Anthropic Builds In-House Silicon Team for Custom AI Chips to Power Claude Models

Anthropic confirms plans to build an in-house silicon team

Anthropic is hiring a "custom silicon team" to design chips on which to run its models, the company has revealed. Yesterday, Business Insider noticed a job listing for a senior engineer with experience shipping semiconductor designs. (You can see listings for a Silicon Engineer and a Technical Program Manager, Silicon on Anthropic's job board right now.) A spokesperson for Anthropic then confirmed the plans to both Business Insider and TechCrunch. The spokesperson clarified that Anthropic will still take a "multi-chip approach," with plans to use hardware from other companies alongside its own designs as it continues to scale up. This is confirmation of a rumor that has been circulating for a little bit; The Information previously reported that Anthropic was considering working with Samsung as a hardware manufacturing partner. Anthropic is not alone in walking this path. Its competitor, OpenAI, recently announced a new custom chip called Jalapeño designed for large language model inference in data centers. OpenAI partnered with Broadcom to develop the chip. Google has been running its models on its own hardware for awhile, Meta has also designed and deployed its own chips, and Mistral is reportedly looking into doing the same. There are a few reasons AI providers are doing this. First, much of the industry is heavily reliant on Nvidia for the hardware the companies' models run on, and Nvidia's continued leverage there is a potential strategic vulnerability, especially as AI companies operate in an environment where compute infrastructure is highly competitive as demand continues to outstrip current capacity. Second, designing chips for specific models and vice versa could lead to better performance. So for example, if OpenAI can reap the rewards of that vertical integration, you can bet Anthropic and other frontier model providers will want that advantage as well. To that point, Anthropic says its teams will co-design new hardware and models side by side. It has co-designed certain hardware with partners before, but the plan is now to bring more silicon expertise inside Anthropic itself. Anthropic may also hope this could help its frontier models get some extra competitive edge as software developers and other users begin exploring running cheaper, smaller, or open-weight models on their own hardware or on edge devices. However, since Anthropic is still in the process of hiring key team members, it will be a long while before either the company or its users see any benefits.

Anthropic is hiring an AI chip design team

Anthropic is building a team to design its own custom chips for AI usage, Business Insider reports. The Claude maker said it is planning to co-design hardware and models to help its technology run faster and more efficiently. Last month, The Information reported that Anthropic was scouting Samsung as a potential partner for building such chips. Anthropic's decision to design its own chips comes as demand for Claude rises while AI companies snatch up as many AI infrastructure deals as they can. For its part, Anthropic has inked deals with AWS, Google, Nvidia and AMD to access AI computing hardware. But to really scale to meet the level of demand, relying on others clearly isn't enough. Anthropic isn't the first AI company to decide to build its own chip. In June, OpenAI unveiled its Broadcom-built Jalapeño chip, which is designed specifically for inference workloads. Google DeepMind has long relied on Alphabet's TPU chips to power its AI models, while Meta has been developing its own MTIA accelerators for AI workloads. The company is seeking engineers with experience in chip design for its "custom silicon team," per a job listing. Anthropic did not immediately return a request for comment.

Anthropic co-designing custom AI inference chips to bypass costly Nvidia GPUs -- Samsung reported as manufacturing partner for Claude maker

It joins Amazon, Meta, OpenAI, and Google with its new "multi-chip" strategy. Anthropic has announced it's building an in-house chip development team to co-design its own custom ASIC processors for handling AI inferencing workloads. As described to Business Insider, Anthropic is starting to hire engineers to design the chips with an unspecified partner, and it looks set to do it at pace, with the job listing saying that any potential hiree would need to be ready to work to a schedule and get the chip design over the line. This is just the latest major AI company to announce it's developing its own custom hardware. As the global economic shortages squeeze chip supply and models increasingly lean on optimizations to make workloads more efficient and potentially profitable, making custom silicon for your own data centers makes a lot of sense. Anthropic now joins the likes of Google, Meta, Microsoft, Amazon, and OpenAI in building their own chips for the job. If you want something done right... It's no secret that if you want to train an advanced AI model, you need Nvidia GPUs. Even Chinese AI developers, who have the ruling party leaning on them and limited access to Nvidia hardware, still use Nvidia GPUs -- even if they have to smuggle them first. But if you're looking to run AI to perform inferencing workloads for agentic and generative AI models, you can use a much wider array of hardware. Nvidia GPUs are good, but they're expensive -- custom silicon can have a lower total cost of ownership of up to 65% -- and power-hungry, and there are much more efficient options available. Chinese labs are using domestic Chinese hardware, and many Western AI developers have their own solutions; those that don't are making them. Google has been building its Tensor Processing Unit (TPU) chips for 12 years, working with Broadcom to develop each generation. Amazon has its Trainium and Inferentia chips, and Meta recently announced several new MTIA designs for deployment through 2027. Microsoft has its Maia line, and Tesla recently pivoted to its AI5 and AI6 chip designs after years of developing Dojo. And now Anthropic is getting in on the act, and for much the same reasons. Anthropic told Business Insider that it was co-designing the chips so that they would allow Claude to run faster and more efficiently at the scale its customers need. Indeed, Anthropic has seen explosive growth in the past year, seeing huge expansion in the consumer space and taking on significant government contracts -- not to mention agentic AI requiring far more tokens than traditional single-prompt interactions. Anthropic hasn't revealed which firm it's working with on the design and development. Although Broadcom and Marvell are the two largest companies in the ASIC co-design market, representing some 95% of it, The Information reported last month that Anthropic was in talks with Samsung for manufacturing. The shovel sellers always benefit Designing, packaging, and manufacturing your own custom ASIC for AI inferencing isn't cheap, and it isn't easy. Alongside developing the hardware, you need the software stack to utilize it, and you want models that are optimized to run on it to make the most of its potential advantages. That makes it more worthwhile for most companies to simply use other firms' hardware and more general-purpose GPUs. But for major AI companies with enough money to burn and the ability to scale up to maximize efficiency gains, it's well worth the investment. But the AI developers aren't the only ones who benefit. Nvidia has been one of the few companies to make enormous profits from the AI boom, while the likes of Meta, Google, Microsoft, and OpenAI are all losing enormous sums of money on their AI efforts. There are very real winners from the custom ASIC design and build market, too: TSMC, Broadcom, and Marvell. Broadcom has been Google's co-design partner for years, and was also recently tapped to help build OpenAI's inferencing chips. It also works with Meta on its MTIA design, as well as holding contracts for other custom ASIC designs with ByteDance and Fujitsu. It also produces strong interconnect and networking hardware, which allows it to offer customers a more complete solution. It claims to have a $73 billion backlog of orders to work through, and expects to generate over $100 billion in annual AI chip revenue by the end of 2027. Marvell holds massive contracts with Amazon for its Trainium chips and Microsoft for Maia, and is expected to make upwards of $11 billion for these co-design jobs in 2026. If Samsung ends up as Anthropic's partner, it would be a relatively small player in this particular space, but it would bring enormous manufacturing and chip design expertise to the table, as well as access to the all-important memory that is in such short global supply. The biggest winner of all these initiatives, though, is arguably TSMC. The Taiwanese company produces the majority of the world's cutting-edge silicon and is involved in the production of almost all the chips discussed here. They need TSMC's CoWoS advanced packaging technologies for integration with HBM. It also handles much of the packaging of Nvidia's and AMD GPUs, as well as producing much of the underlying wafers. Multi-polar chip world Anthropic joining the custom ASIC race is hardly surprising and further cements the future we seem to be barrelling towards, which is each of the hyperscaler AI companies looking to handle as much of their inferencing with custom hardware as possible. It's more efficient, easier to control for features and specifications, and easier to scale up when optimized for internal models. From a Chinese perspective, it's also easier to avoid problems caused by international trade blockades and tariffs. They'll likely never become 100% reliant on their own chips -- there is just too much AI demand to scale into for that to happen, and Nvidia has been ruthlessly dominating access to the supply chain. But every new chip installed is an Nvidia GPU that won't be used for the same purpose, which may help reduce the stranglehold Nvidia has on the industry. Not for training, though. That's likely to remain Nvidia's biggest appeal for some time to come.

Anthropic Reportedly Wants to Make Its Own AI Chips for Claude

(Credit: Thomas Fuller/SOPA Images/LightRocket via Getty Images) Following in the footsteps of Google, Meta, Amazon, and OpenAI, Claude developer Anthropic is reportedly building a new team to design its own custom AI chips, Business Insider reports. For most companies designing their own chips, this solves two key problems: a demand for chips that has completely outstripped supply and a need to run AI tasks as efficiently as possible. As Anthropic's services have grown more popular, demand has increased dramatically. So, like other AI companies before it, Anthropic is getting into the hardware game and putting together a team for it. It told Business Insider that it would "co-design" the hardware and models, with previous rumors pointing to Samsung Electronics as a potential manufacturing partner. Anthropic says it will take a "multi-chip" approach to AI inference and will continue to use hardware from cloud providers such as Amazon's AWS and Google Cloud, leveraging Nvidia and AMD chips. It seems clear that any chips it makes will be only for inference. If the cutting-edge Chinese developers challenging Anthropic and OpenAI use smuggled and otherwise-blocked Nvidia chips for training, you can bet companies like Anthropic will use them too, given their far easier access to the best of the best. The engineer position that Anthropic is looking to fill says any potential candidates must demonstrate "direct personal contribution" to the final states of shipping semiconductor designs. Although it suggests some semblance of autonomy, this job is about getting a chip design over the line on schedule. Time is the ever-present pressure of the rapidly evolving AI industry. Internal, bespoke chip designs appear to be the way the industry is going, at least for running the models. In China's upstart labs, the ruling party mandates heavy use of domestic chips, and the growth of models like DeepSeek and Kimi K3 suggests that optimizing for domestic hardware may be the best solution. Google continues to develop its TPUs to power its own chips, and Meta has a new AI chip design that is expected to enter production in September. French firm Mistral is also said to be considering developing its own silicon chips. Although designing chips is expensive up-front, the potential efficiency and economic advantages of having an in-house design that your models are optimized for may well outweigh the costs. Especially if you have the kind of money these AI developers have to throw around.

Anthropic to build in-house chip design team for Claude, hire engineers

Aug 5 (Reuters) - Anthropic said on Wednesday it is building an in-house team to design custom chips for its Claude AI models, confirming an earlier Reuters report, as the startup responds to a shortage of chips needed to power and develop more advanced AI systems. The company said it was hiring engineers with ⁠experience across the hardware and software stack to help co-design custom chips and AI models that can make Claude run faster and more efficiently at the scale required by customers. Reuters reported in April that Anthropic was mulling designing its own AI chips. The startup said custom silicon was the latest step in its multi-chip ⁠strategy and that it would continue to rely on a diversified hardware stack that includes technology from Amazon Web Services (AMZN.O), opens new tab, Google (GOOGL.O), opens new tab, Nvidia (NVDA.O), opens new tab and AMD (AMD.O), opens new tab. Anthropic ⁠did not provide a timeline for its chip plans or say whether it intends to manufacture them itself. Designing an advanced AI chip can cost ⁠roughly half a billion dollars, according to industry sources, as companies need to employ ⁠skilled engineers and spend to make sure the manufacturing process has no defects. Reporting by Prathik Jayaprakash in Bengaluru; Editing by Jonathan Ananda Our Standards: The Thomson Reuters Trust Principles., opens new tab

Anthropic confirmed it is designing custom chips for Claude. It wants engineers who have "shipped silicon."

Anthropic confirmed an in-house silicon team to design custom chips for Claude. Job listing pays $320K-$485K. Multi-chip approach continues with AWS, Google, Nvidia, and AMD. Anthropic confirmed on Wednesday that it is building an in-house silicon team to design custom chips for Claude, the first time the company has publicly acknowledged the effort. A spokesperson told Business Insider the company would co-design hardware and models, allowing Claude to run faster and more efficiently "at the scale our customers need." The company said it has taken and will continue to take a "multi-chip approach," with hardware from AWS, Google, Nvidia, and AMD remaining central to its scaling. A job listing posted by the company details a "custom silicon team" and seeks engineers with expertise across chip design and verification. The salary range is $320,000 to $485,000. Candidates must demonstrate "direct personal contribution" to the finalisation and shipping of semiconductor designs. "This is a role for someone who has shipped silicon, has a realistic relationship with schedules, and is comfortable making consequential calls without a large organization behind them," the listing reads. The confirmation follows months of signals. Reuters reported in April that Anthropic was exploring custom chips as Claude's run-rate revenue surged past $30 billion. At the time, no dedicated team existed. The Information reported last month that Anthropic had held talks with Samsung as a potential manufacturing partner. The hire of Clive Chan, who previously helped build OpenAI's chip programme, signalled the company was moving from exploration to active development. Today's confirmation makes it official. Anthropic is not alone. OpenAI unveiled Jalapeño in June, a custom inference chip developed with Broadcom that targets late 2026 deployment. Meta plans to put its in-house "Iris" chip into production in September. Mistral's CEO has said the French company is considering building its own silicon. Designing an AI chip costs roughly $500 million, according to industry sources, but the economics improve at Anthropic's scale. The company currently runs Claude across Google TPUs, Amazon Trainium, Nvidia GPUs, and AMD hardware. Adding its own silicon would give it a fifth option, one it controls entirely.

Anthropic building in-house custom AI chip design team for Claude

Anthropic confirmed on Wednesday that it is assembling an in-house team to design custom chips for its Claude AI models, as the company looks to run its technology faster and more cost-efficiently at the scale its customers require. The company said it is looking to bring on engineers with backgrounds spanning hardware and software who will work on developing chips and AI models in tandem. A job listing described the group as a "custom silicon team," according to TechCrunch. The company offered no indication of when its chip efforts might bear fruit, nor did it clarify whether Anthropic plans to handle manufacturing on its own.

Anthropic confirms plans to build own chips amid global shortage

The Claude-creator will also put together a new team in charge of designing the custom-made chips. First reported by the Business Insider, artificial intelligence company Anthropic has confirmed plans to design its own chips, in response to a worldwide shortage and increased pressure to develop faster, more advanced AI systems. In April of this year it was reported by Reuters that the organisation was strongly considering building its own chips, as a means of having improved access to a steady supply and keeping pace with competitors Meta and OpenAI. Both of whom have similar projects underway. The latter previously announced the development of the Broadcom-built Jalapeño chip, designed for inference workloads, while Meta has been developing its own MTIA accelerators for AI workloads. The timeline as to when production might begin is unclear, however, Anthropic are looking to add to their workforce in order to meet future AI development expectations. As per a recent job listing, Anthropic is seeking professionals eager to join a custom silicon team. It is currently unknown if the organisation will manufacture the chips solely by themselves, however, it has been previously reported that Anthropic may be looking at Samsung as a potential partner in the development of the chips. While custom chips is the next step in Anthropic's ongoing AI and chip strategy, reportedly the company still intends to utilise a diversified hardware stack that includes technology from Amazon Web Services, Google, Nvidia and AMD. In late July, Anthropic announced plans to partner with AMD for 2GW of its latest-generation chips, in a bid to boost AI capacity and meet growing demands. The deal between the companies was reported to be worth "tens of billions of dollars". Anthropic is striving for dominance in the AI space ahead of a widely reported IPO, which is expected to value the company at more than $1trn. Don't miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic's digest of need-to-know sci-tech news.

Confirming rumors, Anthropic reveals plan to develop custom chip

Anthropic PBC today disclosed that it plans to develop a custom artificial intelligence chip. A spokesperson told Business Insider that the company will co-design the processor with its future large language models. Usually, co-design initiatives focus on tailoring a chip to a specific workload. Such customization can significantly increase hardware efficiency. Off-the-shelf AI chips' specifications are often mismatched with the models they run. For example, an accelerator might feature slightly more memory than an LLM requires or slightly less. Co-designing processors with the software they run removes such inefficiencies. According to Business Insider, Anthropic is assembling an in-house chip development team to lead its semiconductor push. The company is reportedly seeing processor designers and verification experts. Verification is the process of ensuring that a chip design will work as expected. A job posting indicates that Anthropic plans to automate some verification tasks using AI. According to the listing, the company plans to develop simulations in which Claude can learn how to test newly developed chip designs. The effort will place particular emphasis on an evaluation method called formal valuation. It checks a chip design for flaws by simulating every combination of operating conditions in which it will run. Semiconductor engineers also use a variety of other verification methods. During the initial phase of the testing process, they run a virtual version of their chip in a simulation. They later implement it in an FPGA, or field-programmable gate array. An FPGA is a chip that can mimic other processors at the hardware level, which enables it to provide more detailed telemetry than a simulation. OpenAI Group is also using AI to accelerate its chip design efforts. In June, it debuted a custom inference accelerator called Jalapeño that was developed through a collaboration with Broadcom Inc. The chip took only nine months to design because the companies automated a number of manual tasks with AI. It's likely that Anthropic's chip will be optimized for inference workloads much like Jalapeño. Inference, or the task of running LLMs in production once training is complete, often represents AI providers' biggest infrastructure expense. Custom silicon can be much more cost-efficient than off-the-shelf graphics cards. Rumors of Anthropic's chip design effort first emerged in April. In early June, The Information reported that the company may partner with Samsung Electronics Co. to manufacture its processor. Samsung has a much smaller share of the contract chipmaking market than Taiwan Semiconductor Manufacturing Co. However, it recently introduced a technology called zHBM that could significantly increase the efficiency of AI chips. Partnering with Samsung may enable Anthropic to incorporate zHBM into its upcoming accelerator. A graphics card's logic cores and HBM memory sit next to one another on a shared base layer, or substrate. Samsung says that its zHBM technology makes it possible to place memory directly atop logic cores. That arrangement reduces the distance data must travel between memory and logic circuits, which in turn lowers power use.

Anthropic Begins Hiring for Its Own AI Chip Design Team for Claude Models

Anthropic is planning to hire new personnel for its US offices Anthropic recently relaunched its most capable AI model yet, Claude Mythos, which offers cybersecurity capabilities, along with Claude Fable 5. With the rising demand for AI models, the US-based AI giant could be looking to foray into a new category, as it has started hiring engineers for its Reinforcement Learning team, which will be responsible for designing custom AI chips in-house. A company spokesperson has reportedly confirmed the development as well. The AI chips are said to enhance the performance of Claude AI models. Anthropic is also reportedly in talks with Samsung about manufacturing its chips. Anthropic Is Hiring a Research Engineer for Its Chip Design RL Team A new job listing has been posted on the AI giant's website, confirming that the company has begun hiring a Research Engineer for its Chip Design RL team at its San Francisco and New York City offices. The job description reveals that the Chip Design RL team is responsible for Anthropic's reinforcement learning research and development of Claude AI models, with "significant impacts on the autonomy and coding capabilities of Claude Fable 5 and Opus 4.8". The Research Engineer will help "advance" Claude's AI models' ability to design silicon chips. The new hire will also be responsible for inventing, designing, and implementing reinforcement learning environments and evaluations for agentic RTL generation, design verification, and physical design optimisation. This seemingly confirms that the company plans to begin designing its own AI chips for Claude AI models. Corroborating this, an Anthropic spokesperson confirmed to Business Insider that the US-based AI giant plans to build an in-house team of engineers responsible for designing AI chips for Claude models. The tech firm will reportedly co-design hardware and models, which is said to enable the Claude models to "run faster and more efficiently" at the scale required by Anthropic's customers. As previously mentioned, Anthropic will reportedly design the custom AI chips in-house only. However, it will have to outsource the manufacturing to a different company. The Information reports that Anthropic is currently in talks with Samsung to manufacture the AI giant's custom AI chip. Samsung's System LSI Division, which is responsible for manufacturing chips, could start building Anthropic's AI chips in the future. However, the companies have yet to confirm this development.

Anthropic to build in-house chip design team for Claude, hire engineers

Anthropic is developing an internal team to design custom chips for its Claude AI models. This move addresses the current shortage of essential hardware needed for advanced AI development. The company is hiring engineers to co-design chips and AI models for better performance. Custom silicon represents a new step in their multi-chip strategy. Anthropic will continue using hardware from Amazon, Google, Nvidia, and AMD. Anthropic said on Wednesday it is building an in-house team to design custom chips for its Claude AI models, confirming an earlier Reuters report, as the startup responds to a shortage of chips ⁠needed ⁠to power and develop more advanced AI systems. The company said it was hiring engineers with experience across the hardware and software stack to help co-design custom chips and AI models that can make Claude run faster ⁠and more efficiently at the scale required by customers. Reuters reported in April that ⁠Anthropic was mulling designing its own AI chips. The startup said custom silicon was the latest step in its multi-chip strategy and that it would continue to rely on a diversified hardware stack that includes technology from Amazon Web Services, Google, Nvidia and AMD . Anthropic did not provide a timeline for its chip plans or say whether ⁠it intends to manufacture them itself. Designing an advanced AI chip can cost roughly half a billion dollars, according to industry sources, as companies need to employ skilled engineers and spend to make sure the manufacturing process has no defects.

Anthropic Is Paying Nearly A Million Dollars Per Year To The Engineers Teaching Its AI Models To Design A Chip, But Just $320,000-$485,000 To Those Actually Building Its First ASIC

I guess we are in an era where the zeitgeist is not to teach the man how to fish, but the AI model, ensuring that the AI lab - Anthropic in this case - would then nosh on a lifetime's worth of customized silicon chips. Anthropic is paying $500,000-$850,000 right now to research engineers who are teaching its AI models how to design silicon chips, but just a fraction of this renumeration to the silicon engineers actually building those chips, and that too with nearly identical skillset requirements We reported back in May that Anthropic's passing remarks on "logic chips" in relation to its evolving partnership with Samsung had unleashed a flood of speculation regarding a possible ASIC-related tie-up between the two. Well, just around two months later, Anthropic all but confirmed to Business Insider earlier this week that it indeed plans to build such a custom chip by acknowledging its ongoing efforts to assemble an in-house silicon design team, going on to add that it has and will continue to take a "multi-chip approach." As Anthropic continues to beef up its silicon design team, however, an interesting pay disparity has emerged, with the AI lab willing to pay between $500,000 and $850,000 per year to research engineers who are teaching its AI models how to design silicon chips, but just between $320,000 and $485,000 to the silicon engineers who are actually designing its first ASIC. What's more, this material pay disparity has emerged despite the convergent skillsets required for these two roles, including expertise on full ASIC/FPGA flow, RTL to tape-out, UVM/formal, physical design, PPA, DFT, and EDA tools. Of course, Anthropic is not the first AI lab to try to leverage AI models for chip design processes. After all, Moonshot's viral Kimi K3 model was recently able to design a viable silicon chip completely autonomously within 48 hours, producing a chip design with a 4.0 mm² area and a Nangate 45nm library, all by leveraging open-source EDA tools and building a GPU compiler from scratch. The virtual chip even managed to achieve over 8,700 tokens/second decoding throughput in simulation. Looks like even silicon engineers are not immune from the AI's roving wrath of obsolescence. Follow Wccftech on Google to get more of our news coverage in your feeds.

Anthropic Joins OpenAI, Google in AI Chip Race as Compute Crunch Deepens

Anthropic is building an internal chip-design team to develop custom processors for its Claude artificial intelligence models, a move aimed at reducing its reliance on constrained AI hardware supplies and improving performance at scale. The AI startup is hiring engineers with expertise across both hardware and software, Reuters reported. The effort expands Anthropic's existing multi-chip strategy, with the company signaling it will continue using a mix of third-party infrastructure and accelerators. Its current ecosystem includes hardware and cloud partnerships tied to Amazon Web Services, Google, Nvidia and AMD. Anthropic's move fits into a larger push by leading AI companies to reduce dependence on scarce Nvidia GPUs. The availability of advanced chips has become a bottleneck for training and running frontier AI models, with companies competing for limited accelerator capacity. The chip initiative also highlights the evolving relationship between AI model developers and cloud providers. Anthropic has deep ties with Amazon, which has invested billions of dollars in the startup and provides access to its Trainium and Inferentia chips through Amazon Web Services. In April, Amazon announced that Anthropic would spend more than $100 billion over the next 10 years on AWS technologies. Developing a cutting-edge AI chip can cost roughly $500 million, industry sources told Reuters, reflecting the expense of recruiting specialized engineers, designing advanced architectures and ensuring chips can be manufactured at scale without costly production failures. Anthropic has not disclosed a timeline for when its custom chips could be deployed or whether it plans to manufacture the processors itself. Anthropic is also exploring a $36 billion debt financing package tied to the use of Alphabet Inc.'s Google chips, with Blackstone reportedly holding early discussions with investors regarding demand for the potential deal. The size, structure and leadership of the financing are still under negotiation, and Blackstone may not end up leading the transaction. If completed, the financing would surpass the $35 billion debt package that was agreed upon several months ago, arranged by Apollo Global Management and Blackstone. The deal would support Anthropic's lease of Google's custom artificial intelligence chips. This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

After OpenAI's Jalapeno Chip, Anthropic Now Sets Sights on One of Its Own

Having stayed away from AI infrastructure all these months, the sudden shift towards chip design seems to be a result of OpenAI's own chip release Having gone on record stating that their AI growth plan wouldn't exactly depend on creating their own infrastructure, Anthropic has changed its mind and is now hiring an AI chip design team of its own. The end goal is the obvious development of custom AI chips for use in their own and outside ecosystems. A report published by the Business Insider said the company is setting up a team to meet in-house demand for AI chips, especially in the wake of Anthropic facing high usage and then setting limits for Claude, Claude Pro and Max subscribers in the wake of high demand during peak hours that is defined as between 0500 and 1100 hours Pacific time. The company is hiring engineers to design custom chips for its Claude model, a company statement noted. The purpose is building an in-house silicon team to design chips. The idea is to co-design hardware and models to help the company run its technology faster and much more efficiently. Anthropic was reportedly to be in talks with Samsung Foundry for producing high-end chips. A report published by Newsworks Korea claimed that the company has indeed secured a contract to fabricate proprietary accelerator chips for Anthropic. The chip will use Samsung's 2 nm process node and their advanced packaging. This decision coupled with the reports of the going for its own chip-design only suggests that Anthropic has also gotten into the race amongst AI companies to get as many AI infrastructure deals as they can. In the past, Anthropic has done deals with AWS, Google, Nvidia, and AMD to access AI computing hardware. Several AI bigwigs have gone on record to state that the inability of foundries to meet the growing chip demand is why some of them have moved towards creating some of their own. OpenAI's recent unveiling of their Broadcom-built Jalapeno chip meant for inference workloads is a case in point. Google already has shifted to its own TPUs to power AI models while Meta has also been developing its own accelerators for matching its AI workloads. Others like Amazon too have followed the bandwagon with its Trainium chips for large language model training requirements in the AI ecosystem. In the case of Anthropic too, the latest effort seems to be supplementing their multi-chip approach whereby hardware from AWS, Google, AMD, and Nvidia continues to be central to their efforts of scaling up enterprise business. The latest job posting from the company clearly stated that the position was available for a "custom silicon team." According to Business Insider, the engineering position offers a salary range of between $320,00 to $485,000 a year and requires candidates to show direction personal contribution to the finalisation and shipping of semiconductor designs.

Anthropic confirms in-house AI chip team for Claude models Report

The company will also continue using chips from its existing partners. Anthropic has recently confirmed that it is building an internal team to develop custom computer chips for its Claude AI models. The report suggests that the AI startup with the in-house chips aims to improve the speed and efficiency of Claude while supporting growing customer demand. The company says it wants to co-design AI hardware and models to deliver better performance at scale. Even as it invests in its own chip technology, Anthropic has clarified that it will continue using processors from partners such as AWS, Google, Nvidia and AMD as part of its broader AI infrastructure strategy. According to a Business Insider report, Anthropic has confirmed that it is creating an internal team to design its own computer chips for its Claude AI models. This is the first time the company has openly said that it is working on developing its own chip-making technology. Also read: Mark Zuckerberg apologises to govt over CSAM, PM Modi post removal: Report The spokesperson said that Anthropic aims to co-design hardware and AI models, which could help Claude operate faster and more efficiently while meeting the performance demands of customers at scale. However, Anthropic will continue following a 'multi-chip approach', relying on hardware from major partners such as AWS, Google, Nvidia, and AMD. These external chip platforms will remain a key part of the company's strategy for expanding its AI infrastructure. The claims are further supported by the AI startup's recent job posting, which reveals that the company is hiring engineers for a custom chip team. The post reads that the company is looking for specialists who have experience designing, testing and taking computer chips from early development all the way through to final production. The role offers a salary range of $320,000 to $485,000 per year. By creating its own chips, Anthropic hopes to improve speed and efficiency while serving more customers. However, the company said it will continue using chips from other suppliers as part of its wider plan. The company said its aim is to build hardware that matches the needs of its Claude AI models. By creating its own chips, Anthropic hopes to improve speed and efficiency while serving more customers. However, the company said it will continue using chips from other suppliers as part of its wider plan. Also read: Microsoft tells employees to stop tokenmaxxing as it switches to OpenAI's GPT-5.6 Sol Anthropic is not the only company that is aiming to build an AI chipset, as Meta is also reportedly developing a new AI chip that is expected to start production soon. French AI company Mistral is also interested in making its own AI chips. However, OpenAI has developed its own AI chipset called 'Jalapeño' with Broadcom.