In an open letter addressed to the Attorneys General of California and Delaware, the group – which also includes legal experts, corporate governance specialists, AI researchers, and nonprofit representatives – argues that the proposed changes fundamentally threaten OpenAI’s original charitable mission.   

OpenAI was founded with a unique structure. Its core purpose, enshrined in its Articles of Incorporation, is “to ensure that artificial general intelligence benefits all of humanity” rather than serving “the private gain of any person.”

The letter’s signatories contend that the planned restructuring – transforming the current for-profit subsidiary (OpenAI-profit) controlled by the original nonprofit entity (OpenAI-nonprofit) into a Delaware public benefit corporation (PBC) – would dismantle crucial governance safeguards.

This shift, the signatories argue, would transfer ultimate control over the development and deployment of potentially transformative Artificial General Intelligence (AGI) from a charity focused on humanity’s benefit to a for-profit enterprise accountable to shareholders.

Original vision of OpenAI: Nonprofit control as a bulwark

OpenAI defines AGI as “highly autonomous systems that outperform humans at most economically valuable work”. While acknowledging AGI’s potential to “elevate humanity,” OpenAI’s leadership has also warned of “serious risk of misuse, drastic accidents, and societal disruption.”

Co-founder Sam Altman and others have even signed statements equating mitigating AGI extinction risks with preventing pandemics and nuclear war.   

The company’s founders – including Altman, Elon Musk, and Greg Brockman – were initially concerned about AGI being developed by purely commercial entities like Google. They established OpenAI as a nonprofit specifically “unconstrained by a need to generate financial return”. As Altman stated in 2017, “The only people we want to be accountable to is humanity as a whole.”

Even when OpenAI introduced a “capped-profit” subsidiary in 2019 to attract necessary investment, it emphasised that the nonprofit parent would retain control and that the mission remained paramount. Key safeguards included:   

• Nonprofit control: The for-profit subsidiary was explicitly “controlled by OpenAI Nonprofit’s board”.   
• Capped profits: Investor returns were capped, with excess value flowing back to the nonprofit for humanity’s benefit.   
• Independent board: A majority of nonprofit board members were required to be independent, holding no financial stake in the subsidiary.   
• Fiduciary duty: The board’s legal duty was solely to the nonprofit’s mission, not to maximising investor profit.   
• AGI ownership: AGI technologies were explicitly reserved for the nonprofit to govern.

Altman himself testified to Congress in 2023 that this “unusual structure” “ensures it remains focused on [its] long-term mission.”

A threat to the mission?

The critics argue the move to a PBC structure would jeopardise these safeguards:   

• Subordination of mission: A PBC board – while able to consider public benefit – would also have duties to shareholders, potentially balancing profit against the mission rather than prioritising the mission above all else.   
• Loss of enforceable duty: The current structure gives Attorneys General the power to enforce the nonprofit’s duty to the public. Under a PBC, this direct public accountability – enforceable by regulators – would likely vanish, leaving shareholder derivative suits as the primary enforcement mechanism.   
• Uncapped profits?: Reports suggest the profit cap might be removed, potentially reallocating vast future wealth from the public benefit mission to private shareholders.   
• Board independence uncertain: Commitments to a majority-independent board overseeing AI development could disappear.   
• AGI control shifts: Ownership and control of AGI would likely default to the PBC and its investors, not the mission-focused nonprofit. Reports even suggest OpenAI and Microsoft have discussed removing contractual restrictions on Microsoft’s access to future AGI.   
• Charter commitments at risk: Commitments like the “stop-and-assist” clause (pausing competition to help a safer, aligned AGI project) might not be honoured by a profit-driven entity.  

OpenAI has publicly cited competitive pressures (i.e. attracting investment and talent against rivals with conventional equity structures) as reasons for the change.

However, the letter counters that competitive advantage isn’t the charitable purpose of OpenAI and that its unique nonprofit structure was designed to impose certain competitive costs in favour of safety and public benefit. 

“Obtaining a competitive advantage by abandoning the very governance safeguards designed to ensure OpenAI remains true to its mission is unlikely to, on balance, advance the mission,” the letter states.   

The authors also question why OpenAI abandoning nonprofit control is necessary merely to simplify the capital structure, suggesting the core issue is the subordination of investor interests to the mission. They argue that while the nonprofit board can consider investor interests if it serves the mission, the restructuring appears aimed at allowing these interests to prevail at the expense of the mission.

Many of these arguments have also been pushed by Elon Musk in his legal action against OpenAI. Earlier this month, OpenAI counter-sued Musk for allegedly orchestrating a “relentless” and “malicious” campaign designed to “take down OpenAI” after he left the company years ago and started rival AI firm xAI.

Call for intervention

The signatories of the open letter urge intervention, demanding answers from OpenAI about how the restructuring away from a nonprofit serves its mission and why safeguards previously deemed essential are now obstacles.

Furthemore, the signatories request a halt to the restructuring, preservation of nonprofit control and other safeguards, and measures to ensure the board’s independence and ability to oversee management effectively in line with the charitable purpose.   

“The proposed restructuring would eliminate essential safeguards, effectively handing control of, and profits from, what could be the most powerful technology ever created to a for-profit entity with legal duties to prioritise shareholder returns,” the signatories conclude.

See also: How does AI judge? Anthropic studies the values of Claude

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In court documents filed yesterday, OpenAI accuses Musk of orchestrating a “relentless” and “malicious” campaign designed to “take down OpenAI” after he left the organisation years ago.

The court filing, submitted to the US District Court for the Northern District of California, alleges Musk could not tolerate OpenAI’s success after he had “abandoned and declared [it] doomed.”

OpenAI is now seeking legal remedies, including an injunction to stop Musk’s alleged “unlawful and unfair action” and compensation for damages already caused.   

Origin story of OpenAI and the departure of Elon Musk

The legal documents recount OpenAI’s origins in 2015, stemming from an idea discussed by current CEO Sam Altman and President Greg Brockman to create an AI lab focused on developing artificial general intelligence (AGI) – AI capable of outperforming humans – for the “benefit of all humanity.”

Musk was involved in the launch, serving on the initial non-profit board and pledging $1 billion in donations.   

However, the relationship fractured. OpenAI claims that between 2017 and 2018, Musk’s demands for “absolute control” of the enterprise – or its potential absorption into Tesla – were rebuffed by Altman, Brockman, and then-Chief Scientist Ilya Sutskever. The filing quotes Sutskever warning Musk against creating an “AGI dictatorship.”

Following this disagreement, OpenAI alleges Elon Musk quit in February 2018, declaring the venture would fail without him and that he would pursue AGI development at Tesla instead. Critically, OpenAI contends the pledged $1 billion “was never satisfied—not even close”.   

Restructuring, success, and Musk’s alleged ‘malicious’ campaign

Facing escalating costs for computing power and talent retention, OpenAI restructured and created a “capped-profit” entity in 2019 to attract investment while remaining controlled by the non-profit board and bound by its mission. This structure, OpenAI states, was announced publicly and Musk was offered equity in the new entity but declined and raised no objection at the time.   

OpenAI highlights its subsequent breakthroughs – including GPT-3, ChatGPT, and GPT-4 – achieved massive public adoption and critical acclaim. These successes, OpenAI emphasises, were made after the departure of Elon Musk and allegedly spurred his antagonism.

The filing details a chronology of alleged actions by Elon Musk aimed at harming OpenAI:   

• Founding xAI: Musk “quietly created” his competitor, xAI, in March 2023.   
• Moratorium call: Days later, Musk supported a call for a development moratorium on AI more advanced than GPT-4, a move OpenAI claims was intended “to stall OpenAI while all others, most notably Musk, caught up”.   
• Records demand: Musk allegedly made a “pretextual demand” for confidential OpenAI documents, feigning concern while secretly building xAI.   
• Public attacks: Using his social media platform X (formerly Twitter), Musk allegedly broadcast “press attacks” and “malicious campaigns” to his vast following, labelling OpenAI a “lie,” “evil,” and a “total scam”.   
• Legal actions: Musk filed lawsuits, first in state court (later withdrawn) and then the current federal action, based on what OpenAI dismisses as meritless claims of a “Founding Agreement” breach.   
• Regulatory pressure: Musk allegedly urged state Attorneys General to investigate OpenAI and force an asset auction.   
• “Sham bid”: In February 2025, a Musk-led consortium made a purported $97.375 billion offer for OpenAI, Inc.’s assets. OpenAI derides this as a “sham bid” and a “stunt” lacking evidence of financing and designed purely to disrupt OpenAI’s operations, potential restructuring, fundraising, and relationships with investors and employees, particularly as OpenAI considers evolving its capped-profit arm into a Public Benefit Corporation (PBC). One investor involved allegedly admitted the bid’s aim was to gain “discovery”.   

Based on these allegations, OpenAI asserts two primary counterclaims against both Elon Musk and xAI:

• Unfair competition: Alleging the “sham bid” constitutes an unfair and fraudulent business practice under California law, intended to disrupt OpenAI and gain an unfair advantage for xAI.   
• Tortious interference with prospective economic advantage: Claiming the sham bid intentionally disrupted OpenAI’s existing and potential relationships with investors, employees, and customers. 

OpenAI argues Musk’s actions have forced it to divert resources and expend funds, causing harm. They claim his campaign threatens “irreparable harm” to their mission, governance, and crucial business relationships. The filing also touches upon concerns regarding xAI’s own safety record, citing reports of its AI Grok generating harmful content and misinformation.

The counterclaims mark a dramatic escalation in the legal battle between the AI pioneer and its departed co-founder. While Elon Musk initially sued OpenAI alleging a betrayal of its founding non-profit, open-source principles, OpenAI now contends Musk’s actions are a self-serving attempt to undermine a competitor he couldn’t control.

With billions at stake and the future direction of AGI in the balance, this dispute is far from over.

See also: Deep Cogito open LLMs use IDA to outperform same size models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The San Francisco-based company, which states its mission is “building general superintelligence,” has launched preview versions of LLMs in 3B, 8B, 14B, 32B, and 70B parameter sizes. Deep Cogito asserts that “each model outperforms the best available open models of the same size, including counterparts from LLAMA, DeepSeek, and Qwen, across most standard benchmarks”.

Impressively, the 70B model from Deep Cogito even surpasses the performance of the recently released Llama 4 109B Mixture-of-Experts (MoE) model.   

Iterated Distillation and Amplification (IDA)

Central to this release is a novel training methodology called Iterated Distillation and Amplification (IDA). 

Deep Cogito describes IDA as “a scalable and efficient alignment strategy for general superintelligence using iterative self-improvement”. This technique aims to overcome the inherent limitations of current LLM training paradigms, where model intelligence is often capped by the capabilities of larger “overseer” models or human curators.

The IDA process involves two key steps iterated repeatedly:

• Amplification: Using more computation to enable the model to derive better solutions or capabilities, akin to advanced reasoning techniques.
• Distillation: Internalising these amplified capabilities back into the model’s parameters.

Deep Cogito says this creates a “positive feedback loop” where model intelligence scales more directly with computational resources and the efficiency of the IDA process, rather than being strictly bounded by overseer intelligence.

“When we study superintelligent systems,” the research notes, referencing successes like AlphaGo, “we find two key ingredients enabled this breakthrough: Advanced Reasoning and Iterative Self-Improvement”. IDA is presented as a way to integrate both into LLM training.

Deep Cogito claims IDA is efficient, stating the new models were developed by a small team in approximately 75 days. They also highlight IDA’s potential scalability compared to methods like Reinforcement Learning from Human Feedback (RLHF) or standard distillation from larger models.

As evidence, the company points to their 70B model outperforming Llama 3.3 70B (distilled from a 405B model) and Llama 4 Scout 109B (distilled from a 2T parameter model).

Capabilities and performance of Deep Cogito models

The newly released Cogito models – based on Llama and Qwen checkpoints – are optimised for coding, function calling, and agentic use cases.

A key feature is their dual functionality: “Each model can answer directly (standard LLM), or self-reflect before answering (like reasoning models),” similar to capabilities seen in models like Claude 3.5. However, Deep Cogito notes they “have not optimised for very long reasoning chains,” citing user preference for faster answers and the efficiency of distilling shorter chains.

Extensive benchmark results are provided, comparing Cogito models against size-equivalent state-of-the-art open models in both direct (standard) and reasoning modes.

Across various benchmarks (MMLU, MMLU-Pro, ARC, GSM8K, MATH, etc.) and model sizes (3B, 8B, 14B, 32B, 70B,) the Cogito models generally show significant performance gains over counterparts like Llama 3.1/3.2/3.3 and Qwen 2.5, particularly in reasoning mode.

For instance, the Cogito 70B model achieves 91.73% on MMLU in standard mode (+6.40% vs Llama 3.3 70B) and 91.00% in thinking mode (+4.40% vs Deepseek R1 Distill 70B). Livebench scores also show improvements.

Here are benchmarks of 14B models for a medium-sized comparison:

While acknowledging benchmarks don’t fully capture real-world utility, Deep Cogito expresses confidence in practical performance.

This release is labelled a preview, with Deep Cogito stating they are “still in the early stages of this scaling curve”. They plan to release improved checkpoints for the current sizes and introduce larger MoE models (109B, 400B, 671B) “in the coming weeks / months”. All future models will also be open-source.

(Photo by Pietro Mattia)

See also: Alibaba Cloud targets global AI growth with new models and tools

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post Deep Cogito open LLMs use IDA to outperform same size models appeared first on AI News.

According to the report, titled ‘Rebooting Copyright: How the UK Can Be a Global Leader in the Arts and AI,’ the global race for cultural and technological leadership is still up for grabs, and the UK has a golden opportunity to take the lead.

The report emphasises that countries that “embrace change and harness the power of artificial intelligence in creative ways will set the technical, aesthetic, and regulatory standards for others to follow.”

Highlighting that we are in the midst of another revolution in media and communication, the report notes that AI is disrupting how textual, visual, and auditive content is created, distributed, and experienced, much like the printing press, gramophone, and camera did before it.

“AI will usher in a new era of interactive and bespoke works, as well as a counter-revolution that celebrates everything that AI can never be,” the report states.

However, far from signalling the end of human creativity, the TBI suggests AI will open up “new ways of being original.”

The AI revolution’s impact isn’t limited to the creative industries; it’s being felt across all areas of society. Scientists are using AI to accelerate discoveries, healthcare providers are employing it to analyse X-ray images, and emergency services utilise it to locate houses damaged by earthquakes.

The report stresses that these cross-industry advancements are just the beginning, with future AI systems set to become increasingly capable, fuelled by advancements in computing power, data, model architectures, and access to talent.

The UK government has expressed its ambition to be a global leader in AI through its AI Opportunities Action Plan, announced by Prime Minister Keir Starmer on 13 January 2025. For its part, the TBI welcomes the UK government’s ambition, stating that “if properly designed and deployed, AI can make human lives healthier, safer, and more prosperous.”

However, the rapid spread of AI across sectors raises urgent policy questions, particularly concerning the data used for AI training. The application of UK copyright law to the training of AI models is currently contested, with the debate often framed as a “zero-sum game” between AI developers and rights holders. The TBI argues that this framing “misrepresents the nature of the challenge and the opportunity before us.”

The report emphasises that “bold policy solutions are needed to provide all parties with legal clarity and unlock investments that spur innovation, job creation, and economic growth.”

According to the TBI, AI presents opportunities for creators—noting its use in various fields from podcasts to filmmaking. The report draws parallels with past technological innovations – such as the printing press and the internet – which were initially met with resistance, but ultimately led to societal adaptation and human ingenuity prevailing.

The TBI proposes that the solution lies not in clinging to outdated copyright laws but in allowing them to “co-evolve with technological change” to remain effective in the age of AI.

The UK government has proposed a text and data mining exception with an opt-out option for rights holders. While the TBI views this as a good starting point for balancing stakeholder interests, it acknowledges the “significant implementation and enforcement challenges” that come with it, spanning legal, technical, and geopolitical dimensions.

In the report, the Tony Blair Institute for Global Change “assesses the merits of the UK government’s proposal and outlines a holistic policy framework to make it work in practice.”

The report includes recommendations and examines novel forms of art that will emerge from AI. It also delves into the disagreement between rights holders and developers on copyright, the wider implications of copyright policy, and the serious hurdles the UK’s text and data mining proposal faces.

Furthermore, the Tony Blair Institute explores the challenges of governing an opt-out policy, implementation problems with opt-outs, making opt-outs useful and accessible, and tackling the diffusion problem. AI summaries and the problems they present regarding identity are also addressed, along with defensive tools as a partial solution and solving licensing problems.

The report also seeks to clarify the standards on human creativity, address digital watermarking, and discuss the uncertainty around the impact of generative AI on the industry. It proposes establishing a Centre for AI and the Creative Industries and discusses the risk of judicial review, the benefits of a remuneration scheme, and the advantages of a targeted levy on ISPs to raise funding for the Centre.

However, the report has faced strong criticism. Ed Newton-Rex, CEO of Fairly Trained, raised several concerns on Bluesky. These concerns include:

• The report repeats the “misleading claim” that existing UK copyright law is uncertain, which Newton-Rex asserts is not the case.
• The suggestion that an opt-out scheme would give rights holders more control over how their works are used is misleading. Newton-Rex argues that licensing is currently required by law, so moving to an opt-out system would actually decrease control, as some rights holders will inevitably miss the opt-out.
• The report likens machine learning (ML) training to human learning, a comparison that Newton-Rex finds shocking, given the vastly different scalability of the two.
• The report’s claim that AI developers won’t make long-term profits from training on people’s work is questioned, with Newton-Rex pointing to the significant funding raised by companies like OpenAI.
• Newton-Rex suggests the report uses strawman arguments, such as stating that generative AI may not replace all human paid activities.
• A key criticism is that the report omits data showing how generative AI replaces demand for human creative labour.
• Newton-Rex also criticises the report’s proposed solutions, specifically the suggestion to set up an academic centre, which he notes “no one has asked for.”
• Furthermore, he highlights the proposal to tax every household in the UK to fund this academic centre, arguing that this would place the financial burden on consumers rather than the AI companies themselves, and the revenue wouldn’t even go to creators.

Adding to these criticisms, British novelist and author Jonathan Coe noted that “the five co-authors of this report on copyright, AI, and the arts are all from the science and technology sectors. Not one artist or creator among them.”

While the report from Tony Blair Institute for Global Change supports the government’s ambition to be an AI leader, it also raises critical policy questions—particularly around copyright law and AI training data.

(Photo by Jez Timms)

See also: Amazon Nova Act: A step towards smarter, web-native AI agents

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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As AI progresses from performing narrow tasks to demonstrating general, adaptive intelligence, the ARC-AGI-2 challenges aim to uncover capability gaps and actively guide innovation.

“Good AGI benchmarks act as useful progress indicators. Better AGI benchmarks clearly discern capabilities. The best AGI benchmarks do all this and actively inspire research and guide innovation,” the ARC Prize team states.

ARC-AGI-2 is setting out to achieve the “best” category.

Beyond memorisation

Since its inception in 2019, ARC Prize has served as a “North Star” for researchers striving toward AGI by creating enduring benchmarks. 

Benchmarks like ARC-AGI-1 leaned into measuring fluid intelligence (i.e., the ability to adapt learning to new unseen tasks.) It represented a clear departure from datasets that reward memorisation alone.

ARC Prize’s mission is also forward-thinking, aiming to accelerate timelines for scientific breakthroughs. Its benchmarks are designed not just to measure progress but to inspire new ideas.

Researchers observed a critical shift with the debut of OpenAI’s o3 in late 2024, evaluated using ARC-AGI-1. Combining deep learning-based large language models (LLMs) with reasoning synthesis engines, o3 marked a breakthrough where AI transitioned beyond rote memorisation.

Yet, despite progress, systems like o3 remain inefficient and require significant human oversight during training processes. To challenge these systems for true adaptability and efficiency, ARC Prize introduced ARC-AGI-2.

ARC-AGI-2: Closing the human-machine gap

The ARC-AGI-2 benchmark is tougher for AI yet retains its accessibility for humans. While frontier AI reasoning systems continue to score in single-digit percentages on ARC-AGI-2, humans can solve every task in under two attempts.

So, what sets ARC-AGI apart? Its design philosophy chooses tasks that are “relatively easy for humans, yet hard, or impossible, for AI.”

The benchmark includes datasets with varying visibility and the following characteristics:

• Symbolic interpretation: AI struggles to assign semantic significance to symbols, instead focusing on shallow comparisons like symmetry checks.
• Compositional reasoning: AI falters when it needs to apply multiple interacting rules simultaneously.
• Contextual rule application: Systems fail to apply rules differently based on complex contexts, often fixating on surface-level patterns.

Most existing benchmarks focus on superhuman capabilities, testing advanced, specialised skills at scales unattainable for most individuals. 

ARC-AGI flips the script and highlights what AI can’t yet do; specifically the adaptability that defines human intelligence. When the gap between tasks that are easy for humans but difficult for AI eventually reaches zero, AGI can be declared achieved.

However, achieving AGI isn’t limited to the ability to solve tasks; efficiency – the cost and resources required to find solutions – is emerging as a crucial defining factor.

The role of efficiency

Measuring performance by cost per task is essential to gauge intelligence as not just problem-solving capability but the ability to do so efficiently.

Real-world examples are already showing efficiency gaps between humans and frontier AI systems:

• Human panel efficiency: Passes ARC-AGI-2 tasks with 100% accuracy at $17/task.
• OpenAI o3: Early estimates suggest a 4% success rate at an eye-watering $200 per task.

These metrics underline disparities in adaptability and resource consumption between humans and AI. ARC Prize has committed to reporting on efficiency alongside scores across future leaderboards.

The focus on efficiency prevents brute-force solutions from being considered “true intelligence.”

Intelligence, according to ARC Prize, encompasses finding solutions with minimal resources—a quality distinctly human but still elusive for AI.

ARC Prize 2025

ARC Prize 2025 launches on Kaggle this week, promising $1 million in total prizes and showcasing a live leaderboard for open-source breakthroughs. The contest aims to drive progress toward systems that can efficiently tackle ARC-AGI-2 challenges. 

Among the prize categories, which have increased from 2024 totals, are:

• Grand prize: $700,000 for reaching 85% success within Kaggle efficiency limits.
• Top score prize: $75,000 for the highest-scoring submission.
• Paper prize: $50,000 for transformative ideas contributing to solving ARC-AGI tasks.
• Additional prizes: $175,000, with details pending announcements during the competition.

These incentives ensure fair and meaningful progress while fostering collaboration among researchers, labs, and independent teams.

Last year, ARC Prize 2024 saw 1,500 competitor teams, resulting in 40 papers of acclaimed industry influence. This year’s increased stakes aim to nurture even greater success.

ARC Prize believes progress hinges on novel ideas rather than merely scaling existing systems. The next breakthrough in efficient general systems might not originate from current tech giants but from bold, creative researchers embracing complexity and curious experimentation.

(Image credit: ARC Prize)

See also: DeepSeek V3-0324 tops non-reasoning AI models in open-source first

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Unlike anything seen to date, the Manus AI agent doesn’t just chat with users – it is allegedly capable of independently tackling complex multi-step tasks with minimal human guidance.

Developed by Chinese startup Butterfly Effect with financial backing from tech giant Tencent Holdings, Manus AI agent has captured global attention for its ability to bridge the gap between theoretical AI capabilities and practical, real-world applications. It uses an innovative multi-model architecture that combines the strengths of multiple leading language models.

Breakthrough autonomous task execution

In a post on X, Peak Ji Yichao, co-founder and chief scientist at Butterfly Effect, said that the agentic AI was built using existing large language models, including Anthropic’s Claude and fine-tuned versions of Alibaba’s open-source Qwen.

Its multi-model nature allows Manus to use different AI strengths according to what’s demanded of it, resulting in more sophisticated reasoning and execution capabilities.

“The Manus AI agent represents a fundamentally different approach to artificial intelligence,” CNN Business stated. According to coverage, Manus “can carry out complex, multi-step tasks like screening resumés and creating a website,” and “doesn’t only generate ideas but delivers tangible results, like producing a report recommending properties to buy based on specific criteria.”

Real-world performance assessment

In an extensive hands-on evaluation, MIT Technology Review tested the Manus AI agent in three distinct task categories: compiling comprehensive journalist lists, conducting real estate searches with complex parameters, and identifying candidates for its prestigious Innovators Under 35 program.

“Using Manus feels like collaborating with a highly intelligent and efficient intern,” wrote Caiwei Chen in the assessment. “While it occasionally lacks understanding of what it’s being asked to do, makes incorrect assumptions, or cuts corners to expedite tasks, it explains its reasoning clearly, is remarkably adaptable, and can improve substantially when provided with detailed instructions or feedback.”

The evaluation revealed one of the Manus AI agent’s most distinctive features – its “Manus’s Computer” interface, which provides unprecedented transparency into the AI’s decision-making process.

The application window lets users observe the agent’s actions in real time and intervene when necessary, creating a collaborative human-AI workflow that maintains user control while automating complex processes.

Technical implementation challenges

Despite impressive capabilities, the Manus AI agent faces significant technical hurdles in its current implementation.MIT Technology Reviewdocumented frequent system crashes and timeout errors during extended use.

The platform displayed error messages, citing “high service load,” suggesting that computational infrastructure remains a limitation.

The technical constraints have contributed to highly restricted access, with less than 1% of wait-listed users receiving invite codes – the official Manus Discord channel has already accumulated over 186,000 members.

According to reporting from Chinese technology publication36Kr, the Manus AI agent’s operational costs remain relatively competitive at approximately $2 per task.

Strategic partnership with Alibaba Cloud

The creators of the Manus AI agent have announced a partnership with Alibaba’s cloud computing division. According to a South China Morning Post report dated March 11, “Manus will engage in strategic cooperation with Alibaba’s Qwen team to meet the needs of Chinese users.”

The partnership aims to make Manus available on “domestic models and computing platforms,” although implementation timelines remain unspecified.

Parallel advancements in foundation models

The Manus-Alibaba partnership coincides with Alibaba’s advances in AI foundation model technology. On March 6, the company published its QwQ-32B reasoning model, claiming performance characteristics that surpass OpenAI’s o1-mini and rivalling DeepSeek’s R1 model, despite a lower parameter count.

CNN Businessreported, “Alibaba touted its new model, QwQ-32B, in an online statement as delivering exceptional performance, almost entirely surpassing OpenAI-o1-mini and rivalling the strongest open-source reasoning model, DeepSeek-R1.”

The claimed efficiency gains are particularly noteworthy – Alibaba says QwQ-32B achieves competitive performance with just 32 billion parameters, compared to the 671 billion parameters in DeepSeek’s R1 model. The reduced model size suggests substantially lower computational requirements for training and inference with advanced reasoning capabilities.

China’s strategic AI investments

The Manus AI agent and Alibaba’s model advancements reflect China’s broader strategic emphasis on artificial intelligence development. The Chinese government has pledged explicit support for “emerging industries and industries of the future,” with artificial intelligence receiving particular focus alongside quantum computing and robotics.

Alibaba will invest 380 billion yuan (approximately $52.4 billion) in AI and cloud computing infrastructure in the next three years, a figure the company notes exceeds its total investments in these sectors during the previous decade.

As MIT Technology Review’s Caiwei Chen said, “Chinese AI companies are not just following in the footsteps of their Western counterparts. Rather than just innovating on base models, they are actively shaping the adoption of autonomous AI agents in their way.”

The Manus AI agent also exemplifies how China’s artificial intelligence ecosystem has evolved beyond merely replicating Western advances. Government policies promoting technological self-reliance, substantial funding initiatives, and a growing pipeline of specialised AI talent from Chinese universities have created conditions for original innovation.

Rather than a single approach to artificial intelligence, we are witnessing diverse implementation philosophies likely resulting in complementary systems optimised for different uses and cultural contexts.

The post Manus AI agent: breakthrough in China’s agentic AI appeared first on AI News.

However, this development comes against the backdrop of mounting controversies that have drawn parallels to the TikTok saga.

Today, DeepSeek shared its intentions in a tweet that outlined its vision of open collaboration: “We’re a tiny team at DeepSeek exploring AGI. Starting next week, we’ll be open-sourcing five repos, sharing our small but sincere progress with full transparency.”

The repositories – which the company describes as “documented, deployed, and battle-tested in production” – include fundamental building blocks of DeepSeek’s online service.

By open-sourcing its tools, DeepSeek hopes to contribute to the broader AI research community.

“As part of the open-source community, we believe that every line shared becomes collective momentum that accelerates the journey. No ivory towers – just pure garage-energy and community-driven innovation,” the company said.

This philosophy has drawn praise for fostering collaboration in a field that often suffers from secrecy, but DeepSeek’s rapid rise has also raised eyebrows.

Despite being a small team with a mission rooted in transparency, the company has been under intense scrutiny amid allegations of data misuse and geopolitical entanglements.

Rising fast, under fire

Practically unknown until recently, DeepSeek burst onto the scene with a business model that stood in stark contrast to more established players like OpenAI and Google.

Offering its advanced AI capabilities for free, DeepSeek quickly gained global acclaim for its cutting-edge performance. However, its exponential rise has also sparked debates about the trade-offs between innovation and privacy.

US lawmakers are now pushing for a ban on DeepSeek after security researchers found the app transferring user data to a banned state-owned company.

A probe has also been launched by Microsoft and OpenAI over a breach of the latter’s systems by a group allegedly linked to DeepSeek.

Concerns about data collection and potential misuse have triggered comparisons to the controversies surrounding TikTok, another Chinese tech success story grappling with regulatory pushback in the West.

DeepSeek continues AGI innovation amid controversy

DeepSeek’s commitment to open-source its technology appears timed to deflect criticism and reassure sceptics about its intentions.

Open-sourcing has long been heralded as a way to democratise technology and increase transparency, and DeepSeek’s “daily unlocks,” that are set to begin soon, could offer the community reassuring insight into its operations.

Nevertheless, questions remain over how much of the technology will be open for scrutiny and whether the move is an attempt to shift the narrative amid growing political and regulatory pressure.

It’s unclear whether this balancing act will be enough to satisfy lawmakers or deter critics, but one thing is certain: DeepSeek’s open-source leap marks another turn in its dramatic rise.

While the company’s motto of “garage-energy and community-driven innovation” resonates with developers eager for open collaboration, its future may rest as much on its ability to address security concerns as on its technical prowess.

(Photo by Solen Feyissa)

See also: DeepSeek’s AI dominance expands from EVs to e-scooters in China

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including IoT Tech Expo, Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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OpenAI describes Deep Research as a significant milestone in its journey toward artificial general intelligence (AGI).

“The ability to synthesise knowledge is a prerequisite for creating new knowledge,” says OpenAI. “For this reason, Deep Research marks a significant step toward our broader goal of developing AGI.”

Agentic AI enables ChatGPT to assist with complex research

Deep Research empowers ChatGPT to find, analyse, and synthesise information from hundreds of online sources autonomously. With just a prompt from the user, the tool can deliver a comprehensive report, comparable to the output of a research analyst, according to OpenAI.

Drawing capabilities from a variant of OpenAI’s upcoming “o3” model, the aim is to free users from time-consuming, labour-intensive information gathering. Whether it’s a competitive analysis of streaming platforms, an informed policy review, or even personalised recommendations for a new commuter bike, Deep Research promises precise and reliable results.

Importantly, every output includes full citations and transparent documentation—enabling users to verify the findings with ease.

The tool appears particularly adept at uncovering niche or non-intuitive insights, making it an invaluable asset across industries like finance, science, policymaking, and engineering. But OpenAI also envisions Deep Research being useful for the average user, such as shoppers looking for hyper-personalised recommendations or a specific product.

This latest agentic capability operates through the user interface of ChatGPT; users simply select the “Deep Research” option in the message composer and type their query. Supporting files or spreadsheets can also be uploaded for additional context.

Once initiated, the AI embarks on a rigorous multi-step process, which may take 5-30 minutes to complete. A sidebar provides updates on the actions taken and the sources consulted. Users can carry on with other tasks and will be notified when the final report is ready. 

The results are presented in the chat as detailed, well-documented reports. In the coming weeks, OpenAI plans to enhance these outputs further by embedding images, data visualisations, and graphs to deliver even greater clarity and context.

Unlike GPT-4o – which excels in real-time, multimodal conversations – Deep Research prioritises depth and detail. Its ability to rigorously cite sources and provide comprehensive analysis sets it apart—shifting the focus from fast, summarised answers to well-documented, research-grade insights.

Built for real-world challenges

Deep Rsearch leverages sophisticated training methodologies, grounded in real-world browsing and reasoning tasks across diverse domains. Its model was trained via reinforcement learning to autonomously plan and execute multi-step research processes, including backtracking and adaptively refining its approach as new information becomes available. 

The tool can browse user-uploaded files, generate and iterate on graphs using Python, embed media such as generated images and web pages into responses, and cite exact sentences or passages from its sources. The result of this extensive training is a highly capable agent for tackling complex real-world problems.

OpenAI evaluated Deep Research across a broad set of expert-level exams known as “Humanity’s Last Exam”. The exams – comprising over 3,000 questions covering topics from rocket science and linguistics to ecology and classics – test an AI’s competence in solving multifaceted problems.

The results were impressive, with the model achieving a record-breaking 26.6% accuracy across these domains:

• GPT-4o: 3.3%
• Grok-2: 3.8%
• Claude 3.5 Sonnet: 4.3%
• OpenAI o1: 9.1%
• DeepSeek-R1: 9.4%
• Deep research: 26.6% (with browsing + Python tools)

Deep Research also reached a new state-of-the-art performance on the GAIA benchmark, which evaluates AI models on real-world questions requiring reasoning, multi-modal fluency, and tool-use proficiency. Deep Research topped the leaderboard with a score of 72.57%.

Limitations and challenges

While the Deep Research agentic AI capability in ChatGPT signifies a bold step forward, OpenAI acknowledges that the technology is still in its early stages and comes with limitations.

The system occasionally “hallucinates” facts or offers incorrect inferences, albeit at a notably reduced rate compared to existing GPT models, according to OpenAI. It also faces challenges in differentiating between authoritative sources and speculative content, and it struggles to calibrate its confidence levels—often displaying undue certainty for potentially uncertain findings.

Minor formatting errors in reports and citations, as well as delays in initiating tasks, could also frustrate initial users. OpenAI says these issues are expected to improve over time with more usage and iterative refinements.

OpenAI is rolling out the capability gradually, starting with Pro users, who will have access to up to 100 queries per month. Plus and Team tiers will follow suit, with Enterprise access arriving next. 

UK, Swiss, and European Economic Area residents are not yet able to access the feature, but OpenAI says it’s working on expanding its rollout to these regions.

In the weeks ahead, OpenAI will expand the feature to ChatGPT’s mobile and desktop platforms. The long-term vision includes enabling connections to subscription-based or proprietary data sources, further enhancing the robustness and personalisation of its outputs.

Looking further ahead, OpenAI envisions integrating Deep Research with “Operator,” an existing chatbot capability that takes real-world actions. This integration would allow ChatGPT to seamlessly handle tasks that require both asynchronous online research and real-world execution.

(Photo by John Schnobrich)

See also: Microsoft and OpenAI probe alleged data theft by DeepSeek

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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With ChatGPT recently marking its second anniversary, Altman outlines OpenAI’s achievements, ongoing challenges, and vision for the future of AI.

“The second birthday of ChatGPT was only a little over a month ago, and now we have transitioned into the next paradigm of models that can do complex reasoning,” Altman reflects.

A bold mission to achieve AGI and superintelligence

OpenAI was founded in 2015 with a clear, albeit bold, mission: to develop AGI and ensure it benefits all of humanity.

Altman and the founding team believed AGI could become “the most impactful technology in human history.” Yet, he recalls, the world wasn’t particularly interested in their quest back then.

“At the time, very few people cared, and if they did, it was mostly because they thought we had no chance of success,” Altman explains.

Fast forward to 2022, OpenAI was still a relatively quiet research facility testing what was then referred to as ‘Chat With GPT-3.5.’ Developers had been exploring the capabilities of its API, and the excitement sparked the idea of launching a user-ready demo.

This demo led to the creation of ChatGPT, which Altman acknowledges benefited from “mercifully” better branding than its initial name. When it launched on 30 November 2022, ChatGPT proved to be a tipping point.

“The launch of ChatGPT kicked off a growth curve like nothing we have ever seen—in our company, our industry, and the world broadly,” he says

OpenAI has since witnessed an evolution marked by staggering interest, not just in its tools but in the broader possibilities of AI.

Building at breakneck speed  

Altman admits that scaling OpenAI into a global tech powerhouse came with significant challenges.

“In the last two years, we had to build an entire company, almost from scratch, around this new technology,” he notes, adding, “There is no way to train people for this except by doing it.”

Operating in uncharted waters, the OpenAI team often faced ambiguity—making decisions on the fly and dealing with the inevitable missteps.

“Building up a company at such high velocity with so little training is a messy process,” Altman explains. “It’s often two steps forward, one step back (and sometimes, one step forward and two steps back).”

Yet, despite the chaos, Altman credits the team’s resilience and ability to adapt.

OpenAI now boasts over 300 million weekly active users, a sharp increase from the 100 million reported just a year ago. Much of this success lies in the organisation’s ethos of learning by doing, combined with a commitment to putting “technology out into the world that people genuinely seem to love and that solves real problems.”

‘A big failure of governance’

Of course, the journey so far hasn’t been without turmoil. Altman recounts a particularly difficult chapter from November 2023 when he was suddenly ousted as CEO, briefly recruited by Microsoft, only to be reinstated by OpenAI days later amid industry backlash and staff protests.

Speaking openly, Altman highlights the need for better governance structures in organisations tackling critical technologies like AI.  

“The whole event was, in my opinion, a big failure of governance by well-meaning people, myself included,” he admits. “Looking back, I certainly wish I had done things differently, and I’d like to believe I’m a better, more thoughtful leader today than I was a year ago.”

The episode served as a stark reminder of the complexity of managing rapid growth and the stakes involved in AI development. It also drove OpenAI to forge new governance structures “that enable us to pursue our mission of ensuring that AGI benefits all of humanity.”

Altman expressed deep gratitude for the support OpenAI received during the crisis from employees, partners, and customers. “My biggest takeaway is how much I have to be thankful for and how many people I owe gratitude towards,” he emphasises.

Pivoting towards superintelligence  

Looking forward, Altman says OpenAI is beginning to aim beyond AGI towards the development of “superintelligence”—AI systems that far surpass human cognitive capabilities.

“We are now confident we know how to build AGI as we have traditionally understood it,” Altman shares. OpenAI predicts that by the end of this year, AI agents will significantly “join the workforce,” revolutionising industries with smarter automation and companion systems.

Achieving superintelligence would be especially transformative for society, with the potential to accelerate scientific discoveries, but also poses the most significant dangers.

“We believe in the importance of being world leaders on safety and alignment research … OpenAI cannot be a normal company,” he notes, underscoring the need to approach innovation responsibly.

OpenAI’s strategy includes gradually introducing breakthroughs into the world, allowing for society to adapt alongside AI’s rapid evolution. “Iteratively putting great tools in the hands of people leads to great, broadly-distributed outcomes,” Altman argues.

Reflecting on the organisation’s trajectory, Altman admits OpenAI’s path has been defined by both extraordinary breakthroughs and significant challenges—from scaling teams to navigating public scrutiny. 

“Nine years ago, we really had no idea what we were eventually going to become; even now, we only sort of know,” he says.

What remains clear is his unwavering commitment to OpenAI’s vision. “Our vision won’t change; our tactics will continue to evolve,” Altman claims, attributing the company’s remarkable progress to the team’s willingness to rethink processes and embrace challenges.

As AI continues to reshape industries and daily life, Altman’s central message is evident: While the journey has been anything but smooth, OpenAI is steadfast in its mission to unlock the benefits of AI for all.

“How lucky and humbling it is to be able to play a role in this work,” Altman concludes.

See also: OpenAI funds $1 million study on AI and morality at Duke University

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Released in November 2024, the Commission’s annual report outlined 32 recommendations that could fundamentally alter how the two countries interact, with artificial intelligence taking centre stage in a new chapter of strategic rivalry.

US-China: the AGI moonshot and critical tech controls

At the heart of the report lies an ambitious proposal: establishing a government-backed programme to develop AGI – AI systems that could match and potentially exceed human cognitive abilities. 

However, the recommendation is just one piece of a larger technological puzzle, including export controls, investment screening, and new trade policies to preserve US technological advantages. 

The proposed AGI initiative would provide multi-year contracts to leading AI companies, cloud providers, and data centre operators. It would be backed by the Defense Department’s highest priority, “DX Rating” – a designation typically reserved for critical national security projects. 

This level of government involvement in AI development mirrors the urgency seen in previous technological races. It raises crucial questions about the role of state intervention in an industry primarily driven by private sector innovation.

The Commission’s tech-focused recommendations extend beyond AI. Notable proposals include restricting imports of Chinese-made autonomous humanoid robots with advanced dexterity, locomotion, and intelligence capabilities. 

The report also targets energy infrastructure products with remote monitoring capabilities, reflecting growing concerns about connected technologies in critical infrastructure. The report builds on existing export controls in the semiconductor space by recommending stronger oversight of technology transfers and investment flows. 

This comes as China continues to build domestic chip-making capabilities despite international restrictions. The Commission suggests creating an Outbound Investment Office that prevents US capital and expertise from advancing China’s technological capabilities in sensitive sectors.

Reshaping trade relations and investment flows

Perhaps most significantly, the report recommends eliminating China’s Permanent Normal Trade Relations (PNTR) status—a move that could reshape the technology supply chain and trade flows that have defined the global tech industry for decades. This recommendation acknowledges how deeply intertwined the US and Chinese tech ecosystems have become, while suggesting that this interdependence may now pose more risks than benefits.

Data transparency is another key theme, with recommendations for expanded reporting requirements on investments and technology transfers. The Commission calls for better tracking of investments flowing through offshore entities, addressing a significant blind-spot in current oversight mechanisms.

The report’s release comes at a critical juncture in technological development. China’s push for self-sufficiency in vital technologies and its “new quality productive forces” initiative demonstrates Beijing’s determination to lead in next-generation technologies. Meanwhile, AI capabilities and quantum computing breakthroughs have raised the stakes in technology competition.

However, the Commission’s recommendations face practical challenges. Achieving AGI remains a complex scientific challenge that may not yield quick results, regardless of funding levels. Additionally, restrictions on technology transfers and investment could have unintended consequences for global innovation networks that have historically benefited both nations.

If these recommendations are implemented, the tech industry may need to navigate an increasingly complex regulatory landscape. Companies would face new compliance requirements for international investments, technology transfers, and collaborative research projects.

Challenges and future implications

The effectiveness of the proposed measures will likely depend on coordination with allies and partners who share similar technological capabilities and concerns. The report acknowledges this by recommending multilateral approaches to export controls and investment screening.

US-China technological competition has entered a new phase where government policy may play a more direct role in shaping development. Whether this approach accelerates or hinders innovation remains to be seen, but the tech industry should prepare for increased scrutiny and regulation of international technological collaboration.

(Photo by Nathan Bingle)

See also: Chinese firms use cloud loophole to access US AI tech

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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AIRIS represents the first proto-AGI (Artificial General Intelligence) to harness a comprehensive tech stack across the alliance.

SingularityNET, founded by renowned AI researcher Dr Ben Goertzel, uses agent technology from Fetch.ai, incorporates Ocean Data for long-term memory capabilities, and is soon expected to integrate CUDOS Compute infrastructure for scalable processing power.

“AIRIS is a significant step in the direction of practical, scalable neural-symbolic learning, and – alongside its already powerful and valuable functionality – it illustrates several general points about neural-symbolic systems, such as their ability to learn precise generalisable conclusions from small amounts of data,” explains Goertzel.

According to the company, this alliance-driven procedure propels AIRIS towards AGI—crafting one of the first intelligent systems with autonomous and adaptive learning that holds practical applications for real-world scenarios.

AIRIS’ learning mechanisms

AIRIS is crafted to enhance its understanding by interacting directly with its environment, venturing beyond the traditional AI limitations that depend on predefined rules or vast datasets. Instead, AIRIS evolves through observation, experimentation, and continual refinement of its unique “rule set.”

This system facilitates a profound level of problem-solving and contextual comprehension, with its implementation in Minecraft setting a new benchmark for AI interaction with both digital and tangible landscapes.

Shifting from a controlled 2D grid to the sophisticated 3D world of Minecraft, AIRIS faced numerous challenges—including terrain navigation and adaptive problem-solving in a dynamic environment. This transition underscores AIRIS’ autonomy in navigation, exploration, and learning.

The AIRIS Minecraft Agent distinguishes itself from other AI entities through several key features:

• Dynamic navigation: AIRIS initially evaluates its milieu to formulate movement strategies, adapting to new environments in real-time. Its capabilities include manoeuvring around obstacles, jumping over barriers, and anticipating reactions to varied terrains.

• Obstacle adaptation: It learns to navigate around impediments like cliffs and forested areas, refining its rule set with every new challenge to avoid redundant errors and minimise needless trial-and-error efforts.

• Efficient pathfinding: Via continuous optimisation, AIRIS advances from initially complex navigation paths to streamlined, direct routes as it “comprehends” Minecraft dynamics.

• Real-time environmental adaptation: Contrasting with conventional reinforcement learning systems that demand extensive retraining for new environments, AIRIS adapts immediately to unfamiliar regions, crafting new rules based on partial observations dynamically.

AIRIS’ adeptness in dealing with fluctuating terrains, including water bodies and cave systems, introduces sophisticated rule refinement founded on hands-on experience. Additionally, AIRIS boasts optimised computational efficiency—enabling real-time management of complex rules without performance compromises.

Future applications

Minecraft serves as an excellent launchpad for AIRIS’ prospective applications, establishing a solid foundation for expansive implementations:

• Enhanced object interaction: Forthcoming stages will empower AIRIS to engage more profoundly with its surroundings, improving capabilities in object manipulation, construction, and even crafting. This development will necessitate AIRIS to develop a more refined decision-making framework for contextual tasks.

• Social AI collaboration: Plans are underway to incorporate AIRIS in multi-agent scenarios, where agents learn, interact, and fulfil shared objectives, simulating real-world social dynamics and problem-solving collaboratively.

• Abstract and strategic reasoning: Expanded developments will enhance AIRIS’s reasoning, enabling it to tackle complex goals such as resource management and prioritisation, moving beyond basic navigation towards strategic gameplay.

The transition of AIRIS to 3D environments signifies a pivotal advancement in the ASI Alliance’s mission to cultivate AGI. Through AIRIS’s achievements in navigating and learning within Minecraft, the ASI Alliance aspires to expedite its deployment in the real world, pioneering applications for autonomous robots, intelligent home assistants, and other systems requiring adaptive learning and problem-solving capacities.

Berick Cook, AI Developer at SingularityNET and creator of AIRIS, said: “AIRIS is a whole new way of approaching the problem of machine learning. We are only just beginning to explore its capabilities. We are excited to see how we can apply it to problems that have posed a significant challenge for traditional reinforcement learning.

“The most important aspect of AIRIS to me is its transparency and explainability. Moving away from ‘Black Box’ AI represents a significant leap forward in the pursuit of safe, ethical, and beneficial AI.”

The innovative approach to AI evident in AIRIS – emphasising self-directed learning and continuous rule refinement – lays the foundation for AI systems capable of independent functioning in unpredictable real-world environments. Minecraft’s intricate ecosystem enables the system to hone its skills within a controlled yet expansive virtual setting, effectively bridging the divide between simulation and reality.

The AIRIS Minecraft Agent represents the inaugural tangible step towards an AI that learns from, adapts to and makes autonomous decisions about its environment. This accomplishment illustrates the potential of such technology to re-envision AI’s role across various industries.

(Image by SkyeWeste)

See also: SingularityNET bets on supercomputer network to deliver AGI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Sutskever, who left OpenAI in May this year following a failed attempt to oust CEO Sam Altman, established SSI to develop ‘safe’ AI models. The company’s mission is to create AI systems that are both highly capable and aligned with human interests.

‘We’ve identified a new mountain to climb that is slightly different from what I was working on previously. We’re not trying to go down the same path faster. If you do something different, it becomes possible for you to do something special, Sutskever told the Financial Times.

The substantial funding will be used to acquire computing resources necessary for AI model development and to expand SSI’s current team of 10 employees. The company actively recruits and offers positions in Palo Alto, California, and Tel Aviv, Israel.

With its focus on safety and alignment, SSI’s approach differs from that of other AI companies. Take firms like OpenAI, Anthropic, and Elon Musk’s xAI, which are all developing AI models for various consumer and business applications. SSI, on the other hand, is focusing solely on creating what it calls a ‘straight shot to safe superintelligence’.

Daniel Gross, SSI’s chief executive, emphasised the importance of this focused approach in a statement to Reuters: “It’s important for us to be surrounded by investors who understand, respect and support our mission, which is to make a straight shot to safe superintelligence and in particular to spend a couple of years doing R&D on our product before bringing it to market.”

It is also interesting to point out that despite not having a product yet, the company’s significant valuation and funding highlight the intense interest and investment in safe AI research. This is amid growing concerns about the potential risks associated with increasingly powerful AI systems.

Even Sutskever’s departure from OpenAI was reportedly due to disagreements over the company’s direction and the pace of AI development. At OpenAI, he led the ‘alignment’ team, which focused on ensuring that advanced AI systems would act in humanity’s best interests.

What is clear, however, is that the formation of SSI and its rapid funding success reflect a broader trend in the AI industry towards addressing safety concerns alongside capability advancements. This approach aligns with calls from AI researchers and ethicists for more responsible development of artificial intelligence.

Today, SSI joins a competitive field of well-funded AI companies. OpenAI is reportedly in talks to raise funds at a valuation exceeding $100 billion, while Anthropic and xAI were recently valued at around $20 billion.

However, the crowded market did not dim SSI’s unique focus on safety or its high-profile founding team, both of which have clearly resonated with investors. 

“We are assembling a lean, cracked team of the world’s best engineers and researchers dedicated to focusing on SSI and nothing else. We offer an opportunity to do your life’s work and help solve our age’s most important technical challenge,” the company’s website states.

For now, the company’s progress will be closely watched by both the tech industry and those concerned with the ethical implications of AI development.

See also: OpenAI hit by leadership exodus as three key figures depart

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post OpenAI co-founder’s Safe Superintelligence Inc secures $1B appeared first on AI News.