AI News Archive: June 4, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
- The Sequence Opinion #872: The Cake Is a Battlefield: Who Really Controls the AI Stack
Full stacks vs layer specialists. That's the AI race.
- Fault Tolerance in LangGraph: Retries, Timeouts, and Error Handlers
Implementing fault tolerance in LangGraph
- Why 80% of AI Projects Never Make It Past the Trial Phase
The overlooked deployment problem costing companies billions in unrealized AI value. Created by Gemini Imagine spending millions of dollars building the world’s fastest Formula 1 car. The engineers are brilliant, the technology is groundbreaking, and every test run is a success. Investors celebrate. Executives call it the future. Then race day arrives — and the car never leaves the garage. Not because it failed, but because nobody figured out how to get it onto the track. Sounds unbelievable? That’s exactly what happens to most AI projects today. When AI Ambition Meets Reality Since generative AI entered the mainstream, companies across industries have rushed to adopt it. Banks are building virtual assistants, hospitals are exploring AI-powered diagnostics, retailers are improving demand forecasting, and manufacturers are automating operations. The potential is enormous, and few organizations want to be left behind. However, many AI projects never move beyond the testing phase. They secure funding, generate excitement, and produce promising results, only to disappear before creating meaningful business impact. The problem is not that AI doesn’t work. In many cases, it performs exactly as expected. The real challenge is turning a successful experiment into a system that people can reliably use in the real world. A Retail Giant’s Million-Dollar Prediction Problem Consider a large retail company that develops an AI-powered forecasting system to predict customer demand. During pilot testing, the model delivers impressive results, identifying seasonal trends, predicting inventory shortages, and outperforming traditional forecasting methods. From a technical perspective, the project is a success. Executives see opportunities to reduce waste, lower costs, and improve product availability. However, scaling the system proves far more difficult than building it. Different stores use different software platforms, data is recorded inconsistently, and many managers continue relying on spreadsheets and experience. As implementation challenges grow, the project begins to stall. The AI continues producing accurate forecasts, but the organization struggles to integrate those insights into daily decision-making. This example highlights a common cause of Pilot Purgatory: success in a pilot environment does not guarantee success at scale. Without data quality, workflow integration, employee adoption, and ownership, even the most accurate AI system may fail to create meaningful business impact. IBM Watson: The AI That Was Supposed to Transform Cancer Care In 2011, IBM’s Watson defeated human champions on the quiz show Jeopardy! , becoming a symbol of what artificial intelligence might achieve. A few years later, IBM introduced Watson for Oncology, an AI system designed to analyze medical records, research papers, and clinical studies to help doctors identify treatment options for cancer patients. The project generated enormous excitement, with many seeing it as a major breakthrough in healthcare. However, hospitals soon discovered that real-world medicine is far more complex than a controlled environment. Every patient is different, and treatment decisions often depend on factors that cannot be neatly captured in data. As healthcare providers attempted to integrate Watson into their workflows, adoption remained limited. Watson’s story highlights an important lesson: building an intelligent system is only half the battle. The harder challenge is making that intelligence work effectively in the real world. When Amazon Learned That AI Can Inherit Human Bias One of the most frequently cited examples of AI deployment challenges comes from Amazon’s experimental recruiting tool. The company trained an AI system on historical hiring data to identify promising job candidates and improve recruitment efficiency. However, the project revealed a fundamental challenge: models learn from the data they are given, including its flaws. Instead of acting as an objective evaluator, the system began reflecting patterns present in past recruitment decisions. The issue was not that the AI stopped working. It was doing exactly what it had been designed to do — identify patterns from previous hiring outcomes. The problem was that some of those patterns conflicted with the company’s goals and values. As concerns about fairness and reliability grew, Amazon ultimately discontinued the project. The case highlights an important reality: success is not determined solely by technical performance. Organizations must also consider fairness, transparency, compliance, and trust. In the context of Pilot Purgatory, Amazon’s recruiting tool demonstrates that the greatest obstacles to AI adoption are often organizational and ethical rather than technological. The Real Problem Isn’t Building AI Most people assume creating the model is the difficult part. Often, it’s the easiest part. The real challenge begins afterward. Think about everything an AI system must survive before reaching production: Security reviews Compliance checks Legal approval Data integration Employee training Infrastructure scaling Budget reviews Executive expectations An AI prototype only needs to impress a small group of people. A production system must survive an entire organization. Why Employees Can Become the Biggest Obstacle to AI Adoption One overlooked reason AI projects stall is surprisingly human. People don’t always trust machines. Imagine you’ve worked in customer service for fifteen years. One day management introduces an AI tool that claims it can answer customer questions faster than you. Would you immediately trust it? Probably not. Many organizations discover that their biggest challenge isn’t technical. It’s behavioral. Employees often continue using familiar tools because familiarity feels safer than innovation. A perfect algorithm means very little if nobody wants to use it. Self-Driving Cars: The Ultimate Test of AI Deployment Self-driving cars are one of the clearest examples of Pilot Purgatory. For years, companies have demonstrated vehicles that can navigate roads, recognize traffic signals, and make driving decisions without human input. The technology is impressive, yet large-scale adoption remains limited. The reason is simple: driving is not a controlled environment. Road construction, bad weather, unpredictable pedestrians, and countless rare situations create challenges that are difficult to anticipate. The question is no longer whether AI can drive a car — it can. The real question is whether it can do so safely and reliably millions of times. This gap between a successful demonstration and widespread deployment perfectly illustrates the challenge facing many AI projects today. The Hidden Cost Nobody Calculates When people discuss AI projects, they usually focus on development costs. What they rarely discuss is the cost of unfinished projects. Every stalled pilot represents: Wasted engineering hours Lost business opportunities Delayed innovation Executive skepticism Reduced future investment An organization that repeatedly fails to deploy AI often becomes hesitant to fund future initiatives. In this way, one failed pilot can affect many future projects. The Companies Winning the AI Race Are Different The organizations creating real value from AI aren’t necessarily building the smartest models. They’re building systems people actually use. Instead of chasing flashy demonstrations, they focus on practical outcomes: Faster customer support Better fraud detection Improved logistics Reduced operational costs Increased productivity Their goal isn’t to impress executives during a presentation. Their goal is to solve a problem every single day. That difference changes everything. Final Thoughts The biggest myth in modern technology is that AI success depends entirely on intelligence. In reality, intelligence is only the beginning. The history of technology is full of brilliant inventions that never changed the world because they never escaped the laboratory. AI faces the same challenge. Years from now, the most important question won’t be: “Which company built the smartest AI?” It will be: “Which company figured out how to make AI useful?” Because in business, a revolutionary idea locked inside a pilot program creates exactly the same value as no idea at all. Why 80% of AI Projects Never Make It Past the Trial Phase was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
- AI reshapes jobs while increasing mental demands
AI boosts productivity for SA’s workforce, but significantly increases cognitive load and workplace mental strain, a study finds.
Score: 35🌐 MovesJun 4, 2026https://www.itweb.co.za/article/ai-reshapes-jobs-while-increasing-mental-demands/G98YdMLGjLR7X2PD - AI and the new productivity curve for developers
AI and the new productivity curve for developers YourStory.com
Score: 35🌐 MovesJun 4, 2026https://yourstory.com/2026/06/ai-and-the-new-productivity-curve-for-developers - Radiant Logic extends identity visibility platform to enterprise AI agents with real-time risk scoring
Radiant Logic Inc., a platform that provides identity visibility and intelligence, today announced it’s extending its services to agentic artificial intelligence to help companies control and govern this new type of autonomous software. As autonomous AI systems proliferate, including AI agents, and begin to join employees within corporate networks, these systems are being given deep […] The post Radiant Logic extends identity visibility platform to enterprise AI agents with real-time risk scoring appeared first on SiliconANGLE .
- Wheels beat legs in the robot race
Today, we look at the Chinese robotics unicorn wagering on wheels over legs and a European quantum computing startup planting its flag in Singapore.
- Rillet CEO Nicolas Kopp says A.I. ERP is ‘next wave’ after cloud ERP
Rillet CEO and founder Nicolas Kopp says A.I. ERP is ‘next wave’ after cloud ERP
- AI is ushering in a new era of colonialism
As AI changes the way the world gathers information, some critics say that it is perpetuating stereotypes and erasing cultural nuances for Indigenous groups and people of color. Why it matters: Most mainstream models are trained on the work of Western writers — particularly white men — and regularly mimic those values, writing styles , viewpoints and biases . Some critics say the data grab is a new form of colonialism , where information gathering replaces Imperial-era land seizures while the AI companies — rather than a conquering nation — reap profits from marginalized groups. Data collection from these groups is often done without their consent or any verification that the information is accurate. What they're saying: "Colonialism is always portrayed as something that happened in the past … many countries got independence, and then the textbooks say 'colonialism is over,'" Julian Posada, a Yale professor who studies the relationship between human labor and data production, tells Axios. Posada says that modern-day colonialism still exists, but people often fail to recognize it. Context: Most large language models are made by the WEIRD: Western, Educated, Industrialized, Rich, and Democratic societies, and pull data from social media, websites, news archives, and digitized materials that largely originate in North America and Europe. Those training materials have resulted in LLMs inventing details based on Western assumptions about cultural traditions or values, and those errors persist despite Big Tech putting in work to train them with more diverse viewpoints and data. Case in point: Aditya Vashistha, a professor at Cornell University, tells Axios that AI models will often say all Indian food is "rich and aromatic and spicy," but some isn't, flattening the diversity of the Indian palette. "You will find different regional cuisines which differ in the spices which are used, or in what moderation, like the amounts they use." Zoom out: Taking the data itself is a "deeply colonial act," Nick Couldry, co-author of "Data Grab: The New Colonialism of Big Tech and How to Fight Back," tells Axios. "To say, 'well, it's just out there. We can just take it.' That was what colonialism was about, just taking everything." "Not only can we take it, but we should take it, and we're entitled to take it and make everything we want out of it, extract as much profit." Zoom in: Big Tech's push to move fast and generate profit exacerbates the problem, Michael Sherbert, an Algonquin of Pikwàkanagàn First Nation and a fellow at Queen's University, tells Axios. "A lot of American AI companies are trying to outperform Chinese companies, right? Taking time to discuss issues and knowledge with indigenous communities is very costly. It takes a lot of time, and could make them fall behind." Brian Ritchie, founder of kama.ai and a member of Ontario's Chapleau Cree First Nation, tells Axios he's attended many summits with Indigenous leaders and hasn't personally "seen any history where indigenous people have been involved" in training AI. Worth noting: Many Indigenous traditions are not being accounted for by AI because they are passed down through oral history rather than written words that an LLM can access. And other knowledge is intentionally kept private, Sherbert says. The bottom line: "It's not just misinformation that's the problem," Sherbert says. "These systems, the answers that these LLMs are giving, are increasingly shaping how people understand themselves, culture, history, identity, and even what's true and legitimate." Go deeper: The continuing problem of AI bias
- Anthropic President Daniela Amodei was asked about tokenmaxxing. Here's what she said.
Anthropic President Daniela Amodei was asked about tokenmaxxing. Here's what she said. Business Insider
Score: 35🌐 MovesJun 4, 2026https://www.businessinsider.com/anthropic-president-daniela-amodei-tokenmaxxing-ai-coding-claude-code-2026-6 - These States Are Winning The Race For Federal AI Education Funding
A new analysis of NSF awards has surprising findings on where colleges are attracting the most AI-related research, internships and workforce development programs.
- The skeptic’s guide to humanoid robots going viral on the Internet
Robot demonstrations can distort public perceptions of robotic capabilities.
Score: 35🌐 MovesJun 4, 2026https://arstechnica.com/ai/2026/06/the-skeptics-guide-to-humanoid-robots-going-viral-on-the-internet/ - AI boom creates connectivity challenge for integrators
The latest industry insight from Altnets aligns with the findings of other recent research and product launches from major IT providers, finding that thanks to the seemingly endless rise of artificial intelligence (AI), the next major challenge facing integrators will be the need to deploy the physical infrastructure needed to support AI’s growth. The report forms the second instalment in Altnets’ whitepaper series exploring market shifts, fibre demand and the infrastructure strategies shaping the future of digital connectivity. The first paper examined the evolving fibre shortage landscape and mitigation strategies. The latest edition explores how accelerating AI adoption, datacentre expansion and growing digital demand is placing increasing pressure on networks, fibre infrastructure and supply chains. Indeed, it noted that what began as a surge in AI software innovation has quickly evolved into one of the largest infrastructure expansion cycles the technology sector has ever experienced. It stressed that the AI boom is not just a computer story, but a connectivity story. The whitepaper explained how AI is increasingly becoming an infrastructure challenge as much as a technology one, and that infrastructure such as fibre networks, optical connectivity, backhaul capacity and interconnect architecture is rapidly becoming the foundation that future digital economies will rely on. At the same time, broader societal and technological shifts – including automation, fixed wireless access (FWA), edge computing, the internet of things (IoT) and increasingly mobile-first behaviours – are continuing to drive significant increases in global data consumption. According to the paper, this is accelerating demand for dense fibre connectivity across centralised datacentre environments and, increasingly, distributed edge and wireless infrastructure. As a result, the industry is entering a new phase of infrastructure development, where resilience is no longer simply about mitigating disruption. Organisations that invest in scalable connectivity and long-term infrastructure strategy today will be better positioned to support the demands of tomorrow’s AI-driven economy Andy Ainsley, Altnets Fibre was pinpointed as the strategic resource behind AI. As AI workloads continue to scale, fibre and optical connectivity are emerging as critical infrastructure, and the challenge is shifting from generating compute power to transporting vast volumes of data across increasingly distributed environments with speed, reliability and ultra-low latency. Altnets noted that AI models require enormous amounts of data to move continuously between hyperscale datacentres, cloud environments, metro networks and edge infrastructure. In addition, connectivity infrastructure in the age of AI was shown to be no longer just enabling digital transformation – rather, it is shaping the speed, scale and competitiveness of entire digital economies. The paper highlighted how across the globe, hyperscale datacentres are expanding at an accelerated pace as developers race to increase compute capacity, process larger AI workloads and support the growing demands of automation, cloud services and real-time digital connectivity . It also highlighted a number of industry trends that the connectivity firm said were “significant”. First, it cited ABI Research showing that global active datacentre capacity is forecast to increase almost sixfold between 2025 and 2035, rising from 24.4GW to 147.1GW. It also cited JLL data predicting that AI workloads could account for approximately 50% of total global datacentre capacity by the end of the decade. It added that proposed AI-related datacentre projects currently seeking UK grid connections could require around 50GW of electricity capacity, exceeding Great Britain’s current peak demand. Another key finding was that for integrators, the challenge is no longer simply expanding capacity, but about building scalable, future-ready networks capable of supporting unknown future demand in an increasingly distributed and AI-driven digital landscape. The integrators best positioned to lead the next phase of digital infrastructure growth will be those capable of combining intelligent network design, resilient supply ecosystems, strategic collaboration and future-ready infrastructure planning into long-term operational advantage. Altnets suggested that as fibre, backhaul and interconnect architecture become increasingly strategic, providers will need partners that understand not only product availability, but also network architecture, supply chain management and long-term deployment resilience. “The industry is moving into a new era where network resilience and infrastructure readiness are becoming just as important as capacity itself. Organisations that invest in scalable connectivity and long-term infrastructure strategy today will be better positioned to support the demands of tomorrow’s AI-driven economy,” remarked Altnets commercial director Andy Ainsley. Read more about AI in networking Colt expands network in Istanbul to support AI‑ready infrastructure : Global digital infrastructure provider expands major connectivity hub for Asia and Europe, allowing local and international users to access wider portfolio of scalable networking services. Motive offers vision of new era of physical AI operations : New products aim to shift operational burden from people to technology, automating ‘busy work’ so fleets can prioritise strategic safety, productivity and profitability. Implementation gap threatens progress in AI and 5G : Despite current patchy deployment of key 5G services, study finds that across regions, company sizes and markets, telecoms leaders are strikingly confident about their ability to capture the next wave of growth. Nokia enters cognitive broadband era with agentic AI capabilities : As the telecoms industry looks to invest heavily in agentic AI, Nokia unveils a plan to tackle fibre and Wi-Fi challenges, boost user experience and increase operational efficiency.
Score: 34🌐 MovesJun 4, 2026https://www.computerweekly.com/news/366643917/AI-boom-creates-connectivity-challenge-for-integrators - Sitar-agent: Building a reliable dynamic configuration sidecar at scale
How Airbnb built a Kubernetes sidecar to deliver dynamic configuration reliably at scale. By : Bo Teng , Cosmo Qiu , Siyuan Zhou , Ankur Soni , Xin Huang , Willis Harvey Introduction In our previous post , we explored Airbnb’s dynamic configuration system, Sitar, with a focus on service architecture and configuration change safety. Now for the harder question: once a config change is committed, which happens several times each minute, how does it actually reach the thousands of Airbnb’s service instances reliably, quickly, and without redeploying the services? This post describes sitar agent: a lightweight Kubernetes sidecar that runs alongside every subscribed service pod, continuously synchronizing the latest configurations from the service backend and making them available on the local filesystem for reads. In this post, we will first go through the configuration delivery life cycle, and then discuss some key design choices for the sitar-agent sidecar. Config delivery life cycle The diagram below illustrates the end-to-end journey of a configuration change, from the developer-facing layer to the production service fleet. Sitar config delivery lifecycle Step 1 — Config creation/update Developers create or update configuration values through either Git flow or the web UI. These changes are committed to the Sitar Service, where they are stored with full versioning, change logs, and ACL enforcement. Step 2 — Hourly snapshot upload The Snapshot Service periodically packages the full state of all config groups and uploads compressed snapshots to AWS S3. Step 3.1 — Preload snapshot from S3 (on pod startup) When a production service pod starts, the sitar-agent sidecar runs first. It downloads the latest snapshot for each subscribed tenant’s configs from S3 to the mounted disk (shared between sitar-agent and the main container). This allows the agent to bootstrap from a known-good state without fetching every config from the Sitar Service from scratch on every restart. Preloading the snapshots from S3 enables faster restarts, makes the service resilient to transient Sitar Service unavailability, and avoids load spikes during deployments. Step 3.2 — Preload latest config from Sitar Service (on pod startup) After loading the S3 snapshot, the agent performs an initial sync with the Sitar Service to catch up on any changes published since the last snapshot. Once this step succeeds, the agent signals readiness, unblocking the application main container from starting. Step 4 — Periodic update After startup, the agent enters a continuous polling loop (order of seconds with jitter). On each cycle, the sitar agent queries the Sitar Service for changes across all subscribed groups. Step 5 — Read config The application main container reads configurations from the mounted disk through the Sitar client library, which maintains an in-memory cache. The client detects file changes and refreshes its cache transparently. With the delivery lifecycle in mind, the following sections walk through the major architectural choices that shaped the sidecar’s design. Key design decisions In 2024, the sitar-agent underwent a full rewrite from Ruby to Java, Airbnb’s mainstream JVM language, giving the team an opportunity to modernize the architecture alongside the language migration. The snapshot-based S3 preload introduced in the previous section is one outcome of this effort: it dramatically reduces cold start time for the pod and decouples startup reliability from Sitar Service availability. The rewrite also led to several other deliberate design decisions around reliability, performance, and operational safety. The sections below walk through each of these choices. Requirements for the Sitar System Before diving into specific design choices, it helps to understand the constraints that shaped every decision. At Airbnb, dynamic configuration delivery isn’t just a convenience: it controls critical features across thousands of services. That means configs must always be available, even when the Sitar Service itself is down; a slightly stale value is tolerable, but an unreadable config is not. At the same time, when an engineer pushes a change, it needs to reach every subscribed service within tens of seconds, not minutes. Making that work at scale is non-trivial: with tens of thousands of pods fetching updates simultaneously, the system has to absorb that load without degrading. And since Airbnb’s service fleet spans Java, Python, Go, Typescript, and Ruby, the solution needs to serve all of them, ideally minimizing the effort of maintaining separate per-language implementations. The above requirements for reliability, performance, scalability, and multi-language support aren’t independent. As you’ll see, most of our design decisions, described below, come back to balancing one against another. Main container vs sidecar The question of whether sitar-agent should run as a sidecar container or a process in the main container surfaced as a key architectural decision during the Java rewrite. We evaluated the pros and cons of each option as follows: Pros of moving to the main container: Cost reduction. This is the main driver for moving to the main container: running the agent as a library eliminates the per-pod JVM overhead, allowing memory and CPU to be shared with the main container. Reduced operational surface. One fewer container means one fewer component for service owners to configure and tune. However, this advantage weakens when considering Airbnb’s multi-language service fleet. Cons of moving to the main container: Multi-language complexity. Airbnb service languages span Java, Python, Go, Typescript, and Ruby. A library approach would require the existing sidecar logic to be implemented in all languages, significantly increasing development and maintenance effort. No isolation. Bugs or resource spikes in sitar logic can crash or starve the main container, and vice versa. This coupling increases incident blast radius and complicates resource attribution during debugging. Operational noise. Having the logs for Sitar and its cpu/memory usage mixed with the main process logs and its metrics makes it harder to debug both sitar and main process issues. Optimizability. Having a separate container allows the container to be optimized for its purpose, and eases testing and debugging. Decision: Despite the cost savings and reduced operational surface which would result from moving the sitar-agent logic to the main container, the projected savings were insufficient to justify the tradeoffs in reliability and operational overhead, and the development overhead of supporting the sidecar logic in multiple languages. We therefore decided to maintain the sitar-agent as an isolated sidecar container. The pull model and server-side optimization Sitar-agent fetches configuration updates by polling the Sitar service every 10 seconds. This is a pull model: the agent drives the update cycle by periodically asking the server for changes. This pull-based architecture, while being simple and easy to maintain, generates unnecessary load on the server when there is no update needed. A push-based architecture change can greatly reduce the server-side load and change propagation time, at the expense of a more complicated architecture. In order to keep the current simple architecture while reducing the service-side load, the sitar system implements the following optimizations: Since the sitar config is mostly changed manually, which takes longer than several seconds, a slight delay in config update delivery is acceptable. Therefore, a server-side cache with a short TTL (10s) is a great way to reduce sitar server-side processing. Most of the sitar-agent calls to services hit the cache layer without triggering heavy server-side compute or database access, thus greatly reducing the resource usage of handling requests. When there is a cache miss and the request actually triggers database access, it passes along a token (last scanned db row), which tells the service to skip scanning for changes before the last fetch, thus greatly reducing server-side processing and database access time during each periodic pull. Given the above optimizations, the sitar-service can scale and perform quite well in handling the pull request from all service pods at Airbnb, and we can preserve the simple, stateless server-with-pull architecture. Decision: For sitar’s use case, polling latency on the order of seconds is acceptable; dynamic config is not a real-time signaling mechanism, and most config changes are manual, making a few seconds of propagation delay inconsequential. The pull model’s stateless simplicity is a strong operational advantage at Airbnb’s scale. The team elected to keep the pull model and invest instead in reducing per-poll cost. Local datastore selection Sitar-agent maintains a local on-disk key-value store that the main container reads from. The legacy datastore is a Sparkey -backed internal implementation , with a thin layer around the Sparkey datastore for concurrent coordination. As the usage of Sitar continues to grow and evolve, the mismatch of the Sparkey-backed datastore and sitar’s needs have become evident: Sparkey is purpose-built for write-once, read-many workloads with no support for multi-thread read-write coordination. This requires a wrapper around the Sparkey datastore for concurrent coordination to support sitar’s frequent write to the datastore, adding to complexity and potentially becoming a source of latent bugs. Sparkey doesn’t include native concurrency support by design, and we needed an external locking mechanism that locks the entire datastore file on write. As update frequency increased across the datastore, this lock contention began to limit concurrent read/write performance. Since Sparkey’s design requires re-indexing of the entire datastore on each write, writing frequently to the Sparkey backed datastore became increasingly expensive. However, as Sitar has become widely used across almost all Airbnb services, the write to the datastore is very frequent; we see updates in configs in almost every pull cycle (every ~10 seconds) Sparkey has limited multi-language support: it does not have implementations in all languages Airbnb services require, and supporting all languages in Airbnb would require complex interop. The team evaluated and benchmarked two candidates to replace the legacy Sparkey-based datastore: SQLite and RocksDB. A matrix of experiments were run across varying dataset sizes, read QPS, and memory allocations, fixing two of the three dimensions and varying the third in each run. We also researched community support, open source activity, supported languages, and adoption breadth of both. The following summarizes our findings: SQLite: Pros: Mature, widely-adopted library with officially maintained bindings for Java, TypeScript/Node.js, Python, Go and Ruby: all languages used by sitar’s service consumers. Built-in write-ahead logging (WAL) mode supports concurrent reads during writes, eliminating the need for a custom concurrency wrapper. Simple operational model: a single file, no background compaction or tuning required. Read and write performance is dramatically better than the Sparkey-backed datastore, and sufficient for sitar’s workload. Cons: Read latency is 2–3x slower than RocksDB, and increases linearly with data size. Write latency also increases with larger data sizes. RocksDB: Pros: Best raw read/write performance across all test dimensions. Consistent read high-QPS performance; tested up to 1500 ops/sec with minimal degradation. Cons: A more complex operational model; requires tuning of compaction, block cache, column families, and memory settings. The multi-language library ecosystem is less mature and less uniformly maintained than SQLite’s. Higher operational burden for a team without deep RocksDB expertise. Decision: In our tests, both RocksDB and SQLite significantly outperform Sparkey-backed datastores for our workload across all three test dimensions: data size, memory allocation, and read QPS. While RocksDB delivers better raw performance, sitar-agent’s workload operates comfortably within SQLite’s envelope. SQLite’s first-class multi-language library support, native WAL-based concurrent access model, and simpler operational footprint made it the better overall fit for a team supporting multiple language runtimes. The team selected SQLite as the replacement for the Sparkey-backed datastore. Safe migration from Sparkey to SQLite Operational safety was a top priority. Beyond extensive testing, we also we relied on two mechanisms to keep the rollout safe: Shadow reads : Before migrating each service, we ran a shadow read-and-compare phase; services continued reading from Sparkey while SQLite results were fetched in parallel for validation. Feature flag-gated gradual rollout : We migrated incrementally, starting from the least critical services and progressing toward the most critical. Some critical Tier 0 services were onboarded last, with dedicated coordination at each step. Conclusions Sitar-agent sits at the core of Airbnb’s dynamic configuration delivery system. This post walked through how it works and the key tradeoffs we navigated during the Java rewrite: between cost and isolation, simplicity and push-based efficiency, and raw performance and operational practicality. Every decision came back to the same constraints: configs must always be available, changes must propagate quickly across a fleet of tens of thousands of pods, and the solution must work across Airbnb’s polyglot service stack without compounding the maintenance burden. If this type of work interests you, check out some of our related positions ! Acknowledgments Our progress with Sitar would not have been possible without the support and contributions of many people. We’d like to thank Craig Sosin, Nikolaj Nielsen, Daniel Fagnan, Alex Edwards, Nick Morgan, Carolina Calderon, Hanfei Lin, Yunong Liu, Lucas Rosa Galego, Yann Ramin, Denis Sheahan, Richa Khandelwal, Swetha Vaidy, Adam Kocoloski, Adam Miskiewicz, and all the other engineers and teams at Airbnb who joined design reviews and offered valuable feedback, as this work would not have been possible without them. All product names, logos, and brands are property of their respective owners. All company, product, and service names used in this website are for identification purposes only. Use of these names, logos, and brands does not imply endorsement. Sitar-agent: Building a reliable dynamic configuration sidecar at scale was originally published in The Airbnb Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.
- IRCTC reinforces its AI monitoring system to ensure hygiene and quality of food in trains
IRCTC reinforces its AI monitoring system to ensure hygiene and quality of food in trains
- Token anxiety and the hidden cost of control in the AI era
Park Chul-wan The author is a professor of Department of Smart Automotive Engineering and Future Mobility Master’s Program at Seojeong University. The atmosphere at Silicon Valley house parties is changing. Developers no longer keep checking social media feeds or stock prices. Instead, many find themselves constantly monitoring the status of AI agents running in the background. Venture capitalist Nikunj Kothari has called this phenomenon “token anxiety.” A token is the basic unit of computation used by artificial intelligence. As AI systems work around the clock, humans increasingly worry about what those systems are doing. A staff member explains how to build and run Claw Agents at the Computex Taipei exhibition in Taipei, Taiwan, Wednesday, June 3. [AP/YONHAP] Tokens also carry a financial cost. The more tokens an AI system consumes, the higher the bill. Yet the deeper issue is not computing expenses themselves but the cost of maintaining human control over increasingly autonomous systems. The trend is closely tied to the rise of “vibe coding.” Rather than writing code line by line, developers describe a desired outcome in natural language and let large language models handle the implementation. Users see only the finished result, much like a completed dish placed on a dining table. Hidden from view are countless model calls, debugging cycles and iterations taking place behind the scenes. The less transparent the process becomes, the more invisible costs accumulate. During the prototype stage, this approach can appear revolutionary. Once a product enters real-world operation, however, the tradeoffs become clear. If developers do not fully understand AI-generated code, even minor bugs can be difficult to fix. Users become less like programmers and more like managers. They spend additional time providing context, validating outputs and monitoring errors. When the technical debt and maintenance burden left by AI-generated code are taken into account, the long-term cost of control can easily exceed visible server expenses. Related Article Agentic AI era demands state-backed industrial strategy Agentic AI ignites efficiency race amid memory crunch Science Ministry launches Agentic AI Alliance consultative body with LG, Kakao The butterfly effect of the Anthropic contract termination Google reportedly developing AI agent ahead of annual conference The same principle applies to physical AI systems operating in the real world. Because humans cannot continuously monitor and intervene in physical space in real time, systems such as autonomous vehicles must make reliable decisions within milliseconds. Competitive advantage does not come from processing ever-larger amounts of data. It comes from achieving a level of reliability that people can trust while using the minimum amount of computation necessary. For that reason, the next stage of AI competition will not be defined solely by model performance. Systems that achieve greater reliability with fewer tokens are likely to outperform larger systems that consume ever-increasing computational resources. The key measure is not volume of computation but the density of meaningful information. Ultimately, token anxiety reflects fear of losing control rather than concern about computing costs. Automation that lacks clear rules about when AI should stop and when human verification should begin creates a new form of waste. An uncontrolled loop is inefficiency disguised as efficiency. This article was originally written in Korean and translated by a bilingual reporter with the help of generative AI tools. It was then edited by a native English-speaking editor. All AI-assisted translations are reviewed and refined by our newsroom.
- Integra and SettleMint to expand AI blockchain-enabled real estate tokenization to the UAE and USA
Companies will establish a structured framework to integrate Integra’s real estate ecosystem platform with SettleMint’s DALP platform
- Irish doctor launches artificial intelligence app to transform medical training
Real-time learning platform designed by RCSI graduate helps medical students get the most from hospital placements
- “코딩 AI 비용 폭탄 막는다” IBM 작업 쪼개 최적 모델 골라주는 ‘밥’으로 코딩 시장 정조준
기존 AI 코딩 서비스가 우수한 자체 모델을 기반으로 코딩 실력을 내세웠다면, IBM은 여러 회사의 모델을 종합적으로 가져와 ‘비용 효율’을 내세웠다. AI 코딩 및 IT 업무에 사용량 기반 과금이 보편화되면서 기업의 비용 관리 부담이 커진 상황 에서, IBM의 ‘밥’은 작업 난이도에 따라 최적의 모델을 실시간으로 선택·전환해 비용을 통제한다. 또한 보안 기능을 강화해 단순한 AI 코딩 도구를 넘어 엔터프라이즈 AI 플랫폼으로 자리매김하겠다는 구상이다. IBM 밥 솔루션 부사장 겸 캐나다 연구소장 마이클 쿽 (Michael Kwok)은 “기존 AI 코딩 도구들이 코드 작성이나 코드 조각 생성에 집중했다면, 실제 기업 환경에서는 테스트, 보안, 거버넌스, 레거시 시스템 의존성 등 보이지 않는 요소들이 개발 속도를 결정한다”며 “밥은 SDLC 전 과정을 지원하는 파트너로 설계됐다”고 설명했다. IBM에 따르면 밥은 코드 생성뿐 아니라 코드 분석, 문서화, 아키텍처 다이어그램 작성, 테스트, 보안 취약점 분석, 애플리케이션 현대화(modernization)까지 수행한다. 개발자가 작업 중인 IDE나 터미널 환경에서 동작하며, 전체 코드베이스를 이해해 맥락 기반 작업을 지원한다. 이미 IBM 내부에서는 매일 10만 명 이상의 개발자가 밥을 사용하고 있다. IBM에 따르면 밥의 코드 가운데 약 40%도 밥이 직접 작성했다. 자체 평가 결과, 소프트웨어 개발 사이클 전반에서 평균 45% 이상의 생산성 향상을 달성한 것으로 나타났다. IBM 사내 400개 이상의 제품에 밥을 적용하고 있으며, 외부에서도 지난 반년여 동안 50곳 이상의 고객사가 밥을 도입했다. IBM 밥 선임 기술 책임자 제이 탈레카 (Jay Talekar)는 밥의 경쟁력으로 비용 효율성을 강조했다. 그는 “밥은 입력·출력·캐시 토큰을 기준으로 과금하며, 보통 이 중 출력 토큰이 가장 비싸다”고 말했다. 밥은 개발자가 작업 하나를 맡기면 이를 여러 하위 작업으로 분할한다. 복잡한 하위 작업은 프런티어(최신 대형) 모델로 보내고, 테스트 케이스 생성처럼 고성능 모델이 필요 없는 작업은 IBM의 그래니트(Granite) 같은 소형 모델로 처리한다. 덕분에 간단한 작업까지 비싼 최신 모델로 무조건 처리하는 상황을 피해 토큰 비용을 줄인다는 것이다. 동시에 밥은 비용 관점에서 모델 선택에 따르는 고민의 부담을 더는 데도 집중했다. 그는 “밥에는 모델을 고르는 드롭다운 메뉴가 없다. 어떤 모델을 쓸지는 런타임에 결정되며, 개발자에게는 투명하게 처리된다”고 설명했다. 밥은 클로드 계열 모델, 오픈AI 계열 모델, IBM의 그래니트, 메타의 라마(Llama), 미스트랄(Mistral) 계열 모델 등 주요 모델을 끌어다 쓰지만, 어떤 작업에 어떤 모델을 배정할지는 IBM의 자체 기술로 정해진다. IBM 10만 개발자의 실사용 데이터를 바탕으로 ‘같은 결과를 더 낮은 비용에 내는 모델’을 지속적으로 평가해 결정하는 식이다. IBM은 이들 모델을 개별적으로 사용했을 때와 비교해 어느 정도의 비용 효율성을 확보할 수 있는지에 대한 구체적인 수치는 공개하지 않았다. 다만 쿽은 “고객들이 직접 ‘밥이 훨씬 비용 효율적’이라고 말한다”고 강조했다. 탈레카는 “항상 프런티어 모델을 쓰는 것은 아니다. 소형 언어모델이나, 자바(Java)용으로 우리가 직접 학습시킨 자체 모델처럼 다른 곳에는 없는 모델도 쓴다”고 했다. 우수연 IBM 전문위원은 시연을 통해 밥의 실제 활용 사례를 소개했다. 가상의 은행 시스템 ‘IBM 뱅크’를 개발하는 시나리오로 진행된 데모에서는 신규 개발자가 기존 코드베이스를 분석하고 문서를 생성한 뒤 신규 기능 개발, 테스트, 깃허브 풀 리퀘스트(PR) 생성까지 수행하는 과정을 보여줬다. 밥은 전체 코드베이스를 분석해 자동으로 문서를 생성하고 이를 웹 기반 아키텍처 다이어그램으로 시각화했다. 이후 깃허브 이슈에 등록된 요구사항을 기반으로 개발 계획을 수립하고, IBM의 디자인 시스템 ‘카본(Carbon)’을 적용해 코드를 생성했다. 이어 브라우저 기반 자동 테스트를 수행하고, 기업 규칙에 맞는 커밋 메시지와 PR까지 자동으로 작성했다. 우 전문위원은 “기존에는 신입 개발자가 수주에서 수개월에 걸쳐 익혀야 했던 업무를 30분 안에 수행하는 시나리오”라며 “AI 네이티브 방식으로 개발 업무를 위임할 수 있다는 점이 큰 변화”라고 설명했다. “배포 직전은 늦다” 개발 시작부터 취약점 잡는 ’시프트 레프트’ 보안 IBM이 강조한 밥의 또 다른 차별점은 ‘시프트 레프트(Shift-Left) 보안’이다. 기존에는 코드 배포 단계에서 보안 취약점을 점검하는 것이 일반적이었지만, 밥은 개발 시작 단계부터 보안을 설계에 내재화한다. 탈레카는 “밥에는 가드레일과 거버넌스가 내장돼 있어 모든 작업에 대해 감사(auditability)와 추적(traceability)이 가능하다”고 밝혔다. 이를 보여주는 사례로 IBM은 미국 정부기관 대상 클라우드 보안 인증 제도인 페드램프(FedRAMP)를 들었다. IBM 제품 중 하나인 ‘콘서트(Concert) 제품’이 페드램프를 인증을 획득하는 데 기존에는 30일이 걸렸지만 밥 도입 후 2일로 단축됐으며, 비용도 8만 4,000달러 이상 절감됐다고 소개했다. 우수연 전문위원의 데모에서는 취약점 분석 기능이 시연됐다. 코드에 하드코딩된 패스워드 등 14개의 보안 취약점을 밥이 자동으로 탐지하고, 클릭 한 번으로 수정까지 진행하는 흐름이 소개됐다. 그는 “기존에는 PR을 올린 뒤 리뷰어가 지적하거나 정적 분석 툴이 돌아야 발견됐던 문제를, 개발 환경 안에서 미리 잡아낼 수 있다”고 설명했다. 쿽 부사장은 AI 코딩 도구의 한계도 지적했다. 그는 개발자들이 생산성 향상을 기대하지만, 실제 대규모 코드베이스에서는 AI 활용이 반드시 개발 속도 향상으로 이어지지 않는다는 연구 결과도 있다고 소개했다. AI가 개별 작업은 빠르게 처리하더라도 인프라, 텔레메트리, 보안 정책, 레거시 시스템 의존성 등 기업 환경의 복잡한 요소들로 인해 배포 단계에서 병목이 발생할 수 있다는 설명이다. 그는 “AI는 맥락이 명확할 때 힘을 발휘하지만, 엔터프라이즈 환경은 맥락이 불명확하고 가려진 영역이 많다”고 덧붙였다. 밥은 현재 SaaS 형태로 체험·이용할 수 있으며, 방화벽 내부에 설치하는 온프레미스·에어갭 버전은 올해 9월 출시된다. 한국어를 포함해 12개 이상의 언어를 지원한다. jihyun.lee@foundryco.com
- LTM launches Cisco-powered SSE platform for enterprise AI
LTM has launched a Managed Secure Service Edge (SSE) solution built on Cisco Secure Access, aimed at helping enterprises strengthen zero-trust security, simplify access management, and securely deploy generative AI […] The post LTM launches Cisco-powered SSE platform for enterprise AI appeared first on Express Computer .
Score: 33🌐 MovesJun 4, 2026https://www.expresscomputer.in/news/ltm-launches-cisco-powered-sse-platform-for-enterprise-ai/135697/ - Partnering with FPT, 100-year-old Japanese Enterprise Leverages AI to Solve Multinational Workforce Training Challenges
Partnering with FPT, 100-year-old Japanese Enterprise Leverages AI to Solve Multinational Workforce Training Challenges The Straits Times
- NC AI develops welding AI for Hanwha Ocean
NC AI, the artificial intelligence subsidiary of gaming giant NCSoft, said Thursday it has won a project from Hanwha Ocean to develop AI-powered autonomous welding technology for shipbuilding sites. The project involves a vision-based welding model and a collaborative robot system designed to identify welding targets and carry out work with minimal human intervention. NC AI said the technology will be applied to Hanwha Ocean’s commercial and special-purpose vessel production sites, where welding
- I tried Google Drive's new AI cleanup tool to fix 14 years of storage clutter - here's the result
With Organize My Files, can Gemini clean up my messy Google Drive and save me money on storage? Let's see.
Score: 33🌐 MovesJun 4, 2026https://www.zdnet.com/article/i-tried-google-drives-new-ai-cleanup-tool-storage-clutter/ - Sixteen schemes for AI safety
These days, I often run across whippersnappers excited to do something for AI safety — but aren’t quite sure what. One of the fun things about the Future Fund era were the big lists of project ideas ; as we enter a new era of crazy money sloshing around, it might be time to bring back the lists! Note that these ideas range from “very confident this is good” to “completely harebrained”; I’m not telling you which are which. If you’re excited for ideas like these, consider joining Surplus, our upcoming software incubator: https://manifund.org/surplus Jobs, jobs, jobs Already, the top problem for most AI safety orgs is hiring good people. Vast torrents of funding will only exacerbate the imbalance between available money and people to hire. So now is a great time to figure out how to discover new talent & match people to jobs. 1. Triplebyte for AI safety jobs Triplebyte would interview a tech candidate once, then forward the results to a bunch of different companies. This would reduce the O(MN) problem in hiring between M orgs and N people to O(M+N), saving applicants and interviewers time. Most obviously, you could just do this for technical AI safety researchers, but maybe could extend to other subfields that are growing rapidly — policy work, generalists, etc. Also, there’s probably room to “do hiring better” with AI-based interviewing. (How to do this effectively and respectfully remains an open problem, curious what the current SOTA is.) Warning: Triplebyte eventually went out of business, so you want to figure out how not to do that. 2. Database of every single AI safety person Imagine a public query-able database that has every single human’s employment info, current job status. Just starting with “Better LinkedIn” could go a long way. Can scrape LinkedIn, socials, personal websites, then allow the person to make edits. Sprinkle in some AI-powered features. Waypoint, Lightcone’s new conference app for LessOnline and Manifest this year, does a lot of this, so look at that for inspiration. Beyond recruiting, this could help with outreach (eg for finding speakers for a conference), organizing (eg for canvassing voters for good candidates). See also my notes on EA/AIS People DB , see also LongtermWiki . Related idea: database of every single AI safety org. 3. Intro AI safety megaconference A large, open-access conference (2,000+ people, potentially up to 4,000) focused on introducing people to AI safety ideas and possibly finding jobs. Could be ungated (no application required), unlike EAG which rejects many applicants (eg me, the first time I applied) — see Scott Alexander’s proposal to Open EAG . Key features: career fair with orgs hiring, talks from famous speakers, focus on recruiting and giving orgs access to candidates. This seems especially tractable for Generator; both Manifest and Curve were organized in ~3 months. Would likely be in San Francisco/Bay Area. Could be self-funding by charging admission & charging orgs for sponsorships. Alternative framings: less jobs-y and more fun, in the vein of large music festivals, or anime/comic conventions. 4. Unionize the lab employees Frontier lab employees currently have significant bargaining power (as evidenced by high salaries), but this may not last. So: help lab employees organize to steer companies towards public good. Eg towards more permissibility & transparency, freedom of speech. Eg towards redistribution of windfall. Maybe OpenAI employees get to vote on a chunk of how OpenAI Foundation gives its funding. (Maybe OpenAI employees get regranting budgets to give to 501c3s.) Eg towards delaying dangerous capabilities, audits. You might try to create one union per lab (one for A\, one for OAI, one for GDM). Or you might try to have all eg technical folks unionize, for cross lab solidarity. You might not want to call it a “union” or use typical union norms (eg standardizing pay based on seniority doesn’t make much sense here). Lab employee interests may be more aligned with society than leadership? Warning: very unclear if feasible, very unclear if good. By default, I’m anti- most unions (eg dockworker unions, trucker unions). And there are few examples of legit unions in tech or labs. Though there’s at least one recent precedent: during the OpenAI board drama, the letter to bring Sam back was kind of an impromptu union-y thing. 5. Safety scorecards to inform job applicants Employees/talent remain a major important scarce input for labs (and other AI safety orgs). One way to improve the ecosystem: differentially help orgs that are doing better safety work, and punish orgs that are not — and build common knowledge about which is which. People have some sense of the top 3 labs, but less for neolabs eg Thinking Machines, SSI, Goodfire etc, and a lot less for new startups. One [big donor] told me “I’ve spoken to [neolab founder] a bunch of times now and I still have no idea what they think about AI safety.” Basically, kind of like AI Lab Watch , but up-to-date/good, and maybe more focused on the recruiting/talent side. 6. Help people move to US (and maybe Bay Area specifically) Help with finding jobs (everything above, I guess). Help with housing. Help with finding friends & community. Help with visas — eg see Researchers and Founders: Join Mox’s J-1 Global Expert Fellowship! Help with marrying — eg a dating platform to match US citizens to international folks? (In an entirely legal way?) See also Itsi’s call for animal welfare folks to move to SF . Cargo-culting from academia What structures and institutions serve established fields of research? How might they be adapted to serve the relatively-nascent AI safety community? What are they good for at their best; where could they be improved? (Disclaimer: I’ve never done technical AI safety research, so my ideas here are even more suspect than usual) 7. AI Safety Nobel Prize Create a big splashy prestigious prize specifically for AI safety work. Very ambitiously: actually convincing the Nobel Committee (or Turing, or something) to add AI safety as a category. More prosaically, just create a new independent prize. Probably have multiple categories: technical research, policy work, movement building etc. Time 100 AI exists but includes both safety and capabilities work. Maybe run this every quarter instead of every year, given short timelines and rapid pace of development. Could include funding (eg $1m per prize), but honor is probably the real important prize. Some goals would be to: legitimize the field and help universities/institutions recognize AI safety as a serious research area. Help the field build consensus on what areas are valuable. Nudge outsiders to try to work on AI safety areas. This idea would benefit from someone with standing/ability to convince prominent figures to be judges (ideally, lab CEOs and heads of major safety orgs). 8. University of AI Safety De novo universities are pretty rare but there’s something about a physical cloistered institution that helps with intellectual discovery. Also something about a “university” that creates legitimacy for an agenda. Also AI safety has enough subfields now to have a whole slate of professors. You could lean hard into AI-based curriculum for undergrads. You could probably buy a cheap university campus someplace. See also: FHI, FHI of the west . Also: Is MATS basically a university? Is Constellation basically a university? 9. Good AI safety research journal Could try to do peer review etc much better than existing journals. Move faster, automate well. Maybe not a traditional journal, and more of a magazine or index over existing Arxiv. See also notes on aligned arxiv , distill.pub , AI Alignment Forum. 10. Legit, academic AI safety conference Conferences remain great ; NeurIPS/ICML is probably not enough (also these aren’t safety focused). Once again you could work on smoothing out the parts that everyone hates (eg peer review). Technical AI safety is the most obvious fit for something like this (as the field with the most participants, and also the most academic-like). Thought experiment: what’s the equivalent of an academic conference for policy? for movement building? Products I would consume Here are a random collection of projects that could be “shut up and take my money”, given sufficiently good execution. (Many are ideas I’ve explored doing, myself.) 11. Turn the AI 2027 TTX into a mass market product Could be a web game or video game. Could be a board game; pretty easy to self-publish these now — see also Daybreak (climate change board game, by Pandemic creators). (Doesn’t have to be AI 2027 specifically.) See also our notes on AI takeoff board game , and David Abecassis’s attempt . Why? Because the experience of playing through the TTX provides a qualitatively different way to “feel the AGI” than just reading AI 2027; and as people wake up to importance of AI, there’ll be more mass market demand for understanding things at play. Why not? The TTX may be out of date now. Maybe most of the value comes from the facilitator being knowledgeable about race dynamics, and it’s too hard to manage without that much context. I do think the AI 2027 team themselves tried to do this in house at one point; dunno what happened with that, probably check in with them. 12. “Lighthaven/Constellation/Mox in DC” Now published by John here ! 13. Common application for AI safety funding Or broadly helping orgs and individuals navigate the landscape, eg with a flowchart or LLM-powered advice chatbot. Or maybe open source S-Process. See also out notes on EA Common App , and https://www.grantmaking.ai/ 14. Givewell for AI safety It really feels like someone should be publicly evaluating AI safety nonprofits; CG and Longview publish ~nothing. See our notes on Proposal: “Givewell of AI Safety” . (This idea has been Manifund’s white whale for a while — if you have an angle of attack, please reach out.) 15. In-depth profiles of AI safety leaders Could be a podcast, like Dwarkesh but more scoped towards AI safety; or like Social Radars . Could be an interview article series, like Mercury’s Meridian . Could be more inside-baseball (what’s going on in EA-space) or more translating ideas to a general broad audience. Could be covering fast-moving trends and new projects (eg someone to talk about the Pope’s encyclical from an AIS perspective). Why? People working in the space are super busy and rarely have time to sit down and write their thoughts in longform, but are happy to go speak on a podcast or interview or talk. Some enterprising smart dedicated writer could go around profiling them in depth. Best for someone with a good nose for under-exposed people, and also able to get a few high profile folks. 16. “ what-is-agi.com “ Public-facing microsite explaining “AGI” and other important concepts in transformative AI, for a broad general audience. Aim to be a definitive source that is easy to share & reference. Explore different operationalizations, their strengths and weaknesses (eg “Drop in remote worker”, vs Ajeya’s Self-sufficient AI ). Might have something about timelines to AGI, either expert surveys or other kinds of graphs (METR time horizon, Epoch company revenue). Maaaybe something about p(doom). (Maybe that’s a different site.) Partly inspired by Leo Gao discovering 30% of sampled NeurIPS attendees don’t know what AGI even stands for. (Probably many more have poor operationalizations.) Adam Schleris apparently owns agi.fyi , could borrow/buy the domain from him. Questions to generate project ideas “What pain points do I, a member of the AI safety community, personally experience?” See Paul Graham : The way to get startup ideas is not to try to think of startup ideas. It’s to look for problems, preferably problems you have yourself. The very best startup ideas tend to have three things in common: they’re something the founders themselves want, that they themselves can build, and that few others realize are worth doing. Microsoft, Apple, Yahoo, Google, and Facebook all began this way. Why is it so important to work on a problem you have? Among other things, it ensures the problem really exists. It sounds obvious to say you should only work on problems that exist. And yet by far the most common mistake startups make is to solve problems no one has. “Who do I specifically understand, care about, empathize with, want to help?” Manifold was really easy to work on because I loved the specific kind of nerd who would opine on prediction market mechanism design. “What things do AI safety people/orgs/community currently spend a lot of money on? (or time, or focus, or energy)” Classic Mom Test advice: when interviewing users, it’s better to ask about past behavior rather than future hypotheticals. “What seems really easy for me to do? Where is everyone else dropping the ball ?” This helps with generating angles of attack, and finding projects that require low activation energy. “What projects do people keep trying to do but failing at?” Just because someone’s tried something, doesn’t mean you shouldn’t also try it — it’s evidence the problem space matters! Google was not the first search engine, more like the 20th. Other notes and advice The label “AI safety” isn’t perfect ; I use it a lot above but really I mean something like “alignment / xrisk / navigating transformative AI / also maybe post-AGI and human flourishing and AI rights and welfare / maybe even broad-tent EA including GHD/AW/abundance”. As a phrase, “AI safety” might not be the right one for this conflationary alliance . “Safety” as a concept doesn’t really speak to me (I’m a risk junkie) and is broadly kind of uncool (see: the AISI renaming). Unfortunately, I don’t have a better label atm; let me know if you do. Keep in mind: “ideas are cheap, execution is everything” . It’s easy to say “we should have an AI safety nobel prize” and hard to make it happen well, reliably, at a high degree of reliability. Thinking about ideas only gets you so far, you have to talk to users — though, talking to users only gets you so far, you also have to ship. You should have an “angle of attack”, a specific set of actions you could imagine doing yourself, without needing buy-in/permission from others. See also: “Tabooing EA should” . See also: my notes on Starting projects and How to build a field ; other lists of ideas from Forethought and Fin Moorhouse . Once again, if you’re excited to work on ideas like these, consider joining Surplus ! Discuss
Score: 33🌐 MovesJun 4, 2026https://www.lesswrong.com/posts/jTyfbs7GW5rf78adK/sixteen-schemes-for-ai-safety - Embracing complexity: Parloa’s vision for next-gen customer experience
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Score: 32🌐 MovesJun 4, 2026https://www.bloomberg.com/news/videos/2026-06-04/consumer-tech-leaders-discuss-the-industry-future-ai-video - India to use AI for machine-readable standards: Consumer Affairs Secretary
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- How a 20-engineer team delivers enterprise AI systems at Fortune 500 scale
When I took over responsibility for technical function at akirolabs more than three years ago, I inherited a procurement SaaS platform that was still at a very early stage, with development fully outsourced outside Europe. However, the company had already reached early product-market fit, and first customers demonstrated their interest, which made product ownership and scalable delivery increasingly critical. After evaluating several approaches, I decided to fully insource development from South Asia to Europe and rebuild the entire technology function in-house, including engineering, product ownership, infrastructure and delivery processes. It was clearly the highest-risk option but also the one with the biggest long-term potential. Preparing to the implementation of this decision, I kept hearing advice which was quite consistent: if we wanted to build and operate an enterprise-grade procurement SaaS platform for global corporations, we needed to hire aggressively. Most estimates I received started at around 50 engineers. At the time, the recommendation sounded logical. Expectations around reliability, security and delivery speed were extremely high. But I also believed the standard enterprise scaling model had a flaw that many companies underestimate: larger engineering organizations often become slower precisely because they are larger. Instead of scaling through headcount, I decided to scale through organizational design. Our first fully production-ready in-house platform release was delivered in less than 12 months from the day when I hired the first engineer in-house. I delivered the full-scope release with a team of roughly a dozen engineers and without any AI co-pilots – the tooling simply was not mature enough yet in 2023 and early 2024. Later, I added a dedicated data science team and evolved the platform into an AI-augmented system with a fully redesigned UI/UX. Today, three years into this transformation, akirolabs has evolved into a recognized category leader. Over the next few years our current customer base has extended to power the strategic procurement operations of enterprises such as Bertelsmann, Raiffeisen Bank International, IFF, UCB Pharma, Axpo and others. This proven scale demonstrates that in enterprise AI, operating model matters more than team size. Why large engineering organizations slow down One thing I’ve repeatedly seen in enterprise technology is that communication complexity grows faster than headcount. Fred Brooks described this decades ago in Brooks’s Law, arguing that adding manpower to a late software project often makes it later. The reason is not simply onboarding overhead. It is the explosion of coordination paths inside the organization itself. The communication channel formula is straightforward – with 12 engineers, there are 66 possible communication paths. At 50 engineers, there are 1,225. In practice, that complexity becomes operational drag. Teams spend more time aligning than building. Meetings multiply. Ownership becomes blurred. Delivery slows down even though payroll grows. As CTO at akirolabs, I wanted to avoid that trap from the beginning. We intentionally kept the organization lean and structured it around small, highly autonomous functional groups. Instead of creating rigid specialization silos, we hired engineers who were comfortable operating across adjacent disciplines. This mirrors a broader industry shift toward cross-functional platform and engineering teams. That cross-functional flexibility became one of the biggest advantages for the company. Backend engineers supported infrastructure automation. QAs worked closely with Security engineers. Frontend developers handled portions of UX implementation directly. Our data science team owned significant parts of the MLOps lifecycle. As CTO, I also absorbed part of the product management layer myself to reduce decision latency and preserve execution speed. This model only works with senior engineers, high trust and strong ownership culture. In our case, insourcing development also fundamentally changed our delivery velocity. Compared to the outsourced structure we previously operated under, product delivery accelerated by more than 2x. We gained direct control over infrastructure, intellectual property, architectural decisions and security processes. It also enabled long-term planning because our engineers were building systems with full awareness of platform requirements. That visibility allowed us to reduce cloud infrastructure costs by more than 30%. Keeping the engineering organization intentionally compact helped us also avoid the architectural sprawl early on, a dynamic which is often described through Conway’s Law . Once the people designing the architecture are also accountable for long-term scalability, infrastructure decisions become dramatically more intentional, enabling the platform to scale and secure akirolabs recognition as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024 and Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025. Replacing Scrum with continuous delivery Another major decision we made was abandoning Scrum. That statement usually generates strong reactions because Scrum has become very popular across enterprise IT. But in our experience, traditional sprint-based delivery models created too much operational overhead for the type of work we were doing. Enterprise AI systems evolve continuously: data pipelines change or models require retraining. Trying to force that environment into rigid sprint cycles often created artificial planning friction instead of predictability. We moved fully to Kanban and continuous delivery. The difference was noticeable almost immediately. Our roadmap became directional rather than fixed. Features shipped when they were production-ready, validated and needed by customers – not because a quarterly milestone required a release. We focused heavily on limiting work in progress, shortening feedback loops and reducing context switching. Removing process overhead had a measurable impact on productivity. Internally, we estimated that eliminating Scrum rituals alone recovered roughly 10% of engineering capacity. Lead time for medium-sized changes dropped from days or sometimes weeks to hours. Release cadence stabilized around monthly production deployments instead of quarterly while urgent fixes and improvements could move significantly faster. That mattered more than any sprint ritual. Communication discipline was equally important. We consolidated nearly all meetings into mornings and invited only people directly involved in the decision being discussed. If information was declarative or preparatory, it was documented asynchronously through structured chat channels or internal wiki pages instead of calls. We also minimized email usage almost completely. Because the company operated remote-first, we complemented this lightweight communication model with intensive in-person workshops three or four times per year across different European cities. Those workshops helped maintain strategic alignment while day-to-day execution remained highly asynchronous. Combined, these changes improved overall team productivity by an estimated 20% to 30%. AI-assisted development later became another major productivity lever for the team. Today, we use AI copilots extensively across coding, code reviews, task analysis and debugging. Based on our internal observations, these tools contribute an additional 20% to 25% productivity improvement when used within mature engineering processes. But I do not believe AI tooling alone solves enterprise delivery problems. Without clear ownership structures and discipline, AI often amplifies organizational chaos rather than reducing it. Lean teams can still satisfy enterprise governance One of the most common assumptions I hear from enterprise leaders is that lean engineering organizations eventually break down under compliance pressure. Our experience was the opposite. With essentially the same compact organizational structure, we achieved ISO 27001 certification under 9 months without any external help. Also, we aligned our governance processes with the requirements of the EU AI Act, which supported our one-million public government grant from Investitionsbank Berlin in 2024 for breakthrough AI innovations. None of that required building large governance departments or adding layers of bureaucracy. What mattered far more was clear ownership and direct accountability between the people building systems and the people operating them. We also embedded prioritization deeply into our culture. Across the organization, teams actively use principles like the Eisenhower Matrix to distinguish urgent work from strategically important work. That may sound simple but in high-growth technology environments, prioritization discipline often becomes the difference between scalable execution and constant operational overload. Perhaps one of the clearest validations of our operational model has been retention. Over a three-year period of my leadership at akirolabs, only one engineer voluntarily left the organization. To me, that says something important about how experienced engineers want to work today . Engineers generally do not want to spend their time navigating endless approval chains or low-value meetings. They want ownership, autonomy and the ability to see direct impact from their work. That is especially true in enterprise AI where iteration speed and adaptability increasingly determine competitive advantage. For years, the enterprise technology sector has operated on the assumption that scale requires organizational expansion. My experience building enterprise AI systems suggests the opposite may often be true. Sometimes the fastest way to scale is to stay intentionally small. This article is published as part of the Foundry Expert Contributor Network. Want to join?
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Hitachi and Columbia University Publish Joint Research Report on AI and Sustainability Transitions rd.hitachi.com
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Score: 30🌐 MovesJun 4, 2026https://siliconangle.com/2026/06/04/healthcare-ai-data-foundations-models-snowflakesummit/ - Alef Education completes landmark migration of AI-powered solutions to Microsoft Azure
Supported by Core42 sovereign cloud capabilities