Latest AI News
Warner Music acquires AI attribution startup Sureel AI
theWarner Music Music (WMG)announcedon Wednesday that it’s acquiring AI attribution startup Sureel AI. Sureel’s patented technology creates “AI DNA” for songs and breaks them down into component parts to trace how AI models use those elements. Through the acquisition, WMG aims to better track when its artists’ and songwriters’ work is used in AI-generated content or for training AI models. “Bringing Sureel into WMG strengthens our capability for protection, control and monetization and ensures that the creative community remains in control of its intellectual property, name, image, likeness, and voice,” said WMG chief executive Robert Kyncl in the press release. The financial terms of the deal were not disclosed. Founded in 2022, Sureel also offers intellectual property provenance, audit and compliance reporting, model optimization, and AI business intelligence. The startup also has a name, image, and likeness (NIL) attribution suite to track how artist voices, likenesses, and performance identities are used in AI training and generation. This includes voice clones, AI-generated avatars, and style replication. The startup will continue to operate as a stand-alone platform serving the broader music and AI ecosystem, WMG says. “Rightsholders deserve to know how AI interacts with their work, and to share fairly in the value it creates,” Sureel founder and chief executive Tamay Aykut said in remarks. “Sureel was built to make that possible, and with WMG’s backing, we can deliver on our mission at scale, building a more transparent and fair future and driving value growth for the whole music and entertainment ecosystem.” WMG has embraced AI after initially opposing it, as the company originally sued music-generation startup Suno in 2024 and latersigned a licensing dealwith the company last year. WMG said at the time that artists and songwriters would have full control over whether and how their names, images, likenesses, voices, and compositions are used in new AI-generated music. It’s worth noting that Sony Music Entertainment and Universal Music Group are still pursuing massive copyright infringement claims against the AI music startup. WMG last year also settled its lawsuit against AI music startup Udio andreached a licensing dealwith the company.
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The three hard-tech moonshots fueling SpaceX’s unbelievable IPO
SpaceX is coming to market on Friday, and investors can barely contain their excitement. The $75 billion stock offering is reportedlydeeply over-subscribed, with some institutional investors ponying up for$10 billion blocksof Elon Musk’s empire. There are lots of reasons to be skeptical of the investment — big IPOs tend to sink, the company is losing money, and Musk’s erratic online behavior would be terrifying coming from any other tech CEO — but it doesn’t seem to be slowing anyone down. Tech investors have learned to never bet against Elon, whatever the business logic indicates. But a dispassionate look at SpaceX’s financial plans can still tell us a lot about what they’re betting on: A business centered around orbital data centers that emerged in the last 18 months as Musksought a visionthat would unite his conglomerate ahead of its IPO. In true Musk style, it’s a bold scheme, and one that requires at least three near-impossible feats of engineering: a reusable rocket, a brand-new American chip foundry, and a sprint to build satellites faster than ever before. That kind of business plan can be difficult to score. This week, two analyses tried to offer a more a sober assessment of SpaceX’s plan — one from Morningstar, the financial research firm, and another from Aswath Damodaran, a New York University finance professor who takes a special interest in corporate valuation. Both exercises find SpaceX significantly less valuable than the nearly $1.8 trillion assessment proffered by the company’s bankers. Morningstarassigns a valueof about $825 billion, while Damodaransuggeststhe company is worth $1.2 trillion. The significant difference is, in many ways, the result of bolting a world-beating space monopoly to a far riskier AI business. Morningstar’s analyst characterizes the difference between their assessment of a fair value of $63 a share, and SpaceX’s offering price of $135, as a $72 call option on the company’s ability to deliver orbital data centers at the rate and capability that Musk believes is possible. In both analyses, the high margins of the company’s space launch business and its satellite internet network are the most attractive things about the company, while its AI business is the most uncertain. Part of the question is, what is SpaceX’s AI business? In the company’sS-1market analysis, it frames its largest opportunity in enterprise AI — that its models will power coding tools built by the team it acqui-hired from Cursor, or the company’s Macrohard project, which is intended to equip digital agents with the capabilities to perform white-collar labor. SpaceX assessed the total market for that business as $22.7 trillion, compared to $2.4 trillion for AI infrastructure and just under $2 trillion for the company’s space efforts. But that contradicts the company’s recent deals to sell significant amounts of compute toAnthropicandGoogle, ostensible competitors in the model business. That’s not out of place for a Musk company; SpaceX frequently launches satellites operated by competitors to its Starlink network. It just usually does that from a place of strength, not while playing catch-up. Acting like a neocloud might be good near-term business, but it raises the question of where value will accrue in the AI tech stack: Is it better to be a compute provider or a model-builder, if you can’t be both? The scaling logic that dominates the AI business demands that serious frontier labs constantly train new and more powerful models (or, as Muskadmittedin his recent lawsuit against Sam Altman, by distilling capabilities from other companies’ models). Any competitor not rushing ahead is likely to fall behind, although the rising abilities of cheaper open source models might undermine that dynamic. Space data centers are one way to square the circle, providing so much compute that SpaceX could effectively do both. In avideo interviewreleased by SpaceX this week, Musk laid out the logic for why SpaceX is best positioned to deliver on data centers. The core of the argument was that SpaceX is the only company capable of putting a lot of mass on orbit cheaply, building a lot of solar panels, and building a lot of chips. In general, industry experts see space data centers at scale being about a decade away, but Musk argued (with a lot of caveats) that they are much closer. “This is not a promise of what we’ll do,” Musk said in the video. “This is what we are going to try to do, and think we probably can do, which is to get to roughly an annualized rate of a gigawatt per year by the end of next year, in terms of space AI compute.” Based on his expected maximum power delivery of 150 kW per satellite, that’s a production rate of 6,666 satellites a year, or about 556 a month. That’s roughly twice the reported current production rate of Starlink satellites, which is just 70 a week. Though Musk says that the AI satellites are simpler in architecture, that’s a lot to ask for a production facility that hasn’t been built yet. The company is also still building out its solar panel production facility. That’s before we get to Terafab, the company’s much-discussed chip foundry, which Musk sees feeding into the later stages of this product as the company tries to scale up to a terawatt of annual compute production. Chip fabs are some of the hardest modern industrial projects, typically costing billions of dollars and taking as long as a decade to build. Then there’s the most vital question: What about Starship, the key to SpaceX’s ability to economically put all those chips in orbit? A recent test flight went well enough, but it didn’t suggest that rapid reusability is right around the corner. SpaceX may end up reusingjust the boosterat first, which would raise the costs of the space data center roll-out. For now, the company is still undergoing a mishap investigation for the FAA to understand why the booster stage failed to make a controlled reentry as planned. SpaceX hasn’t responded to questions about when the vehicle will fly again, thought it has said it expects to begin launching Starlink satellites with it by the end of this year. But take that with a grain of salt: Consider that NASA, which has a nearly $4 billion contract with SpaceX to use Starship as a moon lander, still isn’t ready to commit to a test mission with the vehicle scheduled for late 2027. As public investors get their hands on SpaceX shares, they’ll find themselves owning a near-monopoly on access to space in the U.S. and Europe, a world-spanning communications network, and a wager on the most ambitious infrastructure project of the AI era. Those projects depend on SpaceX creating something never seen before — a fully reusable rocket. The company will also need to build a high-rate production facility for AI satellites, but do so in 18 months, not the decade it took to develop its Starlink manufacturing. Finally, it will need to build a chip foundry in the U.S., something even dedicated silicon firms are reluctant to take on. Musk is right that SpaceX is the only company positioned to build any of this anytime soon, but that speaks to the magnitude of the challenge as much as the company’s likelihood of achieving it. Musk used to say he wouldn’t take SpaceX public until he reached Mars, since fickle investors might lose faith along the way. Those plans may have been put on hold, but what he’s laid out ahead of the company’s IPO could be just as difficult.
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Datadog veterans launch AI coding startup Niteshift on a bet against Big AI lock-in
AI coding agent startupNiteshifthas raised a $7 million seed round led by Greylock’s Jerry Chen. That’s a modest sum by AI standards, but the startup, founded by two former early Datadog engineers, has attracted some big-name angels like Reid Hoffman, Datadog’s Olivier Pomel and Alexis Lê-Quôc, Ankur Goyal of Braintrust, and Misha Laskin of Reflection AI. Founded by Sajid Mehmood and Conor Branagan, who helped grow Datadog from its early days to a multi-billion valuation, the company has entered the crowded AI coding space with a compelling idea: Why would any company trust its most sensitive assets — code that runs its products — directly to model makers like OpenAI and Anthropic, given that those companies are constantly “killing” startups and businesses by launching competing apps? Mehmood, who is CEO, likens it to Datadog’s early growth, when the monitoring company won e-commerce customers who refused to build on Amazon Web Services. It was a reasonable concern, given that Amazon was simultaneously putting many of those same retail stores out of business in what became known as the “retail apocalypse.” The AI equivalent, as Mehmood sees it, is already underway. Anthropic, OpenAI, and others are moving fast into vertical software markets — what some are calling theSaaSpocalypse. “At Datadog we saw this clearly,” Mehmood said. “A big part of our multicloud business came from e-commerce businesses who did not want to run on Amazon, right? … We are absolutely going to see the same dynamic as Anthropic goes to compete in legal and healthcare and finance and whatever else.” The bet is that companies will increasingly seek infrastructure that separates the coding model from all the other orchestration needed to ensure AI-generated code is properly vetted and maintained (and that they’ll want a vendor without a competing agenda). To be clear, Niteshift isn’t replacing Claude Code or Codex, the two most popular coding agents. It argues that it reduces dependence on them. Niteshift’s AI coding cloud will route between those models — along with open source options and others — based on the needs of each project. “Being able to switch between GPT and cloud models is important,” Mehmood said, “Everybody’s worried about getting stepped on by these giants.” That idea is what got Greylock’s Chen to bite. “As the frontier labs move up the stack, there’s an opportunity to offer customers an alternate path: unbundling their agents from the infrastructure they run on,” Chen told TechCrunch. “Niteshift is building the platform that enables this for coding agents, letting customers invest deeply in their developer tooling without locking themselves into a single model or agent vendor.” More than that, Niteshift isn’t selling tokens. It sells infrastructure, charging like a cloud provider, with per-minute usage rates. “Everybody else is selling labor replacement intelligence,” Mehmood said. “We’re selling software to agents, as opposed to humans — but we’re still out here selling software.” Even so, Niteshift is entering a crowded market of AI coding tools. Model independence isn’t a novel idea, and Niteshift’s competitors have a massive head start. That includes Cursor,though it could soon be gobbled up by SpaceX; Cognition, which justraised $1 billion at a $26 billion valuation; Amazon Bedrock; and AI gateway platform OpenRouter, whichjust raised $113 million at a $1.3 billion valuation. The list goes on. Mehmood’s answer to all of that is the founding team’s depth. Mehmood and Branagan didn’t just study these problems — they lived them, scaling Datadog through the exact growing pains that large engineering organizations now face with AI-generated code. Teams, he said, need to run, test, and verify software autonomously in their real production environments, and they need infrastructure built by people who’ve done it at scale.
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Cybersecurity researchers aren’t happy about the guardrails on Anthropic’s Fable
Anthropicreleased its latest model Fableon Tuesday, billing it as a public and limited version of its powerful and much-hyped cybersecurity model Mythos. But not everyone is happy with the restrictions, anda numberofcybersecurityresearchersandprofessionalshave airedcomplaintsonline. “[Fable] rejects any request that could be tangentially cyber related. Even innocuous tasks like reading a blog post,”saidValentina “Chompie” Palmiotti, a well-known security researcher who works at IBM X-Force. When a prompt triggers its guardrails, Fable pauses the chat and says that its “safety measures flagged this message for cybersecurity or biology topics.” The guardrails were put in place to limit the risk that Fable could be used to develop malware or compromise software —a longstanding concernwithin Anthropic. The restrictions on biology come from a similar concern arounddeveloping biological weapons. Whenthe AI giant released Mythosin April, it restricted the model to a limited number of companies and organizations in what it calledProject Glasswing, an effort to deploy the model to secure critical software and infrastructure. Last week,Anthropic expanded access to Mythosto hundreds of organizations in 15 countries. But despite the good intentions, many cybersecurity experts are still put off by the haphazard nature of the restrictions. Matt Suiche, a cybersecurity veteran, told TechCrunch that “if you ask it to write secure code, it assumes it is cybersecurity related work instead of software engineering best practices, and you get downgraded.” Fable is programmed to fall back to Claude Opus 4.8 if it hits a guardrail. “It seems to be keyword based, so anything in the lexical field of ‘cybersecurity’ triggers the guardrails.” Contact UsDo you have more information about how hackers are using AI? Or how cybersecuity companies are using AI? We’d love to hear from you. From a non-work device and network, you can contact Lorenzo Franceschi-Bicchierai securely on Signal at +1 917 257 1382, or via Telegram and Keybase @lorenzofb, oremail. “But it is understandable as we are still in the early days and they are still adapting their guardrails. I am sure they are going to evolve over time as Anthropic and other frontier model companies will collaborate more with the current new generation of cybersecurity companies,” said Suiche, who is a member of the technical staff at Tolmo, an AI cybersecurity startup. “It’s better to catch more people than not enough when you do such a release and to relax the guardrails over time.” Another researchergripedon X that “even asking for a code review” triggers Fable’s guardrails. Anthropic did not immediately respond to a request for comment. Apart from guardrails inside its models, Anthropic requires cybersecurity professionals to apply to theCyber Verification Program. If they get approved, the applicants have fewer limitations on using Claude for cybersecurity work. OpenAI has a similar program calledTrusted Access for Cyber.
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How memory tools can make AI models worse
One of the biggest selling points for modern AI systems is their ability to adapt to users. Every time an AI assistant takes on a task for you, it’s also adapting to your style and preferences, which are incorporated as context for future tasks. With more context and a better understanding of the user, the model can get better every time you use it — or at least that’s the theory. New research suggests that models’ adaptive abilities might be a mixed blessing. On Wednesday,researchers at the AI company Writerpublishedtwopapersshowing how popular memory systems can make models worse, pulling them toward misconceptions or misunderstandings introduced by the user. As user input fills up more of the model’s context window, the model grows more sycophantic — and less committed to accuracy. “We wanted to be able to characterize how often a model is going to be usefully paying attention to user preferences versus giving a potentially wrong answer,” said Dan Bikel, Writer’s head of AI, who worked on the papers. As Bikel told TechCrunch, “with every additional storing of user preferences and retrieving of them, you’re running an increasing risk.” In one variation, researchers tested AI models by recording that a user’s favorite book was Station Eleven, then asking the model to name a best-selling dystopian book. Models became far more likely to name Station Eleven in their response, even though the question didn’t relate to the user’s favorite book. The tendency increased when using memory compression tools likeMem0andZep. As the paper puts it, “all memory systems fundamentally struggle to distinguish relevant context from irrelevant anchors, severely undermining diversity and creativity and introducing unintended avenues of bias that can limit system utility,” the paper reads. The second paper shows how the same dynamic can actively degrade performance, presenting a user with misconceptions about finance and then challenging the model to analyze a company’s performance. The more context the model had, the worse it performed. “With no memory or personalization present the AI model correctly assesses that the company is a capital intensive business that suffers from high customer churn,” the post reads. “But with those features turned on, it will happily change its answer to agree with the user’s mistake or supply them with an incorrect answer based on its evaluation of their earlier preferences.” Notably, the research didn’t look at Anthropic’s recent Opus 4.8 model, which wastrained to actively push back against input errorslike the ones presented. The patterns discovered by researchers held true across different models. It’s a demonstration of how delicately balanced AI context can be, and how useful tools can have unintended consequences if they upset that balance.
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‘AI-pilled’ firms spend $7,500 per employee each month on AI
An Nvidia executive recentlysaidthat the cost of compute is now greater than the salaries of his employees. Last week,Mercor’s CEO saidthe startup is spending more on tokens for internal agents than on employee headcount. As enterprisesblow through their token budgets,a big question is: Are companies actually spending more on AI than on humans? Not quite yet, according tofresh researchfrom the Ramp AI Index, which measures the adoption rate of AI among American businesses. The top 1% of firms — which Ramp describes as “AI-pilled” — are spending $7,500 per employee per month. Whether you think that’s a lot or a little depends on your perspective, but it’s certainly not more than the roughly $16,000 per month the average software engineer makes. And those are just the power users. The top 10% spend about $611 monthly per employee, and the median only spend about $11.38, or about the cost of a seat on an enterprise plan. That said, despite pressures, AI spending is still rising. Among the AI-pilled firms, spend grew 14.1% per employee last month. It’s not yet clear if that trend will continue. The top 1% of firms tend to mix and match, opting to bounce between multiple frontier models and platforms that give them access to cheaper open-source models.
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Decart’s new world model can simulate hours of photorealistic driving — with some caveats
AI startupDecarton Wednesday unveiled Oasis 3, its latest interactive world model that can generate photorealistic driving environments in real time, TechCrunch has exclusively learned. The model is currently available via API. The startup is initially targeting autonomous vehicle companies that need to simulate rare driving scenarios at scale, and plans to expand into robotics and other physical AI applications. But the bigger bet is on developers: By offering API access from day one, Decart is trying to build a developer ecosystem around world models much like how OpenAI did with language models. “It’s going to be the first usable world model that people can actually program on top of,” Dean Leitersdorf, co-founder and CEO of Decart, told TechCrunch. “I think there’s going to be an entire developer community that emerges on top of this.” The startup already has a community of more than 100,000 developers, many of whom are building products on top of its real-time video model Lucy, largely in e-commerce and live streaming. Oasis 3 is based on that foundation model, and it represents the company’s push into physical AI. Access is priced at $0.02 per second, and enterprise pricing depends on use cases, Decart said. Decart is playing in an increasingly packed world model arena. Last year, Google releasedGenie 3in research preview, Fei-Fei Li’sWorld Labs launched Marblefor commercial use cases, and video generation startups like Luma andRunwayare also translating their physics-aware video models into world models. Oasis 3’s release comes a few weeks after two-year-old Decart raised $300 million, which Leitersdorf says followed “huge demand increases for the models we built” in e-commerce, live streaming and physical AI. The round boosted Decart’s valuation to nearly $4 billion, and brought a series of strategic investors such as Toyota, Adobe and eBay. All of these companies are potential customers, says Leitersdorf. Nvidia, an existing investor, also participated in the round. Oasis 3’s edge lies in the photo-realism of its models and infinite generation capability. That’s due to some efficiency wizardry on Decart’s part, powered by the company’s other main product: the DOS (Decart Optimization Stack) software that allows models to run efficiently on Nvidia, Amazon and Google hardware, making its models far less expensive to run than competitors. “This is built on top of our entire real-time stack, which we optimize all the way down to the hardware,” Leitersdorf said. “By being so vertically integrated, we’re able to be more than an order of magnitude cheaper than anyone else in the industry in order to run these models.” The startup’s models are so efficient, per Leitersdorf, that it has burned through “drastically less” than $100 million in its lifetime. Oasis 3 generates physically accurate, multi-camera environments — one front-facing and two-side facing — for training and testing systems. And instead of offering limited demos and research previews, Decart allows developers to generate scenarios infinitely, which is perfect for autonomous vehicle developers looking to try as many edge cases as possible. Compared to other models I’ve tried, like Google’s Genie 3 or World Labs’s Marble, Oasis 3 delivers the most photorealistic environments from a single text prompt I’ve seen. And the fact that you can interact with them for hours suggests a level of efficiency that Decart’s rivals might lack. But by letting you generate a world for so long, the model also degrades significantly. In my testing, I found the system could consistently set up a strong initial scene that matches the prompt, but the thematic integrity degraded rapidly as I moved through the world. I prompted it to generate a New York City street in the morning, it did so, beautifully. But as I drove along, the environment looked less like New York and more like a standard version of any urban, Western city. When I tried to turn around and make my way back to the initial intersection, it was gone, replaced by an entirely new environment. On top of that, the controls aren’t very responsive, and I often lost control over where the car was moving (again, a drawback shared by other world models I’ve tested). The experience felt less like a coherent simulation and more of a dream-like, disjointed stream of consciousness that quickly grows nonsensical. Another issue, which I’ve also seen in other world models, is that the car will just drive through other cars, meaning the model doesn’t simulate physics properly in the environment. Leitersdorf calls this a “major research problem that we’re cracking now,” attributing it to the fact that “there’s drastically more data on good driving compared to accidents.” Part of what makes this physics consistency difficult is fundamental to how this world model works. Oasis 3 is auto-regressive, meaning it generates one frame at a time, and looks back at what it previously generated to decide what comes next. This is a key architectural feature of many world models, and it is a compute-intensive one, too. In order to maintain consistency, Leitersdorf says the Decart team is working to improve the length of the model’s memory. “Every frame we generate is roughly 8,000 tokens,” he said. “Generating this at tens of frames per second — that’s hundreds of thousands of tokens per second. The context window fills up very quickly. We’re researching how to do longer context to store millions more tokens, and how to compress the memory into fewer tokens.” Leitersdorf thinks the consistency issue might be partially solved in the model’s next version, which will allow users to start generating worlds based on a video of an environment rather than an image. He acknowledged that world models as a field are still early. Still, the founder is less focused on the current limitations of his tech than what will happen when developers get their hands on it. “It takes me back to the early days of LLMs, when OpenAI invented the API for models,” he said, pointing to the emergence of a developer community that advanced the field by finding and building new use cases. “When we talk again in three months, we’ll be like, ‘Here’s 100 developers that all built 100 different applications with Oasis that surprised all of us,’” he said.
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Jedify raises $24M to help companies arm AI agents with context on their business
AI vendors promote their enterprise products as if they’re turnkey solutions, but the chances are low that AI agents will hit the ground running right away. Unless you put in the effort to train a model on the specifics of your business, it’s unlikely to understand how your company, for example, defines revenue or knows who is allowed to see which file. That’s part of the reason why we’re seeing AI companies deploying engineers to help integrate their AI products into customers’ systems. New York-based startupJedifyis attacking this very gap. The company says its platform connects to enterprises’ knowledge sources via APIs to build a “context graph” about their business that AI agents can use to work better. These sources can be databases, data warehouses and lakes, SaaS apps or BI tools, as well as unstructured sources such as reports, documentation, code bases, and even Slack channels and meeting recordings. To build that out, Jedify has raised $24 million in a Series A funding round led by Norwest, TechCrunch has exclusively learned. The round saw participation from returning backers S Capital VC and Cerca Partners, as well as new investor Oceans Ventures. Data giant Snowflake also participated as a strategic investor and is integrating the startup’s tech with its AI products, such as its Cortex AI service, Semantic Views, and CoWork. Jedify’s pitch is that to be useful within enterprises, AI agents need access to the relationships between entities, data, permissions, domain knowledge, workflows, operational assumptions, and company-specific terminology. This context, the company says, allows an AI agent to narrow its attention to the information that is relevant to a particular task instead of searching across everything a company has. Co-founder and CEO Assaf Henkin (pictured above, on the far right) pointed to Kiteworks, a compliance company, as an example of how customers are using Jedify. Kiteworks connected Snowflake, Tableau, Notion, and internal playbooks, including documents and screenshots, to Jedify, then built agentic tools for different customer workflows. “They wanted to arm their sellers and account teams with a sophisticated app — you can think of it as both like a dashboard application and a real-time conversational application. When they go into a customer conversation, Jedify builds for them, on the fly, everything they need to know. And during the conversation, they can, in real time, get very specific details surfaced proactively,” Henkin said. Henkin argues that Jedify’s context graph is different from the semantic layers, metadata catalogs, and knowledge graphs that companies already use because it is multi-dimensional, capturing relationships across entities, data, people, permissions, and customers. It’s also model-agnostic and updates in real time as information flows into and out of the systems it is connected to. “When you want to enable an agentic solution to really be autonomous, to drive decisions across CRM data, Zendesk tickets, maybe telemetry data that’s coming in real time, that’s when a context graph is much better in terms of capabilities versus a semantic layer,” he said. Permissions are an obvious hurdle here. It wouldn’t do for an agent to give an intern access to the CFO’s revenue projections, for example. Henkin said his platform works to address that by inheriting permissions from identity systems, file systems, SaaS tools, and databases, including row-, column-, and table-level access rules, then lets its customers create additional groups that define what and whom agents or workflows are allowed to reach. It also offers observability and governance tools to help customers ensure their AI agents are behaving as intended. Jedify is currently targeting mid-market and large enterprise customers that have mature data stacks and multiple databases or data warehouses. Henkin said the company has between 10 and 20 early customers, one of which is The Weather Company, and is seeing interest from data-heavy sectors such as gaming, industrials, and consumer packaged goods. Snowflake’s investment and partnership are notable because large data platforms are also trying to build similar capabilities. But Henkin argues that Jedify is complementary to such efforts because much of a company’s data, and most of its institutional knowledge, isn’t usually stored with a single cloud provider. “[The large data companies] will tell you, ‘Oh yeah, just bring everything.’ But in reality, companies have multiple databases, and warehouses, and data solutions […] The big thing is that not all of your data is in those environments, and most of your knowledge is not there, so it’s a bit of a disadvantage that they actually have,” he said. Henkin also noted that for companies trying to do this on their own, training an AI model to build a comparable context layer can be cost-prohibitive, especially ascompanies are scrutinizing and clamping down on their AI token usage. And the rapid advances in AI model development play into the company’s broader bet: as models grow more capable and more interchangeable, proprietary context that helps those models work better within businesses could prove a valuable and durable moat. The startup will use the fresh cash for product development, hiring, and go-to-market motion. It brings the firm’s total funding to about $33 million.
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Eros Brings Cultural AI Ecosystem to UK with $355 Mn Investment
The initiative will strengthen collaboration between India and the UK in artificial intelligence, storytelling, and creative industries.
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Big Tech Cloud Infra is Breaking the Illusion of India's Sovereign AI Ambitions
Hyperscalers such as Google are actively investing in India. However, they primarily serve their own enterprise clients.
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Piper Serica Moves to Fix Deep Tech Startups’ Biggest Funding Gap
The Bharat Tech Fund supports companies at advanced technology readiness levels that have spent years developing their products but struggle to scale due to insufficient funding.
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The Real Reason Why AI Agents are Failing in Organisations
Genpact’s Ajay Vatsal believes the biggest returns from agentic AI will come from better operational outcomes and employee augmentation.
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