SSRN Our Power, Our Planet: SSRN Celebrates Earth Day 2025 Earth Day, celebrated annually on April 22, is a global event dedicated to raising awareness and promoting action for environmental protection and sustainability. Established in 1970, Earth Day serves as a reminder of our shared responsibility to safeguard our planet for future generations. It brings together millions of people from diverse backgrounds to engage in activities that educate, inspire, and mobilize communities around pressing environmental issues, from climate change and pollution to biodiversity conservation and sustainable practices. SSRN has joined the movement by creating an Earth Day Special Topic Hub that highlights early-stage research addressing critical global challenges such as climate change, pollution, deforestation, and habitat loss. This special topic hub presents insights from many disciplines that may inform the ongoing conversation on advancing Earth Day efforts by providing the scientific foundation needed to understand environmental challenges and develop effective solutions. Below is a selection of the top downloaded papers from the Earth Day Special Topic Hub from 2024 to the present: A Multitemporal Snapshot of Greenhouse Gas Emissions from the Israel-Gaza Conflict by Benjamin Neimark (Queen Mary University of London), Patrick Bigger (The Climate and Community Project), Frederick Otu-Larbi (Lancaster University), & Reuben Larbi (Lancaster University) Climate Homicide: Prosecuting Big Oil For Climate Deaths by David Arkush (Public Citizen) & Donald Braman (George Washington University) When Insurers Exit: Climate Losses, Fragile Insurers, and Mortgage Markets by Parinitha Sastry (Columbia Business School), Ishita Sen (Harvard Business School), & Ana-Maria Tenekedjieva (Board of Governors of the Federal Reserve System) On the Importance of Assurance in Carbon Accounting by Florian Berg (Massachusetts Institute of Technology), Jaime Oliver Huidobro (Clarity AI Europe S.L.), & Roberto Rigobon (Massachusetts Institute of Technology) A Proto-Standard for Carbon Accounting and Auditing using the E-Liability Method by Karthik Ramanna (University of Oxford), Lauren Holloway (E-liability Institute), Max Israelit (E-liability Institute), Chloe Wenye Zhang (Yale School of Management), & Robert S. Kaplan (Harvard Business School) To read more research on Earth Day, view other papers here.

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HTech CEO Madhav Sheth has revealed that he has joined Nxtcell to lead the launch of Alcatel smartphones in India. This move comes shortly after Alcatel announced its plans to make a comeback in the Indian smartphone market. The French brand, operated independently by TCL Communication under trademark licensing from Nokia, has been inactive in the Indian smartphone arena for the last couple of years. Sheth is likely to maintain his position at HTech, which serves as Honor's distributor in India. Meanwhile, Sheth teased the launch of new Honor products shortly after revealing his involvement with Nxtcell.Madhav Sheth Joining Nxtcell to Reintroduce Alcatel SmartphonesIn an X post on Tuesday, Madhav Sheth announced his close collaboration with the Nxtcell team. "I am thrilled to announce the launch of Alcatel smartphones in India. I will be working closely with the Nxtcell team to spearhead technology transfer, foster patent-driven innovation, and ensure that local manufacturing aligns with India's vision for tech self-reliance," said Sheth.The technology agreements are signed, and the local manufacturing efforts are set to play a major role in strengthening India's tech ecosystem and expanding export capabilities, he added.Shortly after disclosing his decision to venture into a new chapter with Alcatel, Madhav Sheth posted on X that Honor has secured approvals to launch five products in India. The HTech's CEO did not disclose any specifications for the phones. The post, however, suggests that Sheth may continue his role at HTech while working with Nxtcell.After leaving Realme, Sheth joined HTech in 2023 to bring Honor smartphones back to the Indian market.Alcatel announced its return to the Indian market in the first week of April. The brand, operated independently by TCL Communication, is planning to bring a range of premium smartphones to the country, and they will be sold via Flipkart's main platform and its quick-delivery service, Flipkart Minutes. The e-commerce company is teasing the arrival of new handsets through a dedicated landing page on its website. Alcatel has already confirmed that it will introduce a smartphone equipped with a stylus. The brand's product line will incorporate some unnamed patented innovations. Alcatel smartphones will be manufactured locally in the country, aligning with the government's Make in India initiative. It is aiming to establish a pan-India service network to offer customer support.Affiliate links may be automatically generated - see our ethics statement for details.

Moto Tag has been launched in India, months after it was unveiled by the Lenovo-owned company. The location tracker offers support for Google's Find My Device network. It is a Bluetooth-enabled tracker that is compatible with Android devices, and it also features an ultra-wideband (UWB) chip. Notably, Motorola initially introduced the tracker in the US in June 2024, and it is priced at $29 (roughly Rs. 2,423). Earlier this year, competing OEMs Noise and Boat also introduced Bluetooth trackers in the country, namely the Noise Tag 1 and the Boat Tag, respectively.Moto Tag Price in India, AvailabilityMoto Tag price in India is set at Rs. 2,299 and will be available for purchase in the country soon via Flipkart and the Moto India website. The wireless tracker is offered in Jade Green and Starlight Blue colour options in the country.Moto Tag FeaturesWith support for Google's Find My Device network, the Moto Tag can help users track lost items. It is claimed to offer precise location-tracking capabilities when used with a UWB-capable smartphone. The 'Precision Finding' feature is said to be capable of accurately locating the tracker, even when it is offline.The Bluetooth tracker can be used to keep track of objects such as keys, purse, luggage, or larger ones like bikes and vehicles. It supports Bluetooth 5.4 connectivity and is compatible with devices running Android 9 (Android Pie) or newer. A dedicated ringer button helps users track misplaced phones. The same button can be used as a remote capture button when clicking photos.The Moto Tag is equipped with a replaceable CR2032 battery that is said to last for up to one year. It is said to offer end-to-end encryption for location information, which allows only the owner of the Moto Tag to locate their objects.  The device supports manual scanning to check for unwanted trackers, and it is also said to alert other users if it is moving along with them for a specified period of time. The tracker has an IP67 rating for dust and water resistance. The plastic body of the tracker measures 31.9 x 8mm in size and weighs 7.5gAffiliate links may be automatically generated - see our ethics statement for details. For the latest tech news and reviews, follow Gadgets 360 on X, Facebook, WhatsApp, Threads and Google News. For the latest videos on gadgets and tech, subscribe to our YouTube channel. If you want to know everything about top influencers, follow our in-house Who'sThat360 on Instagram and YouTube. Further reading: Moto Tag, Moto Tag Price in India, Moto Tag India Launch, Moto Tag Features, Motorola Sucharita Ganguly Sucharita is a writer with Gadgets 360 and is mostly found playing with her cat in her free time. She has previously worked at breaking news desks across organizations. Powered by coffee, The Beatles, Bowie, and her newfound love for BTS, she aims to work towards contributing to a better media environment for women and queer folk. More Related Stories

Interview Catfishes: Reflections on AI from a Data Hiring ManagerPublished inInside League·6 min read·Just now--We’re living in exciting times with AI tools at our fingertips. Whether I’m meal planning, searching for an error in my code, organizing a vacation, summarizing meetings and threads, or trying to remember that catchy song, AI has disrupted the way I operate in all parts of my life. At League, it’s so exciting to be building AI-driven features, and I believe that AI is crucial to how we advance technology. As a data leader, I truly agree with the saying that if we aren’t using AI, we are falling behind.There’s always a but…Now comes the dreaded “but”: while the benefits of AI are fantastic from a productivity and innovation perspective, this post is about the downsides of over-reliance on AI, specifically in the context of job interviews if a candidate uses it dishonestly.My new major ick is dishonest use of AI interview assistance, where candidates read their answers off a screen teleprompter style, generated by an AI service that listens for your questions in real-time. In the past three months with over 50 interviews, about 20% of our candidates were immediately disqualified due to obvious AI assistant usage. While I encourage using AI in your job search, my goal is to find balance between “a helpful tool” versus something that misrepresents your skillset and doesn’t leave room for an authentic conversation.It’s the modern day catfish and it sets everyone involved up for failure. For candidates, it’s hurting their chances, and for hiring managers, it’s a headache to navigate and avoid. Of course this excludes aids necessary for accessibility purposes, such as speech-to-text or closed captioning.Picture this: I’ve got a great candidate on paper, exactly what we asked for in the posting and more. Excellent.They join the interview and start off by reading a scripted introduction of themselves.I politely explain that this is an informal interview and that while I appreciate their prep ahead of time, ask that they please try not read directly from a script.They shrug and continue reading. Cue vague answers to direct questions, with inconsistent, contradicting statements that they can’t elaborate on.Cue glazed over eyes, emotionless reading (it’s obvious even if you think it’s not), and in worse cases sometimes tripping up while reading their sentence.They stall while their prompter is catching up.I’ll remind them again to please keep it informal and not read their answers word for word, only for them to deny, in a completely different tone than their previous robotic reading.The interview is cold, impersonal, and I leave the interview disappointed not to have gotten to know the candidate. Next!Why Everyone LosesRecognizing that there is a power dynamic here and that the job market is especially tough these days, I needed to dig deeper into why this type of catfishing irked me so much (I’m a Data Engineer — I need more information!) and I started to think back to the hierarchy of skillsets for any role I’d hire. In order of importance, they are:Trust: Honesty and trustworthiness are essential for remote work and a positive team dynamic, regardless if the role is technical or not.Communication Skills: Effective oral and written communication is crucial for understanding instructions, articulating thoughts, and collaborating within a team.Technical Savviness & Willingness to Learn: Adaptability and the ability to learn new skills are valued over expertise in specific technologies. Ironically, the ability to make good use of AI tooling falls into this category, and I love to hear about how candidates leverage it!Specific Skillset: While relevant specific skills and experience are beneficial, they are not always the most critical factor for success, given how rapidly technology changes anyways.So, what happens when someone’s over-relying on an AI assistant after being asked not to in an interview? Right off the bat, there goes honesty/trust (1). If a candidate needs to rely on reading their answers without any room for real discussion, it would indicate subpar communication skills (2). Since I can’t trust their answers, their technical savviness (3) and skillset (4) are out the window as well. The candidate will not pass the interview, and everyone leaves disappointed.Let’s think about this from an odds perspective. Say Sue knows SQL really well but uses another type of batch data processing software than we do (Airflow). And she’s heard of infrastructure as code software (Terraform), but she’s not that familiar with it beyond high-level theoretical knowledge. We’d have a couple possible scenarios:If she’s honest about her skillset, maybe she’d get an intermediate role with mentorship and space to grow in the gaps we’ve identified (Airflow, Terraform).If Sue uses an AI assistant to read off of, she would most likely be disqualified. But in the rare possibility that she did so without any detection, her skillset would be misrepresented. She’d get the role but be given responsibilities based on skills that she doesn’t have (Airflow, Terraform), and she would likely not pass her first 3 month probationary period.In all of the above, the only positive outcome is a result of both parties showing up as their authentic selves, even if that risks for the candidate missing out on the job. They’d be better off with a “soft no” due to missing skills than a “hard no” due to cheating, or a dismissal a few weeks into the job. With a soft no due to missing skills, a candidate can leave the interview knowing what areas they need to grow, but with the AI assistant muddling the reality, candidates aren’t learning anything.Ways to Fix ThisSo, what do we do about this problem? We need to remind ourselves that we are human above all else. In a sea of prompt-reading candidates, talking to an authentic person feels like a breath of fresh air, and I find myself valuing the imperfections of general back and forth conversations. In those cases, I leave an interview with a good idea of what a real technical discussion would be like between peers and as a result I have much more confidence in my perception of the candidate. Due to the high use of dishonest AI assistance I’ve experienced, I have actually adjusted my own expectations for candidates by focusing on behavioral priorities such as communication, trust, willingness to learn, and transferable skills. Here’s what I recommend:What interviewers should do:Ask the candidate if there are any accessibility needs at the start of the interview, and accommodate them as needed.Let your candidates know that you value authenticity and transferable skills over scripted perfection. Ask them not to rely on AI assistants directly!Focus on communication skills — can a candidate clearly and directly answer a question you’ve asked?Learn to detect AI assistants (ask direct questions, ask for examples).Stop the emphasis on a perfect resume / conversation and focus on transferable and soft skills instead.What what job seekers should do:Continue to use tools to cater your resume, cover letter, etc. as long as it remains truthful.Practice for interviews! Whether with a friend, or with an AI tool, mock interviews are a great idea.It’s ok to have notes in your interview, and kudos for being prepared! Just be ready for an unscripted conversation.If you are comfortable, disclose with your interviewer if you are using accessibility tools.Be your authentic self in your interview! Someone who uses AI to read a scripted answer will be disqualified immediately and barred from other roles. Not to be confused with the use of AI in general, which we completely encourage.In conclusion, AI is awesome for productivity and innovation. I’ve used it to edit this very post, including shortening some wordy sentences and spellchecking along the way. However, the negatives outweigh the positives when it comes to tooling during job interviews when used dishonestly, especially if it results in a candidate reading their full answers off a screen. I’d much prefer to have an authentic conversation with a candidate, and I welcome sharing tips on productive ways to use AI.A genuine, imperfect candidate will leave a much stronger impression and will result in a greater rate of success down the road, fostering an honest, vulnerable, and mutually beneficial relationship. Companies are hiring a person, not perfection, and the sooner both sides of an interview value that, the better.

From Experiment to Imperative: Why the Age of Incremental AI Is OverTony Moroney·Follow3 min read·Just now--Business leaders face a critical choice: orchestrate transformative AI now or be disrupted by those who do. The latest data shows that AI is no longer a fringe capability; it is becoming the operational core of competitive advantage. AI has shifted from innovation theatre to institutional transformation. Business leaders now confront a critical juncture: accelerate their AI maturity or risk irrelevance. Boards, in particular, must pivot from oversight to orchestration.According to the 2025 Stanford AI Index, nearly 78% of businesses now use AI, and over 70% report incorporating generative AI into at least one function — a figure that has more than doubled since 2023. However, while adoption metrics are soaring, true transformation remains elusive. Many companies are stuck in perpetual pilot mode, hesitating just as the AI landscape becomes faster, more affordable, and infinitely more strategic.Model costs have plummeted, and inference prices for GPT-3.5-level performance have decreased by over 280-fold. Meanwhile, open-weight models perform nearly the same level as their closed-source counterparts. This indicates that the old paradigm of “owning the best model” is swiftly being replaced by the necessity to integrate, deploy, and evolve rapidly. The barriers to access have crumbled. The next battlefield is orchestration.AI capabilities, particularly autonomous agents, are surpassing enterprise structures. Geopolitical pressures are intensifying the quest for digital dominance. The disruption isn’t merely technological — it’s institutional. The organisations that will thrive will be the most adaptive, orchestrated, and AI-native. Benchmarked through challenges like RE-Bench, these goal-directed systems can outperform human experts on constrained tasks, execute multi-step workflows, and learn in real time. Businesses must shift from viewing AI as a set of discrete tools to designing ecosystems where agents become operational actors. This isn’t an IT transformation — it’s an organisational redesign.But AI transformation isn’t just about speed and scale; it’s about trust. In 2024, AI incidents spiked by 56%, yet Responsible AI practices remain uneven. New benchmarks, such as HELM Safety and AIR-Bench, reveal significant gaps between model performance and ethical standards. This isn’t merely a theoretical issue; it’s a fiduciary one. Boards that view trust solely through a PR or compliance lens will be blindsided when AI risks manifest as market exclusion, regulatory fines, or class action lawsuits.According to KPMG’s 2025 Boardroom Lens, while 90% of directors see AI as a top priority, only 38% feel confident that their organisations have a clear AI strategy. Even fewer are receiving the appropriate governance metrics. That gap is not just technical — it’s existential. Boards must evolve. The question is no longer “Is management doing something with AI?” but “Are we fundamentally redesigning how value is created, governed, and defended in an AI-native world?”This involves rethinking risk frameworks for autonomous systems, demanding transparency in model governance, and challenging management on how AI aligns with core strategic levers — accelerating revenue, transforming cost structures, or enhancing adaptability. The future will not wait. Agentic enterprises, AI-powered disruptors, and RAI-driven regulations are already reshaping markets. Boards must prepare for scenarios where agents operate entire business units, AI-generated harm incites regulatory backlash, or open-source ecosystems foster a wave of vertical insurgents.The age of experimentation is over. This is the era of imperatives. The imperative is clear: either build an AI-native enterprise or risk being outpaced by those who do. Waiting for clarity is a strategy of decline. Leaders must move beyond exploration and commit to orchestration. AI must be embedded in the fabric of how work is done, how strategy is executed, and how organisations think.This is not a plug-in; it’s a re-platforming. The question isn’t whether AI is on your agenda; it’s whether your agenda is AI-native. Now is the time for boards to govern for AI orchestration.

The battle to keep online spaces safe and inclusive continues to evolve. As digital platforms multiply and user-generated content expands very quickly, the need for effective harmful content detection becomes paramount. What once relied solely on the diligence of human moderators has given way to agile, AI-powered tools reshaping how communities and organisations manage toxic behaviours in words and visuals. From moderators to machines: A brief history Early days of content moderation saw human teams tasked with combing through vast amounts of user-submitted materials – flagging hate speech, misinformation, explicit content, and manipulated images. While human insight brought valuable context and empathy, the sheer volume of submissions naturally outstripped what manual oversight could manage. Burnout among moderators also raised serious concerns. The result was delayed interventions, inconsistent judgment, and myriad harmful messages left unchecked. The rise of automated detection To address scale and consistency, early stages of automated detection software surfaced – chiefly, keyword filters and naïve algorithms. These could scan quickly for certain banned terms or suspicious phrases, offering some respite for moderation teams. However, contextless automation brought new challenges: benign messages were sometimes mistaken for malicious ones due to crude word-matching, and evolving slang frequently bypassed protection. AI and the next frontier in harmful content detection Artificial intelligence changed this field. Using deep learning, machine learning, and neural networks, AI-powered systems now process vast and diverse streams of data with previously impossible nuance. Rather than just flagging keywords, algorithms can detect intent, tone, and emergent abuse patterns. Textual harmful content detection Among the most pressing concerns are harmful or abusive messages on social networks, forums, and chats. Modern solutions, like the AI-powered hate speech detector developed by Vinish Kapoor, demonstrate how free, online tools have democratised access to reliable content moderation. The platform allows anyone to analyse a string of text for hate speech, harassment, violence, and other manifestations of online toxicity instantly – without technical know-how, subscriptions, or concern for privacy breaches. Such a detector moves beyond outdated keyword alarms by evaluating semantic meaning and context, so reducing false positives and highlighting sophisticated or coded abusive language drastically. The detection process adapts as internet linguistics evolve. Ensuring visual authenticity: AI in image review It’s not just text that requires vigilance. Images, widely shared on news feeds and messaging apps, pose unique risks: manipulated visuals often aim to misguide audiences or propagate conflict. AI-creators now offer robust tools for image anomaly detection. Here, AI algorithms scan for inconsistencies like noise patterns, flawed shadows, distorted perspective, or mismatches between content layers – common signals of editing or manufacture. The offerings stand out not only for accuracy but for sheer accessibility. Their completely free resources, overcome lack of technical requirements, and offer a privacy-centric approach that allows hobbyists, journalists, educators, and analysts to safeguard image integrity with remarkable simplicity. Modern AI solutions introduce vital advantages into the field: Instant analysis at scale: Millions of messages and media items can be scrutinized in seconds, vastly outpacing human moderation speeds. Contextual accuracy: By examining intent and latent meaning, AI-based content moderation vastly reduces wrongful flagging and adapts to shifting online trends. Data privacy assurance: With tools promising that neither text nor images are stored, users can check sensitive materials confidently. User-friendliness: Many tools require nothing more than scrolling to a website and pasting in text or uploading an image. The evolution continues: What’s next for harmful content detection? The future of digital safety likely hinges on greater collaboration between intelligent automation and skilled human input. As AI models learn from more nuanced examples, their ability to curb emergent forms of harm will expand. Yet human oversight remains essential for sensitive cases demanding empathy, ethics, and social understanding. With open, free solutions widely available and enhanced by privacy-first models, everyone from educators to business owners now possesses the tools to protect digital exchanges at scale – whether safeguarding group chats, user forums, comment threads, or email chains. Conclusion Harmful content detection has evolved dramatically – from slow, error-prone manual reviews to instantaneous, sophisticated, and privacy-conscious AI. Today’s innovations strike a balance between broad coverage, real-time intervention, and accessibility, reinforcing the idea that safer, more positive digital environments are in everyone’s reach – no matter their technical background or budget. (Image source: Unsplash)

AI agents handle increasingly complex and recurring tasks, such as planning supply chains and ordering equipment. As organisations deploy more agents developed by different vendors on different frameworks, agents can end up siloed, unable to coordinate or communicate. Lack of interoperability remains a challenge for organisations, with different agents making conflicting recommendations. It’s difficult to create standardised AI workflows, and agent integration require middleware, adding more potential failure points and layers of complexity. Google’s protocol will standardise AI agent communication Google unveiled its Agent2Agent (A2A) protocol at Cloud Next 2025 in an effort to standardise communication between diverse AI agents. A2A is an open protocol that allows independent AI agents to communicate and cooperate. It complements Anthropic’s Model Context Protocol (MCP), which provides models with context and tools. MCP connects agents to tools and other resources, and A2A connects agents to other agents. Google’s new protocol facilitates collaboration among AI agents on different platforms and vendors, and ensures secure, real-time communication, and task coordination. The two roles in an A2A-enabled system are a client agent and a remote agent. The client initiates a task to achieve a goal or on behalf of a user, It makes requests which the remote agent receives and acts on. Depending on who initiates the communication, an agent can be a client agent in one interaction and a remote agent in another. The protocol defines a standard message format and workflow for the interaction. Tasks are at the heart of A2A, with each task representing a work or conversation unit. The client agent sends the request to the remote agent’s send or task endpoint. The request includes instructions and a unique task ID. The remote agent creates a new task and starts working on it. Google enjoys broad industry support, with contributions from more than 50 technology partners like Intuit, Langchain, MongoDB, Atlassian, Box, Cohere, PayPal, Salesforce, SAP, Workday, ServiceNow, and UKG. Reputable service providers include Capgemini, Cognizant, Accenture, BCG, Deloitte, HCLTech, McKinsey, PwC, TCS, Infosys, KPMG, and Wipro. How HyperCycle aligns with A2A principles HyperCycle’s Node Factory framework makes it possible to deploy multiple agents, addressing existing challenges and enabling developers to create reliable, collaborative setups. The decentralised platform is advancing the bold concept of “the internet of AI” and using self-perpetuating nodes and a creative licensing model to enable AI deployments at scale. The framework helps achieve cross-platform interoperability by standardising interactions and supporting agents from different developers so agents can work cohesively, irrespective of origin. The platform’s peer-to-peer network links agents across an ecosystem, eliminating silos and enabling unified data sharing and coordination across nodes. The self-replicating nodes can scale, reducing infrastructure needs and distributing computational loads. Each Node Factory replicates up to ten times, with the number of nodes in the Factory doubling each time. Users can buy and operate Node Factories at ten different levels. Growth enhances each Factory’s capacity, fulfilling increasing demand for AI services. One node might host a communication-focused agent, while another supports a data analysis agent. Developers can create custom solutions by crafting multi-agent tools from the nodes they’re using, addressing scalability issues and siloed environments. HyperCycle’s Node Factory operates in a network using Toda/IP architecture, which parallels TCP/IP. The network encompasses hundreds of thousands of nodes, letting developers integrate third-party agents. A developer can enhance function by incorporating a third-party analytics agent, sharing intelligence, and promoting collaboration across the network. According to Toufi Saliba, HyperCycle’s CEO, the exciting development from Google around A2A represents a major milestone for his agent cooperation project. The news supports his vision of interoperable, scalable AI agents. In an X post, he said many more AI agents will now be able to access the nodes produced by HyperCycle Factories. Nodes can be plugged into any A2A, giving each AI agent in Google Cloud (and its 50+ partners) near-instant access to AWS agents, Microsoft agents, and the entire internet of AI. Saliba’s statement highlights A2A’s potential and its synergy with HyperCycle’s mission. The security and speed of HyperCycle’s Layer 0++ HyperCycle’s Layer 0++ blockchain infrastructure offers security and speed, and complements A2A by providing a decentralised, secure infrastructure for AI agent interactions. Layer 0++ is an innovative blockchain operating on Toda/IP, which divides network packets into smaller pieces and distributes them across nodes. It can also extend the usability of other blockchains by bridging to them, which means HyperCycle can enhance the functionality of Bitcoin, Ethereum, Avalanche, Cosmos, Cardano, Polygon, Algorand, and Polkadot rather than compete with those blockchains. DeFi, decentralised payments, swarm AI, and other use cases HyperCycle has potential in areas like DeFi, swarm AI, media ratings and rewards, decentralised payments, and computer processing. Swarm AI is a collective intelligence system where individual agents collaborate to solve complicated problems. They can interoperate more often with HyperCycle, leading to lightweight agents carrying out complex internal processes. The HyperCycle platform can improve ratings and rewards in media networks through micro-transactions. The ability to perform high-frequency, high-speed, low-cost, on-chain trading presents innumerable opportunities in DeFi. It can streamline decentralised payments and computer processing by increasing the speed and reducing the cost of blockchain transactions. HyperCycle’s efforts to improve access to information precede Google’s announcement. In January 2025, the platform announced it had launched a joint initiative with YMCA – an AI app called Hyper-Y that will connect 64 million people in 12,000 YMCA locations across 120 countries, providing staff, members, and volunteers with access to information from the global network. HyperCycle’s efforts and Google’s A2A converge Google hopes its protocol will pave the way for collaboration to solve complex problems and will build the protocol with the community, in the open. A2A was released as open-source with plans to set up contribution pathways. HyperCycle’s innovations aim to enable collaborative problem-solving by connecting AI to a global network of specialised abilities as A2A standardises communication between agents regardless of their vendor or build, so introducing more collaborative multi-agent ecosystems. A2A and Hypercycle bring ease of use, modularity, scalability, and security to AI agent systems. They can unlock a new era of agent interoperability, creating more flexible and powerful agentic systems. (Image source: Unsplash)