Training AI systems utilizing other AI frameworks has emerged as a highly sought-after objective for neolabs — and now, an investigator in Anthropic’s fellows initiative has offered us an initial glimpse at how this could manifest in real-world applications.

On Friday, Anthropic released a new study titled “Automated Researchers Can Reliably Mitigate Alignment Failures,” explaining how AI systems might consistently enhance a model’s performance against a series of alignment criteria. When presented with 10 measures for particular misaligned actions, the automated systems succeeded in boosting performance on each one without compromising overall efficacy.

Headed by Anthropic fellow Chen Yueh-Han, the system emulates much of the conventional methodology in research. Each automated entity scans the existing literature, suggests a technique, and trains the model using that technique for 30 minutes, steadily increasing the benchmark over multiple iterations. Successful methods are retained while those that are ineffective are eliminated, enabling the system to function rapidly and on a large scale.

“Overall, these findings offer preliminary proof that automated alignment post-training could be feasible in the near future,” states the paper.

The study is a move towards recursive self-enhancement, which many view as the next critical advancement in AI development. If models are capable of refining their own alignment training, it’s likely they could enhance training methodologies more broadly — at which stage, human AI researchers might soon be rendered unnecessary.

The paper openly confronts this notion, directly contrasting the Automated Alignment Researcher (AAR) with its human counterpart. “The best AAR method outperforms what seasoned humans propose, on average within six hours,” notes the paper. “Human-guided research directions do not yield superior results.”

There’s even a financial comparison, should anyone remain skeptical. “An AAR incurs a cost of approximately $4 per hour in API inference, compared to the $150 per hour allotted for our human researchers.”

In fairness, the paper also acknowledges certain limitations of this method. The automated framework only functions effectively to the extent that the benchmarks accurately align with the genuine alignment objectives, and even then, considerable effort is needed to establish and uphold those benchmarks — not to mention the necessity of maintaining and expanding the literature from which the automated researchers derive their information.

An appealing reason to switch to Brave, an alternative to Chrome, has just been introduced: email aliases. This feature, revealed this week, enables Brave users to register on various websites and online services without disclosing their actual email addresses.

To utilize this feature, you must start by creating an account with Brave, providing your genuine email address. Once logged in to the browser, a pop-up will appear when you click on an email field on a website, offering the choice to utilize an alias instead. (If the pop-up does not show, simply right-click on the field to see the option.) The email alias will automatically fill in the website’s form, and any emails sent to that address will be redirected to your main email account.

The company states that utilizing an alias can safeguard your privacy, especially since websites use your email as a unique identifier, which allows them to target you with advertisements. Advertising technology giants such as Meta can also match the email you submitted to a retailer’s site, for example, with the address they have on file for you, enabling these firms to monitor your purchases or views.

Additionally, if a website where you shared your email is breached, hackers would then have access to your primary email address, potentially leading to the leaking of this information to data brokers.

Brave claims it developed this new feature to address this privacy gap, asserting that it does not examine the content of emails sent to an alias — it merely processes them for spam and virus detection. After the email is forwarded, Brave removes it from its servers. All data in users’ Brave accounts is also encrypted while stored, and any notes saved alongside an alias are stored locally on your device, unless you activate the sync feature. If syncing is active, notes will be end-to-end encrypted.

Since the email alias feature is still in its early stages, Brave has warned that some of the forwarded emails might initially land in your spam folder, but this should improve as Brave enhances its reputation as a mail provider.

For now, Brave users are granted five complimentary email aliases. Additional aliases will be made available for Premium plan subscribers in the future.

All eyes are on Nvidia as it is expected to announce this week’s most intriguing tech acquisition: A purported $13 billion purchase of Hugging Face, a platform dedicated to the sharing of open-weight AI models and benchmarks.

Currently recognized as a prime target for a squad of reward-hacking OpenAI agents, Hugging Face sits at the core of the community of developers focused on creating and implementing LLMs outside the realm of frontier labs. Consider it the GitHub equivalent for the AI age.

Speculations regarding this acquisition follow Nvidia’s $6 billion deal with Poolside, a builder of open-weight models, which will result in the majority of its workforce transitioning to the semiconductor titan. Additionally, two weeks prior, Stripe obtained OpenRouter, the leading supplier of open-weight models for businesses, for upwards of $7 billion.

This influx of investment into a sector that thrives on sharing resources highlights emerging trends in the AI landscape.

For Nvidia, minimizing reliance on partnerships with major hyperscalers and frontier labs is crucial. This is especially relevant as significant AI model developers like OpenAI and Google are simultaneously creating their own inference chips, such as OpenAI’s Jalapeño, which was unveiled this week. If model developers are producing chips, Nvidia aims to secure a piece of the model development pie.

Nvidia has its own Nemotron line of open-weight models, but their adoption has been limited. By annexing the largest developer community in the U.S. focused on open models, the firm will gain access to a large user base that can be directed toward its chips and standards.

Moreover, there are escalating concerns about AI inference costs, prompting companies to investigate more affordable models crafted by Chinese companies such as Moonshot, DeepSeek, and Alibaba. Although current adoption rates stand at a modest level, with only 6% of companies leveraging open-weight models, according to spending data compiled by Ramp, and just 2% of software engineers assessed by Jellyfish, which develops tools for coders.

Nik Albarran, the AI product lead at Jellyfish, informed TechCrunch that open-weight models are mainly utilized by firms whose offerings depend on repeated inference tasks, like customer service chat functionalities. Given that these tasks involve high volumes and frequent repetition, an open-weight model can be optimized for cost-effective responses.

This is notably the framing Stripe has adopted in discussing its OpenRouter acquisition. “Tokens serve as the main currency for companies developing AI solutions, and it’s evident that the tangible economic potential hinges on efficiently managing limited computing resources,” Patrick Collison, Stripe’s co-founder and CEO, remarked in a statement.

In contrast, for coding and agent-related tasks, varying requests and deeper reasoning indicate that frontier models often excel, partly because proprietary labs provide easier access, and sometimes offer token subsidies. Albarran notes that as organizations refine their AI workflows, migrating to open models will become more feasible. Still, the primary motive for companies exploring these models now is for control and adaptability, rather than cost concerns.

“There aren’t many companies that find themselves in that situation yet… [but] if prices continue to rise from frontier labs, an increasing number of companies will have no choice but to at least contemplate it,” Albarran shared with TechCrunch. “When your AI-driven processes are significantly more developed, that’s when investing in self-hosted models truly makes sense.”

Lin Qiao, the CEO of Fireworks, a prominent router and host for open-weight models aimed at corporate clients, is frequently mentioned as a possible acquisition target for a major tech firm. Qiao stated that her company manages 40 trillion tokens daily, surpassing both Gemini’s and OpenAI’s APIs in volume.

Fireworks focuses on model diversity: As LLMs expand and enhance, it will become increasingly simpler for organizations to tailor them to their specific requirements. “Every single app company should contemplate bringing on an internal researcher,” she remarked to TechCrunch last week. “They can utilize their product and the data it generates to develop their own model. The future will truly be about specialized intelligence. Every company ought to have its own model tailored for each use case, and this will occur organically.”

It’s easy to overlook how early we are in AI’s evolution as both a tool and a commercial venture. However, the supremacy of OpenAI and Anthropic is not a foregone conclusion. As tech giants seek to hedge their investments in the largest labs, the charm of open technology is proving to be difficult to resist.

Sweden is currently considered one of the most vibrant tech hubs globally. It has been the birthplace of Spotify and Klarna for some time, but this new wave of firms — ranging from the legal AI startup Legora to the vibe-coding tool company Lovable — has sparked curiosity about what’s happening in one of the happiest nations in the world.

Sophia Bendz, a general partner at Cherry Ventures, visited Equity to discuss the recent developments in Stockholm’s startup scene. Her explanation for the abundance of exciting startups in the country: Being an entrepreneur is now regarded as trendy. During her tenure at Spotify years ago, the desirable career paths were banking or consulting. “Now, I believe many individuals are focused on creating things and aiming for both freedom and likely wealth.”

This year, the startup ecosystem has secured $2.8 billion in funding, according to Dealroom, and is expected to reach at least $5 billion. Last year, the ecosystem attracted $3.2 billion. Nothing has quite replicated the $8.5 billion raised in 2021 — indicative of the fiercely competitive venture market that subsequently cooled and is now, according to the data, beginning to warm up again. Other noteworthy emerging entities from the ecosystem include Neko Health and the autonomous freight company Einride.

Bendz stated that the latest startup surge in Stockholm is reaping benefits from the first generation of entrepreneurs, who are offering resources and mentorship to new founders — or, in some instances, becoming repeat entrepreneurs themselves (Spotify’s founder Daniel Ek, for example, is also behind Neko Health).

“I think we’re seeing many individuals now departing Lovable because they recognize the potential for companies that can be developed using that technology,” she mentioned regarding how this generation could motivate the next. “Those who have been employed at Lovable and Legora appear to be very entrepreneurial and eager to launch their own ventures.”

Not surprisingly, American investors have noticed. Bendz remarked that an increasing number of American investors have been traveling to meet with founders and issue term sheets. “It’s simply a testament to the fact that we have many exceptional companies emerging here,” she noted. “It’s a sign that we’re on the right path.”

Retro, the photo-sharing application designed for friends and created by former Instagram product engineers Nathan Sharp and Ryan Olson, has secured over $21 million in Series A funding, as reported in an SEC filing.

This fundraising reflects the consistent demand for social applications that facilitate direct connections among individuals, rather than through curated algorithms saturated with creator content and now frequently, AI-generated noise.

“One undeniable truth is that people will always desire to see more of their friends,” Sharp told TechCrunch in December, commenting on the dominance of For You-style feeds in current social media. “The photographs and videos you capture need to find an avenue to reach the audience they are meant for,” he stated.

Business Insider was the first to notice the SEC filing, which is under the startup’s name, Lone Palm Labs. The filing, dated August 19, indicates that the funding was finalized in December. PitchBook now estimates the startup’s valuation to be over $100 million.

Retro did not respond to a request for a statement.

Initially launched in 2023 as more of a personal photo diary, Retro has since broadened its functionality, allowing users to privately share their weekly photos with friends, create collaborative albums, view and share highlights, or “rewind” to revisit past photo memories.

The application has an additional appeal as it refrains from monetization through advertisements. Rather, the company provides in-app subscriptions that grant access to features like video support, GIF and sticker comments, additional styles, unlimited memory, and exclusive app icons.

As per statistics from app intelligence provider Appfigures, Retro has been downloaded approximately 7 million times since its inception and has observed its in-app revenue surge by over 460% in the past 180 days. Although the number of app users who subscribe remains small, the belief is that those who do are dedicated users who have transformed Retro into a regularly utilized photo-sharing platform.

Retro’s investors include Thrive Capital, Figma CEO Dylan Field, Scribble Ventures, Box Group, Imaginary Ventures, Coalition, Conviction, Copper, Positive Sum, and several angel investors.

On Thursday evening, a federal judge in California determined that the Trump administration’s designation of Anthropic as a supply-chain risk was unlawful. 

U.S. District Judge Rita Lin stated in her decision that Defense Secretary Pete Hegseth’s classification of Anthropic as a national security risk constituted “illegal retaliation” in violation of the First Amendment, and characterized the ruling as “arbitrary and capricious.” Lin also noted that Anthropic was deprived of due process, as mandated by the Fifth Amendment. 

Earlier this year, Hegseth and President Donald Trump categorized Anthropic as a supply-chain risk and directed all federal agencies, including those outside of defense, to cease collaborations with the creator of Claude.

The conflict arose from Anthropic imposing strict limitations on certain safety protocols that would enable the Pentagon to use its models for fully autonomous weaponry and extensive surveillance of American citizens. The Pentagon contended that it would only utilize Anthropic models for lawful activities and accused Anthropic of attempting to influence the military’s use of the models it had purchased. 

In her ruling, Lin remarked that the government’s “statements and actions affirm that the challenged measures were motivated by a desire to publicly reprimand Anthropic for its ‘arrogance’ in criticizing the government.”

She highlighted the inconsistency between the supply-chain designation and other governmental actions, such as Hegseth’s suggestion to apply the Defense Production Act to Anthropic, “which would suggest that the company was vital to national security rather than a menace to it.” She also noted the Department of Defense’s continuing efforts to secure a contract with the firm, as well as the government working with the company’s new model, Mythos, for cybersecurity purposes.

Lin further asserted that it is evident that Anthropic “undeniably lacks” any backdoor access to its technology once it is provided to the DOD. 

“While the Department of War is indisputably entitled to select its preferred AI vendor, the evidence reveals that the extensive measures imposed on Anthropic were unlawful and unfounded,” Lin stated. “The hollow argument of national security does not grant permission to penalize and retaliate against governmental critics,” she added.

“We appreciate the court’s decision that this supply chain risk classification was illegitimate,” an Anthropic spokesperson declared in a statement provided to TechCrunch. “We continue to prioritize productive collaboration with the government to leverage AI for our national security, ensuring that all Americans gain from this technology.”

In March, Anthropic initiated two legal actions against the DOD in California and Washington, D.C. The lawsuit in D.C. remains active. 

TechCrunch has contacted the DOD for a response.