Anthropic has confidentially submitted draft registration paperwork for an initial public offering with the Securities and Exchange Commission, the company announced in a blog post on Monday. The number of shares to be offered and the price have not been set. The filing positions Anthropic to potentially reach the public market as soon as this fall, ahead of rival OpenAI, which is also preparing its own confidential IPO filing in the coming weeks.

The move comes days after Anthropic raised $65 billion in a funding round at a $965 billion valuation, eclipsing OpenAI’s valuation for the first time. The round was led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital. Goldman Sachs, JPMorgan Chase, and Morgan Stanley are expected to be under consideration for lead underwriting roles on both Anthropic’s and OpenAI’s listings.

The revenue trajectory

Anthropic’s financial growth over the past 12 months has been extraordinary even by AI startup standards. The company’s annualised revenue run rate was $4 billion in July 2025. By January 2026 it had surpassed $9 billion. Anthropic has told investors that the run rate will exceed $50 billion by the end of July, representing roughly an 80-fold increase in annualised revenue over two years.

The company expects to report $10.9 billion in revenue for the second quarter of 2026, more than doubling the $4.8 billion it generated in Q1 and exceeding its entire 2025 annual revenue in a single quarter. Anthropic is also on pace for its first profitable quarter, with a projected operating profit of $559 million. Early investors like Accel, whose Anthropic stake has more than quadrupled in months, now have a clear path to liquidity.

The 💜 of EU tech

Advertisement

The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now!

The growth has been driven by a surge in enterprise demand for Claude’s coding capabilities and, more recently, by Anthropic’s Mythos cybersecurity model, which discovered more than 10,000 zero-day vulnerabilities across major operating systems and has become the most discussed AI capability in enterprise security.

The three-way IPO race

Anthropic’s filing sets up what could be the most significant cluster of technology IPOs since the dot-com era. OpenAI is preparing its own confidential filing and has targeted a public debut in the fall at a potential valuation of up to $1 trillion. SpaceX has already filed for the largest IPO in history, with marketing set for early June. All three companies are backed by overlapping pools of sovereign wealth funds and institutional investors, and all three are likely to draw on Goldman Sachs, JPMorgan, and Morgan Stanley for underwriting.

For Anthropic, filing first carries a strategic advantage. The company that reaches the public market ahead of its rival gets first access to the broadest possible pool of institutional and retail investors, sets the valuation benchmark for the AI sector, and defines the narrative around which AI company represents the best public market bet.

The competitive dynamic between the two companies has intensified this year. While Anthropic has posted an 80-fold annualised revenue increase and surpassed OpenAI in valuation, OpenAI has been rethinking its product lineup, shuffling leadership, and confronting reports that it missed certain internal revenue and user targets. OpenAI CFO Sarah Friar has pushed back, telling Bloomberg the company is seeing a “vertical wall of demand” for its products.

Risks in the filing

Anthropic’s S-1, when it becomes public, will need to disclose several material risks. The most prominent is the company’s ongoing legal battle with the US government after the Pentagon declared Anthropic a supply-chain risk, a designation typically reserved for foreign adversaries. The dispute stems from Anthropic’s refusal to grant the military unrestricted access to its models. Anthropic has said the designation could jeopardise billions of dollars in revenue.

The $965 billion valuation also carries scrutiny. At $10.9 billion in projected Q2 revenue, Anthropic would be trading at roughly 22x annualised revenue, a premium that assumes continued hypergrowth. If the $50 billion run rate materialises by July, the multiple compresses to a more defensible 19x, but that figure depends on maintaining the pace of enterprise adoption that drove the Q1-to-Q2 doubling.

There is also the question of whether profitability is sustainable. Anthropic’s projected $559 million operating profit in Q2 represents a roughly 5% margin, thin for a company seeking a near-trillion-dollar public valuation. The economics of running frontier AI models at scale remain expensive, and compute costs could compress margins as the company scales. The University of Michigan turned a $20 million early OpenAI investment into $2 billion, a reminder that early AI bets can produce extraordinary returns, but public market investors will demand more predictable economics.

What it means for the market

Anthropic’s IPO filing, combined with OpenAI’s expected filing and SpaceX’s already-submitted paperwork, means the fall 2026 IPO window could see more than $200 billion in new public market value from three companies alone. The last time a cluster of technology offerings this large reached the public market simultaneously was the 1999-2000 dot-com wave.

The comparison is imperfect. Anthropic and OpenAI have real, rapidly growing revenue. SpaceX is profitable. But the valuations all assume that the AI market will continue expanding at its current pace, that enterprise adoption will not plateau, and that regulatory and legal risks will remain manageable. Anthropic’s confidential filing is the starting gun for the most consequential test of whether the AI boom can sustain public market scrutiny.

Big news in enterprise AI broke over the weekend as Chinese AI startup MiniMax released its highly anticipated M3 large language model on Sunday evening Eastern time, pairing frontier-tier coding and agentic performance with a 1-million-token context window and native multimodality for a fraction of the cost of leading proprietary models, with pricing starting at just $20 per month under its new subscription token plans.

The company’s leadership also announced plans to deliver the model under an open source license including “open weights,” allowing for full enterprise downloading and customizability free-of-charge, coming sometime in the next 10 days. For now, it is available via the MiniMax API at a special discounted price of $0.3 per 1 million input tokens and $1.20 per million output tokens (on fresh cache) for the next week — beating proprietary U.S. giants like Google, OpenAI and Anthropic handily on cost, while also eclipsing the performance of the latest models from the former two on selected benchmarks.

Even at its full price of $0.6/$2.40 per million input/output tokens, MiniMax-M3 remains at just 8-20% the cost of the leading, proprietary U.S. models.

The traditional matrix governing large language model development has long dictated a rigid choice: software developers can either access top-tier closed-source intelligence behind restrictive APIs, or deploy nimble, cost-effective open models that falter on multi-step reasoning, dense coding tasks, and massive data sequences. MiniMax-M3 fundamentally upends this paradigm.

By unifying these two historically separated frontier capabilities, M3 introduces a level of comprehensive utility previously restricted to expensive, closed-source ecosystems, effectively shifting the baseline of open-weights systems while drastically minimizing the operational compute footprint required to execute complex development loops.

VentureBeat Frontier AI Model API Pricing Snapshot

New MiniMax Sparse Attention (MSA) technique helps keep the model’s cost low

At the core of the model’s efficiency lies an architectural departure from classic Transformer networks. Standard attention mechanisms scale quadratically ($O(N^2)$), meaning computational and financial costs explode as text inputs lengthen.

To combat this “inherent flaw,” the engineering team implements MiniMax Sparse Attention (MSA), a clean, extensible sparse attention blueprint.

To visualize this innovation, think of traditional full attention as an editor reading an entire library from scratch every time they need to verify a single sentence. MSA acts as an intelligent indexing clerk, using a pre-filtering phase to partition Key-Value (KV) matrices into highly precise blocks.

At the operator level, MSA uses a “KV outer gather Q” approach. The system treats KV blocks as an outer loop, dynamically aggregating only the specific queries that hit them. Because each data block is read exactly once and memory access remains strictly contiguous, hardware utilization skyrockets.

In internal trials, MSA runs more than 4x faster than alternative open-source solutions like Flash-Sparse-Attention or flash-moba.

When managing a maxed-out context length of 1 million tokens, M3’s per-token compute demand drops to just 1/20th of the previous generation model, translating into a 9x acceleration in the prefilling stage and a 15x boost during decoding.

Rather than taking a pretrained text network and fusing it with a separate vision model, MiniMax engineered M3 as a natively multimodal system from “Step Zero”.

The company overhauled its data ingest machinery to blend naturally interleaved sequences of text, images, and visual components, scaling the total pretraining corpus beyond 100 trillion tokens.

This deep data alignment enables the model to translate complex visual geometries, such as programming charts or coordinate maps, into structural code without losing contextual fidelity. On standardized assessments, M3 validates this engineering path.

The model records a 59.0% on SWE-Bench Pro, an autonomous agent metric, positioning it ahead of closed models like GPT-5.5 and Gemini 3.1 Pro. It achieves a 66.0% on Terminal Bench 2.1, a 74.2% on MCP Atlas, and an 83.5 on BrowseComp—outstripping Claude Opus 4.7’s benchmark score of 79.3 in autonomous browsing and information retrieval.

However, when contrasted with Anthropic’s newly released, premium frontier model, Claude Opus 4.8, from last week, the competitive ceiling of M3’s efficient sparse-attention footprint becomes evident across directly comparable, tool-intensive agent benchmarks.

In the domain of pure code modification on SWE-Bench Pro, M3’s 59.0% score drops behind Opus 4.8’s leading 69.2% threshold.

A similar performance delta manifests in automated system environments via Terminal-Bench 2.1; while M3’s 66.0% terminal execution score effectively runs neck-and-neck with the previous-generation Opus 4.7 baseline of 66.1%, it trails the upgraded Opus 4.8 architecture, which achieves 74.6%.

Furthermore, evaluations tracking continuous GUI interaction on the OSWorld-Verified sandbox place M3’s automated computer use at 70.0%, compared to a higher 83.4% validation rate secured by Opus 4.8.

These standardized evaluations illustrate the structural trade-offs currently defining the ecosystem: closed-source systems like Opus 4.8 maintain absolute margin leads on hyper-complex reasoning vectors, yet M3 delivers a highly capable baseline of local, tier-one automated operation without the compounding premium of closed-door API subscription fees.

When positioned alongside the heavy-duty inference metrics of the newly minted, fellow open weights model DeepSeek-V4 Pro Max, M3 holds its ground across core agentic categories while asserting narrow advantages in specialized code synthesis.

On the software engineering matrix of SWE-Bench Pro, M3’s 59.0% resolution efficiency edges past DeepSeek-V4 Pro Max’s score of 55.4%.

However, the competitive friction tightens in command-line environments; under Terminal Bench evaluations, DeepSeek-V4 Pro Max pulls slightly ahead with a 67.9% execution accuracy over M3’s 66.0% mark.

In web orchestration and open-world browsing simulations, the two architectures reach a virtual statistical parity, with M3 registering an 83.5% on BrowseComp compared to DeepSeek’s 83.4%.

Similarly, on the MCP Atlas tool-use framework, M3 secures a narrow lead at 74.2% against DeepSeek’s 73.6%.

This close alignment demonstrates that while DeepSeek handles a massive 1.6-trillion total parameter footprint with specialized high-effort reasoning modes, MiniMax’s block-filtered sparse attention mechanism yields directly competitive execution efficiencies without requiring extensive parameter activation scaling.

MiniMax Code AI agent offers Agentic Team capabilities

MiniMax translates these architectural gains into immediate utility through an updated product suite divided between standalone applications, customizable subscription tiers, and raw developer infrastructure. For end-user orchestration, the flagship implementation is MiniMax Code, an AI agent product designed to maximize M3’s multi-step capabilities.

Operating via web or native desktop apps, MiniMax Code runs an “Agent Team” capable of breaking massive engineering tasks into multi-stage, concurrent workflows.

The system relies on a “Producer + Verifier” adversarial harness loop. As one agent instance generates code, a secondary verifier instance aggressively tests and reflects upon execution outputs, allowing the network to self-correct and operate autonomously for days without human oversight. Because of its native visual grounding, MiniMax Code supports direct computer use.

A developer can issue a cross-application voice prompt via their phone to have the model open a localized enterprise ERP client and batch-populate data tables directly from an open Excel spreadsheet.

For custom setups, developers can pipeline M3 directly into existing workflows using an API key (sk-cp) compatible with common alternative IDE environments like Claude Code, Cursor, Roo Code, and Cline. The API introduces a toggleable “thinking mode”.

When enabled, M3 routes processing power into deep reasoning and long-horizon planning; when disabled, the model runs at minimal latency for quick text completion. The companion Token Plan models an aggressive pricing strategy structured around shared multimodal quotas. Billed annually, three options are available:

• Plus ($20/month): Supplies ~1.7B tokens per month and handles 3–4 concurrent agents.

• Max ($50/month): Supplies ~5.1B tokens per month, manages 4–5 concurrent agents, and adds 3 automated video clips per day via Hailuo 2.3.

• Ultra ($120/month): Supplies ~9.8B tokens per month, facilitates 6–7 concurrent agents, and extends video capacity to 5 daily clips.

Open weights makes M3 much more attractive for enterprise use

MinMax’s pledge to release M3 under an open-weights license model—with weights and technical documentation launching on HuggingFace and GitHub within 10 days—carries significant strategic weight for enterprise infrastructure managers.

However, it is still to be determined precisely which license the weights will be available under, and whether or not it will be permissible for consumer usage, e.g. MIT, Apache 2.0 or the new OpenMDW license. If so, the calculus looks like this:

By shipping the underlying model weights directly to the community, MiniMax departs from the closed-door approach favored by major American AI labs.

For enterprise users bound by strict compliance and privacy rules, open weights mean they can run M3 locally on internal hardware.

This setup completely removes the risk of data leakage associated with public APIs. Furthermore, it permits engineering teams to run bespoke fine-tuning passes, modify internal architectures, or embed specialized system prompts deep within the model layers—transforming an off-the-shelf system into a highly targeted proprietary asset.

Initial community reactions are resoundingly positive

The developer ecosystem reacted immediately to M3’s operational benchmarks, singling out its long-horizon autonomous behavior and cost-to-performance profile.

A major focal point of discussion is a 12-hour automated verification test where M3 was tasked with reproducing an ICLR 2025 Outstanding Paper Award winner, titled “Learning Dynamics of LLM Finetuning”.

As MiniMax’s own researcher @MikaStars39 highlighted on X:

“M3 ran autonomously for nearly 12 hours, producing 18 commits and 23 experimental figures on its own, and got the core experiments working:

• it matched the predicted probability trends in the SFT stage

• clearly observed the squeezing effect central to the DPO experiments

• validated the Extend mitigation method proposed in the original paper.”

Simultaneously, creators of developer tools highlighted the practical economic advantages of the model’s new attention mechanism. The official team behind the agentic AI coding harness Cline posted an alert confirming day-one compatibility, stating:

“The new MiniMax-M3 is their first model to have 1m context, multimodal, and agentic coding capability. Congratulations to @MiniMax_AI for the breakthrough in sparse-attention architecture cutting compute & cost to 1/20th their previous generation.”

This sharp drop in execution costs shifts how developers view the relationship between financial investment and capability. Tech commentator @jumperz mapped out this disruption, noting how M3 breaks a historical pattern in machine learning pricing:

By addressing context scaling limitations through fundamental attention-level optimizations rather than brute-force hardware scaling, MiniMax has established a highly efficient open-source baseline. M3 demonstrates that the next phase of agent development will not just be driven by larger datasets, but by efficient architectural choices that make frontier-level performance accessible to the broader open-source community.

For enterprises building autonomous software development or agent infrastructure, MiniMax M3 provides the ultimate “bang for the buck.”

While DeepSeek-V4 Pro holds a microscopic price advantage of $0.195 per million tokens, MiniMax M3 justifies its marginal premium by delivering superior autonomous software engineering resolution rates (59.0% SWE-Bench Pro).

More importantly, because M3 is an open-weights model, the calculation extends far beyond the API chart. By deploying M3’s weights locally inside private enterprise clouds, organizations completely bypass cloud data egress tracking, eliminate structural vendor lock-in, and can implement custom prefix-caching models on internal hardware. This technical approach transforms a highly efficient runtime budget into a permanent, privately owned corporate asset.

Expect more than an hour of trailers and reveals.

Nearly 2,000 WordPress websites were infected with malware that relies on Steam Community profile comments to hide command-and-control (C2) data.

The threat actor used invisible Unicode characters to encode a payload that builds a URL to a malicious script. By leveraging Valve’s platform, the attacker avoids maintaining a separate C2 infrastructure and evades traditional detection methods.

Since the campaign was first uncovered in July 2025, GoDaddy security engineers have found malware on approximately 1,980 WordPress websites.

It is unclear how the hackers breach the websites, but researchers assess that the initial infection vector ranges from stolen admin logins or compromised FTP/SFTP credentials to the exploitation of a vulnerable WordPress theme or plugin, or a supply-chain compromise.

The first-stage malware planted on a website uses WordPress page loads to reach specific Steam profiles and extract text from benign-looking comments.

However, the text includes hidden Unicode characters that conceal malicious payloads sometimes disguised as ASCII art.

GoDaddy researchers note in a report that the threat actor uses six invisible Unicode characters for the encoded payload:

• Zero-width non-joiner (U+200C)
• Zero-width joiner (U+200D)
• Function application (U+2061)
• Invisible times (U+2062)
• Invisible separator (U+2063)
• Invisible plus (U+2064)

The decoder ignores any visible character and maps the invisible ones to a corresponding number; then it converts them to binary representation and reconstructs bytes from the binary stream.

“This encoding allows binary data to be embedded within normal-looking text. The visible characters serve as camouflage while the invisible characters carry the actual payload,” GoDaddy says.

According to the researchers, the decoded payload is used to build a hello-mywordl[.]info URL serving JavaScript code that is injected into every frontend WordPress page.

Based on the file names (e.g., asahi-jquery-min-bundle and lodash.core.min.js), the retrieved malware is disguised as a legitimate JavaScript library.

The final stage of the attack is implementing a backdoor that responds to specially crafted POST requests that include a specific authentication cookie. If the “tEcaKKXEsb cookie is present, the backdoor accepts base64-encoded PHP code via POST parameter,” the researchers explain.

GoDaddy describes several evasion mechanisms employed by the malware, including obfuscated strings using octal and hex escapes, randomized function names, fake disabled logging code, and the use of standard WordPress APIs, allowing it to blend with normal activity.

Site owners can defend by checking for references to Steam Community URLs, suspicious external JavaScript injections, outbound connections from WordPress servers to Steam, and unexpected scripts loading from domains such as hello-mywordl[.]info.

Other indicators include invisible Unicode characters, suspicious _transient_caption_ cache entries, disabled SSL verification in cURL requests, and POST requests containing the malware’s authentication cookies or the new_code parameter.

The researchers recommend that security teams prioritize restoring from a known good backup before the infection date. If this is not possible, the manual cleaning process should be thorough because “attackers can reinstall removed code through the backdoor if any component remains active.”

Automated pentesting tools deliver real value, but they were built to answer one question: can an attacker move through the network? They were not built to test whether your controls block threats, your detection rules fire, or your cloud configs hold.

This guide covers the 6 surfaces you actually need to validate.

Download Now

from the goes-wherever-the-hate-leads-him dept

CBP Commander-at-Large Gregory Bovino made that title literal by showing up wherever Trump needed trouble started. Once he had arrived far north of the southern border he was supposed to be patrolling, Bovino (and the people he was “commanding”) found themselves on the receiving end of several lawsuits.

Not only did they find themselves on the receiving end of lawsuits, they — especially Gregory Bovino — found themselves hit with judgments and orders forbidding them from constantly violating the rights of anti-ICE protesters and journalists covering the protests.

After a couple of murders were committed by CBP officers in Minneapolis, Minnesota, the Trump administration decided Bovino was more trouble than he was worth. Sure, he was loyal and loved to personally engage in violence against protesters, but he also loved to see himself on TV and to dress like he’s auditioning for a Hitler Youth leadership position.

Now that he’s back at the border and bored, Bovino appears to be using his free time to push his own personal brand: a buzzcut bigot willing to spread hatred wherever it’s welcomed. Jeff Tischauser points out on Bluesky that Bovino recently headed overseas to help European bigots push their anti-migrant narratives.

Greg Bovino will speak at a white nationalist conference in Portugal tomorrow. He will share the stage with no fewer than five people who idolize Hitler, including one who joined a group created by two Nazi SS members. Another guy is a self-described racist who refers to women as “cockroaches.‘

RESUM26 is this year’s “Remigration Summit,” which was held in Porto, Portugal on May 30. If you’re not familiar with “Remigration” and/or RESUM26, I’ll let the organization speak for itself, even if it can’t seem to limit itself to 14 words.

Remigration is the umbrella term that designates and encompasses a set of fiscal, cultural, economic, social, political, and logistical policies whose objective is to prevent population replacement through the reversal of migratory flows, thereby restoring the sovereignty, independence, and identity of countries, through the defense of their ethnocultural specificity.

It’s almost twice as long as it needs to be at 25 words, but it’s pretty much saying the same thing Nazi supporters have been saying for years. Oh, and since literal Nazi supporters spoke at this event, here’s another reminder that Bovino himself seems to be on the supply side of Nazi sympathy.

If you can’t see the Bluesky post, It’s a screencap of Bovino’s recent X post where he’s captured giving what looks a hell of a lot like a Nazi salute while in his CBP work uniform. The accompanying text suggests Nazi salutes are just another way federal officers can visibly show their support for ICE and its activities.

Here’s one of the multiple anti-migrant luminaries Bovino joined at RESUM26: Alfonso Goncalves, as summarized by the Global Project Against Hate and Extremism:

Gonçalves is a white supremacist and misogynist who fashions himself as an authoritarian leader. He revels in descriptions of himself as “transphobic,” and proudly accepts the characterization that he is “ultranationalist, racist, and xenophobic.” Gonçalves is particularly known for his misogyny, posting bizarre rants about women’s right to vote, casual sex, and women sitting in public. He refers to women as “whores” and “cockroaches.” He has argued that women who get divorced should “not be entitled to receive money/goods” from their husbands and should be mandated to “pay for damages caused to the family.” He says abortions are a “crime against humanity” and has called for women who have the procedure to receive the death penalty. For Gonçalves, “non-traditional families” are an “aberration,” “80% of all divorces are initiated by women” and people of African descent are creating a “population replacement” of people of “native” European descent. He has argued that “African American men are 12x more likely to commit murder than white men.” His bigotry is so extreme that even Elon Musk’s Twitter, known for being lenient towards hateful accounts, permanently suspended both his main and backup accounts, in one instance for “abusive behavior.” 

And that’s just one speaker at this event. Also speaking at RESUM26 were Dutch far-right activist Eva Vlaardingerbroek (great replacement theory proponent), Austrian far-right activist Martin Sellner (great replacement theory proponent), former French National Front politician Jean-Yves Gallou (more of the same), and RESUM co-founder Dries Van Langenhover (who adds some Holocaust denialism to the mix).

Greg Bovino has done this while still employed by the US federal government. Under any normal president, his resignation might have been demanded for choosing to associate with people promoting racist theories. But this isn’t a normal presidency. No one in the DHS is going to criticize Bovino. And, while no one seems all that eager to return Bovino to anti-migration front lines, he’s still going to keep being paid by the US public to cheer on racism from the sidelines, when not traveling overseas to do the same thing from the stage.

Filed Under: bigotry, cbp, gregory bovino, mass deportation, nazis, portugal, trump administration, white nationalism

Companies: resum26

The quantum computing industry has spent the last three years measured almost entirely in qubits. Willow’s 105. Nighthawk’s 120. The 540-qubit superconducting platform that integrated nearly 700 control lines into a single cryostat last year. The qubit count is the headline number, and for good reason.

But inside the labs trying to push superconducting systems past today’s ceiling, engineers spend a surprising amount of time talking about something far less photogenic: the cables.

Every superconducting qubit needs multiple control and readout lines threading from room-temperature instrumentation down to the millikelvin plate where the processor sits. Every additional line carries heat, takes up space, and introduces electromagnetic noise. At a few hundred qubits, the wiring is already hand-built artisanal work. At a few thousand, it stops fitting. At the million-qubit scale fault-tolerant computing requires, the conventional approach simply doesn’t work.

That’s the bottleneck a company called QTREX is positioning itself around.

The Wall the Industry Walked Into

The 💜 of EU tech

The latest rumblings from the EU tech scene, a story from our wise ol’ founder Boris, and some questionable AI art. It’s free, every week, in your inbox. Sign up now!

The “I/O wall”, shorthand for the interconnect problem, has been flagged in the engineering world for years, and the timing is starting to bite. IBM’s roadmap targets near-term quantum advantage by the end of 2026 and a fault-tolerant machine by 2029. None of those roadmaps work if the interconnect layer can’t keep up.

QTREX’s argument is that this isn’t a cable problem. It’s an architecture problem.

The company’s approach aims to replace the conventional assembled bundle, cables, connectors, shielding, thermal anchors, mechanical routing, with one integrated structure manufactured as a single object. The capability comes from Additively Manufactured Electronics, or AME: a multi-material 3D printing platform that deposits conductive and dielectric inks together to produce 3D electronic geometries at micron-level precision. Until recently it was used for high-performance RF circuits and antennas in defense and aerospace. The Israeli company is now applying it to quantum’s hardest hardware constraint.

In plain language, the company’s pitch is that traditional quantum wiring is assembled, while QTREX’s is engineered as a single system. The company claims roughly 20 fully shielded conductors per square centimeter, a density that matters precisely because what limits a cryostat isn’t volume, it’s the thermal budget that volume carries with it.

Why This Could Become a Category

In late April, QTREX signed a joint development agreement with Qarakal Quantum, the Israeli full-stack superconducting quantum company connected to Israel Aerospace Industries and the Hebrew University of Jerusalem. Qarakal built Israel’s first domestically operated quantum computer. Under the agreement, QTREX is supplying 3D-printed structures for testing at milli-Kelvin temperatures inside Qarakal’s cryogenic development environment.

Three weeks later, the company disclosed it had moved into a joint technical evaluation with one of the top five global quantum computing companies. Engineering and integration teams from both sides are testing QTREX’s interconnect components inside the partner’s cryogenic refrigerator. If a definitive agreement follows, QTREX would sit as foundational interconnect technology beneath the partner’s forward quantum hardware roadmap.

Traction extends beyond quantum-native players. A Tier-1 US defense customer has taken delivery of an AME system, and an implementation is underway at one of the Magnificent Seven US technology companies, the cohort that increasingly owns the world’s quantum research budgets.

“Engagement with one of the top five global players in quantum systems reflects the recognition that QTREX’s interconnect approach addresses a complex bottleneck in quantum hardware,” CEO Dagi Ben-Noon said when announcing the evaluation.

The Shift the Industry Is Already Making

The first wave of quantum computing was a physics story: could a working qubit be built and controlled. Those questions are mostly answered. The next wave is an engineering story, whether the surrounding hardware can scale with the processors. In AI infrastructure, the most valuable companies turned out not to be the ones writing the models but the ones building the chips and interconnects underneath. The same shape is starting to appear in quantum.

QTREX is betting the connective layer is where one of those positions opens up. The company still needs to execute commercially, but the problem it is targeting is already recognized across the industry.

Anthropic, the AI lab behind Claude, has filed confidentially for an initial public offering, the company said in a blog post Monday.

The company, which is valued at close to $1 trillion, submitted a draft registration statement to the U.S. Securities and Exchange Commission for a proposed initial public offering. Anthropic has yet to list the number of shares or set the price. Anthropic said the proposed initial public offering will depend on market conditions and other factors.

The filing comes less than a week after Anthropic raised $65 billion in a Series H funding round that pushed its valuation to $965 billion. The round, which was co-led by Altimeter Capital, Dragoneer, Greenoaks, Sequoia Capital, Capital Group, Coatue, and D1 Capital Partners, attracted a bevy of institutional and strategic investors in anticipation of an IPO.

Anthropic’s confidential filing landed in an already white-hot IPO season that includes SpaceX’s initial public offering that is targeting a $2 trillion valuation. SpaceX is seeking to raise more than $75 billion.

It also comes as its rival OpenAI continues to raise funding, notably a $122 billion round in March at an $852 billion post-money valuation, and prepares for its own IPO. OpenAI is expected to file for an initial public offering, setting the stage for an IPO season that will pit the two largest AI labs against each other and test the market’s resolve and interest in artificial intelligence.

Anthropic, now an AI powerhouse that has landed top-tier enterprise customers, was once considered an underdog in the emerging world of large language models. The startup was founded in 2021 by former OpenAI employees and was seen as a distant competitor to OpenAI and its AI chatbot, ChatGPT.

The company has gained investors and customers for the capabilities of Claude and powerful model Mythos, which has been released on a limited basis. That has translated to eye-popping revenue growth. The company said recently that its revenue run-rate had surpassed $47 billion, up from $9 billion at the end of 2025.

That revenue growth rate could accelerate as Anthropic makes its Mythos model more widely available. Anthropic unveiled Mythos in April — along with a warning to software developers that its model had discovered thousands of high-severity bugs that would need to be fixed before it could be made public.

The generative AI lab is poised to give the European Union’s cybersecurity agency access to Mythos, Bloomberg reported, citing anonymous sources.

Gigascale, the venture firm led by former Meta CTO Mike Schroepfer, announced on Monday that it had raised a $250 million fund to back founders who are “rebuilding the physical economy.”

The new fund will focus on energy, grid infrastructure, and critical minerals all through the lens of climate tech. By continuing with the overt climate focus, Gigascale is bucking conventional wisdom which has soured on the “climate tech” thesis.

Gigascale’s second fund is shaping up to be a continuation of the sort of bets that Schrep, as he’s known, has made in the three years since he started Gigascale. The firm has backed some high-profile startups in the climate tech space, including Commonwealth Fusion Systems, Heron Power, Mill, and Form Energy. 

Gigascale emerged from Schrep’s study of climate tech during COVID, and the new fund is the first with an early-stage focus that includes institutional investors.

Climate tech has always been a wide-ranging sector, and Gigascale’s portfolio reflects that. But in recent years, the sector has become increasingly focused on energy and infrastructure, a shift that has been largely driven by the demands of AI. 

It’s no surprise, then, that power is a significant focus of the new fund. With rising demand for electricity, there’s an opportunity to invest in new energy sources and new ways to deliver that to businesses and households.

Schroepfer pointed to solar as a recent example of a clean technology that’s faster and cheaper and winning the market. 

While solar and batteries have come to dominate conversations around clean power, Schroepfer clearly sees more opportunities. AI and broader trends in electrification have made it challenging for companies to connect to the grid. In response, many have been seeking to develop their own power sources, though there, too, competition is stiff. Natural gas turbines, for example, have a waitlist that stretches into the early 2030s.

The power crunch gives energy startups an opening. In energy intensive industries, bring-your-own power “is going to be a competitive advantage over time,” Schroepfer said on the Inevitable podcast last year. Startups that can supply power cheaper or more flexibly — or both — can win on those merits alone.

But Gigascale also expects its energy investments to extend beyond generation, citing grid infrastructure, critical minerals, and physical AI as other places where the company will look for opportunities. 

“The companies we back win because they’re cheaper, faster, and more reliable,” Schroepfer said in a statement. “That’s how adoption scales. Climate impact is the result of better-performing systems.”

FiiO has expanded its hi-fi line-up with three new products, headlined by the JT9 planar magnetic headphones.

The latest products from FiiO lean heavily into high-power desktop audio but keeps portability and modern connectivity in view.