchers have fallen prey to the lure of money as well. For instance, popular AI researcher Epoch faced controversy when it revealed that OpenAI supported the creation of one of its AI benchmarks that was then used to unveil its new o3 model. Epoch's founder also launched a startup with a controversial mission to replace all human workers everywhere with AI agents. Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | REGISTER NOW Like other AI research organizations with commercial ambitions, Konwinski has structured his institute across boundaries: as a nonprofit with a public benefit corporation operating arm. He's dividing his research investments into two buckets that he calls 'Slingshots and Moonshots.' Slingshots are for early-stage research that can benefit from grants and hands-on help. Moonshots are, as the name implies, for 'long-horizon labs tackling species-level challenges like AI for scientific discovery, civic discourse, healthcare, and workforce reskilling.' His lab has, for instance, collaborated with 'terminal-bench,' a Stanford-led benchmark for how well AI agents handle tasks, used by Anthropic. One thing to note, Konwinski's company Laude isn't solely a grant-writing research institute. He also co-founded a for-profit venture fund launched in 2024. The fund's co-founder is former NEA VC Pete Sonsini. As TechCrunch previously reported, Laude led a $12 million investment in AI agent infrastructure startup Arcade. It has quietly backed other startups, too. A Laude spokesperson tells us that while Konwinski has pledged $100 million, he's also looking for, and open to, investment from other successful technologists. As to how Konwinski amassed a fortune enough to guarantee $100 million for this new endeavor: Databricks closed a $15.3 billion funding round in January that valued the company at $62 billion. Perplexity last month secured a $14 billion valuation, too. Does the world really need yet another AI 'good for humanity' research or with a murky nonprofit/commercial structure? No, and yes. AI research has become increasingly muddled. For instance, AI benchmarks designed to prove that a particular vendor's model works best have become plentiful these days. (Even Salesforce has its own LLM benchmark for CRMs.) An alliance that includes the likes of Konwinski, Dean, and Stoica supporting truly independent research that could one day turn into independent and human-helpful commerce could be an attractive alternative.
Business Wire
6 days ago
- Business
- Business Wire
Motivus Partners with Aquila Clouds to Deliver Advanced FinOps Solutions to Enterprise Clients
DALLAS--(BUSINESS WIRE)--Motivus is proud to announce a strategic partnership with Aquila Clouds, a leading cloud and AI financial management platform provider, to deliver comprehensive FinOps assessments that enable enterprises to optimize cloud costs while maintaining performance excellence. This partnership with Aquila Clouds represents a natural extension of our commitment to helping clients maximize their cloud investments through intelligent financial management. This partnership combines Motivus's deep expertise in AI, data, cloud, and enterprise software solutions with Aquila Clouds' advanced FinOps platform, which provides real-time observability, AI-enabled automation, and policy-driven governance for cloud financial management. The collaboration will enable joint clients to establish robust FinOps practices that bring financial accountability to various cloud spending models. Through Aquila Clouds' platform capabilities—including cost optimization recommendations, ML-based forecasting, and automated budget controls—organizations can achieve savings ranging from 25 to 75% of total cloud spend. 'This partnership with Aquila Clouds represents a natural extension of our commitment to helping clients maximize their cloud investments through intelligent financial management,' said Facundo Tomaselli, CTO of Motivus. 'By combining our technical expertise in cloud transformation with Aquila's proven FinOps platform, we can deliver comprehensive solutions that drive both operational efficiency and cost optimization for our Fortune 1000 clients.' The partnership addresses the growing need for sophisticated FinOps capabilities as organizations increasingly adopt multi-cloud strategies and modern workloads including Kubernetes and Databricks. Aquila Clouds' platform provides comprehensive cost analysis across all cloud services while enabling self-service capabilities that allow FinOps practitioners to manage significantly larger cloud footprints. 'We're thrilled to partner with Motivus. Their deep expertise in cloud transformation makes them an ideal partner as we help organizations advance their FinOps journey,' said Desmond Chan, Co-founder and CPO of Aquila Clouds. 'To succeed in the cloud, businesses need both technical excellence and financial discipline. This partnership brings those capabilities together for our mutual clients.' Key benefits of the partnership include: Integration of advanced FinOps capabilities into Motivus's cloud transformation services Enhanced visibility and control over cloud costs and performance metrics Streamlined chargeback and billing operations for federated organizations Automated policy enforcement and compliance management AI/ML-powered cost forecasting and optimization recommendations The partnership reflects both companies' commitment to enabling digital transformation while maintaining strict cost and performance controls, helping organizations make informed business trade-offs between speed, cost, and quality. About Motivus Motivus is a provider of digital engineering, cloud, data, and AI-enabled transformation services dedicated to solving complex technology challenges that empower clients to achieve business growth, increase revenues, and reduce costs. With a global footprint anchored by multiple nearshore development centers across Latin America, Motivus brings expertise and agility to its Fortune 1000 clients worldwide. For more information, please visit About Aquila Clouds Aquila Clouds is a cloud and AI financial management platform that enables managed service providers, cloud resellers, and enterprises to improve cost observability, optimize spend, automate billing, and boost cloud and AI workload performance. Powered by advanced analytics and AI/ML, its FinOps and BillOps solutions help organizations plan budgets, monitor usage, and streamline operations. Customers typically achieve over 20% in cloud cost savings and reduce billing operation time by up to 82%. Learn more at
Tahawul Tech
7 days ago
- Automotive
- Tahawul Tech
AVEVA recognised at annual Data + AI Summit
AVEVA, a global leader in industrial software driving innovation and sustainability, has been named as the 2025 Databricks Manufacturing ISV Partner of the Year. Presented at the annual Data + AI Summit, the award highlights AVEVA's exceptional contributions to innovations in data-powered manufacturing. Over the past year, AVEVA, in strategic partnership with Databricks, the data and AI company, launched a ground-breaking integrative solution through its CONNECT industrial intelligence platform that redefines how industrial and enterprise data can be unified, analysed, and operationalised. This collaboration addresses one of the most pressing challenges in modern industry: turning vast amounts of siloed operational technology (OT) and information technology (IT) data into meaningful insights, while ensuring data integrity, minimising the development and maintenance costs, and significantly shortening time-to-value. By using the Databricks Data Intelligence Platform alongside CONNECT, customers can securely unify industrial data with enterprise business systems in an open, governed, and scalable manner via Delta Sharing, Databricks' open source approach that enables customers to share live data across platforms, clouds, and regions. This enables AI, machine learning, and real-time analytics to be applied to data sets that were historically isolated or underutilised. Manufacturers can now leverage Databricks with CONNECT for smarter decision making, predictive maintenance, demand forecasting, sustainability tracking, safety monitoring and boosted operational efficiency. 'At AVEVA, we're proud to have established Databricks and Delta Sharing as key foundation-stones of our strategy for our industrial intelligence platform, CONNECT', said Bry Dillon, SVP of Partners and Commercial Strategy at AVEVA. 'Together, we're enabling our joint customers to access real-time insights, accelerate AI, and deliver tangible outcomes across the industrial landscape. Our partnership with Databricks marks a pivotal moment in the advancement of industrial AI. This collaboration presents a powerful opportunity to accelerate the deployment of AI-driven solutions and drive greater industry wide collaboration — capabilities that are needed for companies across the industrial sector to stay relevant, remain competitive, and build efficient, sustainable businesses of the future'. 'We are thrilled to name AVEVA the 2025 Databricks Manufacturing ISV Partner of the Year', said Shiv Trisal, Global Manufacturing, Transportation & Energy GTM Leader at Databricks. 'As more enterprises leverage data intelligence to solve challenges across the manufacturing and energy industries, AVEVA's partnership with Databricks is essential to helping organisations everywhere harness the full potential of their data'. Image Credit: AVEVA
Forbes
18-06-2025
- Business
- Forbes
The AI Race Is Now About Databases — Not Just Big Models
The real AI race isn't about smarter models anymore. It's about who can serve fast, reliable data to ... More power agents, copilots and real-time decisions. (Photo by Alive Coverage for Snowflake) When Snowflake announced its $250 million acquisition of Crunchy Data two weeks ago at its annual summit in San Francisco, it was a signal that the battle for AI dominance, which was once the domain of massive language models and advanced GPUs, was shifting straight into what many experts describe as the foundation of AI development: the database layer. Two weeks earlier, Snowflake rival Databricks revealed its own $1 billion acquisition of Neon, another Postgres-native startup. And right about the same time, American cloud-based software company Salesforce made an $8 billion deal to buy data management provider Informatica. Taken together, these aren't just high-profile database deals but proof that the AI infrastructure race is moving down-stack. While sophisticated models may still grab headlines, the battle is now increasingly about who can serve AI-ready data, fast, resiliently and at scale. Despite their limitations, AI tools often produce remarkably human-like results. But these tools, whether in the form of AI copilots, chatbots, or assistants, require lots of data parameters to do that. They demand a steady flow of both structured and unstructured data — fresh, fast and accessible. As one analyst put it, 'AI is stupid without access to good data.' And not just access to a treasure trove of data, but also data orchestration at machine speed. To access and process data quickly, which is how AI chatbots are able to 'reason,' PostgreSQL — the open-source database powering many traditional web apps and enterprise systems — has become a go-to choice for companies modernizing their data infrastructure because it's reliable, widely used and already trusted in enterprise settings. But as Spencer Kimball, cofounder and CEO of Cockroach Labs, noted in an interview, while Postgres was built for batch jobs, periodic queries and peak load measured in thousands, AI doesn't work like that. 'Retrofitting Postgres for real-time, agent-driven workloads exposes architectural limits. AI agents, copilots and real-time pipelines generate nonstop reads and writes. They require globally consistent data in milliseconds and expect systems to absorb failure without flinching,' Kimball said. That's why companies like Snowflake are rethinking what the modern database needs to be. For Snowflake, the answer is to embed transactional and AI-ready systems deep into its platform, giving customers a seamless way to support a new generation of intelligent workflows. At its yearly summit, which had 20,000 attendees this year, Snowflake CEO Sridhar Ramaswamy emphasized a major shift toward 'workflow-native' data platforms. With tools like Openflow, the company is repositioning itself as the connective tissue between corporate data and AI-driven decisions. The idea, according to Ramaswamy, is to make it easier for companies to tap into and connect fragmented data sources — including on-prem databases, SaaS apps and unstructured streams — into unified pipelines and build real-time workflows on top of them. And Snowflake Intelligence is meant to go a step further. By layering generative AI atop enterprise data, the platform lets non-technical employees query across their company's information without writing a single line of SQL or needing engineering support. Ask a natural-language question, and an AI agent finds the answer, with context. Jeff Hollan, head of Cortex AI apps and agents, calls this the next frontier, noting that 'the next generation of apps aren't just data-hungry, but also reasoning-hungry.' That hunger for reasoning is why the database layer is now more critical for enterprises than ever before, Hollan said. Artin Avanes, head of core data Platform at Snowflake, sees this growing demand for platforms that can support continuous data interaction, not just batch analytics, as a reflection of an industry-wide shift that Snowflake is aiming to lead. 'While faster access to data is great, that's not what you really need,' Avanes told me in an interview. 'You need systems that adapt to how decisions are made in real time.' That shift is also fueling the rise of Cortex AI, Snowflake's agentic AI framework, which orchestrates data at scale, automates operations and delivers business value in live environments. It's a far cry from the common situation for most organizations today, where many are still trapped in proof-of-concept cycles, according to Vivek Raghunathan, senior vice president of engineering at Snowflake. 'Everyone's experimenting. But very few are scaling responsibly,' he said. 'Part of the challenge lies in the disconnect between what enterprises think AI will do and what it actually requires.' As Raghunathan further explained, it's an organizational issue. 'Without clear vision and readiness at the infrastructure level,' he noted, 'no amount of AI investment will deliver sustained value.' And that's a reality many enterprise leaders are now coming to terms with. In fact, according to Fivetran's report on AI and data readiness, 42% of enterprises report that over half of their AI projects have been delayed, underperformed, or failed due to poor data readiness, underscoring the growing gap between AI ambition and architecture. Snowflake's move to acquire Crunchy Data, Databricks' Neon acquisition and Salesforce's big money deal are all land grabs in the AI foundation layer. They show that vendors no longer want to sit atop the data stack. They want to own it. For enterprises, that should trigger a serious question: Who do you trust with the foundation of your AI future? Because as AI moves from experimentation to execution, the companies that win won't just build the smart models. They'll be the ones who control the data stack. What that means is that the database layer isn't the back-end of enterprise AI anymore. It's now the front line.
