Latest AI News
Whatnot acquires Shaped to power real-time live shopping recommendations
Livestream shopping appWhatnotannounced Wednesday that it has acquiredShaped, a machine learning company that specializes in real-time recommendation and search systems. The deal is meant to strengthen Whatnot’s discovery and personalization capabilities as the platform continues to expand across new product categories and millions of buyers. According to the company, the acquisition helps Whatnot continue its investment in AI as it looks to solve one of live commerce’s biggest challenges: helping shoppers find the right products while inventory, auctions, and buyer demand change in real time. Unlike traditional e-commerce platforms, where product catalogs remain relatively stable, Whatnot’s marketplace is constantly evolving, and live auctions can end within minutes or last for hours. “By combining Shaped’s technology with Whatnot’s existing systems, we can make recommendations faster, more responsive, and more personalized,” Emmanuel Fuentes, VP of Data and AI at Whatnot, told TechCrunch. “That speed matters because live commerce is a uniquely hard recommendation problem. Inventory changes by the second, shows start and end continuously, and buyer intent shifts throughout a show.” Fuentes said the company has spent the last six years improving the speed of its recommendation engine, reducing recommendation latency from roughly a day to just minutes. Integrating Shaped’s technology is expected to push those recommendations even closer to real time. The company says its systems process more than 500,000 hours of live video and millions of real-time interactions every week, using that data to continuously improve recommendations. Founded to help businesses build AI-powered recommendation systems, Shaped developed technology that combines existing customer data with large language models and machine learning to deliver highly personalized search and discovery experiences. Its customer roster included companies such as Outdoorsy and QVC. As part of the acquisition, Shaped founder and CEO Tullie Murrell, along with nearly a dozen engineers and AI researchers, will join Whatnot. Murrell will lead the company’s newly formed Applied AI Research group. (Notably, Murrell worked at Meta before launching Shaped.) The acquisition comes as Whatnot experiences significant growth. Launched in 2019, the company recentlyrevealedthat sellers have surpassed 1 billion orders. Earlier this year, Whatnot raised$225 millionin Series F funding, giving the company a valuation of more than $11 billion after adding 20 million buyers over the past year. Whatnothas also significantly broadened its marketplace, launching more than 35 new categories last year — including art, golf, and vinyl — and more than 45 additional categories during the first half of 2025, with new subcategories continuing to roll out each month. Additionally, the move comes as resale giants race to integrate AI throughout their platforms, such aseBayandPoshmark.
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Hack suggests AI music generator Suno scraped YouTube for training data
The AI music generator Suno was hacked, according to a report from404 Media. The hacker told the publication that they used a supply chain attack to access an employee’s credentials, allowing them to then access source code showing how Suno allegedly scraped decades of audio from YouTube Music, Deezer, Genius, stock music libraries, and podcast RSS feeds. Suno previouslyadmittedthat it trains its AI on “publicly available music files” on the open internet, arguing that it can train on copyrighted material under the fair use doctrine, a subjective carve-out of copyright law. But according to the major record labels activelysuingSuno, it isillegalunder the Digital Millennium Copyright Act (DMCA) to deliberately circumvent YouTube’s protections against data scraping; it also violates YouTube’s terms of service. Udio, a competitor to Suno, has also been accused of scraping YouTube data. Google, the parent company of YouTube, faces similar allegations ofcopyright infringementfrom a variety of major book publishers. The hacker reportedly accessed customer data includingcustomer emails, phone numbers, and partial credit card numbers in Stripe. Suno did not notify customers about the November 2025 breach and claims that this was a “limited security incident that was quickly contained.”
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Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, Inkling
Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, released its first in-house AI model Wednesday morning, calledInkling. And unlike the flagship models from OpenAI, Anthropic, or Google, it’s open-weight, meaning outside developers and companies can download it and modify it directly. Inkling is a mixture-of-experts system with 975 billion total parameters, though it only draws on a fraction of that — about 41 billion — for any given task, a common design that keeps very large models faster and cheaper to run. It was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all four, according to the company’s own release materials. For now, though, its outputs are limited to text, including code, styled artifacts, and structured data. The model is Thinking Machines Labs’ first public proof point after a year and a half spent building AI infrastructure largely out of public view. Some of that work had already surfaced in aMay research previewof “interaction models” — AI designed to listen and speak (and even interrupt) instead of stop and wait as with typical chatbots. It’s also a test of the central bet behind the startup, which is that AI that organizations can adapt for themselves will outperform the one-size-fits-all models the biggest labs currently sell. Inkling is designed to give calibrated answers, including flagging uncertainty rather than guessing, and lets users dial “thinking effort” up or down when they want to trade for speed. On one benchmark, the company says, Inkling uses a third as many tokens as Nvidia’s Nemotron 3 Ultra — its latest generation open-weight model — to hit the same coding performance. Thinking Machines doesn’t claim Inkling is best-in-class. Its briefing materials state explicitly that Inkling is “not the strongest model available today, closed or open.” What it’s evidently going for instead is well-rounded performance and customizability. That raises the question of who, within the enterprise market it’s targeting, this product is really for. Thinking Machines is, for now, marketing Inkling less as a finished product than as a starting point, something for organizations to fine-tune themselves through Tinker, the company’s model-customization platform. This also means customers, not Thinking Machines, are responsible for making sure their customizations are safe, for example. (Fine-tuning requires serious machine-earning talent.) OpenAI, Anthropic, and Google have all taken a very different approach with ChatGPT, Claude, and Gemini, respectively, which were all built to compete as general-purpose chatbots first, with agentic, autonomous features layered on top. A post published by Thinking Machineslast weekwas clearly meant as the backdrop for this release. AI that’s trained centrally by one company and then set in stone, the company argued in that post, underperforms AI that organizations shape themselves because so much expertise is specific to the people who hold it. It’s an argument that’s gaining steam. In a blog post published Sunday, Microsoft CEO Satya Nadella — whose company has invested billions in both OpenAI and Anthropic — warned that enterprises using proprietary AI modelseffectively pay twice: once in subscription costs, and again by handing over business knowledge embedded in their prompts and corrections, which can be absorbed into future model versions. Hugging Face CEO Clem Delangue made asimilar predictionin conversation with TechCrunch last week. Frontier models, he said, will increasingly be reserved for experimentation and high-value tasks, while most production AI work shifts to private or open-source alternatives — the exact split Thinking Machines is building around. The clearest evidence for Thinking Machines’ argument came from arecent projectwith Bridgewater Associates, the world’s largest hedge fund (which is not, for what it’s worth, a Thinking Machines investor). Researchers from both companies took an existing open-source model and trained it further on Bridgewater’s own financial expertise. The result was said to score 84.7% on financial reasoning tests, beating top proprietary AI models, while costing roughly a fourteenth as much to run — though those results come from the two companies’ own evaluation, not an independent one. Either way, Thinking Machines is emphasizing how quickly it got here. OpenAI took roughly five years to bring its tech to market and show revenue, and Anthropic roughly three. Thinking Machines says it did the same in about nine months. Some will wonder whether Inkling was trained on outputs from competitors’ models, a practice known as “distillation” that hasdrawn scrutinyacross the industry. The short answer, per the company’s own materials, is partly. Thinking Machines pre-trained Inkling from scratch, but it says it used other open-weight models — including Moonshot AI’s Kimi K2.5 — to help generate some of its early post-training data before large-scale reinforcement learning took over. The next model, the company insists, will use fully self-contained post-training instead. On the cost side, Thinking Machines has been more guarded. It struck a partnership with Nvidia in March to deploy a gigawatt of Vera Rubin computing capacity and trained Inkling entirely on Nvidia’s GB300 NVL72 systems — but hasn’t said how it plans to cover those costs, and revenue, by most accounts, hasn’t been a priority. (A reported $50 billion fundraising round was said to be coming together last November but had stalled by January; the company has declined to talk about its funding picture since.) A related question is whether Thinking Machines’ spending will ever reach the scale of OpenAI’s or Anthropic’s, or whether its efficiency-driven approach means the economics look different. Put another way, the company’s bet may be less that it will eventually spend like its larger rivals than that it won’t need to at all — because once weights are public, nothing obligates anyone who downloads them to pay Thinking Machines to run them, unlike the metered access OpenAI and Anthropic sell. It’s Tinker, not the model itself, where the company’s revenue has to come from, via training, fine-tuning, and, now, a cut of the hosting ecosystem built around it. Headcount, at least, looks more settled. Thinking Machines now employs roughly 200 people, up from levels reported after a wave of departures earlier this year, includingtwo co-founders who left for OpenAIin January. Thinking Machines, for its part, doesn’t seem interested in playing up individual moves the way much of the industry does. According to a source inside the company, its culture, by design, favors continuity over reliance on any one personality. It makes sense: it’s less of a setback when people change teams if they were never put on a pedestal to begin with. It’s also a remarkable thing for a company to insist on, given how much of its own story is still associated with the name of its now-famous co-founder, whether she planned it or not.
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SpaceX slips below its $135 IPO price ahead of Starship launch
SpaceX’s shares fell below $135, the price that CEO Elon Musk and his company chose ahead of its blockbuster June 12 IPO that raked in nearly $86 billion. After slipping below that price on Wednesday afternoon to beneath $133 per share, the stock traded back up to the $135 price, and occasionally hovered above it. The dip on Wednesday followed a steady decline in the month since the company went public. SpaceX initially saw its stock price rise to more than $200 in the days after it went public, briefly giving it a valuation that rivaled tech giants like Amazon and Microsoft. Its shares have lost value basically every week since reaching that high point. Some of the volatility is attributable to the fact that just 4% of the company’s total shares are trading on the Nasdaq. That small “float,” as it’s known, combined with an immense amount of constant attention on the company, has created wild swings during the first month of trading. The markets also appear to be sobering up on CEO Elon Musk’s grand vision for the company, part of a broader deflation in tech stocks over the last month. Not only has SpaceX’s stock traded down, but alsobonds the company soldin the wake of the IPO are suffering. A prolonged downturn for SpaceX could have wider effects because the company’s stock price is a sign of how investors view the (literal) otherworldly promises Musk has made about what his company can accomplish. SpaceX’s IPO has also set the table for other Big Tech companies like Anthropic and OpenAI to go public. Both of those companies have filed confidentially for an IPO. While neither has set a date to go public, SpaceX’s stock is being closely watched to gauge how successful those IPOs could be. SpaceX is about to face another early test of the durability of its stock price. On Thursday the company will test launch its Starship rocket for the first time since the IPO. Starship is still very much in development, which means it is prone to failures — the result of SpaceX’s “fly, fail, fix” approach. This will be the first Starship flight since it experienced a booster failure in May. And once again, the company does not plan to try to recover the Starship booster or upper stage on this flight, instead opting to have them simulate a landing in the Gulf of Mexico. That means both parts of the overall Starship rocket system will end in an explosion no matter what, even if they don’t run into any problems during the flight plan.
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LatentView Analytics Appoints Former Wipro Executive Sonal Ramrakhiani as CEO
Ramrakhiani takes over from Rajan Sethuraman, who will remain as strategic advisor for up to six months to support the leadership transition.
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Vint Cerf is working on a plan to unleash AI agents on the open internet
Vint Cerf says his favorite place is where he’s never been before. One of the architects of the protocols behind the open internet, Cerfleft Googleafter 20 years last week, but he’s not done thinking about the digital future. Starting today, he’s advising Innovation Labs, an organization trying to create the open architecture for AI agents to identify themselves. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, which sees domain-name infrastructure as a practical way to hold AI agents accountable and position itself for a future where more online interaction happens between agents than people. Cerf joinsa handfulof other internet luminaries lending their names to the effort. Most AI agents today stay within proprietary systems, calling on internal resources for specific purposes. But businesses are already envisioning a world where they operate far more autonomously across the internet and interact directly with other agents. So far, a key road block has been a lack of a shared standard for identifying and auditing agents. A variety of standards are beginning to emerge, and Innovation Labs has proposedDNSid, aregistry for agent identificationthat links each one to an existing internet domain name and uses cryptographic proofs to log its registration over time. Innovation Labs’ interim CEO Allie Kline says the company is trialing the standards with several unnamed hyperscalers and identity companies. “I felt like I might be able to help them in a period of time when naming and identification is becoming increasingly important,” Cerf told TechCrunch. “This is largely triggered by the notion of AI agents and the question of what authorities they have, where they have derived those authorities, who is accountable for the behavior of an agent in this context, and where and how its identity is established, and why [you’d] trust it.” Those questions promise to be thorny, Cerf says, because AI agents are so much more active than domains, and it’s not yet clear what commitment an organization is making when they register one. “It’s going to be a fascinating — and at the same time maybe even exasperating — period in the evolution of the internet and the things that depend on it, because the functionality is so dramatically powerful,” Cerf said. With multiple solutions to the problem under consideration, Cerf says the key to a wide adoption of any protocol will be its functionality. “Company X uses agent Y’s technology, and company A uses agent C’s technology, and then they don’t interwork with each other,” Cerf said. “Nobody can do everything that you might want every agent to do… and so we’re going to have to rely on the pressure coming from the users. This is what happened with TCP/IP.” One key to Innovation Labs’ proposal is that it does not come with broader plans to do other kinds of AI business or own the registration data, Kline says. “I think there’s a lot of organ rejection to a hyperscaler releasing [a standard] and having that proprietary data,” she told TechCrunch. And does Cerf think the agentic economy is the internet’s destiny? “I don’t think it’s inevitable,” he said. “But what I do think is inevitable is that people will try to do that. We are fundamentally lazy creatures, and if we find a way to have an an agent do something for us, we’re very likely to choose to do that because [it’s] just easier.”
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Indian AI coding startup Emergent becomes a unicorn with $130M Series C
Indian AI coding startupEmergenthas raised $130 million in a Series C funding round at a $1.5 billion post-money valuation, a five-fold jump in six months. The funding round was led by private equity firm Creaegis. New investors MNI Ventures-Claypond, Sentinel Global, and existing backers Khosla Ventures, SoftBank’s Vision Fund 2, Lightspeed, and Y Combinator also participated. The deal takes Emergent’s total funding to $230 million. The startup had previously raiseda $70 million Series Bat a $300 million valuation in January. AI coding has attracted hordes of investors, with startups such asLovable,Replit, andCursorraising billions in funding to develop tools that allow developers to speed up their work. AI labs such as OpenAI and Anthropic have alsopushed deeper into coding. Emergent is looking to gain a share of this crowded market by targeting entrepreneurs looking to start new businesses and small and medium-sized companies that have traditionally relied on email, spreadsheets, and messaging apps to run their operations. “Our thesis has always been to build a production-grade application for serious builders,” Emergent co-founder and chief executive Mukund Jha (pictured above, right) told TechCrunch in an interview. “So you’re basically getting an engineering team in a box.” Jha said the startup has reached an annual run-rate revenue of $120 million, up 70% in the last four months, and has more than 200,000 paying customers. Jha started Emergent with his brother Madhav Jha (CTO) in June last year. Customers include trucking companies building software to track shipments; factories; construction businesses creating enterprise resource planning systems; and property managers developing internal customer management tools. North American customers account for about a third of Emergent’s revenue, Europe makes up another third, and the rest comes from other markets, Jha told TechCrunch. India accounts for about 8% to 9%. Emergent’s focus on small businesses and entrepreneurs pits it directly against Replit, which Jha described as the startup’s closest rival. He sought to distinguish Emergent from developer-focused coding tools such asAnthropic’s Claude Code,OpenAI’s Codex, and Cursor, arguing that non-technical users need a platform that handles deployment, hosting, testing, and debugging alongside the work of programming. However, Jha acknowledged that design remains a weakness, pointing out that many websites built using AI tools tend to look similar. Emergent plans to use the fresh capital to accelerate product development and research, including improving the success rate of applications built on its platform and its core AI agent workflows. The company is working to support more complex AI applications, including those that use local and open source models, Jha said, adding that it will also invest in expanding its go-to-market operations. The company is also considering opening an office in Europe, where Jha said Emergent is seeing significant customer traction. Emergent has about 200 employees, most of whom work in Bengaluru, with a handful in San Francisco. The startup plans to expand its San Francisco office by 30 to 40 people by the end of the year, Jha said.
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Rime picks up $24M Series A to help enterprises field customer calls
Voice AI startups’ biggest unlock has been handling calls for enterprises in areas like sales, marketing, and customer support. Large organizations are offloading calls to voice model developers likeElevenLabsandDeepgram; infrastructure companies likeVapi, Retell, and LiveKit; and dedicated customer support shops like Decagon and Sierra. San Francisco-basedRimeis trying to gain an edge in this crowded market with its voice AI models that are trained on conversational data that it records, aiming to reduce its clients’ customization load. Founded in 2022 by former Stanford PhD student Lily Clifford, ex-Amazon Alexa engineer Brooke Larson, and Stanford engineer Ares Geovanos, Rime has built a recording studio in San Francisco to collect its own conversational data rather than relying on scraping the web for audio. The startup said it focuses on tuning its voice models to nail the pronunciation of different brand entities and industry-specific terms. It employs a phoneme-based architecture to adapt to different pronunciations so that customers don’t have to retrain models for their specific industry. Rime on Wednesday said it has raised $24 million in a Series A funding round that was led by M13 Ventures. Twilio Ventures, Corazon Capital, Unusual Ventures, and other existing investors also participated. Clifford said that despite progress in voice AI development, enterprises still prefer legacy IVR (interactive voice response) implementations, as AI voice technology still can’t match up to IVR’s effectiveness. “The voice technology is still not there to automate the vast majority of enterprise phone calls. LLMs have made it a lot easier to build voice applications that work, but they haven’t changed how it feels to interact. Talking with a voice AI agent is not the most compelling experience for the end user. It’s kinda like a new IVR, but with a better voice,” she said. The startup began with a pipeline of separate models for speech-to-text, text-to-speech, and a large language model. But it is now shifting focus to develop better speech-to-speech models to reduce latency, improve turn-taking, and tackle issues like background noise. The new approach will also serve to decrease reliance on orchestration, so the company doesn’t have to manage a bunch of models. Rime says it has customers in food service, healthcare, airlines, and fintech. The company claims that because of its training data and model positioning, customers stay longer on the call, which has helped it win enterprise contracts from clients like Mayo Clinic, Dialpad, Upstart, and Asurion. With the new funding, Rime is planning to expand its team of 35 people, aiming to hire for model development, engineering, and partnerships. It recently brought on Rafael Valle, who worked on audio understanding at Meta Superintelligence Labs and Nvidia’s applied deep learning audio research team, as its chief scientist. “Companies like ElevenLabs have moved into being an orchestration and the application layer, going head to head with the Sierras and Decagons of the world. I think there’s just so much more to be done technically, and Rime’s approach of pushing forward on the best model with low latency and high reliability in a regulated environment stands out,” M13’s Morgan Blumberg told TechCrunch. It had previously raised $5.5 million ina seed round last May. Blumberg is joining the startup’s board as part of the fundraise.
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Reelful’s AI turns your camera roll into short-form videos for social media
A newiOS app called Reelfuluses AI to automatically turn photos and video clips from your camera roll into polished TikTok- and Instagram Reels-style videos for social media. Reelful is designed for people who want to create social content, but find traditional video editing tools too complex or time-consuming. The app’s launch reflects a broader shift in video creation, as AI is allowing users to move beyond traditional creative tools to AI agents that are capable of automating content creation. Reelful joins a growing wave of AI startups that are reshaping how content is created, includingOpus ClipandCaptions. Reelful, which is currently participating in a16z’s Speedrun program, was founded by Kate Deyneka, a former machine learning engineer at Snapchat who helped develop video and image models. Deyneka left the social media giant to build an agentic video editor that helps people create short-form videos automatically, getting rid of the need to spend time selecting clips, adding effects, recording a voiceover, and fine-tuning edits. “I want to post more on Instagram, TikTok, YouTube Shorts, but video editing takes a lot of time, so much time that I do not even want to spend it because I have a lot of things going on in my life, especially now as an early-stage founder,” Deyneka said in an interview with TechCrunch. “I have a lot of events, I meet a lot of interesting people, and this is what I see for all my founder friends: they have a very active life, especially right now when AI is booming, but we do not have time to edit. I see Reelful as a tool that can help people build their online presence and their personal brand.” Reelful works by getting users to enter a prompt describing the story they want to tell, whether it’s a travel recap, product demo, or event highlight. Users then create a voice clone by recording a 30-second sample, and select photos and videos from their camera roll. Reelful will then plan the video, write the script, add an AI voiceover, and assemble the final edit, complete with captions, music, and sound effects. Reelful will turn still images into AI-generated video clips. For example, if a user includes a photo of someone cutting a mango, Reelful can animate the image into a short video showing the person slicing into the fruit. The AI-generated videos feature a watermark to inform users that it has been created with AI. After Reelful generates a complete video, users can continue editing it further by chatting with the app to do things like swap the soundtrack, revise the script, or adjust other aspects of the video. Deyneka says Reelful’s target audience, at least for now, is founders and business owners who need to consistently create content to build their online presence, personal brand, or company brand. For example, a salon in the Bay Area may have a lot of content on hand about its services and customer transformations, but not have the time or resources to turn that content into polished social media videos. That’s where Reelful comes in, Deyneka says. “My target use case is that you went to an event or you met some cool people, and you recorded a short interview with them and while you are driving back home you just uploaded everything to the app, and by the time you’re home, the video is ready,” Deyneka said. “So I want to make it very effortless for people to share their life, their content, their expertise without actively editing or setting up the things on their laptop.” Reelful offers both one-time purchases and subscription plans. Users can buy video credits in bundles of five videos for $15, 15 videos for $43, or 33 videos for $90. The “Creator” subscription costs $25 per month for 10 videos, while the “Pro” plan offers 25 videos per month for $50. The Studio plan includes 60 videos per month for $100. While Reelful is currently only available on iOS, Deyneka plans to launch Android and web versions in the future.
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Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models
AI models are becoming ever more capable, but exactly what enterprise adoption will look like remains a big question. In a bid to shape that future, labs like Anthropic and OpenAI havespun up separate businessesdedicated to deploying AI engineers to their customers’ offices — a bet that assisting businesses in figuring out how to use their AI models is the next trillion-dollar category. One of those businesses now has a name: Ode with Anthropic is the $1.5 billion, AI implementation company that the AI lab launched in May as part of a joint venture with Blackstone, Hellman & Friedman, Goldman Sachs, and others. The move follows OpenAI’s own take on this, The Deployment Company, underscoring a growing acknowledgement among frontier AI labs that winning enterprise customers requires far more than shipping better models. Ode was originally conceived by Blackstone, which noticed a gap when it had roped in large consulting firms and small AI services boutiques to implement AI across its portfolio companies. One of those boutiques, AI engineering services startup Fractional AI, apparently stood out, and the joint venture acquired the startup shortly after it was announced. (Fractional ended an 11-month partnership with OpenAI when it was acquired.) Fractional has become the foundation of what is now Ode — a kind of “scaled boutique” AI services firm. And its leaders have ambitious goals. “It’s pretty easy to imagine this as a trillion-dollar company someday if we execute well,” Chris Taylor, CEO of Ode and co-founder of Fractional, told TechCrunch in an exclusive interview. “The key challenge of the business is how do you go through that phase of hyper growth without losing the emphasis on quality?” Ode currently employs 100 engineers, and works closely with Anthropic’s applied AI team to identify where the tech can have an impact on different businesses, and create systems tailored to each organization’s operations. Anthropic’s internal team will continue to focus on strategic, mission-aligned deployments, a spokesperson told TechCrunch. The private equity firms backing Ode will funnel their own portfolio companies to the joint venture as potential customers, though Ode will not limit sales of its services to those companies. For Ode, an ideal customer is one whose CEO buys into the promise, according to Taylor. “A lot of the work that we’re doing is the top one or two priority for the CEO of the company,” Taylor said. “It’s the most important product feature that the company is going to build over the course of the next two years, or it’s reworking the most important business process they have.” Ode will operate under a “Claude-first” principle, meaning it will implement Anthropic’s technology, including features likeClaude Tag in Slack, whenever possible. The company isn’t limited to Anthropic’s technology, though, and will use rival AI products if needed. Eddie Siegel, Ode’s chief technologist and a Fractional co-founder, says the venture’s secret sauce is its quality of implementation, and the ability to build custom solutions for business problems. “I think model selection matters, but it’s not where the majority of calories are spent,” Siegel said. “It’s one ingredient in a system that has to be engineered. It’s like the choice of programming language when you build a piece of software […] I would not define an enterprise transformation in terms of whether they choose Python or Java.” Taylor added the founding belief behind Ode is that “non-AI companies are going to be among the big winners of this whole AI moment if they adopt the technology the right way.” But to take AI, “this magic, hallucinating ingredient,” and rewire core business processes or customer experiences with it requires a lot of help, he said. “That requires top-caliber applied AI talent, which is not something most companies have,” Taylor said. Ode’s executives describe their team as elite generalist software engineers, over half of whom are former founders — the kind of people who can “juggle a really challenging technical problem, but also own something end-to-end,” per Siegel. Or as one Blackstone executive put it: a team of “grown-up” engineers, the “special forces” rather than an army of forward-deployed engineers (FDEs). As several people involved in the venture told TechCrunch, demand for such FDE teams far outstrips supply. Ode’s goal is to continue scaling, internationally too, while maintaining its boutique firm positioning — in other words, running constant evaluations to measure the business impact of AI implementations. But in a world where top engineering talent is already scarce, maintaining and growing such a team presents a real challenge. If becoming an elite applied AI engineer requires experience as an entrepreneur, systems-first thinking, AI chops, and enterprise product judgement, would Ode be able to train enough people to meet demand? Compound those difficulties with the fact that Ode will be competing not only with OpenAI’s The Deployment Company, but also with consulting giants likeDeloitteandAccenture, which have created their own FDE teams. Siegel isn’t too worried about a dwindling pool of grown-up generalist engineers. “It has never been an easier time to become an entrepreneur,” he said. “You learn so much by trying to own problems end-to-end, going to try and get product-market fit, move the needle on a business. You learn a lot there that you don’t learn from just solving a narrow problem. That’s the skill set that fits really well with Ode.” Whether enough of those engineers will show up remains an open question. But if Ode and its backers are right, the next great AI race won’t just be about the best models, but about who can successfully put those models to work inside the world’s largest companies.
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Dhruva Space, MAHE Partner to Build Space Technology Centre in Manipal
The ASCENT facility will support satellite missions, research programmes and spacecraft testing infrastructure from late 2026.
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Google’s AI Strategy Is to Win Without Locking Customers Into Gemini
Richard Seroter says enterprises should choose AI models based on workload rather than vendor loyalty, as costs rise and AI adoption matures.
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