Latest news with #AliGhodsi
TechCrunch
22-07-2025
- Business
- TechCrunch
This startup thinks email could be the key to usable AI agents
AI companies are pushing agents as the next Great Workplace Disruptor, but experts say they're still not ready for primetime. They often struggle with autonomous decision-making, can't cooperate with other agents, fail at confidentiality awareness, and integrate poorly into existing systems. Industry pioneers like Andrej Karpathy and Ali Ghodsi have said that, like the deployment of autonomous vehicles, humans need to be in the loop in order for agents to succeed. Startup Mixus understands that and has built an AI agent platform that not only keeps humans in the workflow, but also allows those humans to interact with agents directly from their email or Slack. 'We're meeting customers where they are today,' Elliot Katz, Mixus co-founder, told techCrunch. 'Where is every person in the workforce today? For the most part, they're on email. And so because we can do this through email, we believe that's a way we can democratize access [to agents].' Mixus only beta-launched out of Stanford in late 2024, but it has already raised $2.3 million in pre-seed funding and brought on some customers, including clothing store chain Rainbow Shops, and others across finance and tech. Ease of use is Mixus's biggest selling point, from how it helps create agents to how users can interact with them. Users can create an agent or multiple agents from simple text prompts. For someone in sales, that prompt might look like: 'Create an agent that finds all open tasks in Jira in project mixus-dummy, and send me a report with information on all tasks that are overdue. Draft emails to all the assignees who have overdue tasks, and have me review them in the chat and with simple clear formatting for email (no attachments/docs). Once I verify, send the emails. Run it now. And moving forward, run it every Monday at 7am PST.' If Mixus works reliably, this is a huge unlock for the AI agent space. Most agentic AI tools today either give you a pre-built assistant, a la ChatGPT or Gemini, or require developers to build custom against using frameworks like LangChain, AutoGen, or crewAI. Techcrunch event Tech and VC heavyweights join the Disrupt 2025 agenda Netflix, ElevenLabs, Wayve, Sequoia Capital — just a few of the heavy hitters joining the Disrupt 2025 agenda. They're here to deliver the insights that fuel startup growth and sharpen your edge. Don't miss the 20th anniversary of TechCrunch Disrupt, and a chance to learn from the top voices in tech — grab your ticket now and save up to $675 before prices rise. Tech and VC heavyweights join the Disrupt 2025 agenda Netflix, ElevenLabs, Wayve, Sequoia Capital — just a few of the heavy hitters joining the Disrupt 2025 agenda. They're here to deliver the insights that fuel startup growth and sharpen your edge. Don't miss the 20th anniversary of TechCrunch Disrupt, and a chance to learn from the top voices in tech — grab your ticket now and save up to $675 before prices rise. San Francisco | REGISTER NOW With Mixus, users can set up their agents within Mixus's platform via a chat function – through written or vocal prompts – or by simply emailing instructions to agent@ Then Mixus will build, run, and manage single- or multi-step agents directly from the inbox. 'Most of the world, most of America, doesn't even know what an AI agent is or why it's helpful for them, and they've definitely never used one,' Katz said, noting that older workers might have an especially hard time learning how to use agents. 'We're trying to reach all these people that have never used [agents], but could very much benefit from an AI.' Image Credits:Mixus Katz and his co-founder Shai Magzimof demoed the technology for me, showing how easy it is to add human verifiers for your agents by simply instructing at which step they should come to you for oversight. For example, they ran an agent to do research on TechCrunch reporters before pitching them. The agent would identify and gather the latest technology news and trends, analyze the information to identify potential story angles for a TechCrunch reporter, and compile a research report summarizing the findings. At the last stage, the agent was directed to send the information to Katz for verification. Once approved, the agent would send the completed research report to his Magzimof. The founders stressed that humans can be in the loop as much or as little as a business or enterprise dictates – Magzimof said organizations can set up company-wide rules, like ensuring an email gets checked by a human if it's being sent externally. Mixus doesn't always require human oversight. So, for example, if an agent has already run a Jira integration hundreds of times and hasn't messed it up yet, a human may trust it to continue that task autonomously. Or as Katz put it: 'We enable colleague oversight. We don't mandate colleague oversight.' Bringing other colleagues into the workflow is as easy as tagging them in the chat with an agent or even copying them on the email to the agent. That's another standout compared to agents on the markets today. Most models are single-user, and while Notion AI and Slack GPT allow users to collaborate in shared spaces, they don't take it that step further of letting the AI manage conversations and tasks between teammates in real time. Another core feature of Mixus is its ability to store memory. 'We created Spaces so that every team, every person, every group of people can have a shared memory,' Magzimof said. 'Then all my agents, all my files, all the people can be in that very specific Space's memory.' While ChatGPT and Claude both support memory, their enterprise plans don't yet support shared agent memory across users. What else can Mixus do? A running list of Mixus's capabilities as an AI agent. Image Credits:Mixus The founders ran me through roughly an hour-long demo showing a range of use cases and abilities. Its agents do seem miraculous, reflecting a high degree of autonomy and memory that put Mixus on the high end of the AI agent spectrum. That is, if the product works as reliably as it did in the demo. Like other agents, Mixus can integrate with other tools, from Gmail to Jira, and users can trigger agents to run immediately or on a schedule. Agents in Mixus can run and edit documents or spreadsheets inline, which is similar to ChatGPT, Microsoft Copilot, and Google Gemini, but those are often limited to sandboxed environments. Mixus also enables agents to autonomously navigate organizational context – like figuring out who in an organization owns a particular task by looking through Jira tickets. That kind of cross-tool, org-aware reasoning is still rare among today's agent platforms. Built on a combination of Anthropic's Claude 4 and OpenAI's o3, Mixus agents also have access to the web, which Magzimof says can be used for tasks like live research or monitoring. He described it as 'Google Alerts on steroids.' Taken together, Mixus appears to be less of a productivity tool and more like a tireless digital colleague – one of the most ambitious attempts yet to reimagine AI as a true collaborator. If it works as advertised, your next 'coworker' might not be human, but it might get through your inbox faster than you do. Got a sensitive tip or confidential documents? We're reporting on the inner workings of the AI industry — from the companies shaping its future to the people impacted by their decisions. Reach out to Rebecca Bellan at and Maxwell Zeff at For secure communication, you can contact us via Signal at @rebeccabellan.491 and @mzeff.88.
CNBC
26-06-2025
- Business
- CNBC
AI's reasoning problems -- why 'thinking' models may not actually be smarter
AI reasoning models were supposed to be the industry's next leap, promising smarter systems able to tackle more complex problems and a path to superintelligence. The latest releases from the major players in artificial intelligence, including OpenAI, Anthropic, Alphabet and DeepSeek, have been models with reasoning capabilities. Those reasoning models can execute on tougher tasks by "thinking," or breaking problems into logical steps and showing their work. Now, a string of recent research is calling that into question. In June, a team of Apple researchers released a white paper titled "The Illusion of Thinking," which found that "state-of-the-art [large reasoning models] still fail to develop generalizable problem-solving capabilities, with accuracy ultimately collapsing to zero beyond certain complexities across different environments." In other words, once problems get complex enough, reasoning models stop working. Even more concerning, the models aren't "generalizable," meaning they might be just memorizing patterns instead of coming up with genuinely new solutions. "We can make it do really well on benchmarks. We can make it do really well on specific tasks," said Ali Ghodsi, the CEO of AI data analytics platform Databricks. "Some of the papers you alluded to show it doesn't generalize. So while it's really good at this task, it's awful at very common sense things that you and I would do in our sleep. And that's, I think, a fundamental limitation of reasoning models right now." Researchers at Salesforce, Anthropic and other AI labs have also raised red flags about reasoning models. Salesforce calls it "jagged intelligence" and finds that there's "significant gap between current [large language models] capabilities and real-world enterprise demand." The constraints could indicate cracks in a story that has sent AI infrastructure stocks like Nvidia booming. "The amount of computation we need at this point as a result of agentic AI, as a result of reasoning, is easily a hundred times more than we thought we needed this time last year," Nvidia CEO Jensen Huang said at the company's GTC event in March. To be sure, some experts say Apple's warnings about reasoning models may be the iPhone maker shifting the conversation because it is seen as playing catch up in the AI race. The company has had a series of setbacks with its highly-touted Apple Intelligence suite of AI services. Most notably, Apple had to delay key upgrades to its Siri voice assistant to sometime in 2026, and the company did not make many announcements regarding AI at its annual Worldwide Developers Conference earlier this month. "Apple's putting out papers right now saying LLMs and reasoning don't really work," said Daniel Newman, Futurum Group CEO on CNBC's "The Exchange." Having Apple's paper come out after WWDC "sounds more like 'Oops, look over here, we don't know exactly what we're doing.'" Watch this video to learn more.
Deccan Herald
15-06-2025
- Business
- Deccan Herald
America didn't do immigration policies well: JPMorgan Chase CEO Jamie Dimon
His remarks came during a conversation with Databricks CEO and Co-founder Ali Ghodsi at the Data + AI Summit 2025 in San Francisco last week.
Time of India
13-06-2025
- Business
- Time of India
India's digital businesses are innovating faster with data & AI, says Databricks founder
India's digital-native businesses are artificial intelligence (AI)-hungry and ahead of the curve from global peers when it comes to innovation with data and AI , said Ali Ghodsi, founder and chief executive of Databricks . 'India's great because when the rest of the world is talking about recession, India is on the upswing. And in the last decade, they've built a lot of digital infrastructure in India, which is a game-changer. India's ahead on digital infrastructure compared to most other countries in the world,' Ghodsi said while addressing the media at the Databricks Data + AI Summit in San Francisco on Wednesday. The Silicon Valley's data and AI company Databricks recently committed a $250 million investment in India over the next three years towards local R&D, talent development, and enterprise adoption of AI. "We're doubling down on Bangalore. We hired a huge engineering team. We target the IITs," he said, mentioning an instance where the company received 700 applications from IIT graduates for just four open positions in Bangalore. Ghodsi said that the company is extremely bullish on Asian markets, including India, South Korea, Australia and New Zealand, which are moving faster than the rest of the world on AI because of the relaxed regulatory environment. 'We're investing ahead of the game there. We're not just looking at how much revenue we get? Is the ROI there? Instead. We're saying, let's put even more there than the numbers justify, because we're so bullish on what's happening in Asia,' he said. At the annual conference on Wednesday, Databricks made a slew of bold announcements challenging traditional players in database management, AI apps and agents. Here's a rundown of key announcements: Agent bricks Taking a fresh approach to agentic AI, Databricks is focusing on the quality and cost of productising agents with 'Agent Bricks', an offering that directly challenges Salesforce's Agent Force and Google's Agent Space. 'There are a lot of challenges in the industry around building agents. We can't evaluate the quality of the agents. We don't know how these agents are doing in production,' Ghodsi said, adding that there are no evaluations or benchmarks for judging the performance of agents. Hence, Databricks is introducing LLM judges for automated evaluations. Agent Bricks' auto optimisation techniques, such as knowledge extraction and multi-agent supervisor can refine the agent for the best quality output, sometimes at 10 times lower cost. Lakebase Challenging the traditional database platforms like Oracle Database, MySQL, Microsoft SQL Server, and PostgreSQL, Databricks announced Lakebase, a first-of-its-kind fully-managed Postgres database built for AI. 'We think that's going to disrupt the existing database market, which has really not changed much in 40 years. But I think now is the time where it's actually under a lot of pressure with agents coming in,' Ghodsi said, adding that the company is targeting a $100 billion total addressable market with Lakebase. Databricks, last month, announced the acquisition of Neon, a leading serverless Postgres company, which showed that over 30% of the databases at Neon were actually created by agents, not by database administrators. 'So next year, it's probably 99% plus.' Therefore, in the new AI era, enterprises need different types of databases where compute and storage are completely separated, he explained. 'You just store the database on very cheap cloud storage in an open format so you're not locked into anyone (single vendor).' Over 300 Databricks customers are already using Lakebase, and this transition is going to be the most important marathon for the next five years, he said. Databricks free edition To close the AI talent gap, Databricks also announced the free edition of its platform, along with a $100 million global investment in data and AI education. This initiative gives students, professionals, and institutions free access to Databricks tools and training. Among other notable announcements made was the Lakeflow Designer, a new no-code capability that lets non-technical users create data pipelines using a visual drag-and-drop interface and a natural language GenAI assistant. (The reporter was in San Francisco at the invitation of Databricks)
Yahoo
12-06-2025
- Business
- Yahoo
Databricks introduces Agent Bricks for AI agent development
Databricks has launched Agent Bricks, an automated solution designed to facilitate the creation of AI agents tailored to specific business needs. This tool allows users to input a 'high-level' description of the desired task and connect their enterprise data, with Agent Bricks managing the subsequent processes. The service, now available in Beta, is optimised for various industry applications, including structured information extraction, knowledge assistance, text transformation, and multi-agent systems, the company said. Agent Bricks employs advanced research methodologies from Mosaic AI Research to generate domain-specific synthetic data and task-aware benchmarks. This approach enables automatic optimisation for both cost and quality, streamlining the development process and enhancing production-level accuracy. The integration of governance and enterprise controls allows teams to transition from concept to production efficiently, eliminating the need for disparate tools. The functionality of Agent Bricks includes automatic generation of task-specific evaluations and LLM judges, the creation of synthetic data that mirrors customer data, and a comprehensive search for optimisation techniques. Users can select the iteration that best balances quality and cost, resulting in a production-ready AI agent capable of delivering consistent output, the company's statement added. Agent Bricks supports various customer use cases across multiple sectors. For instance, the Information Extraction Agent converts documents into structured data, while the Knowledge Assistant Agent provides accurate answers based on enterprise data. The Multi-Agent Supervisor facilitates the integration of multiple agents for complex tasks, and the Custom LLM Agent allows for tailored text transformations. Databricks CEO and co-founder Ali Ghodsi said: 'For the first time, businesses can go from idea to production-grade AI on their own data with speed and confidence, with control over quality and cost tradeoffs. 'No manual tuning, no guesswork and all the security and governance Databricks has to offer. It's the breakthrough that finally makes enterprise AI agents both practical and powerful.' In addition to Agent Bricks, Databricks has introduced several features at the Data + AI Summit, including support for serverless GPUs, enabling teams to fine-tune models and run workloads without managing GPU infrastructure. The release of MLflow 3.0, a platform for managing the AI lifecycle, allows users to monitor and optimise AI agents across various environments. In May 2025, Databricks announced the acquisition of Neon, a serverless Postgres database company. "Databricks introduces Agent Bricks for AI agent development" was originally created and published by Verdict, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Sign in to access your portfolio
