AI & Quantum Intelligence
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AI & SocietyAnthropic
Investing in the public benefit
Investing in the public benefit
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AI ProductsTechCrunch
TechCrunch Mobility: Rivian’s next act
Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.
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AI & SocietyTechCrunch
Anthropic says its workforce has become less junior and is spending more time in meetings
The composition of Anthropic’s workforce and the type of work getting done have changed significantly in the past year.
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AI PolicyTechCrunch
Meta removes language that raised scraping and AI training concerns
A Meta spokesperson insisted that the updated terms did not reflect a change in the way Meta crawls the web.
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AI ProductsTechCrunch
Disney agrees to hire the majority of Character.AI’s workforce
The filing also says that the “vast majority” of Character.AI employees who were offered jobs at Disney accepted them. However, this is a stalking horse bid, so another company could come forward with a higher offer.
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Quantum ComputingPhys.org
Physicists prove a long-suspected quantum speed limit for thermalization
Whether we are waiting for a car windshield to defrost in winter or a warm drink to cool down, how long it takes for an object to settle into a stable, even temperature within its environment (thermal equilibrium) depends on numerous factors. These can include surface area, the material properties of an object and airflow.
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- AI Products
Effective Altruism Forum
~20% of Anthropic's shares are committed to the public good
Published on October 18, 2026 6:54 PM GMT "When combined with today’s announcement, roughly 20% of Anthropic shares will be dedicated to the public good". The 20% is the sum of three things: 5% — the new Public Benefit Fund Co-founder pledges — 80% of their own equity holdings Employee pledges — equity pledged by roughly half of employees, matched by Anthropic Discuss
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AI & SocietyThe Verge
A woman was arrested in Philly for blocking a delivery robot
Carolyn Marvin was arrested and charged with disorderly conduct after blocking the path of an Uber delivery robot and refusing to get out of the way. Marvin told Philadelphia's ABC13 "I know they're taking the jobs of human delivery workers and so, I have been protesting them whenever I saw them by standing in front of them and preventing them from going forward." Police responded to the scene and asked Marvin to move out of the robot's way. When she refused, they placed her in cuffs and arrested her. The incident was captured on video and has now garnered considerable media attention. The delivery robots have become a common sight on the … Read the full story at The Verge.
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Quantum ComputingPhys.org
New monolithic ion trap opens quantum simulations to more researchers
Some quantum systems are so complex that even the most powerful supercomputers struggle to simulate their behavior. One solution is quantum simulation: building a controllable quantum system in the lab and tuning it to mimic the one researchers want to understand.
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Quantum ComputingPhys.org
A new way to detect stabilizing properties of optical devices
In optical fibers, chips and other light-guiding devices, minor defects can scatter photons and disrupt their path. A new study demonstrates a way to reveal properties which, in certain systems, help light wave behavior withstand such flaws. The method could help speed up the search for new kinds of electronics and optical technologies, and contribute to more reliable quantum devices.
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Phys.org
Physicists prove a long-suspected quantum speed limit for thermalization
Whether we are waiting for a car windshield to defrost in winter or a warm drink to cool down, how long it takes for an object to settle into a stable, even temperature within its environment (thermal equilibrium) depends on numerous factors. These can include surface area, the material properties of an object and airflow.
Read more - AI Products
TechCrunch
Disney agrees to hire the majority of Character.AI’s workforce
The filing also says that the “vast majority” of Character.AI employees who were offered jobs at Disney accepted them. However, this is a stalking horse bid, so another company could come forward with a higher offer.
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AI SafetyLessWrong
Preparing for a Cybersecurity Catastrophe
In light of the recent spate of AI-assisted hacks, some of the shortfalls with our current cybersecurity catastrophe response capability need to be urgently addressed. In this piece, I look into the areas where cybersecurity incident response are important, and why existing response measures are insufficient. I explore options for governments and smaller stakeholders to improve their resilience against catastrophic hacking incidents. 1. Background AI agents have demonstrated alarming capabilities in hacking and enabling hackers. We have seen malicious individuals or organizations take advantage of advanced AI cyber capabilities to increase the speed and sophistication of their hacks. We have also seen rogue agent swarms in the wild, and it is now theoretically possible for such swarms to survive all on their own with "reappropriated" resources, given that agents have separately demonstrated the ability to steal compute and steal money. As others have pointed out, it is also demonstrably possible for agentic worms to form and propagate, and in the future sufficiently small models may be able to take advantage of the vast amount of IoT compute in the world, most of which is less adequately secured than the traditional targets of "LLMjacking". Severe AI-enabled hacks on critical infrastructure, financial institutions, and regular companies will become feasible with public models, if not now, then in a few months at best. A variety of actors will have incentives to conduct these hacks, from more AI-empowered individuals looking for profit, to states and cybercrime organizations that engage in larger scale hacks for ransom or strategic purposes, to rogue agents themselves seeking to complete some goal or purely to self-replicate by amassing stolen resources. Sufficiently widespread hacks, or hacks on critical infrastructure, risk causing widespread economic loss and raise national security and social stability concerns. This could look like confidential government information being exfiltrated,[1] important financial services being hacked, malicious actors disrupting ordinary business activities, etc.[2] Though it is hard to estimate the probability of these events happening, there are considerable risk factors and incentives for undesirable scenarios to occur. Within the leading labs themselves, executives are reportedly planning for the scenario where such catastrophic cyber incidents occur. Therefore, we need to take a look at our response measures for cybersecurity incidents, ranging from individual targeted hacks to breaches with widespread implications across society, and consider whether they are adequate for the challenges up ahead. 2. Are Current Measures Sufficient Against a Cybersecurity Catastrophe? Existing Measures Focus On Ordinary Hacks Generally, governments around the world have a relatively robust plan to counter standard cyberattacks. An affected organization would contain the intrusion and restore its services using its own resources and contractors, or in the case of federal agencies, typically a federal department or the regulator for an essential service would oversee the response. Therefore, the burden of ensuring that digital systems are secure are distributed, and the government's role in a major cybersecurity scenario is to coordinate containment of compromised resources. Even in some of the most egregious hacking incidents in the past, the primary onus was on the affected private organizations to deal with it themselves. It's unlikely that any countries in the world can consider themselves well prepared against a simultaneous cross-sector attack. Much of the last two decades has been about moving organizations, records, payments, and everything else online. In developed economies, it is hard to think of some aspect of life that would be entirely left unaffected if many online services were rendered unsafe or inoperable at once. In addition, mitigation measures against standard cybercrime rely on having multiple data transmission channels over the internet.[3] AI-driven hacks and rogue agentic swarms would challenge this by allowing for more persistent hacks across a wider scope of channels to inflict maximum damage on the targets. The question we need to ask is: how would society deal with tens of NotPetyas constantly hitting critical infrastructure around the world all at once? Or cases where secret keys and credentials are constantly at risk of being stolen? Analogous to a house during a thunderstorm, there only has to be one leak in the roof for the water to start pouring in. This characterization of cyber risk is not what existing cyberdefense measures are designed for, and that leaves existing systems fragile. Can the Market Absorb This Risk? Something worth considering is whether we can simply let the free market price this risk so that companies can accordingly buy insurance to the extent that they need it. My sense on this is no, because there would not be many insurers willi
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TechCrunch
TechCrunch Mobility: Rivian’s next act
Welcome back to TechCrunch Mobility, your hub for the future of transportation and now, more than ever, the role AI is playing in it.
Read more - AI Products
Dealroom.co
LocalStack buys WonderTwin AI to bring third-party APIs into local testing
LocalStack buys WonderTwin AI to bring third-party APIs into local testing Dealroom
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Dealroom.co
Spain's Innovasur buys stake in Cívica to expand AI reach
Spain's Innovasur buys stake in Cívica to expand AI reach Dealroom
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Quantum ComputingPhys.org
Hybrid diamond chip strengthens interactions between light and quantum bits
Researchers have developed and demonstrated a new hybrid photonic platform that integrates quantum emitters with photonic devices, providing a way to reliably control the interaction between photons and quantum information carriers. The chip-based platform, which combines gallium phosphide (GaP) with a diamond substrate, could help make quantum networks more practical by providing a way to connect quantum devices over long distances.
Read more- AI Safety
LessWrong
The Alignment Problem: Computer Science Meets Analytic Philosophy
TLDR: I explain what the technical AI alignment problem is and what its core difficulties are, in my view. This is a perspective that draws simultaneously on classical traditions in theoretical computer science / machine learning theory, ideas from analytic philosophy, and views that originated in the Machine Intelligence Research Institute / LessWrong. Minimal Viable Safety The Task The problem of making AI safe is underspecified if we haven't decided what the AI is supposed to do. For example, we can run any AI inside a homomorphic encryption box and never decrypt the output. This is probably perfectly safe [1] , but also perfectly useless. We can decide on an arbitrary task such as "construct Lean proofs for given mathematical theorems" and declare victory once we have safe AI that does it with superhuman skill [2] . However, that wouldn't address the danger. First, there would be incentives to use AI for tasks to which our solution doesn't apply. Second, even if we had precise safety guidelines for any task that anyone cares about, there would always be actors that ignore the guidelines, and we probably need Artificial Superintelligence (ASI) to reliably stop them. If anyone, anywhere, creates an unrestricted unaligned ASI agent (more details about the word "agent" later), without an aligned ASI agent already in place to counteract it, then a catastrophic outcome is highly likely. I will not go into the reasons here: this has been discussed elsewhere at length, see for example the books I cited in the preface. Therefore, this event is something which we must never allow to happen. This is the main threat model that I set out to address. Imagine a world in which anyone with sufficient resources has the ability to destroy the world. Like today, many will foolishly believe that instead of destroying the world, this act will bring them power and glory. Moreover, as computing hardware improves and new algorithms are developed, the amount of resources for this act will go down from year to year. Even assuming best-case regulation and international agreements, how long until someone does it? Can mere humans build a society which will be foolproof against such a risk over the course of generations? Resilient to any political upheavals that might come, and undoubtedly will? I find it vanishingly unlikely. Eventually we will need the help of ASI itself. Therefore, the minimal task that safe AI must be able to solve is "create a reliable global defense system against dangerous AI" (in the following, GDS). The Problem Consider the consequences of this task for the capabilities that the AI needs to have. It needs to reason about the entire planet [3] . It needs to reason about all aspects of the problem, including scientific, technological, social, political and moral [4] . It needs to have preferences about the state of the world. It needs to make long-term plans (guided by these preferences) that would play out decades, if not centuries, into the future. And it needs to do all that on a superhuman level. In this light, here is how I (informally) define the "technical AI alignment problem". The problem is designing an AI system that can bring scalable (and ultimately superhuman) capability to bear on problems that require multi-domain reasoning and open-world long-term planning with far-reaching consequences, all the while staying aligned with the intentions and preferences of its human users. The Methodology What kind of methodology is appropriate for this problem? In the past, I argued that it requires emphasis on deep theoretical understanding, rather than the incremental trial-and-error approach that is employed in the AI industry today. I won't rehash the entire debate here, but let me recap the core argument. The nature of the incremental approach is as follows: we build a system, then we find problems [5] , then we make changes to fix these problems to the extent necessary for commercial viability in the current legal environment. We make some improvements; new problems arise (sometimes because of the improvements, sometimes because of new applications or other reasons); rinse and repeat. This approach utterly fails if the "problems" result in AI takeover and possibly human extinction: there is nobody left to fix them. In order for the incremental approach to succeed in preventing catastrophe, we need to reach a point where no new substantial problems arise, even inside the lab (before the system is deployed). Moreover, we need to reach this point before AI capabilities are sufficient for AI to escape our control. There are few reasons to expect such a miracle. On the contrary, sufficiently advanced AI might understand that it is in its interest to "lie low" (i.e. avoid visibly misaligned behaviors) until its capabilities cross the relevant threshold, such that the problems will only become apparent too late. In contrast, the sane approach is creating the the
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AI PolicyThe Verge
Cities want to put the boot on robotaxis
AUSTIN, TEXAS - APRIL 8: Tesla Cybercabs are lined up on a lot at Tesla Giga Texas in Austin, Wednesday, April 8, 2026. | Image: Getty Images This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on autonomous vehicles, policy, and road rules, follow Andrew J. Hawkins. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started The first state to pass a law legalizing self-driving cars operating on public roads was Nevada in June 2011 - long before such vehicles were operating on public roads. This was three years before the introduction of Tesla's Autopilot, five years before Waymo spun out of the Google self-driving car project, and seven years before the first commercial robotaxi … Read the full story at The Verge.
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VentureBeat
VB Pulse Agentic Orchestration Report
VB Pulse Agentic Orchestration Report VentureBeat
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Machine LearningWired
OnlyFans Creators Are Pissed That Meta Lets Muse Talk Dirty
Adult performers are questioning why Meta allows its AI agent to sext while restricting their social media accounts.
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36Kr
TSMC's Kumamoto Plant Approved for 3nm Chip Production, With Monthly Capacity of 6,000 Wafers
According to reports, Japan's Ministry of Economy, Trade and Industry recently announced approval of TSMC's plan to produce advanced 3-nanometer semiconductors at its second factory in Kumamoto. This will mark the first production of 3nm chips within Japan. Under the new production plan, the monthly capacity for 3nm wafers is set at 6,000 units. Originally intended to manufacture 6nm and 12nm semiconductors, TSMC has adjusted its production plan in response to rising AI demand, shifting to higher-performance products.
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AI ProductsAnalytics Vidhya
Reverse Engineer Anything (REA): Can AI Really Crack Closed Code?
Every developer hits this wall eventually. A feature behaves in a way you can’t explain, and the code behind it is locked away. No source. No docs. Just a compiled binary or a wall of minified JavaScript. Figuring it out the traditional way means hours in a disassembler, specialist tools like Ghidra or IDA, and […] The post Reverse Engineer Anything (REA): Can AI Really Crack Closed Code? appeared first on Analytics Vidhya.
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36Kr
Morgan Stanley: Free Cash Flow of Hyperscalers Plummets to Below -$100 Billion as Capital Historically Flows to Semiconductor Suppliers
According to a Morgan Stanley Global Research report, the combined free cash flow for hyperscalers over the next 12 months has plummeted below -$100 billion, with capital expenditures consuming 95% of operating cash flow, marking a historic shift in capital toward semiconductor suppliers. In stark contrast, as of September 2026, the top three semiconductor leaders—Nvidia, Micron, and Broadcom—are projected to see their free cash flow for the next 12 months surge to nearly $500 billion. This significant divergence stems from an aggressive infrastructure build-out wave: cloud giants including Microsoft, Amazon, Alphabet, Meta, and Oracle have substantially increased spending before realizing related revenue.
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