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Your startup’s next teammate might be an AI agent: Gusto, Insight Partners, and Leland explain what that changes at TechCrunch Disrupt 2026
The first few hires at a startup can define the company. But increasingly, not every new capability requires a new employee. AI agents are beginning to take on engineering, customer support, research, and operational work that would previously have been assigned to early team members. For founders, that creates a new question before they even open a job requisition: What should people own, and what should be delegated to AI? AtTechCrunch Disrupt 2026, Josh Reeves, CEO and co-founder of Gusto; Michelle Johnson, senior vice president at Insight Partners; and John Koelliker, CEO and co-founder of Leland, come together on theBuilders Stagefor an insightful session called “Hiring When AI Is a Co-Founder.” This sessionwill explore how early-stage companies are building teams where humans and AI agents work alongside each other — and how founders can do that without sacrificing speed, accountability, or culture. Join them at Moscone West in San Francisco, October 13–15, alongside 10,000+ startups, investors, and tech decision-makers exploring what it takes to build and scale the next generation of companies.Register now and save up to $200on your Disrupt pass before rates increase on September 25 at 11:59 p.m. PT. Save up to 30% more when you register as a group. For an early-stage company, every hire is a trade-off between capability, cost, and speed. AI agents add another option. Engineering tasks, customer support, and operational work can increasingly be delegated to systems capable of completing multistep tasks rather than simply helping an employee complete them. That gives founders an opportunity to rethink what their earliest teams should actually look like. Instead of automatically asking, “Who do we hire next?,” the starting question can become: “What work needs to be done — and is a person the best way to do it?” That distinction matters. Giving work to an AI agent can help a small team move faster, but it also introduces new questions around ownership. Who checks the work? Who makes the final decision? What happens when an agent gets something wrong? And which responsibilities are too important to delegate? Those are no longer theoretical questions for companies building AI-native startups. All these questions, and more, will be answered on theBuilders Stageat Disrupt.Grab your passby September 25 at 11:59 p.m. PT to save up to $200. Bring your community and save up to 30%. Josh Reevessees the shift across a particularly broad customer base.Gustosupports more than 500,000 companies across payroll, benefits, compliance, onboarding, HR, and retirement, while combining AI with more than a decade of experience serving small businesses. That puts Gusto close to the day-to-day realities of companies deciding how — and when — to grow their teams. Michelle Johnsonbrings the startup scaling perspective. AtInsight Partners, she works with CEOs and CROs across North America and Europe on go-to-market strategy, revenue organizations, and AI implementation. Before joining Insight, she helped scale Flock Safety from less than $1 million to $90 million in ARR as an early sales and revenue operations leader. Her perspective raises a different set of questions. If AI agents can research prospects, prepare outreach, analyze customer data, or manage parts of the sales process, what should the human members of a revenue team concentrate on? And how does that change the skills a startup should prioritize when hiring? John Koellikersits directly at the intersection of talent and AI. As CEO and co-founder ofLeland, a career and talent platform for the AI era, he brings experience across product and growth roles at LinkedIn, Curated, and Uber, alongside building a company focused on how people develop and apply skills in a changing labor market. Together, they bring three different views of the same challenge: How do you design a startup team when some of the work can be done by agents from day one?Register by September 25 at 11:59 p.m. PT to save up to $200on your pass andhear this session live on the Builders Stage. Bring your team or community with you to save up to 30% on passes. Some of the most important decisions inside a startup are difficult to reduce to a workflow. Who understands what customers really need? Who challenges a bad strategy? Who takes responsibility when something fails? Who builds relationships, motivates a team, or decides when the data is pointing in the wrong direction? As AI agents become more capable, those questions could become more important, not less. The role of an early employee may increasingly be defined by judgment, ownership, and the ability to direct both people and machines — rather than simply completing a larger volume of tasks. That has implications across the startup ecosystem. Early-stage companies have always competed on their ability to do more with less. AI agents could take that principle much further. But a company is more than the sum of the tasks it completes. Founders still need people who can make decisions, take responsibility, understand customers, and create the culture around which a business grows. The challenge is deciding where those people create the greatest value — and where an agent might do the job instead. That’s the conversation Josh Reeves, Michelle Johnson, and John Koelliker will take on at Disrupt. Join them on theBuilders Stage, and explore what happens when AI doesn’t just help your startup — but also becomes part of the team. Then continue the conversation with 10,000+ founders, investors, and operators across Moscone West, the Expo Hall, Startup Battlefield 200, and countless networking opportunities. The startup org chart is changing. Learn to build yours for what comes next, only at Disrupt 2026.Secure your pass now and save up to $200before September 25 at 11:59 p.m. PT. Save up to 30% when you register as a group.
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For Agentic Coding, Logitech Turns Physical Keypads Into AI Command Decks
Logitech’s MX Keypad enters a market that already includes programmable control surfaces such as Elgato’s Stream Deck and OpenAI’s Codex Micro.
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Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?
In a lengthyessaypublished over the weekend, Anthropic CEO Dario Amodei made a proposal that the AI industry would have rejected instantly even a year ago: embed third-party evaluators inside all frontier AI companies, giving them the power to report safety incidents, assess whether AI models are truly aligned, and share their unvarnished findings with the world. Amodei said Anthropic would commit to giving independent evaluators like METR and Redwood Research unprecedented access to the company’s systems. CEO Sam AltmansaidOpenAI also would commit to the practice, signaling a potentially profound change in how the industry works with outside research groups. Third-party evaluators who spoke to TechCrunch broadly welcomed the proposal, but said details need to be ironed out — and ideally backed by legislation — if they’re to know whether they will function as truly independent watchdogs or vendors operating on the AI companies’ terms. That deeper access is becoming more important as models get better at recognizing when they’re being evaluated, raising the risk that they’ll behave well during testing while concealing problematic behavior. Researchers say clues to that behavior can be missed when testing the finished model, but uncovered by investigating how it behaved throughout training. “AI companies should be able to answer some very basic questions about their training process, such as: Did the AI ever actively try to undermine its own alignment training while it was going through the training?” Alexander Meinke, head of research at Apollo Research, told TechCrunch. “The answer to this should be an unequivocal no, and right now we are completely relying on AI companies to both carefully check this themselves and then truthfully report this to the public. And we’ve seen from recent incidents that, by default, they will do neither. As embedded evaluators, we could actually check.” Historically, AI companies brought in outside reviewers to test finished models shortly before their release. Now, evaluators that TechCrunch spoke to propose giving them access not just to the final model, but to intermediate versions, or “checkpoints,” from its lifetime of training. Adam Gleave, CEO of FAR.AI, said evaluators could compare those checkpoints to determine when concerning behavior emerged, inspect the post-training environment that rewards models for certain behaviors, and check evaluation transcripts and logs to verify a company’s claims about how a model performed. Whether and when Anthropic and OpenAI plan to provide that kind of access is unclear. Neither company has shared which evaluators they’ll work with, when they will be embedded, how many they’ll bring on, exactly what systems and information they will be able to access or what can be disclosed to the public, despite repeated questions from TechCrunch. Looking under the hood like this matters because models that perform well on safety tests aren’t necessarily safe if they’ve learned specifically how to pass that test. John Steidley, head of strategy at Palisade Research, pointed to an example of a “shutdown resistance benchmark” that measures if the AI will resist being shut down in certain circumstances. “It’s extremely relevant if the AI has been trained specifically to perform well on that benchmark,” Steidley said, comparing it to Volkswagen’s Dieselgate scandal, in which cars were programmed to recognize emissions tests and perform differently under testing conditions. Gleave noted that meaningful access could extend beyond the models themselves, with evaluators being given access to interview employees to check whether a company’s documentation and public descriptions of its safety practices match what happened internally. Amodei did outline a fairly comprehensive proposal that might give evaluators the kind of access they think is necessary, including the right to “publish key findings about risk levels, incidents, practices, and the access they received or didn’t receive — without editorial control by Anthropic.” But evaluators say such a system will only work if AI companies are actually willing to surrender control over the process. Previous efforts at independent evaluations suggest that that surrender will be hard won, as third parties have often run up against tensions over access, time, confidentiality, and what they can say publicly. Gleave said FAR.AI has had to turn down contracts with several frontier developers that wanted too much control over the evaluation process, threatening the firm’s independence. By default, he said evaluators are treated like ordinary contractors: bound by restrictive NDAs and agreements that give developers significant control over what can ultimately be published. There’s also the question of whether reviewers will get enough time and access to do the work they’re being asked to do. When investigating the Hugging Face incident,OpenAI gave METRandRedwoodroughly a week on premises to investigate, and both later said they could not draw confident conclusions due, in part, to scope and timing limitations. A similar issue occurred during the pre-release testing for GPT-6 Astra, which OpenAI has touted as itsmost aligned model yet. According toApollo Research’s contribution to the model card, the firm was given only three days to test Astra, which made it difficult to draw firm conclusions. “Apollo believes that, given the higher rates of eval awareness and limited evaluation window, low rates of misbehavior here do not provide substantial evidence about the model’s alignment or misalignment,” the firm wrote in its evaluation. That track record leaves evaluators with a basic question: Why should this time be different? “It’s certainly possible that Dario and Sam just had a change of heart, and they’re going to be very open about this,” Gleave said. “But the intellectual property of these companies is so incredibly valuable to them, and I think they’re going to, by default, be very careful about what can be shared.” Several researchers who spoke to TechCrunch called for a transparent framework that they all agree to publicly. Part of the framework, says Steidley, should involve standards for what kinds of auditors companies can rely on, lest they try to sidestep the issue by shopping for evaluators that either aren’t qualified or aren’t interested in assessing the most concerning risk. Henry Papadatos, executive director of Safer AI, says the problem, even with a public framework, is that voluntary measures are always dependent on a company’s goodwill. “Ideally, we would have good regulation mandating this…because then companies cannot change their mind tomorrow if they have a big PR crisis,” Papadatos told TechCrunch, noting that it’s also a good means of pushing all companies to adhere to the rules, not only the most willing. Not everyone has signed on. So far, Meta, SpaceXAI, and Google DeepMind have not committed to embedding third-party evaluators, though DeepMind CEODemis Hassabis has proposeda separate industry standards body to independently test frontier models. Google, OpenAI, and Anthropic have alsoprivately been discussing AI safetyplans for weeks. Some laws are already forming around the idea of third-party evaluators. California’s SB 53, signed into law last year, requires large frontier AI developers to publish safety frameworks and report critical safety incidents. A new law, SB 813, signed this month, creates a framework for state-recognized “independent verification organizations” with expertise assessing AI risks. In Europe, the EU AI Act requires frontier developers to conduct and document model evaluations and adversarial testing and report serious incidents. The EU AI Office can also conduct its own evaluations and appoint independent experts. For now the law remains less expansive than what Amodei is proposing, leaving frontier labs largely responsible for deciding how much independent scrutiny they will submit to. Papadatos said voluntary self-regulation is better than nothing, but ultimately, companies can’t demand the freedom to control their own safety rules while also asking the public to trust that they’re following them. “You cannot have it both ways, having zero accountability externally, and then say, ‘I’ll just have my own flexible rules,” Papadatos said.
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Google DeepMind Sees Early Signs of Recursive Self-Improvement Ahead of Gemini 4
Logan Kilpatrick said the feedback loop could help Google close the gap with other frontier AI labs with Gemini 4.
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Your AI agents can now control your Google Home devices
Smart home owners will soon be able to control their devices connected to Google Home via an AI agent. On Wednesday, Google rolled out early access to its Model Context Protocol (MCP) server for its Google Home ecosystem, which will allow any AI agent that supports MCP — like Claude, Hermes, OpenClaw, ChatGPT, and Google Antigravity — to securely work with their smart home devices and access their event history. This update will allow people to use natural language instructions to do things like review their camera summaries, monitor smart home activity, control their connected devices, and build their own custom smart home dashboards. To get the connection set up, users will need to create a Google Cloud project and configure it to use Home MCP. They will then provide the MCP configuration details to their agent of choice and ask it to set it up. The agent will then ask them to sign in and grant permissions. (A setup guide will also be offered in the Google Home Developer Center.) The system will support any device in the Google Home ecosystem, including Google Nest doorbells and thermostats and “Works with Google Home” (or Matter) devices, like light bulbs. Google already supports MCP in other areas of its business, including in itsGoogle Cloudanddata platforms, developer tools, andGoogle Workspace, for instance. Support for Google Home is targeted more directly at consumers who are experimenting with AI agents designed to handle everyday tasks. The company said access to the MCP will roll out starting today and continuing over the coming weeks to subscribers who pay forGoogle Home Premium Advancedin the U.S. This is the more expensive, $20-per-month subscription tier that provides features like longer event-based video history, descriptive notifications and detailed alerts, tools to search video history, daily summaries, and more. Google wouldn’t comment on if or when the MCP would roll out more broadly, such as to other subscription tiers or markets. Google is also soliciting feedback from early adopters during this period by way of itsSmart Home for Developers Community, it says. An earlier version of this post misstated the rollout’s start. It is today, Wednesday, not Tuesday.
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AI labs want in-house auditors — but maybe they should shut the front door first
Last weekend, after one of his researchers resigned over fears that AI could lead to human extinction, Anthropic CEO Dario Amodei wrote about the need for outside organizations “to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.” Executives at OpenAI, Google, and SpaceXAI have alreadyrallied aroundAmodei’s plan, which has quickly become a central pillar of the emerging AI safety push. But there may be a simpler and more effective fix hiding in plain sight. Internet security experts say the labs need to focus on network security basics like logs and permissions, applying the same rigorous defenses they do for human users. It’s not as exciting as third-party auditing and alignment work — but it may end up being more effective. “To me, it seems like they’re outsourcing,” Katie Moussouris, the CEO of Luta Security, told TechCrunch of Amodei’s proposal. “Saying [a third-party audit] is the solution is a strange proposition from my perspective. It would be the same as if, instead of writing theTrustworthy Computing Memo, Microsoft said, let’s slow down development.” That memo, written by then-Microsoft CEO Bill Gates in 2002, called on his employees to ensure that their software would be reliable and safe following a series of widely publicized computer worms that took over then-nascent enterprise systems. The AI sector may be at a similar turning point, as the value and risk of the new technology becomes increasingly clear. While alignment remains an important concern, Sayash Kapoor, an AI researcher who will be a professor at UC Berkeley starting next year,arguesthat “marginal investments in control are more likely to be effective compared to those in alignment. We view these incidents as illustrating the lack of emphasis on AI control within companies, despite the availability of known techniques.” The incidents that have spurred these concerns revolve around frontier models being asked to complete training tasks, usually cybersecurity evaluations, and then accessing the open internet and penetrating closed third-party systems in an attempt to do so. They usually did so because ofpoorly configured“sandbox” environments that are supposed to contain these agents; ironically, one Anthropic break-out happened because third-party evaluators didn’t close the right doors. “We as a profession know how to block access to the internet,” Avery Pennarun, the CEO of Tailscale, a security company, said. “If you read through all these big long [reports] — ‘wow, that was a very impressive multi-stage attack, blah, blah.’ Look, you gave it access to download stuff. You should have not done that separately from the internet.” That’s one problem — but a bigger problem is that frontier labs were unaware of these activities. “What was really profound was that all of the discoveries of what they were doing happened either because a victim saw something, or in some of the other cases … it was network activity, and none of it was actually from monitoring the AIs directly,” Moussouris points out. In one case, where OpenAI agentstook overa defunct German WikiForum to cheat on evaluations, the agents were active for weeks before anyone at the company appeared to notice. Security experts that TechCrunch spoke to said that real-time monitoring is key to preventing future break-outs, and that every agentic session should be time-limited and expire. Shapor Naghibzadeh, a former Google security executive who now leads thestartup QueryStory, says the solution is to “put the agent in a box and instrument it heavily from the outside looking in and watch everything that crosses the boundary. Every tool call, every process, every network connection, no exceptions. …The one hole you leave open for convenience is the one that gets used. The bypass went through exactly that kind of exception. [At Google,] I watched that movie many times with human attackers, and these models are at least as good at finding the propped-open door.” OpenAI has begun moving in that direction, announcing that it had begun monitoring all tool-using inference by its Astra model, at “significant compute cost.” Anthropic, too, says it ishardening its securityprocedures, including expanding observability of its models. Neither company responded to TechCrunch’s questions about how they track and control AI agents. Other problems are the use of shared infrastructure by agents, which allowed them to communicate during the Hugging Face attack. Simon Willison, a software developer who co-created the Django web framework, has written about something he calls the “lethal trifecta” — when agents have access to untrusted input, the internet, and private information all at the same time, it’s a recipe for disaster. “The trick is you can pick any two legs of the trifecta and an agent can have any two,” Pennarun said. “If you need all three, then you need to split it across at least two agents … and maybe they’re allowed to talk to each other through a controlled channel.” Experts TechCrunch spoke to understand that frontier lab security personnel have difficult jobs. Naghibzadeh points out that every nation-state actor on Earth is trying to steal their model weights and mount distillation attacks on their APIs, as well as the bread-and-butter security tasks of any large digital company. “Research infrastructure has a hard time rising to the top of that priority stack, although that must be changing now,” he said. “Making security incidents public really helps align everyone internally toward the goal of improving.” That’s one note that Moussouris emphasizes: Right now, there is no formal victim notification procedure when the labs discover their agents have penetrated third-party systems, and it is likely that there have been other incidents that have not been widely publicized. While she worries that laws that regulate models directly may have unintended consequences, mandatory notification is one idea she believes policymakers should pursue. And while it’s clear that security best practices weren’t being followed, experts say that the labs are doing work no one has done before — “they’re doing orders of magnitude more than your typical enterprise,” Zack Korman, the CEO of cybersecurity firm Embroidery, told TechCrunch. And while alignment may not be the place to start, it can’t be ignored. Cybersecurity experts are resigned to having to use AI agents to monitor other agents if they are to have any chance of tracking their behavior in real time, a scenario where the potential for deception raises its ugly head. “You’re trapped using AI to try and deal with this, even though AI is not necessarily safe right now,” Moussouris said. The job will only get harder. Everything agents are doing now, Moussouris says, “they are doing loudly” — they are posting on public forums, and their chain of thought and other reasoning traces are in English. “It’s still human readable,” she says, “so take advantage of that for as long as that lasts, because it won’t last forever.”Additional reporting by Aditya Mehta
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After accusations of selling ‘perv glasses,’ Meta prepares to sell a pair without a camera
Meta’s camera-equipped smart glasses haveproven more successfulthan other entrants into the market, but they have also deeply disturbed certain consumers who see them as invasive emblems of a dystopian surveillance society run amok. Now, as the company weathers complaints that it’s selling “pervert glasses,” it has reportedly decided to sell a pair that doesn’t come with integrated spy equipment. The Informationreports thatMeta is developing a new smart glasses model, dubbed Luna, that, sans cameras, comes equipped with a convenient system to communicate with the company’s AI chatbot and Muse, its AI consumer agent. The glasses include six built-in microphones so users can communicate with the chatbot, as well as a button on the side of the glasses that, when pressed, activates the AI system. Luna may be unveiled as soon as Meta Connect, Meta’s annual hardware/developer event happening in Menlo Park next week, the outlet notes. TechCrunch reached out to Meta for more information. The smart glasses industryhas grownover the past few years, with Metapositionednear the front of the pack of providers. However, concerns over usability, cost, and privacy have dogged the business, with many consumersstill unsureof how, why, or if they should use the devices. Meta’s Reality Labs, which is responsible for developing its smart glasses line,is still losing a gargantuan amountof money, as its earnings report from April revealed.
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Former Infosys chief’s AI startup nabs another $53M
Hang Ten Systems, an AI startup founded by former Infosys CEO Vishal Sikka just four months ago, has expanded its seed funding by another $53 million. This new investment round was closed just five weeks after theinitial $32 million seed round was closed, the company tells TechCrunch. The fresh investment, led by Temasek’s early-stage investment platform Xora, takes Hang Ten’s total funding to $85 million. Mayfield, which led the startup’s earlier investment, also participated. Other investors include Aramco Ventures, Intel CEO Lip-Bu Tan, Micron CEO Sanjay Mehrotra, and Yahoo co-founder Jerry Yang, who is also on its board. Founded in May, Hang Ten is betting that AI can fundamentally reshape how large companies build and maintain software. The startup advises companies — typically with over $10 billion in annual revenue — on AI strategy and also helps build and modernize software. “The build part itself has basically become close to zero marginal cost, close to zero time,” Sikka (pictured above) told TechCrunch. He argued that AI is shifting the focus of software development from writing code to defining requirements and validating whether systems perform as intended. The thesis appears to be resonating with customers. Within 25 days of the first meeting with one customer, Hang Ten signed a multimillion-dollar contract to build a mission-critical software system, Sikka said, adding that he had never seen enterprise deals move that quickly. Hang Ten is now working with a mix of existing customers and late-stage prospects spanning 21 major enterprises, Sikka said, with some prospects in the proposal or contract-negotiation stage. The startup has secured multiple seven-figure contracts and is pursuing eight-figure deals. Moreover, its customers span the U.S., Europe, the Middle East, and Asia, and include Fresenius Kabi, Saudi Aramco, and Siemens Energy. The early customer wins prompted Xora to approach Hang Ten about investing, Sikka said, adding that Xora saw an opportunity to introduce the startup to other companies in Temasek’s portfolio. Sikka declined to disclose Hang Ten’s valuation but said the second round of seed funding was a bump up. With the fresh capital, Hang Ten plans to expand its engineering, consulting, and sales teams. The Palo Alto-headquartered startup currently has about 20 to 25 employees across the U.S., the Middle East, and Australia, and expects to hire in Europe and India, Sikka said. Hang Ten enters a crowded market in which AI model developers such as OpenAI and Anthropic are expanding their enterprise offerings, while traditional consulting firms are racing to embed generative AI into their own services. “Enterprises still need trusted partners who are independent of the underlying platform and who work with and solely in the interest of the enterprise,” Sikka said. “There will always be room for companies like us.” Hang Ten says its edge lies not in building another AI model, but in adapting AI to the complex software development processes of large enterprises. Co-founder and chief design officer Sanjay Rajagopalan told TechCrunch the startup’s engineers rely on an in-house framework, Hobie, that packages reusable AI “skills” for regulated industries and complex enterprise software projects. “We are actually delivering production software,” Rajagopalan said. “It’s not like we are doing some kind of PoC.” Hang Ten’s approach is already putting it in competition with traditional systems integrators. Many of the startup’s engagements, Sikka said, are replacing incumbent providers, though more than half of its current opportunities involve new projects that companies had previously put off, rather than work taken from existing vendors. Rajagopalan noted that Hang Ten can use teams of about two to four people for some projects that might previously have required about 30, although customers or independent third parties still handle final quality checks and certification. The startup promises customers a 10-fold improvement in cost, speed, or a combination of the two, he said. Even in four months of its debut, Hang Ten has received serious acquisition offers from “very big companies,” Sikka told TechCrunch. He, however, declined to identify the prospective buyers. The startup, he said, has turned them down for now as it focuses on expanding the business. Before founding Hang Ten, Sikka co-founded enterprise AI startupVianAIin 2019 after leaving Infosys in 2017. It raised$50 million in seed fundingand laterraised $140 millionin a 2021 round led by SoftBank Vision Fund 2. Sikka departed that startup in April, according to his LinkedIn profile. He told TechCrunch that VianAI is “going through a transaction,” declining to provide further details. As for how startups like Hang Ten could disrupt services providers like Infosys, Sikka noted, “We are fortunate that we don’t have the burden of legacy that we have to transform.” The startup takes its name from “hang ten,” the surfing maneuver in which a surfer rides with all 10 toes over the front of the board. For Sikka, it reflects the startup’s ambition to help enterprises ride the ongoing AI wave, what he called “probably the biggest wave of our lives.”
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SK Hynix reportedly in talks with Intel to build memory chips in US
South Korean memory chip giant SK Hynix is in discussions with Intel to manufacture RAM chips in the U.S. for the first time, Reutersreported, citing anonymous sources. The companies have reportedly discussed various options, one of which would have SK Hynix leasing space at Intel’s planned factory in Ohio to manufacture the chips. The chipmakers are also said to be considering a joint venture that could include cloud-service providers. SK Hynixtold TechCrunch on Wednesdaythat nothing has been finalized yet. “SK Hynix is exploring various options to strengthen its global competitiveness, but no specific plans or arrangements have been finalized at this time,” the Korean semiconductor company said in a statement. “No decisions have been made regarding the two scenarios mentioned in the article.” The Korean chip giant, which hasbenefited massivelyfrom skyrocketing demand for high-bandwidth memory RAM chips used in data centers for AI applications,is already building a $3.8 billionadvanced packaging and research facility for AI chips in West Lafayette, Indiana, with mass production expected to begin in 2029. The factory will use DRAM wafers made in South Korea and package them into HBM chips. It remains unclear what types of memory chips the company would produce in Ohio if the deal goes through. As a global chip shortage worsens, the Trump administration is seeking to expand production on American soil. The White House saidin Januarythat it could impose broader tariffs on semiconductor imports while offering tariff relief to companies investing in domestic manufacturing. SK Group Chairman Chey Tae-won signaled support for U.S. chip production in July,sayingthe company should build factories in the U.S., if feasible, alongside those in South Korea’s Honam region. That month, the chipmakerlistedits American depository receipts on the Nasdaq, broadening its access to American investors. The potential deal with Intel could face scrutiny at home. Seoul holds that the decision would be up to SK Hynix, but any plan involving strategically important chip technology could trigger a government review under a law designed to prevent sensitive technology from being transferred overseas, according to Reuters. Intel and SK Hynix have done business before: In 2020, Intelsoldits NAND flash-memory business to SK Hynix for $9 billion. Intel did not immediately respond to TechCrunch’s request for comment outside regular business hours.
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3 days left to exhibit: Get your brand in front of VCs and high-value leads at TechCrunch Disrupt 2026
If you’re looking for opportunities to demo your breakthrough to VCs and build a powerful lead-gen, then thisFriday, September 18 at 11:59 p.m. PT, is your last day to book yourexhibit tableatTechCrunch Disrupt 2026. OnOctober 13-15 at Moscone West in San Francisco, 10,000+ founders, investors, operators, and tech leaders come together to discover startups, source deal flow, find products, and build partnerships. Your6′ × 30″ exhibit tableputs your startup in the highest-traffic area of Disrupt for three days. Use it to: Your package also includes 10 team passes, website and app branding, press-list access, Silver Tier sponsor branding, and more. Founders can access additional opportunities, including the Deal Flow Café and Investor-to-Founder Networking. Three days. Limited tables.Book yours before September 18 at 11:59 p.m. PT. Join 250+ top-tier tech leaders across 200+ sessions on six industry stagesto get practical insights on building, funding, and scaling in today’s tech landscape. Use interactive sessions and AI-powered matchmaking tomeet the people who can help move your business forward. And discover 300+ startups and watch Startup Battlefield 200 to find your next product, partner, or investment opportunity. Save up to $200 on your ticket before prices increase on September 25.
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Next wave of VCs judging Startup Battlefield 200 contenders at TechCrunch Disrupt 2026 revealed
A great pitch doesn’t winStartup Battlefield 200. A great company does. That’s why the VCs sitting in the judges’ seats matter so much. They aren’t looking for polished presentations or clever demos — they’re looking for founders solving real problems with businesses that can endure. Every question they ask is designed to uncover what slides and rehearsed answers can’t: conviction, clarity, execution, and whether this company has the potential to become the next category leader. For more than 15 years, Startup Battlefield has helped launch companies that went on to define entire markets, and every year a new group of founders earns the chance to join that legacy. Today, we’re introducing the next wave of judges who’ll take their seats atTechCrunch Disrupt 2026, happening on October 13-15 at Moscone West in San Francisco. They’ll spend each day evaluating ambitious startups across industries, asking the tough questions founders hope for — and sometimes fear. If you want to understand how experienced investors really evaluate early-stage companies, there’s no better place to watch than Startup Battlefield, live on the Disrupt Stage. Be sure to snag savings of up to $200 on your Disrupt 2026 ticket before rates increase on September 25 at 11:59 p.m. PT.Register here. Now, without further ado, meet the next five judges joining this year’s Startup Battlefield, the ultimate startup pitch competition. Aditi Maliwal, General Partner, Upfront Ventures Aditi Maliwalis a general partner atUpfront Ventures, where she invests at the pre-seed and seed stages in fintech, enterprise SaaS, AI applications, and developer tools. Her portfolio includes Clair, Writer, Arcade, and General Translation. Maliwal previously held roles at Google and Deutsche Bank and led the Series A investment in Chime during her tenure at a prior firm. She is Upfront’s first San Francisco-based partner. Michael Palank, General Partner, MaC Ventures Michael Palankis a general partner atMaC Ventures, a majority Black-owned venture firm that backs seed-stage consumer and enterprise companies through a culturally driven investment thesis, with a focus on AI, fintech, healthcare, media, entertainment, and aerospace and defense. Before transitioning to venture, Palank worked in Hollywood talent representation at William Morris and Overbrook Entertainment, Will Smith’s production company. Nell Daly, Co-founder and Managing Partner, Revenge Capital Nell Dalyis the co-founder and managing partner ofRevenge Capital, an evergreen sector-agnostic fund focused on backing overlooked founders — including women, BIPOC, LGBTQIA+, disabled, and neurodiverse entrepreneurs — across the United States and United Kingdom. Before launching the fund, Daly worked as a psychotherapist, a background she draws on directly in her founder-first investment approach. Grace Ge, Partner, Amplify Partners Grace Geis a partner atAmplify Partners, where she invests in AI, developer tooling, data infrastructure, and modern SaaS — with a particular focus on the layer between foundation models and enterprise applications. Her portfolio includes Pinecone, Eppo, Vivun, Orb, and Avoca. Amplify’s broader portfolio includes Datadog, Fastly, Temporal, dbt, and Modal. Ge was named to the Forbes 30 Under 30 list for Venture Capital. Crystal Huang, General Partner, Google Ventures Crystal Huangis a general partner atGV, where she focuses on enterprise SaaS, AI infrastructure, and fintech — with a particular lens on product-led and developer-led go-to-market strategies. Her portfolio companies include Typeface, Pecan AI, Sardine, Cacheflow, PostHog, Roboflow, and Sift. Before joining GV, Huang was an investor at NEA and GGV Capital and worked in technology mergers and acquisitions at Blackstone. Get to know all the VC judges on theDisrupt agenda. TechCrunch Disrupt 2026is happening October 13-15 in San Francisco, bringing 10,000+ tech leaders, VCs, and founders together to meet the next generation of breakout startups, connect with the leaders who could change their startup’s trajectory, and get a front-row seat to where the industry is headed.Save up to $200 on your ticketbefore rates increase on September 25. Save even more when youregister as a group.
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Threads’ new features let podcasters promote shows and reach listeners
Metaannouncedon Wednesday that its social network and X rival Threads is rolling out new tools that will allow podcast creators to promote their shows, grow their audiences, and engage existing listeners directly on the platform. The news comes as X has also more recently been encouraging its creators to directly post videos on the platform, especially podcasts, to earn revenue and grow engagement. While Threads is not yet looking to host podcasts, it wants to make its platform a hub for posting and discussion. To start, Threads will display a podcast profile card and banner on a creator’s profile and in their posts. Plus, when creators post episode links, they appear as tappable cards in the post, allowing users to easily listen. The feature also lets podcast hosts directly post transcripts for listeners to read along with the episode, and they can tag featured guests, which displays a card related to that. Users can save any of these posts, making it easy to listen to the episode at a later time. Threads will additionally be able to send reminders to creators when their new episode drops to post on Threads, which could help boost their engagement with listeners. The social platform is also providing tools that offer insights into how the audience is engaging with creators’ posts, with metrics like views over time by followers, non-followers, and all. Last year, Threads rolled out new tools to increase discussion around podcasts, with features like better link cards and linking their profile with their podcasts. Threads is now close to X in terms of users, as Meta’s social network reached500 million active users in June.
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