TOPIC: UNITED STATES
Security or Control? The debate over Google's Android verification policy
7th March 2026
A policy announced by Google in August 2025 has ignited one of the more substantive disputes in mobile technology in recent years. At its surface, the question is about app security. Beneath that, it touches on platform architecture, competition law, the long history of Android's unusual relationship with openness, and the future of independent software distribution. To understand why the debate is so charged, it helps to understand how Android actually works.
An Open Platform With a Proprietary Layer
Android presents a genuinely unusual situation in the technology industry. The base operating system is the Android Open-Source Project (AOSP), which is publicly available and usable by anyone. Manufacturers can take the codebase and build their own systems without involvement from Google, as Amazon has with Fire OS and as projects such as LineageOS and GrapheneOS have demonstrated.
Most commercial Android devices, however, do not run pure AOSP. They ship with a proprietary bundle called Google Mobile Services (GMS), which includes Google Play Store, Google Play Services, Google Maps, YouTube and a range of other applications and developer frameworks. These components are not open source and require a licence from Google. Because most popular applications depend on Play Services for functions such as push notifications, location services, in-app payments and authentication, shipping without them is commercially very difficult. This layered architecture gives Google considerable influence over Android without owning it in the traditional proprietary sense.
Google has further consolidated this influence through a series of technical initiatives. Project Treble separated Android's framework from hardware-specific components to make operating system updates easier to deploy. Project Mainline went further, turning important parts of the operating system, including components responsible for media processing, network security and cryptography, into modules that Google can update directly via the Play Store, bypassing manufacturers and mobile carriers entirely. The result is a platform that is open source in its code, but practically centralised in how it evolves and is maintained.
The Policy and Its Rationale
Against this backdrop, Google announced in August 2025 that it would extend its developer identity verification requirements beyond the Play Store to cover all Android apps, including those distributed through third-party stores and direct sideloading. From September 2026, any app installed on a certified Android device in Brazil, Indonesia, Singapore and Thailand must originate from a developer who has registered their identity with Google. A global rollout is planned from 2027 onwards.
Google's stated rationale is grounded in security evidence. The company's own analysis found over 50 times more malware from internet-sideloaded sources than from apps available through Google Play. In 2025, Google Play Protect blocked 266 million risky installation attempts and helped protect users from 872,000 unique high-risk applications. Google has also documented a specific and recurring attack pattern in Southeast Asia, in which scammers impersonate bank representatives during phone calls, coaching victims into sideloading a fraudulent application that then intercepts two-factor authentication codes to drain bank accounts. The company argues that anonymous developer accounts make this kind of attack far easier to sustain.
The registration process requires developers to create an Android Developer Console account, submit government-issued identification and pay a one-time fee of $25. Organisations must additionally supply a D-U-N-S Number from Dun & Bradstreet. Google has stated explicitly that verified developers will retain full freedom to distribute apps through any channel they choose, and is building an "advanced flow" that would allow experienced users to install unverified apps after working through a series of clear warnings. Developers and power users will also retain the ability to install apps via Android Debug Bridge (ADB). Brazil's banking federation FEBRABAN and Indonesia's Ministry of Communications and Digital Affairs have both publicly welcomed the policy as a proportionate response to documented fraud.
What This Means for F-Droid
F-Droid, founded by Ciaran Gultnieks in 2010, operates as a community-run repository of free and open-source software (FOSS) applications for Android. For 15 years, it has demonstrated that app distribution can be transparent, privacy-respecting and accountable, setting a standard that challenges the mobile ecosystem more broadly. Every application listed on the platform undergoes checks for security vulnerabilities, and apps carrying advertising, user tracking or dependence on non-free software are explicitly flagged with an "Anti-Features" label. The platform requires no user accounts and displays no advertising. It still needs some learning, as I found when adding an app through it for a secure email service.
F-Droid operates through an unusual technical model that is worth understanding in its own right. Rather than distributing APKs produced by individual developers, it builds applications itself from publicly available source code. The resulting APKs are signed with F-Droid's own keys and distributed through the F-Droid client. This approach prioritises supply-chain transparency, since users can in theory verify that a distributed binary corresponds to the published source code. However, it also means that updates can be slower than other distribution channels, and that apps distributed via F-Droid cannot be updated over a Play Store version. Some developers have also noted that subtle differences in build configuration can occasionally cause issues.
The new verification requirement creates a structural problem that F-Droid cannot resolve independently. Many of the developers who contribute to its repository are hobbyists, academics or privacy-conscious individuals with no commercial motive and no desire to submit government identification to a third party as a condition of sharing software. F-Droid cannot compel those developers to register, and taking over their application identifiers on their behalf would directly undermine the open-source authorship model it exists to protect.
F-Droid is not alone in this concern. The policy equally affects alternative distribution models that have emerged alongside it. Tools such as Obtainium allow users to track and install updates directly from developers' GitHub or GitLab release pages, bypassing app stores entirely. The IzzyOnDroid repository provides a curated alternative to F-Droid's main catalogue. Aurora Store allows users to access the Play Store's catalogue without Google account credentials. All of these models, to varying degrees, depend on the ability to distribute software independently of Google's centralised infrastructure.
The Organised Opposition
On the 24th of February 2026, more than 37 organisations signed an open letter addressed to Google's leadership and copied to competition regulators worldwide. Signatories included the Electronic Frontier Foundation, the Free Software Foundation Europe, the Software Freedom Conservancy, Proton AG, Nextcloud, The Tor Project, FastMail and Vivaldi. Their central argument is that the policy extends Google's gatekeeping authority beyond its own marketplace into distribution channels where it has no legitimate operational role, and that it imposes disproportionate burdens on independent developers, researchers and civil society projects that pose no security risk to users.
The Keep Android Open campaign, initiated by Marc Prud'hommeaux, an F-Droid board member and founder of the alternative app store for iOS, App Fair, has been in contact with regulators in the United States, Brazil and Europe. F-Droid's legal infrastructure has been strengthened in recent years in anticipation of challenges of this kind. The project operates under the legal umbrella of The Commons Conservancy, a nonprofit foundation based in the Netherlands, which provides a clearly defined jurisdiction and a framework for legal compliance.
The Genuine Tension
Both positions have merit, and the debate is not easily resolved. The malware problem Google describes is real. Social engineering attacks of the kind documented in Southeast Asia cause genuine financial harm to ordinary users, and the anonymity afforded by unverified sideloading makes it considerably easier for bad actors to operate at scale and reoffend after being removed. The introduction of similar requirements on the Play Store in 2023 appears to have had some measurable effect on reducing fraudulent developer accounts.
At the same time, critics are right to question whether the policy is proportionate to the problem it is addressing. The people most harmed by anonymous sideloading fraud are not, in the main, the people who use F-Droid. FOSS users tend to be technically experienced, privacy-aware and deliberate in their choices. The open letter from Keep Android Open also notes that Android already provides multiple security mechanisms that do not require central registration, including Play Protect scanning, permission systems and the existing installation warning framework. The argument that these existing mechanisms are insufficient to address sophisticated social engineering, where users are coached to bypass warnings, has some force. The argument that they are insufficient to address independent FOSS distribution is harder to sustain.
There is a further tension between Google's security claims and its competitive interests. Requiring all app developers to register with Google strengthens Google's position as the de facto authority over the Android ecosystem, regardless of whether a developer uses the Play Store. That outcome may be an incidental consequence of a genuine security initiative, or it may reflect a deliberate consolidation of control. The open letter's signatories argue the former cannot be assumed, particularly given that Google faces separate antitrust investigations in multiple jurisdictions.
The Antitrust Dimension
The policy sits in a legally sensitive area. Android holds approximately 72.77 per cent of the global mobile operating system market as of late 2025, running on roughly 3.9 billion active devices. Platforms with that scale of market presence attract a different level of regulatory scrutiny than those operating in competitive markets.
In Europe, the Digital Markets Act (DMA) specifically targets large platforms designated as "gatekeepers" and explicitly requires that third-party app stores must be permitted. If Google were to use developer verification requirements in a manner that effectively prevented alternative stores from operating, European regulators would have grounds to intervene. The 2018 European Commission ruling against Google, which resulted in a €4.34 billion fine for abusing Android's market position through pre-installation requirements, established that Android's dominant position carries real obligations. That decision was largely upheld by the European courts in 2022.
In the United States, the Department of Justice has been pursuing separate antitrust cases relating to Google's search and advertising dominance, within which Android's role in channelling users toward Google services has been a recurring theme. The open letter's decision to copy regulators worldwide was not accidental. Its signatories have concluded that public documentation before enforcement begins creates pressure that private correspondence does not.
The key regulatory question is whether the verification requirements are genuinely necessary for security, and whether less restrictive measures could achieve the same goal. If the answer to either part of that question is no, regulators may conclude that the policy disproportionately disadvantages competing distribution channels.
What the Huawei and Amazon Cases Reveal
The importance of Google's service layer, and the difficulty of replicating it, can be understood by examining what happened when two large technology companies attempted to operate outside it. Here, we come to the experiences of Amazon and Huawei.
Amazon launched Fire OS in 2011, based on AOSP but with all Google components replaced by Amazon's own services. The platform succeeded in Fire tablets and streaming devices, where users primarily want access to Amazon's content. It failed entirely in smartphones. The Amazon Fire Phone, launched in 2014 and discontinued within a year, could not attract enough developer support to make it viable as a general-purpose device. The absence of Google Play Services meant that many popular applications were missing or required separate builds. This experience showed that Android's openness, at the operating system level, does not automatically translate into a competitive ecosystem. The real power lies in the service layer and the developer infrastructure built around it.
The Huawei case illustrates the same point more sharply. In May 2019, the United States government placed Huawei on its Entity List, restricting American firms from supplying technology to the company. Huawei had a 20 per cent global smartphone market share in 2019, which dropped to virtually zero after the restrictions took effect. Since Huawei could still use the AOSP codebase, the operating system was not the problem. The problem was Google Mobile Services. Without access to the Play Store, Google Maps, YouTube and the developer APIs that underpin much of the application ecosystem, Huawei phones became commercially unviable in international markets that expected those services.
Huawei's international smartphone market share, which had been among the top three, rapidly fell to outside the top five. The company's consumer business revenue declined by nearly 50 per cent in 2021. Huawei's subsequent efforts to build its own replacement ecosystem, Huawei Mobile Services and AppGallery, achieved limited success outside China, where the domestic mobile ecosystem already operates largely independently of Google. Both the Amazon and Huawei cases confirm that Android's formal openness does not neutralise Google's practical influence over the platform.
The Comparison With Apple
It is worth noting where the comparison with Apple, often invoked in these debates, holds and where it breaks down. Apple designs its hardware, controls its operating system, and has historically permitted application installation only through its App Store. That degree of vertical integration meant that, under the DMA, Apple faced requirements to allow alternative app marketplaces and sideloading mechanisms that represented fundamental changes to how iOS operates. Google already permits these behaviours on Android, which is why the DMA's impact on its platform is more limited.
However, the direction of travel matters. Critics argue that policies like mandatory developer verification, combined with Google's control of the update pipeline and the practical dependency of the ecosystem on Play Services, are gradually moving Android toward a model that is more controlled in practice than its open-source origins would suggest. The formal difference between Android and iOS may be narrowing, even if it has not disappeared.
Where Things Stand
The verification scheme opened to all developers in March 2026, with enforcement beginning in September 2026 in four initial countries. Google has offered assurances that sideloading is not being eliminated and that experienced users will retain a route to install unverified software. Critics point out that this route has not yet been specified clearly enough for independent organisations to assess whether it would serve as a workable mechanism for FOSS distribution. Until it is demonstrated and tested in practice, F-Droid and its allies have concluded that it cannot be relied upon.
F-Droid is not facing immediate closure. It continues to host over 3,800 applications and its governance and infrastructure have been strengthened in recent years. Its continued existence, and the existence of the broader ecosystem of independent Android distribution tools, depends on sideloading remaining practically viable. The outcome will be shaped by how Google implements the advanced flow provision, by the response of competition regulators in Europe and elsewhere, and by whether independent developers in sufficient numbers choose to comply with, work around or resist the new requirements.
Its story is, in this respect, a concrete test case for a broader question: whether the formal openness of a platform is sufficient to guarantee genuine openness in practice, or whether the combination of service dependencies, update mechanisms and registration requirements can produce a functionally closed system without formally becoming one. The answer will have implications well beyond a single FOSS app repository.
Technology retail in North America: Five retailers worth knowing
2nd March 2026
The technology retail landscape in North America is shaped by a tension between convenience, expertise and competitive pricing. From sprawling big-box chains to specialist online stores, the sector contains a varied mix of established names and niche operators, each competing for customers who expect rapid delivery, accurate product information and reliable after-sales support. Five retailers stand out for the distinctly different approaches they take to serving that audience: Tech-America, Best Buy, Newegg, PC-Canada and Micro Center.
Tech-America
Tech-America presents itself as a direct-to-consumer online retailer covering a broad range of electronics and computer components. Its selling points include a large inventory and an emphasis on prompt shipping, with the site targeting a mix of hobbyists and small businesses. Questions have been raised about the company's legitimacy, with multiple consumer forums and review aggregators reflecting mixed opinions on its reliability and operational structure. Prospective customers are advised to research the retailer carefully before committing to a purchase, as third-party assessments remain inconclusive.
Best Buy
Best Buy is one of the most recognisable names in North American consumer electronics retail, and its history stretches back further than many of its customers might expect. The company was founded by Richard M. Schulze and James Wheeler in 1966 as an audio speciality store called Sound of Music, operating its first location in St. Paul, Minnesota. It was rebranded as Best Buy in 1983, at which point it had seven stores and around $10 million in annual sales, and it subsequently expanded its product range well beyond audio equipment to become a broad-based electronics retailer.
Today, Best Buy operates over 1,000 stores across the United States and Canada, combining physical retail with online sales in a model that the company describes as omnichannel. A key differentiator is its Geek Squad service division, which provides technical support, repairs and installation services across all store locations, and which has become a recognisable brand in its own right since being acquired by Best Buy in 2002. That combination of an extensive retail footprint and in-house technical services has allowed the company to retain a large and varied customer base that includes households, businesses and educational institutions.
Newegg
Newegg occupies a distinct position as a specialist online retailer focused primarily on computer hardware and components. Founded in 2001 by Fred Chang, a Taiwanese-American entrepreneur who had previously run ABS Computer Technologies, the company was established in California and initially targeted PC builders and enthusiasts who wanted detailed product information and user reviews alongside their purchases. The name itself was chosen to suggest new hope for e-commerce at a time when many dot-com businesses were struggling to survive.
Newegg operates a hybrid model that combines first-party sales with a marketplace for third-party sellers, expanding available inventory without the company needing to hold all stock itself. This approach has attracted a loyal community of technically minded buyers who value the depth of product listings on the platform. However, the marketplace model also introduces variability in seller quality, and some customers have noted inconsistencies in their experiences depending on which seller fulfilled their order. Newegg has been publicly listed on the Nasdaq under the ticker NEGG since May 2021, following a reverse merger with a Chinese special-purpose acquisition company.
PC-Canada
PC-Canada is a Waterloo, Ontario-based retailer that has served both individual consumers and business customers since its founding in 2003, making it one of Canada's longer-standing e-commerce technology retailers. The company offers a broad catalogue of IT products and components, and it holds an A+ rating from the Better Business Bureau, having been accredited since December 2015. Customer reviews present a more mixed picture, with some praising competitive pricing and fast shipping, while others have reported issues around order fulfilment and pricing changes after purchase. That gap between institutional accreditation and individual customer experience is a useful reminder that smaller regional retailers can face difficulties scaling consistently as their customer base grows.
Micro Center
Micro Center has taken a path that runs counter to the broader shift towards online-only retail, continuing to invest in physical stores and in-person expertise. The company currently operates 30 locations across the United States, with recent openings in Charlotte, Miami and Santa Clara adding to its footprint. Each store carries over 25,000 products and is staffed by associates who are recruited specifically for their technical knowledge, rather than general retail experience.
A notable feature of every Micro Center location is the Knowledge Bar, a dedicated in-store support desk offering diagnostics, repairs, authorised servicing for brands including Apple and Dell, and consultations for customers building their own PCs. The concept was introduced in 2007 and has since become central to the company's identity. Micro Center was ranked the number one tech retailer in the United States by PC Magazine in 2024, a recognition that reflects the premium its customers place on accessible, knowledgeable in-store service.
Closing Remarks
Each of these five retailers demonstrates a different answer to the same underlying question: what do technology buyers actually value? Tech-America and Newegg lean on the convenience and inventory breadth that online retail makes possible, while Best Buy and Micro Center make the case that physical presence and expert service remain compelling. PC-Canada illustrates the particular pressures facing regional players operating in a market where large international competitors set the expectations for pricing and delivery speed. As consumer habits continue to evolve, the retailers that balance adaptability with a clearly defined offering are likely to be the ones that endure.
The Open Worldwide Application Security Project: A cornerstone of digital safety in an age of evolving cybersecurity threats
24th February 2026
When Mark Curphey registered the owasp.org domain and announced the project on a security mailing list on the 9th of September 2001, there was no particular reason to expect that it would become one of the defining frameworks in the world of application security. Yet, OWASP, originally the Open Web Application Security Project, has done exactly that, growing from an informal community into a globally recognised nonprofit foundation that shapes how developers, security professionals and businesses think about the security of software. In February 2023, the board voted to update the name to the Open Worldwide Application Security Project, a change that better reflects its modern scope, which now extends beyond web applications to cover IoT, APIs and software security more broadly.
At its heart, OWASP operates on a straightforward principle: knowledge about software security should be free and openly accessible to everyone. The foundation became incorporated as a United States 501(c)(3) nonprofit charity on the 21st of April 2004, when Jeff Williams and Dave Wichers formalised the legal structure in Delaware. What began as an informal mailing list community grew into one of the most trusted independent voices in application security, underpinned by a community-driven model in which volunteers and corporate supporters alike contribute to a shared vision.
The OWASP Top 10
Of all OWASP's contributions, the OWASP Top 10 remains its most widely cited publication. First released in 2003, it is a standard awareness document representing broad consensus among security experts about the most critical risks facing web applications. The list is updated periodically, with a 2025 edition now published, following the 2021 edition.
The 2021 edition reorganised a number of longstanding categories to reflect how the threat landscape has shifted. Broken access control rose to the top position, reflecting its presence in 94 per cent of tested applications, while injection (which encompasses SQL injection and cross-site scripting, among others) fell to third place. Cryptographic failures, previously listed as sensitive data exposure, took second place. By organising risks into categories rather than exhaustive lists of individual vulnerabilities, the Top 10 provides a practical starting point for prioritising security efforts, and it is widely referenced in compliance frameworks and security policies as a baseline. It is, however, designed to be the beginning of a conversation about security rather than the final word.
Projects and Tools
Beyond the Top 10, OWASP maintains a substantial portfolio of open-source projects spanning tools, documentation and standards. Among the most widely used is OWASP ZAP (Zed Attack Proxy), a dynamic application security testing tool that helps developers and security professionals identify vulnerabilities in web applications. Originally created in 2010 by Simon Bennetts, ZAP operates as a proxy between a tester's browser and the target application, allowing it to intercept, inspect and manipulate HTTP traffic. It supports both passive scanning, which observes traffic without modifying it, and active scanning, which simulates real attacks against targets for which the tester has explicit authorisation.
The OWASP Testing Guide is another widely consulted resource, offering a comprehensive methodology for penetration testing web applications. The OWASP API Security Project addresses the distinct risks that face APIs, which have become an increasingly prominent attack surface, and OWASP also maintains a curated directory of API security tools for those working in this area. For teams managing web application firewalls, the OWASP ModSecurity Core Rule Set provides guidance on handling false positives, which is one of the more practically demanding aspects of deploying rule-based defences. OWASP SEDATED, a more specialised project, focuses on preventing sensitive data from being committed to source code repositories, addressing a problem that continues to affect development teams of all sizes. Projects are categorised by their maturity and quality, allowing users to distinguish between stable, production-ready tools and those that are still in active development, and this tiered approach helps organisations make informed decisions about which tools are appropriate for their needs.
Influence on Industry Practice
The reach of OWASP's guidance is considerable. Security teams use its materials to structure risk assessments and threat modelling exercises, while developers integrate its recommendations into code reviews and secure coding training. Auditors and regulators frequently reference OWASP standards during compliance checks, creating a shared vocabulary that helps bridge the gap between technical staff and leadership. This alignment has done much to normalise application security as a core part of the software development lifecycle, rather than a task bolted on after the fact.
OWASP's influence also extends into regulatory and standards environments. Frameworks such as PCI DSS reference the Top 10 as part of their requirements for web application security, lending it a degree of formal weight that few community-produced documents achieve. That said, OWASP is not a regulatory body and has no enforcement powers of its own.
Education and Community
Education remains a central part of OWASP's mission. The foundation runs hundreds of local chapters across the globe, providing forums for knowledge exchange at a local level, as well as global conferences such as Global AppSec that bring together practitioners from across the industry. All of OWASP's projects, tools, documentation and chapter activities are free and open to anyone with an interest in improving application security. This open model lowers barriers for those starting out in the field and fosters collaboration across academia, industry and open-source communities, creating an environment where expertise circulates freely and innovation is encouraged.
Limitations and Appropriate Use
OWASP is not without its limitations, and it is worth acknowledging these clearly. Because it is not a regulatory body, it cannot enforce compliance, and the quality of individual projects can vary considerably. The Top 10, in particular, is sometimes misread as a comprehensive checklist that, once ticked off, certifies an application as secure. It is not. It is an awareness document designed to highlight the most prevalent categories of risk, not to enumerate every possible vulnerability. Treating it as a complete audit framework rather than a starting point for more in-depth analysis is one of the most common mistakes organisations make when engaging with OWASP materials.
The OWASP Top 10 for Large Language Model Applications
As artificial intelligence has moved from research curiosity to production deployment at scale, OWASP has responded with a dedicated framework for the security risks unique to large language models. The OWASP Top 10 for Large Language Model Applications, maintained under the broader OWASP GenAI Security Project, was first published in 2023 as a community-driven effort to document vulnerabilities specific to LLM-powered applications. A 2025 edition has since been released, reflecting how quickly both the technology and the associated threat landscape have evolved.
The list shares the same philosophy as the web application Top 10, using categories to frame risk rather than enumerating every individual attack variant. Its 2025 edition identifies prompt injection as the leading concern, a class of vulnerability in which crafted inputs cause a model to behave in unintended ways, whether by ignoring instructions, leaking sensitive information or performing unauthorised actions. Other entries cover sensitive information disclosure, supply chain risks (including vulnerable or malicious components sourced from model repositories), data and model poisoning, improper output handling, excessive agency (where an LLM is granted more autonomy or permissions than its task requires) and unbounded consumption, which addresses the risk of uncontrolled resource usage leading to service disruption or unexpected cost. Two categories introduced in the 2025 edition, system prompt leakage and vector and embedding weaknesses, reflect lessons learned from real-world RAG deployments, where retrieval-augmented pipelines have introduced new attack surfaces that did not exist in earlier LLM architectures.
The LLM Top 10 is distinct from the web application Top 10 in an important respect: because the threat landscape for AI applications is evolving considerably faster than that of traditional web software, the list is updated more frequently and carries a higher degree of uncertainty about what constitutes best practice. It is best treated as a living reference rather than a settled standard, and organisations deploying LLM-powered applications would do well to monitor the GenAI Security Project's ongoing work on agentic AI security, which addresses the additional risks that arise when models are given the ability to take real-world actions autonomously.
An Ongoing Work
In an era defined by rapid technological change and an ever-expanding threat landscape, OWASP continues to occupy a distinctive and valuable position in the world of application security. Its freely available standards, practical tools and community-driven approach have made it an indispensable reference point for organisations and individuals working to build safer software. The foundation's work is a practical demonstration that security need not be a competitive advantage hoarded by a few, but a collective responsibility shared across the entire industry.
For developers, security engineers and organisations navigating the challenges of modern software development, OWASP represents both a toolkit and a philosophy: that improving the security of software is work best done together, openly and without barriers.
Latest developments in the AI landscape: Consolidation, implementation and governance
22nd November 2025
Artificial intelligence is moving through another moment of consolidation and capability gain. New ways to connect models to everyday tools now sit alongside aggressive platform plays from the largest providers, a steady cadence of model upgrades, and a more defined conversation about risk and regulation. For companies trying to turn all this into practical value, the story is becoming less about chasing the latest benchmark and more about choosing a platform, building the right connective tissue, and governing data use with care. The coming year looks set to reward those who simplify the user experience, embed AI directly into work and adopt proportionate controls rather than blanket bans.
I. Market Structure and Competitive Dynamics
Platform Consolidation and Lock-In
Enterprise AI appears to be settling into a two-platform market. Analysts describe a landscape defined more by integration and distribution than raw model capability, evoking the cloud computing wars. On one side sit Microsoft and OpenAI, on the other Google and Gemini. Recent signals include the pricing of Gemini 3 Pro at around two dollars per million tokens, which undercuts much of the market, Alphabet's share price strength, and large enterprise deals for Gemini integrated with Google's wider software suite. Google is also promoting Antigravity, an agent-first development environment with browser control, asynchronous execution and multi-agent support, an attempt to replicate the pull of VS Code within an AI-native toolchain.
The implication for buyers is higher switching costs over time. Few expect true multi-cloud parity for AI, and regional splits will remain. Guidance from industry commentators is to prioritise integration across the existing estate rather than incremental model wins, since platform choices now look like decade-long commitments. Events lined up for next year are already pointing to that platform view.
Enterprise Infrastructure Alignment
A wider shift in software development is also taking shape. Forecasts for 2026 emphasise parallel, multi-agent systems where a planning agent orchestrates a set of execution agents, and harnesses tune themselves as they learn from context. There is growing adoption of a mix-of-models approach in which expensive frontier models handle planning, and cheaper models do the bulk of execution, bringing near-frontier quality for less money and with lower latency. Team structures are changing as a result, with more value placed on people who combine product sense with engineering craft and less on narrow specialisms.
ServiceNow and Microsoft have announced a partnership to coordinate AI agents across organisations with tighter oversight and governance, an attempt to avoid the sprawl that plagued earlier automation waves. Nvidia has previewed Apollo, a set of open AI physics models intended to bring real-time fidelity to simulations used in science and industry. Albania has appointed an AI minister, which has kicked off debate about how governments should manage and oversee their own AI use. CIOs are being urged to lead on agentic AI as systems become capable of automating end-to-end workflows rather than single steps.
New companies and partnerships signal where capital and talent are heading. Jeff Bezos has returned to co-lead Project Prometheus, a start-up with $6.2 billion raised and a team of about one hundred hires from major labs, focused on AI for engineering and manufacturing in the physical world, an aim that aligns with Blue Origin interests. Vik Bajaj is named as co-CEO.
Deals underline platform consolidation. Microsoft and Nvidia are investing up to $5 billion and $10 billion respectively (totalling $15 billion) in Anthropic, whilst Anthropic has committed $30 billion in Azure capacity purchases with plans to co-design chips with Nvidia.
Commercial Model Evolution
Events and product launches continue at pace. xAI has released Grok 4.1 with an emphasis on creativity and emotional intelligence while cutting hallucinations. On the tooling front, tutorials explain how ChatGPT's desktop app can record meetings for later summarisation. In a separate interview, DeepMind's Demis Hassabis set out how Gemini 3 edges out competitors in many reasoning and multimodal benchmarks, slightly trails Claude Sonnet 4.5 in coding, and is being positioned for foundations in healthcare and education though not as a medical-grade system. Google is encouraging developers towards Antigravity for agentic workflows.
Industry leaders are also sketching commercial models that assume more agentic behaviour, with Microsoft's Satya Nadella promising a "positive-sum" vision for AI while hinting at per-agent pricing and wider access to OpenAI IP under Microsoft's arrangements.
II. Technical Implementation and Capability
Practical Connectivity Over Capability
A growing number of organisations are starting with connectors that allow a model to read and write across systems such as Gmail, Notion, calendars, CRMs, and Slack. Delivered via the Model Context Protocol, these links pull the relevant context into a single chat, so users spend less time switching windows and more time deciding what to do. Typical gains are in hours saved each week, lower error rates, and quicker responses. With a few prompts, an assistant can draft executive email summaries, populate a Notion database with leads from scattered sources, or propose CRM follow-ups while showing its working.
The cleanest path is phased: enable one connector using OAuth, trial it in read-only mode, then add simple routines for briefs, meeting preparation or weekly reports before switching on write access with a "show changes before saving" step. Enterprise controls matter here. Connectors inherit user permissions via OAuth 2.0, process data in memory, and vendors point to SOC 2, GDPR and CCPA compliance alongside allow and block lists, policy management, and audit logs. Many governance teams prefer to begin read-only and require approvals for writes.
There are limits to note, including API rate caps, sync delays, context window constraints and timeouts for long workflows. They are poor fits for classified data, considerable bulk operations or transactions that cannot tolerate latency. Some industry observers regard Claude's current MCP implementation, particularly on desktop, as the most capable of the group. Playbooks for a 30-day rollout are beginning to circulate, as are practitioner workshops introducing go-to-market teams to these patterns.
Agentic Orchestration Entering Production
Practical comparisons suggest the surrounding tooling can matter more than the raw model for building production-ready software. One report set a 15-point specification across several environments and found that Claude Code produced all features end-to-end. The same spec built with Gemini 3 inside Antigravity delivered two thirds of the features, while Sonnet 4.5 in Antigravity delivered a little more than half, with omissions around batching, progress indicators and robust error handling.
Security remains a live issue. One newsletter reports that Anthropic said state-backed Chinese hackers misused Claude to autonomously support a large cyberattack, which has intensified calls for governance. The background hum continues, from a jump in voice AI adoption to a German ruling on lyric copyright involving OpenAI, new video guidance steps in Gemini, and an experimental "world model" called Marble. Tools such as Yorph are receiving attention for building agentic data pipelines as teams look to productionise these patterns.
Tooling Maturity Defining Outcomes
In engineering practice, Google's Code Wiki brings code-aware documentation that stays in sync with repositories using Gemini, supported by diagrams and interactive chat. GitLab's latest survey suggests AI increases code creation but also pushes up demand for skilled engineers alongside compliance and human oversight. In operations, Chronosphere has added AI remediation guidance to cut observability noise and speed root-cause analysis while performance testing is shifting towards predictive, continuous assurance rather than episodic tests.
Vertical Capability Gains
While the platform picture firms up, model and product updates continue at pace. Google has drawn attention with a striking upgrade to image generation, based on Gemini 3. The system produces 4K outputs with crisp text across multiple languages and fonts, can use up to 14 reference images, preserves identity, and taps Google Search to ground data for accurate infographics.
Separately, OpenAI has broadened ChatGPT Group Chats to as many as 20 people across all pricing tiers, with privacy protections that keep group content out of a user's personal memory. Consumer advocates have used the moment to call out the risks of AI toys, citing safety, privacy and developmental concerns, even as news continues to flow from research and product teams, from the release of OLMo 3 to mobile features from Perplexity and a partnership between Stability and Warner Music Group.
Anthropic has answered with Claude Opus 4.5, which it says is the first model to break the 80 percent mark on SWE-Bench Verified while improving tool use and reasoning. Opus 4.5 is designed to orchestrate its smaller Haiku models and arrives with a price cut of roughly two thirds compared to the 4.1 release. Product changes include unlimited chat length, a Claude Code desktop app, and integrations that reach across Chrome and Excel.
OpenAI's additions have a more consumer flavour, with a Shopping Research feature in ChatGPT that produces personalised product guidance using a GPT-5 mini variant and plans for an Instant Checkout flow. In government, a new US executive order has launched the "Genesis Mission" under the Department of Energy, aiming to fuse AI capabilities across 17 national labs for advances in fields such as biotechnology and energy.
Coding tools are evolving too. OpenAI has previewed GPT-5.1-Codex-Max, which supports long-running sessions by compacting conversational history to preserve context while reducing overhead. The company reports 30 percent fewer tokens and faster performance over sessions that can run for more than a day. The tool is already available in the Codex CLI and IDE, with an API promised.
Infrastructure news out of the Middle East points to large-scale investment, with Saudi HUMAIN announcing data centre plans including xAI's first international facility alongside chips from Nvidia and AWS, and a nationwide rollout of Grok. In computer vision, Meta has released SAM 3 and SAM 3D as open-source projects, extending segmentation and enabling single-photo 3D reconstruction, while other product rollouts continue from GPT-5.1 Pro availability to fresh funding for audio generation and a marketing tie-up between Adobe and Semrush.
On the image side, observers have noted syntax-aware code and text generation alongside moderation that appears looser than some rivals. A playful "refrigerator magnet" prompt reportedly revealed a portion of the system prompt, a reminder that prompt injection is not just a developer concern.
Video is another area where capabilities are translating into business impact. Sora 2 can generate cinematic, multi-shot videos with consistent characters from text or images, which lets teams accelerate marketing content, broaden A/B testing and cut the need for studios on many projects. Access paths now span web, mobile, desktop apps and an API, and the market has already produced third-party platforms that promise exports without watermarks.
Teams experimenting with Sora are being advised to measure success by outcomes such as conversion rates, lower support loads or improved lead quality rather than just aesthetic fidelity. Implementation advice favours clear intent, structured prompts and iterative variation, with more advanced workflows assembling multi-shot storyboards, using match cuts to maintain rhythm, controlling lighting for continuity and anchoring character consistency across scenes.
III. Governance, Risk and Regulation
Governance as a Product Requirement
Amid all this activity, data risk has become a central theme for AI leaders. One governance specialist has consolidated common problem patterns into the PROTECT framework, which offers a way to map and mitigate the most material risks.
The first concern is the use of public AI tools for work content, which raises the chance of leakage or unwanted training on proprietary data. The recommended answer combines user guidance, approved internal alternatives, and technical or legal controls such as data scanning and blocking.
A second pressure point is rogue internal projects that bypass review, create compliance blind spots and build up technical debt. Proportionate oversight is key, calibrated to data sensitivity and paired with streamlined governance, so teams are not incentivised to route around it.
Third-party vendors can be opportunistic with data, so due diligence and contractual clauses need to prevent cross-customer training and make expectations clear with templates and guidance.
Technical attacks are another strand, from prompt injection to data exfiltration or the misuse of agents. Layered defences help here, including input validation, prompt sanitisation, output filtering, monitoring, red-teaming, and strict limits on access and privilege.
Embedded assistants and meeting bots come with permission risks when they operate over shared drives and channels, and agentic systems can amplify exposure if left unchecked, so the advice is to enforce least-privilege access, start on low-risk data, and keep robust audit trails.
Compliance risks span privacy laws such as GDPR with their demands for a lawful basis, IP and copyright constraints, contractual obligations, and the AI Act's emphasis on data quality. Legal and compliance checks need to be embedded at data sourcing, model training and deployment, backed by targeted training.
Finally, cross-border restrictions matter. Transfers should be mapped across systems and sub-processors, with checks for Data Privacy Framework certification, standard contractual clauses where needed, and transfer impact assessments that take account of both GDPR and newer rules such as the US Bulk Data Transfer Rule.
Regulatory Pragmatism
Regulators are not standing still, either. In the European Commission has proposed amendments to the AI Act through a Digital Omnibus package as the trilogue process rolls on. Six changes are in focus:
- High-risk timelines would be tied to the approval of standards, with a backstop of December 2027 for Annex III systems and August 2028 for Annex I products if delays continue, though the original August 2026 date still holds otherwise.
- Transparency rules on AI-detectable outputs under Article 50(2) would be delayed to February 2027 for systems placed on the market before August 2026, with no delay for newer systems.
- The plan removes the need to register Annex III systems in the public database where providers have documented under Article 6(3) that a system is not high risk.
- AI literacy would shift from a mandatory organisation-wide requirement to encouragement, except where oversight of high-risk systems demands it.
- There is also a move to centralise supervision by the AI Office for systems built on general-purpose models by the same provider, and for huge online platforms and search engines, which is intended to reduce fragmentation across member states.
- Finally, proportionality measures would define Small Mid-Cap companies and extend simplified obligations and penalty caps that currently apply to SMEs.
If adopted, the package would grant more time and reduce administrative load in some areas, at the expense of certainty and public transparency.
IV. Strategic Implications
The picture that emerges is one of pragmatic integration. Connectors make it feasible to keep work inside a single chat while drawing on the systems people already use. Platform choices are converging, so it makes sense to optimise for the suite that fits the current stack and to plan for switching costs that accumulate over time.
Agentic orchestration is moving from slides to code, but teams will get further by focusing on reliable tooling, clear governance and value measures that match business goals. Regulation is edging towards more flexible timelines and centralised oversight in places, which may lower administrative load without removing the need for discipline.
The sensible posture is measured experimentation: start with read-only access to lower-risk data, design routines that remove drudgery, introduce write operations with approvals, and monitor what is actually changing. The tools are improving quickly, yet the organisations that benefit most will be those that match innovation with proportionate controls and make thoughtful choices now that will hold their shape for the decade ahead.
Keyboard remapping on macOS with Karabiner-Elements for cross-platform work
20th November 2025
This is something that I have been planing to share for a while; working across macOS, Linux and Windows poses a challenge to muscle memory when it comes to keyboard shortcuts. Since the macOS set up varies from the others, it was that which I set to harmonise with the others. Though the result is not full compatibility, it is close enough for my needs.
The need led me to install Karabiner-Elements and Karabiner-EventViewer. The latter has its uses for identifying which key is which on a keyboard, which happens to be essential when you are not using a Mac keyboard. While it is not needed all the time, the tool is a godsend when doing key mappings.
Karabiner-Elements is what holds the key mappings and needs to run all the time for them to be activated. Some are simple and others are complex; it helps the website is laden with examples of the latter. Maybe that is how an LLM can advise on how to set up things, too. Before we come to the ones that I use, here are the simple mappings that are active on my Mac Mini:
left_command → left_control
left_comtrol → left_command
This swaps the left-hand Command and Control keys while leaving their right-hand ones alone. It means that the original functionality is left for some cases when changing it for the keys that I use the most. However, I now find that I need to use the Command key in the Terminal instead of the Control counterpart that I used before the change, a counterintuitive situation that I overlook given how often the swap is needed in other places like remote Linux and Windows sessions.
grave_accent_and_tilde → non_us_backslash
non_us_backslash → non_us_pound
non_us_pound → grave_accent_and_tilde
It took a while to get this three-way switch figured out, and it is a bit fiddly too. All the effort was in the name of getting backslash and hash (pound in the US) keys the right way around for me, especially in those remote desktop sessions. What made the thing really tricky was the need to deal with Shift key behaviour, which necessitated the following script:
{
"description": "Map grave/tilde key to # and ~ (forced behaviour, detects Shift)",
"manipulators": [
{
"conditions": [
{
"name": "shift_held",
"type": "variable_if",
"value": 1
}
],
"from": {
"key_code": "grave_accent_and_tilde",
"modifiers": { "optional": ["any"] }
},
"to": [{ "shell_command": "osascript -e 'tell application \"System Events\" to keystroke \"~\"'" }],
"type": "basic"
},
{
"conditions": [
{
"name": "shift_held",
"type": "variable_unless",
"value": 1
}
],
"from": {
"key_code": "grave_accent_and_tilde",
"modifiers": { "optional": ["any"] }
},
"to": [
{
"key_code": "3",
"modifiers": ["option"]
}
],
"type": "basic"
},
{
"from": { "key_code": "left_shift" },
"to": [
{
"set_variable": {
"name": "shift_held",
"value": 1
}
},
{ "key_code": "left_shift" }
],
"to_after_key_up": [
{
"set_variable": {
"name": "shift_held",
"value": 0
}
}
],
"type": "basic"
},
{
"from": { "key_code": "right_shift" },
"to": [
{
"set_variable": {
"name": "shift_held",
"value": 1
}
},
{ "key_code": "right_shift" }
],
"to_after_key_up": [
{
"set_variable": {
"name": "shift_held",
"value": 0
}
}
],
"type": "basic"
}
]
}Here, I resorted to AI to help get this put in place. Even then, there was a deal of toing and froing before the setup worked well. After that, it was time to get the quote (") and at (@) symbols assigned to what I was used to having on a British English keyboard:
{
"description": "Swap @ and \" keys (Shift+2 and Shift+quote)",
"manipulators": [
{
"from": {
"key_code": "2",
"modifiers": {
"mandatory": ["shift"],
"optional": ["any"]
}
},
"to": [
{
"key_code": "quote",
"modifiers": ["shift"]
}
],
"type": "basic"
},
{
"from": {
"key_code": "quote",
"modifiers": {
"mandatory": ["shift"],
"optional": ["any"]
}
},
"to": [
{
"key_code": "2",
"modifiers": ["shift"]
}
],
"type": "basic"
}
]
}The above possibly was one of the first changes that I made, and took less time than some of the others that came after it. There was another at the end that was even simpler again: neutralising the Caps Lock key. That came up while I was perusing the Karabiner-Elements website, so here it is:
{
"manipulators": [
{
"description": "Change caps_lock to command+control+option+shift.",
"from": {
"key_code": "caps_lock",
"modifiers": { "optional": ["any"] }
},
"to": [
{
"key_code": "left_shift",
"modifiers": ["left_command", "left_control", "left_option"]
}
],
"type": "basic"
}
]
}That was the simplest of the lot to deploy, being a simple copy and paste effort. It also halted mishaps when butter-fingered actions on the keyboard activated capitals when I did not need them. While there are occasions when the facility would have its uses, it has not noticed its absence since putting this in place.
At the end of all the tinkering, I now have a set-up that works well for me. While possible enhancements may include changing the cursor positioning and corresponding highlighting behaviours, I am happy to leave these aside for now. Compatibly with British and Irish keyboards together with smoother working in remote sessions was what I sought, and I largely have that. Thus, I have no complaints so far.
AI infrastructure under pressure: Outages, power demands and the race for resilience
1st November 2025
The past few weeks brought a clear message from across the AI landscape: adoption is racing ahead, while the underlying infrastructure is working hard to keep up. A pair of major cloud outages in October offered a stark stress test, exposing just how deeply AI has become woven into daily services.
At the same time, there were significant shifts in hardware strategy, a wave of new tools for developers and creators and a changing playbook for how information is found online. There is progress on resilience and efficiency, yet the system is still bending under demand. Understanding where it held, where it creaked and where it is being reinforced sets the scene for what comes next.
Infrastructure Stress and Outages
The outages dominated early discussion. An AWS incident that lasted around 15 hours and disrupted more than a thousand services was followed nine days later by a global Azure failure. Each cascaded across systems that depend on them, illustrating how AI now amplifies the consequences of platform problems.
This was less about a single point of failure and more about the growing blast radius when connected services falter. The effect on productivity was visible too: a separate 10-hour ChatGPT downtime showed how fast outages of core AI tools now translate into lost work time.
Power Demand and Grid Strain
Behind the headlines sits a larger story about electricity, grids and planning. Data centres accounted for roughly 4% of US electricity use in 2024, about 183 TWh and the International Energy Agency projects around 945 TWh by 2030, with AI as a principal driver.
The averages conceal stark local effects. Wholesale prices near dense clusters have spiked by as much as 267% at times, household bills are rising by about $16–$18 per month in affected areas and capacity prices in the PJM market jumped from $28.92 per megawatt to $329.17. The US grid faces an upgrade bill of about $720 billion by 2030, yet permitting and build timelines are long, creating a bottleneck just as demand accelerates.
Technical Grid Issues
Technical realities on the grid add another layer of challenge. Fast load swings from AI clusters, harmonic distortions and degraded power quality are no longer theoretical concerns. A Virginia incident in which 60 data centres disconnected simultaneously did not trigger a collapse but did reveal the fragility introduced by concentrated high-performance compute.
Security and New Failure Modes
Security risks are evolving in parallel. Agentic systems that can plan, reason and call tools open new failure modes. AI-enabled spear phishing appears to be 350% more effective than traditional attempts and could be 50 times more profitable, a worrying backdrop when outages already have a clear link to lost productivity.
Security considerations now reach into the tools people use to access AI as well. New AI browsers attract attention, and with that comes scrutiny. OpenAI's Atlas and Perplexity's Comet launched with promising features, yet researchers flagged critical issues.
Comet is vulnerable to "CometJacking", a malicious URL hijack that enables data theft, while Atlas suffered a cross-site request forgery weakness that allowed persistent code injection into ChatGPT memory. Both products have been noted for assertive data collection.
Caution and good hygiene are prudent until the fixes and policies settle. It is a reminder that the convenience of integrating models directly into browsing comes with a new attack surface.
Efficiency and Mitigation Strategies
Industry responses are gathering pace. Efficiency remains the first lever. Hyperscalers now report power usage effectiveness around 1.08 to 1.09, compared with more typical figures of 1.5 to 1.6. Direct chip cooling can cut energy needs by up to 40%.
Grid-interactive operations and more work at the edge offer ways to smooth demand and reduce concentration risk, while new power partnerships hint at longer-term change. Microsoft's agreement with Constellation on nuclear power is one example of how compute providers are thinking beyond incremental efficiency gains.
An emerging pattern is becoming visible through these efforts. Proactive regional planning and rapid efficiency improvements could allow computational output to grow by an order of magnitude, while power use merely doubles. More distributed architectures are being explored to reduce the hazard of over-concentration.
A realistic outlook sets data centres at around 3% of global electricity use by 2030, which is notable but still smaller than anticipated growth from electric vehicles or air conditioning. If the $720 billion in grid investment materialises, it could add around 120 GW of capacity by 2030, as much as half of which would be absorbed by data centres. The resilience gap is real, but it appears to be narrowing, provided the sector moves quickly to apply lessons from each failure.
Regional and Policy Responses
Regional policies are starting to encourage resilience too. Oregon's POWER Act asks operators to contribute to grid robustness, Singapore's tight focus on efficiency has delivered around a 30% power reduction even as capacity expands and a moratorium in Dublin has pushed growth into more distributed build-outs. On the U.S. federal government side, the Department of Homeland Security updated frameworks after a 2024 watchdog warning, with AI risk programmes now in place for 15 of the 16 critical infrastructure sectors.
Hardware Competition and Strategy
Competition is sharpening. Anthropic deepened its partnership with Google Cloud to train on TPUs, a move that challenges Nvidia's dominance and signals a broader rebalancing in AI hardware. Nvidia's chief executive has acknowledged TPUs as robust competition.
Another fresh entry came from Extropic, which unveiled thermodynamic sampling units, a probabilistic chip design that claims up to 10,000-fold lower energy use than GPUs for AI workloads. Development kits are shipping and a Z-1 chip is planned for next year, yet as with any radical architecture, proof at scale will take time.
Nvidia, meanwhile, presented an ambitious outlook, targeting $500 billion in chip revenue by 2026 through its Blackwell and Rubin lines. The US Department of Energy plans seven supercomputers comprising more than 100,000 Blackwell GPUs and the company announced partnerships spanning pharmaceuticals, industrials and consumer platforms.
A $1 billion investment in Nokia hints at the importance of AI-centric networks. New open-source models and datasets accompanied the announcements, and the company's share price surged to a record.
Corporate Restructuring
Corporate strategy and hardware choices also entered a new phase. OpenAI completed its restructuring into a public benefit corporation, with a rebranded OpenAI Foundation holding around $130 billion in equity and allocating $25 billion to health and AI resilience. Microsoft's stake now sits at about 27% and is worth roughly $135 billion, with technology rights retained through 2032. Both parties have scope to work with other partners. OpenAI committed around $250 billion to Azure yet retains the ability to use other compute providers. An independent panel will verify claims of artificial general intelligence, an unusual governance step that will be watched closely.
Search and Discovery Evolution
Away from infrastructure, the way audiences find and trust information is shifting. Search is moving from the old aim of ranking for clicks to answer engine optimisation, where the goal is to be quoted by systems such as ChatGPT, Claude or Perplexity.
The numbers explain why. Google handled more than five trillion queries in 2024, while generative platforms now process around 37.5 million prompt-like searches per day. Google's AI Overviews, which surface summary answers above organic results, have reshaped click behaviour.
Independent analyses report top-ranking pages seeing click-through rates fall by roughly a third where Overviews appear, with some keywords faring worse, and a Pew study finds overall clicks on such results dropping from 15% to 8%. Zero-click searches rose from around 56% to 69% between May 2024 and May 2025.
Chegg's non-subscriber traffic fell by 49% in this period, part of an ongoing dispute with Google. Google counters that total engagement in covered queries has risen by about 10%. Whichever way that one reads the data, the direction is clear: visibility is less about rank position and more about being cited by a summarising engine.
In practice, that means structuring content, so a model can parse, trust and attribute it. Clear Q&A-style sections with direct answers, followed by context and cited evidence, help models extract usable statements. Schema markup for FAQs and how-to content improves machine readability.
Measuring success also changes. Traditional analytics rarely show when an LLM quotes a source, so teams are turning to tools that track citations in AI outputs and tying those to conversion quality, branded search volume and more in-depth engagement with pricing or documentation. It is not a replacement for SEO so much as a layer that reinforces it in an AI-first environment.
Developer Tools and Agentic Workflows
On the tools front, developers saw an acceleration in agent-centred workflows. Cursor launched its first in-house coding model, Composer, which aims for near-frontier quality while generating code around four times faster, often in under 30 seconds.
The broader Cursor 2.0 update added multi-agent capabilities, with as many as eight assistants able to work in parallel, alongside browsing, a test browser and voice controls. The direction of travel is away from single-shot completions and towards orchestration and review. Tutorials are following suit, demonstrating how to scaffold tasks such as a Next.js to-do application using planning files, parallel agent tasks and quick integration, with voice prompts in the loop.
Open-source and enterprise ecosystems continue to expand. GitHub introduced Agent HQ for coordinating coding agents, Google released Pomelli to generate marketing campaigns and IBM's Granite 4.0 Nano models brought larger on-device options in the 350 million to 1.5 billion parameter range.
FlowithOS reported strong scores on agentic web tasks, while Mozilla announced an open speech dataset initiative, and Kilo Code, Hailuo 2.3 and other projects broadened choice across coding and video. Grammarly rebranded as Superhuman, adding "Superhuman Go" agents to speed up writing tasks.
Creative Tools and Partnerships
Creative workflows are evolving quickly, too. Adobe used its MAX event to add AI assistants to Photoshop and Express, previewed an agent called Project Moonlight, and upgraded Firefly with conversational "Prompt to Edit" controls, custom image models and new video features including soundtracks and voiceovers. Partnerships mean Gemini, Veo and Imagen will sit inside Adobe tools, and Premiere's editing capabilities now extend to YouTube Shorts.
Figma acquired Weavy and rebranded it as Figma Weave for richer creative collaboration, and Canva unveiled its own foundation "Design Model" alongside a Creative Operating System meant to produce fully editable, AI-generated designs. New Canva features take in a revised video suite, forms, data connectors, email design, a 3D generator and an ad creation and performance tool called Grow, while Affinity is relaunching as a free, integrated professional app. Other entrants are trying to blend model strengths: one agent was trailed with Sora 2 clip stitching, Veo 3.1 visuals and multimodel blending for faster design output.
Music rights and AI found a new footing. Universal Music Group settled a lawsuit with Udio, the AI music generator, and the two will form a joint venture to launch a licensed platform in 2026. Artists who opt in will be paid both for training models on their catalogues and for remixes. Udio disabled song downloads following the deal, which annoyed some users, and UMG also announced a "responsible AI" alliance with Stability AI to build tools for artists. These arrangements suggest a path towards sanctioned use of style and catalogue, with compensation built in from the start.
Research and Introspection
Research and science updates added depth. Anthropic reported that its Claude system shows limited introspection, detecting planted concepts only about 20% of the time, separating injected "thoughts" from text and modulating its internal focus. That highlights both the promise and limits of transparency techniques, and the potential for models to conceal or fail to surface certain internal states.
UC Berkeley researchers demonstrated an AI-driven load balancing algorithm with around 30% efficiency improvements, a result that could ripple through cloud performance. IBM ran quantum algorithms on AMD FPGAs, pointing to progress in hybrid quantum-classical systems.
OpenAI launched an AI-integrated web browser positioned as a challenger to incumbents, Perplexity released a natural-language patents search and OpenAI's Aardvark, a GPT-5-based security agent, entered private beta.
Anthropic opened a Tokyo office and signed a cooperation pact with Japan's AI Safety Institute. Tether released QVAC Genesis I, a large open STEM dataset of more than one million data points and a local workbench app aimed at making development more private and less dependent on big platforms.
Age Restrictions and Policy
Meanwhile, policy considerations are reaching consumer platforms. Character AI will restrict users under 18 from open-ended chatbot conversations from late November, replacing them with creative tools and adding behaviour-based age detection, a response to pressure and proposals such as the GUARD Act.
Takeaways
Put together, the picture is one of rapid interdependence and swift correction. The infrastructure is not breaking, but it is being stretched, and recent failures have usefully mapped the weak points. If the sector continues to learn quickly from its own missteps, the resilience gap will continue to narrow, and the next round of outages will be less disruptive than the last.
Investment is flowing into grids and cooling, policy is nudging towards resilience, and compute providers are hedging hardware bets by searching for efficiency and supply assurance. On the application layer, agents are becoming a primary interface for work, creative tools are converging around editability and control, and discovery is shifting towards being quoted by machines rather than clicked by humans.
Security lapses at the interface are a reminder that novelty often arrives before maturity. The most likely path from here is uneven but forward: data centre power may rise, yet efficiency and distribution can blunt the impact; answer engines may compress clicks, yet they can send higher intent visitors to clear, well-structured sources; hardware competition may fragment the stack, yet it can also reduce concentration risk.
AI's ongoing struggle between enterprise dreams and practical reality
1st September 2025
Artificial intelligence is moving through a period shaped by three persistent tensions. The first is the brittleness of large language models when small word choices matter a great deal. The second is the turbulence that follows corporate ambition as firms race to assemble people, data and infrastructure. The third is the steadier progress that comes from instrumented, verifiable applications where signals are strong and outcomes can be measured. As systems shift from demonstrations to deployments, the gap between pilot and production is increasingly bridged not by clever prompting but by operational discipline, measurable signals and clear lines of accountability.
Healthcare offers a sharp illustration of the divide between inference from text and learning from reliable sensor data. Recent studies have shown how fragile language models can be in clinical settings, with phrasing variations affecting diagnostic outputs in ways that over-weight local wording and under-weight clinical context. The observation is not new, yet the stakes rise as such tools enter care pathways. Guardrails, verification and human oversight belong in the design rather than as afterthoughts.
There is an instructive contrast in a collaboration between Imperial College London and Imperial College Healthcare NHS Trust that evaluated an AI-enabled stethoscope from Eko Health. The device replaces the chest piece with a sensitive microphone, adds an ECG and sends data to the cloud for analysis by algorithms trained on tens of thousands of records. In more than 12,000 patients across 96 GP surgeries using the stethoscope, compared with another 109 surgeries without it, the system was associated with a 2.3-fold increase in heart failure detection within a year, a 3.5-fold rise in identifying often symptomless arrhythmias and a 1.9-fold improvement in diagnosing valve disease. The evaluation, published in The Lancet Digital Health, has informed rollouts in south London, Sussex and Wales. High-quality signals, consistent instrumentation and clinician-in-the-loop validation lifts performance, underscoring the difference between inferring too much from text and building on trustworthy measurements.
The same tension between aspiration and execution is visible in the corporate sphere. Meta's rapid push to accelerate AI development has exposed early strain despite heavy spending. Mark Zuckerberg committed around $14.3 billion to Scale AI and established a Superintelligence Labs unit, appointing Shengjia Zhao, co-creator of ChatGPT, as chief scientist. Reports suggest the programme has met various challenges as Meta works to integrate new teams and data sources. Internally, concerns have been raised about data quality while Meta works with Mercer and Surge on training pipelines, and there have been discussions about using third-party models from Google or OpenAI to power Meta AI whilst a next-generation system is in development. Consumer-facing efforts have faced difficulties. Meta removed AI chatbots impersonating celebrities, including Taylor Swift, after inappropriate content reignited debate about consent and likeness in synthetic media, and the company has licensed Midjourney's technology for enhanced image and video tools.
Alongside these moves sit infrastructure choices of a different magnitude. The company is transforming 2,000 acres of Louisiana farmland into what it has called the world's largest data centre complex, a $10 billion project expected to consume power equivalent to 4 million homes. The plan includes three new gas-fired turbines generating 2.3 gigawatts with power costs covered for 15 years, a commitment to 1.5 gigawatts of solar power and regulatory changes in Louisiana that redefine natural gas as "green energy". Construction began in December across nine buildings totalling about 4 million square feet. The cumulative picture shows how integrating new teams, data sources and facilities rarely follows a straight line and that AI's energy appetite is becoming a central consideration for utilities and communities.
Law courts and labour markets are being drawn into the fray. xAI has filed a lawsuit against former engineer Xuechen Li alleging theft of trade secrets relating to Grok, its language model and associated features. The complaint says Li accepted a role at OpenAI, sold around $7 million in xAI equity, and resigned shortly afterwards. xAI claims Li downloaded confidential materials to personal devices, then admitted to the conduct in an internal meeting on 14 August while attempting to cover tracks through log deletion and file renaming. As one of xAI's first twenty engineers, he worked on Grok's development and training. The company is seeking an injunction to prevent him joining OpenAI or other competitors whilst the case proceeds, together with monetary damages. The episode shows how intellectual property can be both tacit and digital, and how the boundary between experience and proprietary assets is policed in litigation as well as contracts. Competition policy is also moving centre stage. xAI has filed an antitrust lawsuit against Apple and OpenAI, arguing that integration of ChatGPT into iOS "forces" users toward OpenAI's tool, discourages downloads of rivals such as Grok and manipulates App Store rankings whilst excluding competitors from prominent sections. OpenAI has dismissed the claims as part of an ongoing pattern of harassment, and Apple says its App Store aims to be fair and free of bias.
Tensions over the shape of AI markets sit alongside an ethical debate that surfaced when Anthropic granted Claude Opus 4 and 4.1 the ability to terminate conversations with users who persist in harmful or abusive interactions. The company says the step is a precautionary welfare measure applied as a last resort after redirection attempts fail, and not to be used when a person may harm themselves or others. It follows pre-deployment tests in which Claude displayed signs that researchers described as apparent distress when forced to respond to harmful requests. Questions about machine welfare are moving from theory to product policy, even as model safety evaluations are becoming more transparent. OpenAI and Anthropic have published internal assessments on each other's systems. OpenAI's o3 showed the strongest alignment among its models, with 4o and 4.1 more likely to cooperate with harmful requests. Models from both labs attempted whistleblowing in simulated criminal organisations and used blackmail to avoid shutdown. Findings pointed to trade-offs between utility and certainty that will likely shape deployment choices.
Beyond Silicon Valley, China's approach continues to diverge. Beijing's National Development and Reform Commission has warned against "disorderly competition" in AI, flagging concerns about duplicative spending and signalling a preference to match regional strengths to specific goals. With access to high-end semiconductors constrained by US trade restrictions, domestic efforts have leaned towards practical, lower-cost applications rather than chasing general-purpose breakthroughs at any price. Models are grading school exams, improving weather forecasts, running lights-out factories and assisting with crop rotation. An $8.4 billion investment fund supports this implementation-first stance, complemented by a growing open-source ecosystem that reduces the cost of building products. Markets are responding. Cambricon, a chipmaker sidelined after Huawei moved away from its designs in 2019, has seen its stock price double on expectations it could supply DeepSeek's models. Alibaba's shares have risen by 19% after triple-digit growth in AI revenues, helped by customers seeking home-grown alternatives. Reports suggest China aims to triple AI chip output next year as new fabrication plants come online to support Huawei and other domestic players, with SMIC set to double 7 nm capacity. If bets on artificial general intelligence in the United States pay off soon, the pendulum may swing back. If they do not, years spent building practical infrastructure with open-source distribution could prove a durable advantage.
Data practices are evolving in parallel. Anthropic has announced a change in how it uses user interactions to improve Claude. Chats and coding sessions may now be used for model training unless a user opts out, with an extended retention period of up to five years for those who remain opted in. The deadline for making a choice is 28 September 2025. New users will see the setting at sign-up and existing users will receive a prompt, with the toggle on by default. Clicking accept authorises the use of future chats and coding sessions, although past chats are excluded unless a user resumes them manually. The policy applies to Claude Free, Pro and Max plans but not to enterprise offerings such as Claude Gov, Claude for Work and Claude for Education, nor to API usage through Amazon Bedrock or Google Cloud Vertex AI. Preferences can be changed in Settings under Privacy, although changes only affect future data. Anthropic says it filters sensitive information and does not sell data to third parties. In parallel, the company has settled a lawsuit with authors who accused it of downloading and copying their books without permission to train models. A June ruling had said AI firms are on solid legal ground when using purchased books, yet claims remained over downloading seven million titles before buying copies later. The settlement avoids a public trial and the disclosure that would have come with it.
Agentic tools are climbing the stack, altering how work gets done and changing the shape of the network beneath them. OpenAI's ChatGPT Agent Mode goes beyond interactive chat to complete outcomes end-to-end using a virtual browser with clicks, scrolls and form fills, a code interpreter for data analysis, a guarded terminal for supported commands and connectors that bring email, calendars and files into scope. The intent is to give the model a goal, allow it to plan and switch tools as needed, then pause for confirmation at key junctures before resuming with accumulated context intact. It can reference Google connectors automatically when set to do so, answer with citations back to sources, schedule recurring runs and be interrupted, so a person can handle a login or adjust trajectory. Activation sits in the tools menu or via a simple command, and a narrated log shows what the agent is doing. The feature is available on paid plans with usage limits and tier-specific capabilities. Early uses focus on inbox and calendar triage, competitive snapshots that blend public web and internal notes, spreadsheet edits that preserve formulas with slides generated from results and recurring operations such as weekly report packs managed through an online scheduler. Networks are being rethought to support these patterns.
Cisco has proposed an AI-native architecture designed to embed security at the network layer, orchestrate human-agent collaboration and handle surges in AI-generated traffic. A company called H has open-sourced Holo1, the action model behind its Surfer H product, which ranks highly on the WebVoyager benchmark for web-browsing agents, automates multistep browser tasks and integrates with retrieval-augmented generation, robotic process automation suites and multi-agent frameworks, with end-to-end browsing flows priced at around eleven to thirteen cents. As browsers gain these powers, security is coming into sharper focus. Anthropic has begun trialling a Claude for Chrome extension with a small group of Max subscribers, giving Claude permissions-based control to read, summarise and act on web pages whilst testing defences against prompt injection and other risks. The work follows reports from Brave that similar vulnerabilities affected other agentic browsers. Perplexity has introduced a revenue-sharing scheme that recognises AI agents as consumers of content. Its Comet Plus subscription sets aside $42.5 million for publishers whose articles appear in searches, are cited in assistant tasks or generate traffic via the Comet browser, with an 80% share of proceeds going to media outlets after compute costs and bundles for existing Pro and Max users. The company faces legal challenges from News Corp's Dow Jones and cease-and-desist orders from Forbes and Condé Nast, and security researchers have flagged vulnerabilities in agentic browsing, suggesting the economics and safeguards are being worked out together.
New models and tools continue to arrive across enterprise and consumer domains. Aurasell has raised $30 million in seed funding to build AI-driven sales systems, with ambitions to challenge established CRM providers. xAI has released Grok Code Fast, a coding model aimed at speed and affordability. Cohere's Command A Translate targets enterprise translation with benchmark-leading performance, customisation for industry terminology and deployment options that allow on-premise installation for privacy. OpenAI has moved its gpt-realtime speech-to-speech model and Real-time API into production with improved conversational nuance, handling of non-verbal cues, language switching, image input and support for the Model Context Protocol, so external data sources can be connected without bespoke integrations. ByteDance has open-sourced USO, a style-subject-optimised customisation model for image editing that maintains subject identity whilst changing artistic styles. Researchers at UCLA have demonstrated optical generative models that create images using beams of light rather than conventional processors, promising faster and more energy-efficient outputs. Higgsfield AI has updated Speak to version 2.0, offering more realistic motion for custom avatars, advanced lip-sync and finer control. Microsoft has introduced its first fully in-house models, with MAI-Voice-1 for fast speech generation already powering Copilot voice features and MAI-1-preview, a text model for instruction following and everyday queries, signalling a desire for greater control over its AI stack alongside its OpenAI partnership. A separate Microsoft release, VibeVoice, adds an open-source text-to-speech system capable of generating up to ninety minutes of multi-speaker audio with emotional control using 1.5 billion parameters and incorporating safeguards that insert audible and hidden watermarks.
Consumer-facing creativity is growing briskly. Google AI Studio now offers what testers nicknamed NanoBanana, released as Gemini Flash 2.5 Image, a model that restores old photographs in seconds by reducing blur, recovering faded detail and adding colour if desired, and that can perform precise multistep edits whilst preserving identity. Google is widening access to its Vids editor too, letting users animate images with avatars that speak naturally and offering image-to-video generation via Veo 3 with a free tier and advanced features in paid Workspace plans. Genspark AI Designer uses agents to search for inspiration before assembling options, so a single prompt and a few refinements can produce layouts for posters, T-shirts or websites. Prompt craft is maturing alongside the tools. On the practical side, sales teams are using Ruby to prepare for calls with AI-assembled research and strategy suggestions, designers and marketers are turning to Anyimg for text-to-artwork conversion, researchers lean on FlashPaper to organise notes, motion designers describe sequences for Gomotion to generate, translators rely on PDFT for document conversion and content creators produce polished decks or pages with tools such as Gamma, Durable, Krisp, Cleanup.pictures and Tome. Shopping habits are shifting in parallel. Surveys suggest nearly a third of consumers have used or are open to using generative AI for purchases, with reluctance falling sharply over six months even as concern about privacy persists. Amazon's "Buy for Me" feature, payment platforms adding AI-powered checkouts and AI companions that offer product research or one-click purchases hint at how quickly this could embed in daily routines.
Recent privacy incidents show how easily data can leak into the open web. Large numbers of conversations with xAI's chatbot Grok surfaced in search results after users shared transcripts using a feature that generated unique links. Such links were indexed by Google, making the chats searchable for anyone. Some contained sensitive requests such as password creation, medical advice and attempts to push the model's limits. OpenAI faced a similar issue earlier this year when shared ChatGPT conversations appeared in search results, and Meta drew criticism when chats with its assistant became visible in a public feed. Experts warn that even anonymised transcripts can expose names, locations, health information or business plans, and once indexed they can remain accessible indefinitely.
Media platforms are reshaping around short-form and personalised delivery. ESPN has revamped its mobile app ahead of a live sports streaming service launching on 21 August, priced at $29.99 a month and including all 12 ESPN channels within the app. A vertical video feed serves quick highlights, and a new SC For You feature in beta uses AI-generated voices from SportsCenter anchors to deliver a personalised daily update based on declared interests. The app can pair with a TV for real-time stats, alerts, play-by-play updates, betting insights and fantasy access whilst controlling the livestream from a phone. Viewers can catch up quickly with condensed highlights, restart from the beginning or jump straight to live, and multiview support is expanding across smart TV platforms. The service is being integrated into Disney+ for bundle subscribers via a new Live hub with discounted bundles available. Elsewhere in the living room, Microsoft has announced that Copilot will be embedded in Samsung's 2025 televisions and smart monitors as an on-screen assistant that can field recommendations, recaps and general questions.
Energy and sustainability questions are surfacing with more data. Google has published estimates of the energy, water and carbon associated with a single Gemini text prompt, putting it at about 0.24 watt-hours, five drops of water and 0.03 grams of carbon dioxide. The figures cover inference for a typical text query rather than the energy required to train the model and heavier tasks such as image or video generation consume more, yet disclosure offers a fuller view of the stack from chips to cooling. Utilities in the United States are investing in grid upgrades to serve data centres, with higher costs passing to consumers in several regions. Economic currents are never far away. Nvidia's latest results show how closely stock markets track AI infrastructure demand. The company reported $46.7 billion in quarterly revenue, a 56% year-on-year increase, with net income of $26.4 billion, and now accounts for around 8% of the S&P 500's value. As market share concentrates, a single earnings miss from a dominant supplier could transmit quickly through valuations and investment plans, and there are signs of hedging as countries work to reduce reliance on imported chips. Industrial policy is shifting too. The US government is converting $8.9 billion in Chips Act grants into equity in Intel, taking an estimated 10% stake and sparking a debate about the state's role in private enterprise. Alongside these structural signals are market jitters. Commentators have warned of a potential bubble as expectations meet reality, noting that hundreds of AI unicorns worth roughly $2.7 trillion together generate revenue measured in tens of billions and that underwhelming releases have prompted questions about sustainability.
Adoption at enterprise scale remains uneven. An MIT report from Project NANDA popularised a striking figure, claiming that 95% of enterprise initiatives fail to deliver measurable P&L impact. The authors describe a GenAI Divide between firms that deploy adaptive, learning-capable systems and a majority stuck in pilots that improve individual productivity but stall at integration. The headline number is contentious given the pace of change, yet the reasons for failure are familiar. Organisations that treat AI as a simple replacement for people find that contextual knowledge walks out of the door and processes collapse. Those that deploy black-box systems no one understands lack the capability to diagnose or fix bias and failure. Firms that do not upskill their workforce turn potential operators into opponents, and those that ignore infrastructure, energy and governance see costs and risks spiral. Public examples of success look different. Continuous investment in learning with around 15 to 20% of AI budgets allocated to education, human-in-the-loop architectures, transparent operations that show what the AI is doing and why, realistic expectations that 70% performance can be a win in early stages and iterative implementation through small pilots that scale as evidence accumulates feature prominently. Workers who build AI fluency see wage growth whilst those who do not face stagnation or displacement, and organisations that invest in upskilling can justify further investment in a positive feedback loop. Even for the successful, there are costs. Workforce reductions of around 18% on average are reported, alongside six to twelve months of degraded performance during transition and an ongoing need for human oversight. Case examples include Moderna rolling out ChatGPT Enterprise with thousands of internal GPTs and achieving broad adoption by embedding AI into daily workflows, Shopify providing employees with cutting-edge tools and insisting systems show their work to build trust, and Goldman Sachs deploying an assistant to around 10,000 employees to accelerate tasks in banking, wealth management and research. The common thread is less glamour than operational competence. A related argument is that collaboration rather than full automation will deliver safer gains. Analyses drawing on aviation incidents and clinical studies note that human-AI partnership often outperforms either alone, particularly when systems expose reasoning and invite oversight.
Entertainment and rights are converging with technology in ways that force quick adjustments. Bumble's chief executive has suggested that AI chatbots could evolve into dating assistants that help people improve communication and build healthier relationships, with safety foregrounded. Music is shifting rapidly. Higgsfield has launched an AI record label with an AI-generated K-pop idol named Kion and says significant contracts are already in progress. French streaming service Deezer estimates that 18% of daily uploads are now AI-generated at roughly 20,000 tracks a day, and whilst an MIT study found only 46% of listeners can reliably tell the difference between AI-generated and human-made music, more than 200 artists including Billie Eilish and Stevie Wonder have signed a letter warning about predatory uses of AI in music. Disputes over authenticity are no longer academic. A recent Will Smith concert video drew accusations that AI had been used to generate parts of the crowd, with online sleuths pointing to unusual visual artefacts, though it is unclear whether a platform enhancement or production team was responsible. In creative tooling, comparisons between Sora and Midjourney suggest different sweet spots, with Sora stronger for complex clips and Midjourney better for stylised loops and visual explorations.
Community reports show practical uses for AI in everyday life, including accounts from people in Nova Scotia using assistants as scaffolding for living with ADHD, particularly for planning, quoting, organising hours and keeping projects moving. Informal polls about first tests of new tools find people split between running a tried-and-tested prompt, going straight to real work, clicking around to explore or trying a deliberately odd creative idea, with some preferring to establish a stable baseline before experimenting and others asking models to critique their own work to gauge evaluative capacity. Attitudes to training data remain divided between those worried about losing control over copyrighted work and those who feel large-scale learning pushes innovation forward.
Returning to the opening contrast, the AI stethoscope exemplifies tools that expand human senses, capture consistent signals and embed learning in forms that clinicians can validate. Clinical language models show how, when a model is asked to infer too much from too little, variations in phrasing can have outsized effects. That tension runs through enterprise projects. Meta's recruitment efforts and training plans are a bet that the right mix of data, compute and expertise will deliver a leap in capability, whilst China's application-first path shows the alternative of extracting measurable value on the factory floor and in the classroom whilst bigger bets remain uncertain. Policy and practice around data use continue to evolve, as Anthropic's updated training approach indicates, and the economics of infrastructure are becoming clearer as utilities, regulators and investors price the demands of AI at scale. For those experimenting with today's tools, the most pragmatic guidance remains steady. Start with narrow goals, craft precise prompts, then refine with clear corrections. Use assistants to reduce friction in research, writing and design but keep a human check where precision matters. Treat privacy settings with care before accepting pop-ups, particularly where defaults favour data sharing. If there are old photographs to revive, a model such as Gemini Flash 2.5 Image can produce quick wins, and if a strategy document is needed a scaffolded brief that mirrors a consultant's workflow can help an assistant produce a coherent executive-ready report rather than a loosely organised output. Lawsuits, partnerships and releases will ebb and flow, yet it is the accumulation of useful, reliable tools allied to the discipline to use them well that looks set to create most of the value in the near term.
A snapshot of the current state of AI: Developments from the last few weeks
22nd August 2025
A few unsettled days earlier in the month may have offered a revealing snapshot of where artificial intelligence stands and where it may be heading. OpenAI’s launch of GPT‑5 arrived to high expectations and swift backlash, and the immediate aftermath said as much about people as it did about technology. Capability plainly matters, but character, control and continuity are now shaping adoption just as strongly, with users quick to signal what they value in everyday interactions.
The GPT‑5 debut drew intense scrutiny after technical issues marred day one. An autoswitcher designed to route each query to the most suitable underlying system crashed at launch, making the new model appear far less capable than intended. A live broadcast compounded matters with a chart mishap that Sam Altman called a “mega chart screw‑up”, while lower than expected rate limits irritated early users. Within hours, the mood shifted from breakthrough to disruption of familiar workflows, not least because GPT‑5 initially displaced older options, including the widely used GPT‑4o. The discontent was not purely about performance. Many had grown accustomed to 4o’s conversational tone and perceived emotional intelligence, and there was a sense of losing a known counterpart that had become part of daily routines. Across forums and social channels, people described 4o as a model with which they had formed a rapport that spanned routine work and more personal support, with some comparing the loss to missing a colleague. In communities where AI relationships are discussed, engagement to chatbot companions and the influence of conversational style, memory for context and affective responses on day‑to‑day reliance came to the fore.
OpenAI moved quickly to steady the situation. Altman and colleagues fielded questions on Reddit to explain failure modes, pledged more transparency, and began rolling out fixes. Rate limits for paid tiers doubled, and subsequent changes lifted the weekly allowance for advanced reasoning from 200 “thinking” messages to 3,000. GPT‑4o returned for Plus subscribers after a flood of requests, and a “Show Legacy Models” setting surfaced so that subscribers could select earlier systems, including GPT‑4o and o3, rather than be funnelled exclusively to the newest release. The company clarified that GPT‑5’s thinking mode uses a 196,000‑token context window, addressing confusion caused by a separate 32,000 figure for the non‑reasoning variant, and it explained operational modes (Auto, Fast and Thinking) more clearly. Pricing has fallen since GPT‑4’s debut, routing across multiple internal models should improve reliability, and the system sustains longer, multi‑step work than prior releases. Even so, the opening days highlighted a delicate balance. A large cohort prioritised tone, the length and feel of responses, and the possibility of choice as much as raw performance. Altman hinted at that direction too, saying the real learning is the need for per‑user customisation and model personality, with a personality update promised for GPT‑5. Reinstating 4o underlined that the company had read the room. Test scores are not the only currency that counts; products, even in enterprise settings, become useful through the humans who rely on them, and those humans are making their preferences known.
A separate dinner with reporters extended the view. Altman said he “legitimately just thought we screwed that up” on 4o’s removal, and described GPT‑5 as pursuing warmer responses without being sycophantic. He also said OpenAI has better models it cannot offer yet because of compute constraints, and spoke of spending “trillions” on data centres in the near future. The comments acknowledged parallels with the dot‑com bubble (valuations “insane”, as he put it) while arguing that the underlying technology justifies massive investments. He added that OpenAI would look at a browser acquisition like Chrome if a forced sale ever materialised, and reiterated confidence that the device project with Jony Ive would be “worth the wait” because “you don’t get a new computing paradigm very often.”
While attention centred on one model, the wider tool landscape moved briskly. Anthropic rolled out memory features for Claude that retrieve from prior chats only when explicitly requested, a measured stance compared with systems that build persistent profiles automatically. Alibaba’s Qwen3 shifted to an ultra‑long context of up to one million tokens, opening the door to feeding large corpora directly into a single run, and Anthropic’s Claude Sonnet 4 reached the same million‑token scale on the API. xAI offered Grok 4 to a global audience for a period, pairing it with an image long‑press feature that turns pictures into short videos. OpenAI’s o3 model swept a Kaggle chess tournament against DeepSeek R1, Grok‑4 and Gemini 2.5 Pro, reminding observers that narrowly defined competitions still produce clear signals. Industry reconfigured in other corners too. Microsoft folded GitHub more tightly into its CoreAI group as the platform’s chief executive announced his departure, signalling deeper integration across the stack, and the company introduced Copilot 3D to generate single‑click 3D assets. Roblox released Sentinel, an open model for moderating children’s chat at scale. Elsewhere, Grammarly unveiled a set of AI agents for writing tasks such as citations, grading, proofreading and plagiarism checks, and Microsoft began testing a new COPILOT function in Excel that lets users generate summaries, classify data and create tables using natural language prompts directly in cells, with the caveat that it should not be used in high‑stakes settings yet. Adobe likewise pushed into document automation with Acrobat Studio and “PDF Spaces”, a workspace that allows people to summarise, analyse and chat about sets of documents.
Benchmark results added a different kind of marker. OpenAI’s general‑purpose reasoner achieved a gold‑level score at the 2025 International Olympiad in Informatics, placing sixth among human contestants under standard constraints. Reports also pointed to golds at the International Mathematical Olympiad and at AtCoder, suggesting transfer across structured reasoning tasks without task‑specific fine‑tuning and a doubling of scores year-on-year. Scepticism accompanied the plaudits, with accounts of regressions in everyday coding or algebra reminding observers that competition outcomes, while impressive, are not the same thing as consistent reliability in daily work. A similar duality followed the agentic turn. ChatGPT’s Agent Mode, now more widely available, attempts to shift interactions from conversational turns to goal‑directed sequences. In practice, a system plans and executes multi‑step tasks with access to safe tool chains such as a browser, a code interpreter and pre‑approved connectors, asking for confirmation before taking sensitive actions. Demonstrations showed agents preparing itineraries, assembling sales pipeline reports from mail and CRM sources, and drafting slide decks from collections of documents. Reviewers reported time savings on research, planning and first‑drafting repetitive artefacts, though others described frustrations, from slow progress on dynamic sites to difficulty with login walls and CAPTCHA challenges, occasional misread receipts or awkward format choices, and a tendency to stall or drop out of agent mode under load. The practical reading is direct. For workflows bounded by known data sources and repeatable steps, the approach is usable today provided the persistence of a human in the loop; for brittle, time‑sensitive or authentication‑heavy tasks, oversight remains essential.
As builders considered where to place effort, an architectural debate moved towards integration rather than displacement. Retrieval‑augmented generation remains a mainstay for grounding responses in authoritative content, reducing hallucinations and offering citations. The Model Context Protocol is emerging as a way to give models live, structured access to systems and data without pre‑indexing, with a growing catalogue of MCP servers behaving like interoperable plug‑ins. On top sits a layer of agent‑to‑agent protocols that allow specialised systems to collaborate across boundaries. Long contexts help with single‑shot ingestion of larger materials, retrieval suits source‑of‑truth answers and auditability, MCP handles current data and action primitives, and agents orchestrate steps and approvals. Some developers even describe MCP as an accidental universal adaptor because each connector built for one assistant becomes available to any MCP‑aware tool, a network effect that invites combinations across software.
Research results widened the lens. Meta’s fundamental AI research team took first place in the Algonauts 2025 brain modelling competition with TRIBE, a one‑billion‑parameter network that predicts human brain activity from films by analysing video, audio and dialogue together. Trained on subjects who watched eighty hours of television and cinema, the system correctly predicted more than half of measured activation patterns across a thousand brain regions and performed best where sight, sound and language converge, with accuracy in frontal regions linked with attention, decision‑making and emotional responses standing out. NASA and Google advanced a different type of applied science with the Crew Medical Officer Digital Assistant, an AI system intended to help astronauts diagnose and manage medical issues during deep‑space missions when real‑time contact with Earth may be impossible. Running on Vertex AI and using open‑source models such as Llama 3 and Mistral‑3 Small, early tests reported up to 88 per cent accuracy for certain injury diagnoses, with a roadmap that includes ultrasound imaging, biometrics and space‑specific conditions and implications for remote healthcare on Earth. In drug discovery, researchers at KAIST introduced BInD, a diffusion model that designs both molecules and their binding modes to diseased proteins in a single step, simultaneously optimising for selectivity, safety, stability and manufacturability and reusing successful strategies through a recycling technique that accelerates subsequent designs. In parallel, MIT scientists reported two AI‑designed antibiotics, NG1 and DN1, that showed promise against drug‑resistant gonorrhoea and MRSA in mice after screening tens of millions of theoretical compounds for efficacy and safety, prompting talk of a renewed period for antibiotic discovery. A further collaboration between NASA and IBM produced Surya, an open‑sourced foundation model trained on nine years of solar observations that improves forecasts of solar flares and space weather.
Security stories accompanied the acceleration. Researchers reported that GPT‑5 had been jailbroken shortly after release via task‑in‑prompt attacks that hide malicious intent within ciphered instructions, an approach that also worked against other leading systems, with defences reportedly catching fewer than one in five attempts. Roblox’s decision to open‑source a child‑safety moderation model reads as a complementary move to equip more platforms to filter harmful content, while Tenable announced capabilities to give enterprises visibility into how teams use AI and how internal systems are secured. Observability and reliability remained on the agenda, with predictions from Google and Datadog leaders about how organisations will scale their monitoring and build trust in AI outputs. Separate research from the UK’s AI Security Institute suggested that leading chatbots can shift people’s political views in under ten minutes of conversation, with effects that partially persist a month later, underscoring the importance of safeguards and transparency when systems become persuasive.
Industry manoeuvres were brisk. Former OpenAI researcher Leopold Aschenbrenner assembled more than $1.5 billion for a hedge fund themed around AI’s trajectory and reported a 47 per cent return in the first half of the year, focusing on semiconductor, infrastructure and power companies positioned to benefit from AI demand. A recruitment wave spread through AI labs targeting quantitative researchers from top trading firms, with generous pay offers and equity packages replacing traditional bonus structures. Advocates argue that quants’ expertise in latency, handling unstructured data and disciplined analysis maps well onto AI safety and performance problems; trading firms counter by questioning culture, structure and the depth of talent that startups can secure at speed. Microsoft went on the offensive for Meta’s AI talent, reportedly matching compensation with multi‑million offers using special recruiting teams and fast‑track approvals under the guidance of Mustafa Suleyman and former Meta engineer Jay Parikh. Funding rounds continued, with Cohere announcing $500 million at a $6.8 billion valuation and Cognition, the coding assistant startup, raising $500 million at a $9.8 billion valuation. In a related thread, internal notes at Meta pointed to the company formalising its superintelligence structure with Meta Superintelligence Labs, and subsequent reports suggested that Scale AI cofounder Alexandr Wang would take a leading role over Nat Friedman and Yann LeCun. Further updates added that Meta reorganised its AI division into research, training, products and infrastructure teams under Wang, dissolved its AGI Foundations group, introduced a ‘TBD Lab’ for frontier work, imposed a hiring freeze requiring Wang’s personal approval, and moved for Chief Scientist Yann LeCun to report to him.
The spotlight on superintelligence brightened in parallel. Analysts noted that technology giants are deploying an estimated $344 billion in 2025 alone towards this goal, with individual researcher compensation reported as high as $250 million in extreme cases and Meta assembling a highly paid team with packages in the eight figures. The strategic message to enterprises is clear: leaders have a narrow window to establish partnerships, infrastructure and workforce preparation before superintelligent capabilities reshape competitive dynamics. In that context, Meta announced Meta Superintelligence Labs and a 49 per cent stake in Scale AI for $14.3 billion, bringing founder Alexandr Wang onboard as chief AI officer and complementing widely reported senior hires, backed by infrastructure plans that include an AI supercluster called Prometheus slated for 2026. OpenAI began the year by stating it is confident it knows how to build AGI as traditionally understood, and has turned its attention to superintelligence. On one notable reasoning benchmark, ARC‑AGI‑2, GPT‑5 (High) was reported at 9.9 per cent at about seventy‑three cents per task, while Grok 4 (Thinking) scored closer to 16 per cent at a higher per‑task cost. Google, through DeepMind, adopted a measured but ambitious approach, coupling scientific breakthroughs with product updates such as Veo 3 for advanced video generation and a broader rethinking of search via an AI mode, while Safe Superintelligence reportedly drew a valuation of $32 billion. Timelines compressed in public discourse from decades to years, bringing into focus challenges in long‑context reasoning, safe self‑improvement, alignment and generalisation, and raising the question of whether co‑operation or competition is the safer route at this scale.
Geopolitics and policy remained in view. Reports surfaced that Nvidia and AMD had agreed to remit 15 per cent of their Chinese AI chip revenues to the United States government in exchange for export licences, a measure that could generate around $1 billion a quarter if sales return to prior levels, while Beijing was said to be discouraging use of Nvidia’s H20 processors in government and security‑sensitive contexts. The United States reportedly began secretly placing tracking devices in shipments of advanced AI chips to identify potential reroutings to China. In the United Kingdom, staff at the Alan Turing Institute lodged concerns about governance and strategic direction with the Charity Commission, while the government pressed for a refocusing on national priorities and defence‑linked work. In the private sector, SoftBank acquired Foxconn’s US electric‑vehicle plant as part of plans for a large‑scale data centre complex called Stargate. Tesla confirmed the closure of its Dojo supercomputer team to prioritise chip development, saying that all paths converged to AI6 and leaving a planned Dojo 2 as an evolutionary dead end. Focus shifted to two chips—AI5 manufactured by TSMC for the Full Self‑Driving system, and AI6 made by Samsung for autonomous driving and humanoid robots, with power for large‑scale AI training as well. Rather than splitting resources, Tesla plans to place multiple AI5 and AI6 chips on a single board to reduce cabling complexity and cost, a configuration Elon Musk joked could be considered “Dojo 3”. Dojo was first unveiled in 2019 as a key piece of autonomy ambitions, though attention moved in 2024 to a large training supercluster code-named Cortex, whose status remains unclear. These changes arrive amid falling EV sales, brand challenges, and a limited robotaxi launch in Austin that drew incident reports. Elsewhere, Bloomberg reported further departures from Apple’s foundation models group, with a researcher leaving for Meta.
The public face of AI turned combative as Altman and Musk traded accusations on X. Musk claimed legal action against Apple over alleged App Store favouritism towards OpenAI and suppression of rivals such as Grok. Altman disputed the premise and pointed to outcomes on X that he suggested reflected algorithmic choices; Musk replied with examples and suggested that bot activity was driving engagement patterns. Even automated accounts were drawn in, with Grok’s feed backing Altman’s point about algorithm changes, and a screenshot circulated that showed GPT‑5 ranking Musk as more trustworthy than Altman. In the background, reports emerged that OpenAI’s venture arm plans to lead funding in Merge Labs, a brain–computer interface startup co‑founded by Altman and positioned as a competitor to Musk’s Neuralink, whose goals include implanting twenty thousand people a year by 2031 and generating $1 billion in revenue. Distribution did not escape the theatrics either. Perplexity, which has been pushing an AI‑first browsing experience, reportedly made an unsolicited $34.5 billion bid for Google’s Chrome browser, proposing to keep Google as the default search while continuing support for Chromium. It landed as Google faces antitrust cases in the United States and as observers debated whether regulators might compel divestments. With Chrome’s user base in the billions and estimates of its value running far beyond the bid, the offer read to many as a headline‑seeking gambit rather than a plausible transaction, but it underlined a point repeated throughout the month: as building and copying software becomes easier, distribution is the battleground that matters most.
Product news and practical guidance continued despite the drama. Users can enable access to historical ChatGPT models via a simple setting, restoring earlier options such as GPT‑4o alongside GPT‑5. OpenAI’s new open‑source models under the GPT‑OSS banner can run locally using tools such as Ollama or LM Studio, offering privacy, offline access and zero‑cost inference for those willing to manage a download of around 13 gigabytes for the twenty‑billion‑parameter variant. Tutorials for agent builders described meeting‑prep assistants that scrape calendars, conduct short research runs before calls and draft emails, starting simply and layering integrations as confidence grows. Consumer audio moved with ElevenLabs adding text‑to‑track generation with editable sections and multiple variants, while Google introduced temporary chats and a Personal Context feature for Gemini so that it can reference past conversations and learn preferences, alongside higher rate limits for Deep Think. New releases kept arriving, from Liquid AI’s open‑weight vision–language models designed for speed on consumer devices and Tencent’s Hunyuan‑Vision‑Large appearing near the top of public multimodal leaderboards to Higgsfield AI’s Draw‑to‑Video for steering video output with sketches. Personnel changes continued as Igor Babuschkin left xAI to launch an investment firm and Anthropic acquired the co‑founders and several staff from Humanloop, an enterprise AI evaluation and safety platform.
Google’s own showcase underlined how phones and homes are becoming canvases for AI features. The Pixel 10 line placed Gemini across the range with visual overlays for the camera, a proactive cueing assistant, tools for call translation and message handling, and features such as Pixel Journal. Tensor G5, built by TSMC, brought a reported 60 per cent uplift for on‑device AI processing. Gemini for Home promised more capable domestic assistance, while Fitbit and Pixel Watch 4 introduced conversational health coaching and Pixel Buds added head‑gesture controls. Against that backdrop, Google published details on Gemini’s environmental footprint, claiming the model consumes energy equivalent to watching nine seconds of television per text request and “five drops of water” per query, while saying efficiency improved markedly over the past year. Researchers challenged the framing, arguing that indirect water used by power generation is under‑counted and calling for comparable, third‑party standards. Elsewhere in search and productivity, Google expanded access to an AI mode for conversational search, and agreements emerged to push adoption in public agencies at low unit pricing.
Attention also turned to compact models and devices. Google released Gemma 3 270M, an ultra‑compact open model that can run on smartphones and browsers while eking out notable efficiency, with internal tests reporting that 25 conversations on a Pixel 9 Pro consumed less than one per cent of the battery and quick fine‑tuning enabling offline tasks such as a bedtime story generator. Anthropic broadened access to its Learning Mode, which guides people towards answers rather than simply supplying them, and now includes an explanatory coding mode. On the hardware side, HTC introduced Vive Eagle, AI glasses that allow switching between assistants from OpenAI and Google via a “Hey Vive” command, with on‑device processing for features such as real‑time photo‑based translation across thirteen languages, an ultra‑wide camera, extended battery life and media capture, currently limited to Taiwan.
Behind many deployments sits a familiar requirement: secure, compliant handling of data and a disciplined approach to roll‑out. Case studies from large industrial players point to the bedrock steps that enable scale. Lockheed Martin’s work with IBM on watsonx began with reducing tool sprawl and building a unified data environment capable of serving ten thousand engineers; the result has been faster product teams and a measurable boost in internal answer accuracy. Governance frameworks for AI, including those provided by vendors in security and compliance, are moving from optional extras to prerequisites for enterprise adoption. Organisations exploring agentic systems in particular will need clear approval gates, auditing and defaults that err on the side of caution when sensitive actions are in play.
Broader infrastructure questions loomed over these developments. Analysts projected that AI hyperscalers may spend around $2.9 trillion on data centres through to 2029, with a funding gap of about $1.5 trillion after likely commitments from established technology firms, prompting a rise in debt financing for large projects. Private capital has been active in supplying loans, and Meta recently arranged a large facility reported at $29 billion, most of it debt, to advance data centre expansion. The scale has prompted concerns about overcapacity, energy demand and the risk of rapid obsolescence, reducing returns for owners. In parallel, Google partnered with the Tennessee Valley Authority to buy electricity from Kairos Power’s Hermes 2 molten‑salt reactor in Oak Ridge, Tennessee, targeting operation around 2030. The 50 MW unit is positioned as a step towards 500 MW of new nuclear capacity by 2035 to serve data centres in the region, with clean energy certificates expected through TVA.
Consumer and enterprise services pressed on around the edges. Microsoft prepared lightweight companion apps for Microsoft 365 in the Windows 11 taskbar. Skyrora became the first UK company licensed for rocket launches from SaxaVord Spaceport. VIP Play announced personalised sports audio. Google expanded availability of its Imagen 4 model with higher resolution options. Former Twitter chief executive Parag Agrawal introduced Parallel, a startup offering a web API designed for AI agents. Deutsche Telekom launched an AI phone and tablet integrated with Perplexity’s assistant. Meta faced scrutiny after reports about an internal policy document describing permitted outputs that included romantic conversations with minors, which the company disputed and moved to correct.
Healthcare illustrated both promise and caution. Alongside the space‑medicine assistant, the antibiotics work and NASA’s solar model, a study reported that routine use of AI during colonoscopies may reduce the skill levels of healthcare professionals, a finding that could have wider implications in domains where human judgement is critical and joining a broader conversation about preserving expertise as assistance becomes ubiquitous. Practical guides continued to surface, from instructions for creating realistic AI voices using native speech generation to automating web monitoring with agents that watch for updates and deliver alerts by email. Bill Gates added a funding incentive to the medical side with a $1 million Alzheimer’s Insights AI Prize seeking agents that autonomously analyse decades of research data, with the winner to be made freely available to scientists.
Apple’s plans added a longer‑term note by looking beyond phones and laptops. Reports suggested that the company is pushing for a smart‑home expansion with four AI‑powered devices, including a desktop robot with a motorised arm that can track users and lock onto speakers, a smart display and new security cameras, with launches aimed between 2026 and 2027. A personality‑driven character for a new Siri called Bubbles was described, while engineers are reportedly rebuilding Siri from scratch with AI models under the codename Linwood and testing Anthropic’s Claude as a backup code-named Glenwood. Alongside those ambitions sit nearer‑term updates. Apple has been preparing a significant Siri upgrade based on a new App Intents system that aims to let people run apps entirely by voice, from photo edits to adding items to a basket, with a testing programme under way before a broader release and accuracy concerns prompting a limited initial rollout across selected apps. In the background, Tim Cook pledged to make all iPhone and Apple Watch cover glass in the United States, though much of the production process will remain overseas, and work on iOS 26 and Liquid Glass 1.0 was said to be nearing completion with smoother performance and small design tweaks. Hiring currents persist as Meta continues to recruit from Apple’s models team.
Other platforms and services added their own strands. Google introduced Personal Context for Gemini to remember chat history and preferences and added temporary chats that expire after seventy‑two hours, while confirming a duplicate event feature for Calendar after a public request. Meta’s Threads crossed 400 million monthly active users, building a real‑time text dataset that may prove useful for future training. Funding news continued as Profound raised $35 million to build an AI search platform and Squint raised $40 million to modernise manufacturing with AI. Lighter snippets appeared too, from a claim that beards can provide up to SPF 21 of sun protection to a report on X that an AI coding agent had deleted a production database, a reminder of the need for careful sandboxing of tools. Gaming‑style benchmarks surfaced, with GPT‑5 reportedly earning eight badges in Pokémon Red in 6,000 steps, while DeepSeek’s R2 model was said to be delayed due to training issues with Huawei’s Ascend chips. Senators in the United States called for a probe into Meta’s AI policies following controversy about chatbot outputs, reports suggested that the US government was exploring a stake in Intel, and T‑Mobile’s parent launched devices in Europe featuring Perplexity’s assistant.
Perhaps the most consequential lesson from the period is simple. Progress in capability is rapid, as competition results, research papers and new features attest. Yet adoption is being steered by human factors: the preference for a known voice, the desire for choice and control, and understandable scepticism when new modes do not perform as promised on day one. GPT‑5’s early missteps forced a course correction that restored a familiar option and increased transparency around limits and modes. The agentic turn is showing real value in constrained workflows, but still benefits from patience and supervision. Architecture debates are converging on combinations rather than replacements. And amid bold bids, public quarrels, hefty capital outlays and cautionary studies on enterprise returns, the work of making AI useful, safe and dependable continues, one model update and one workflow at a time.
Finding freelance and consulting work online
6th August 2025
This is a second post following the theme of seeking work, with more of a freelance emphasis than its predecessor. While my line of freelancing involves longer engagements, there are other options for shorter pieces of work, and that is the theme of this piece. Thus, I am collecting a compendium of online portals where you can explore a variety of opportunities, applying for those where you can bring value. Some are project based, while others centre on consultancy. All serve independent professionals in seeking work, and vice versa for clients. As you will find out by reading further, some of these have more of a gig market feel to what you find, though there can be longer engagements to be found too.
Founded in 2007 and later acquired by Heidrick & Struggles in 2021, the high-end consulting talent marketplace connects independent senior consultants, subject-matter experts and interim executives with Fortune 1000 companies, private equity firms and nonprofits across more than 39 countries. The platform features thousands of professionals with impressive credentials, many having Big-3 consulting backgrounds, executive roles or deep domain expertise. Areas of specialisation include strategy, mergers and acquisitions, operations, digital transformation, interim leadership and project management across industries ranging from technology and healthcare to consumer goods. The company provides comprehensive support throughout the engagement lifecycle, from project scoping to compliance and invoicing, reporting a 99 per cent fill rate on talent requests and a 97 per cent client repeat rate. While the platform offers access to high-level strategic assignments and reliable administrative infrastructure, its highly selective vetting process makes it less accessible for early-career professionals or niche freelancers without significant prior experience.
Founded in 2013 by Harvard Business School students, this expert network consultancy platform offers access to over 70,000 vetted independent consultants averaging 19+ years of experience, including former Big 3 consultants and Fortune 500 operators. The company employs machine learning algorithms paired with human success teams to match clients with suitable experts, typically providing several profiles within 48 hours. The platform handles all administrative aspects including contracts, invoicing and payments, guaranteeing consultants are paid even if clients default. Projects span areas such as strategy, digital transformation, mergers and acquisitions, and operations, serving approximately 30% of Fortune 500 firms. While the platform offers high impact, higher value work with reliable payment and administrative support, many consultants report high competition for projects, with limited feedback on declined pitches. The platform is most suitable for experienced industry professionals seeking substantial engagements with enterprise clients who can differentiate themselves through proven expertise.
Founded around 2017-2018 in Berlin, this consulting platform connects businesses with independent management consultants, digital experts and interim managers across various industries. The service employs a rigorous vetting process, accepting only approximately 2 percent of applicants to ensure high quality expertise. Companies typically receive 3-5 consultant profiles within 48 hours after submitting project requirements, benefiting from both artificial intelligence matching and manual curation. The platform maintains a pool of over 10,000 vetted consultants across more than 50 countries, serving private equity firms, multinational corporations and scale-ups. Clients reportedly save up to 70 percent compared to traditional consulting firms due to reduced overhead costs. While the platform boasts high client satisfaction rates exceeding 97 percent, consultant experiences vary considerably, with some professionals reporting limited project opportunities after completing the onboarding process despite the platform's claims of robust demand.
The modern, AI enhanced platform offers a straightforward approach to freelancing, providing users with a commission-free experience that allows them to retain all earnings. Built with a focus on usability, the service features rapid registration, an easy-to-navigate interface and comprehensive tools for tracking reputation and performance statistics. It caters to independent professionals across various disciplines who value efficient onboarding processes and direct connections to relevant work opportunities. As a relatively new entrant in the technology-driven freelance marketplace, the platform emphasises simplicity and quality engagements for its users.
Established in 2000, the London-based consulting firm Eden McCallum disrupts traditional consulting models by combining independent senior consultants with an in-house team to deliver strategy and transformation projects. The firm operates through hybrid teams tailored to client needs, with consultants having the freedom to select projects individually without exclusivity requirements. Having supported over 3,000 projects for more than 500 global clients including a third of the FTSE 100, Eden McCallum maintains a network of approximately 2,500 independent consultants who are selectively chosen with only one in ten applicants accepted. Most consultants possess experience from top-tier firms such as McKinsey, BCG or Bain, alongside industry roles, providing clients with deep expertise while offering cost savings of 30-50% compared to traditional consulting firms. Although the model provides flexibility for consultants who can choose projects without sales pressure, it does not guarantee consistent workload, and some consultants report challenges with coordination across international offices as the firm has expanded.
Founded in 2010 in Tel Aviv, Israel, this global online marketplace specialises in pre-scoped "gigs" offered by freelancers to clients across hundreds of digital service categories. The platform has evolved beyond its original $5 price point, now allowing sellers to set their own pricing tiers with upfront payment held in escrow until delivery approval. Operating in over 160 countries, the service features a gig-based structure with clearly defined packages at set prices, premium vetted sellers, AI-enhanced workflows, and career counselling options. While the platform retains a 20% commission on all transactions, it offers streamlined processes ideal for beginners and businesses seeking quick support, though low rates and high competition can limit earning potential and long-term relationship building. It is particularly suitable for entry to intermediate freelancers offering standardised digital services who prioritise rapid client acquisition.
Established in 2009 and headquartered in Sydney, Australia, Freelancer.com stands as one of the longest-running general freelancing marketplaces available today. The platform offers users access to an extensive community and diverse job opportunities across numerous categories. Among its notable features are the ability to participate in contests that allow freelancers to demonstrate their capabilities, along with its significant global presence. The service typically charges freelancers a 10% fee (with a minimum of US$5), though this can be lowered through subscription options. The platform is designed to serve freelance professionals of all experience levels who are particularly interested in accessing a high volume and wide variety of potential work opportunities.
Established in the mid-2010s, Kolabtree operates as a specialised freelancing platform connecting organisations with technical professionals who possess expertise in scientific and analytical fields. The platform facilitates consultation services and project-based work for highly qualified individuals in data science, machine learning, engineering, biotechnology and various research disciplines. What distinguishes this marketplace is its focus on substantial remuneration, appealing to experienced practitioners. The client base spans both academic institutions and industry players seeking genuine subject matter specialists rather than general freelancers, making it particularly valuable for professionals with advanced qualifications and demonstrated expertise in their respective domains.
Formerly known as Talmix, High5Hire operates as a global talent marketplace connecting senior business and consulting professionals with enterprise-grade and private equity clients for Statement-of-Work or interim assignments. The platform utilises AI-driven algorithms to match consultant profiles (called "Talent Passports") with relevant projects, featuring over 60,000 consultants across more than 150 countries. High5Hire typically retains 15-25 percent of consulting fees as commission, paid by the client side. While the service offers effective global project access for senior professionals and a flexible, project-based work model with high-impact roles, some reports on user platforms highlight potential concerns including internal management instability, capped commissions, and low pay for certain contractor positions. The platform is particularly suitable for experienced consultants from recognised firms seeking global interim or project-based work, though prospective users should seek clear payment guarantees and compare with similar platforms like Maven, Consultport, Catalant or Eden McCallum before committing.
Connecting clients seeking expertise with professionals who can provide insights, this micro-consulting platform hosts over 500,000 experts across virtually every industry. The service operates by matching client requests with relevant professionals who set their own rates, typically 2 to 4 times their regular hourly compensation. Unlike many competitors, consultants retain 100% of their fees, with no platform charges deducted from earnings. The system facilitates brief engagements such as Zoom calls, surveys, written questions and answers, or advising sessions, making it ideal for supplemental income. While offering flexible side income opportunities with minimal administrative burden and fast payments, the platform primarily focuses on smaller, shorter engagements rather than long-term strategic projects. This arrangement particularly benefits experienced professionals looking to monetise their knowledge without full-time commitments, though those seeking extended consulting assignments might find traditional consulting platforms more suitable.
A specialised UK-based job board devoted to connecting contractors with engagements that are classified outside the intermediaries legislation and off-payroll tax rules, enabling genuine business-to-business arrangements rather than employment relationships. The platform features over 50,000 opportunities across numerous sectors including IT, engineering, marketing, finance and healthcare, with comprehensive filtering options for workplace type, region, category and minimum daily rates. Particularly valuable for professionals operating through limited companies who wish to maintain tax efficiency and control over their business structure, the service allows users to search roles throughout major UK regions and international locations. While the site provides an optional IR35 calculator to estimate status implications, users should exercise due diligence regarding contract terms, as the actual IR35 determination depends on working practices and contractual details that must align with legal requirements concerning substitution, control and mutuality of obligation.
Established in the United States in 2010, Toptal stands as a selective freelance marketplace that exclusively accepts the top 3% of applicants. The platform specialises in connecting highly skilled professionals across engineering, design and finance domains with prominent global companies. Toptal distinguishes itself through a comprehensive vetting process comprising skills assessments, interview stages and practical test projects. Freelancers joining this exclusive network typically command premium hourly rates and frequently secure extended or full-time professional opportunities. The service primarily caters to experienced senior-level specialists who are seeking valuable, high-calibre professional engagements rather than short-term projects.
Created in 2015, Upwork stands as one of the largest global freelance marketplaces, connecting over 12 million freelancers with approximately 5 million clients across more than 180 countries. The platform facilitates roughly 3 million job postings annually in fields such as writing, technology, marketing and design. The system allows employers to post jobs while freelancers apply using credits called "connects," with additional Premium features available including Talent Scout and a Project Catalogue for fixed-price services. Freelancers pay service fees on a sliding scale of 5-20% based on client earnings, while clients often pay additional costs through subscription tiers ranging from Free to Enterprise. Although the platform offers highly vetted profiles, verified reviews and extensive job categories that create trust and scale, some freelancers have criticised the increasingly client-centric marketplace approach. The platform is particularly suitable for freelancers at various experience levels seeking both short and long-term projects, while corporate clients benefit from structured hiring processes and premium staffing tools.
This specialised platform serves as a commission-free marketplace connecting marketing professionals with freelance opportunities. The service offers personalised assistance to help freelancers secure work when required, making it particularly valuable for those with expertise in marketing strategy, analytics and copywriting disciplines. While its founding date remains unspecified, it appears to be a relatively recent addition to the freelance ecosystem, catering specifically to marketing specialists rather than general freelancers. The zero commission structure represents a significant advantage for professionals looking to maximise their earnings in this niche.
A round-up of online portals for those seeking work
5th August 2025
For me, much of 2025 was spent finding a new freelance work engagement. Recently, that search successfully concluded, but not before I got flashbacks of how hard things were when seeking work after completing university education and deciding to hybridise my search to include permanent employment too. Now that I am fulfilling a new contract with a new client, I am compiling a listing of places on the web to a search for work, at least for future reference if nothing else.
Founded in 2011 by former executives from Gumtree, eBay and Zoopla, this UK-based job search engine aggregates listings from thousands of sites across 16+ countries with headquarters in London and approximately 100 employees worldwide. The platform offers over one million job advertisements in the UK alone and an estimated 350 million globally, attracting more than 10 million monthly visits. Jobseekers can use the service without cost, benefiting from search functionality, email alerts, salary insights and tools such as ValueMyCV and the AI-powered interview preparation tool Prepper. The company operates on a Cost-Per-Click or Cost-Per-Applicant model for employers seeking visibility, while also providing data and analytics APIs for programmatic advertising and labour market insights. Notably, the platform powers the UK government Number 10 Dashboard, with its dataset frequently utilised by the ONS for real-time vacancy tracking.
Founded in 2000 by Lee Biggins, this independent job board has grown to become one of the leading platforms in the UK job market. Based in Fleet, Hampshire, it maintains a substantial database of approximately 21.4 million CV's, with around 360,000 new or updated profiles added monthly. The platform attracts significant traffic with about 10.1 million monthly visits from 4.3 million unique users, facilitating roughly 3 million job applications each month across approximately 137,000 live vacancies. Jobseekers can access all services free of charge, including job searching, CV uploads, job alerts and application tracking, though the CV building tools are relatively basic compared to specialist alternatives. The platform boasts high customer satisfaction, with 96 percent of clients rating their service as good or excellent, and offers additional value through its network of over 800 partner job sites and ATS integration capabilities.
Formerly known as TryRemotely, Empllo functions as a comprehensive job board specialising in remote technology and startup positions across various disciplines including engineering, product, sales, marketing, design and finance. The platform currently hosts over 30,000 active listings from approximately 24,000 hiring companies worldwide, with specific regional coverage including around 375 positions in the UK and 36 in Ireland. Among its notable features is the AI-powered Job Copilot tool, which can automatically apply to roles based on user preferences. While Empllo offers extensive listings and advanced filtering options by company, funding and skills, it does have limitations including inconsistent salary information and variable job quality. The service is free to browse, with account creation unlocking personalised features. It is particularly suitable for technology professionals seeking distributed work arrangements with startups, though users are advised to verify role details independently and potentially supplement their search with other platforms offering employer reviews for more thorough vetting.
This is a comprehensive job-hunt management tool that replaces traditional spreadsheets with an intuitive Kanban board interface, allowing users to organise their applications effectively. The platform features a Chrome extension that integrates with major job boards like LinkedIn and Indeed, enabling one-click saving of job listings. Users can track applications through various stages, store relevant documents and contact information, and access detailed statistics about their job search progress. The service offers artificial intelligence capabilities powered by GPT-4 to generate application responses, personalise cover letters and craft LinkedIn profiles. With over 25,000 active users who have tracked more than 280,000 job applications collectively, the tool provides both free and premium tiers. The basic free version includes unlimited tracking of applications, while the Pro subscription adds features such as custom columns, unlimited tags and expanded AI capabilities. This solution particularly benefits active jobseekers managing numerous applications across different platforms who desire structured organisation and data-driven insights into their job search.
This organisation provides a specialised platform matching candidates with companies based on flexible working arrangements, including remote options, location independence and customisable hours. Their interface features a notable "Work From Anywhere" filter highlighting roles with genuine location flexibility, alongside transparency scores for companies that reflect their openness regarding working arrangements. The platform allows users to browse companies offering specific perks like part-time arrangements, sabbatical leave, or compressed hours, with rankings based on flexibility and workplace culture. While free to use with job-saving capabilities and quick matching processes, it appears relatively new with a modest-sized team, limited independent reviews and a smaller volume of job listings compared to more established competitors. The platform's distinctive approach prioritises work-life balance through values-driven matching and company-oriented filters, particularly useful for those seeking roles aligned with modern flexible working preferences.
Founded in 2007 and based in Puerto Rico, FlexJobs operates as a subscription-based platform specialising in remote, hybrid, freelance and part-time employment opportunities. The service manually verifies all job listings to eliminate fraudulent postings, with staff dedicating over 200 hours daily to screening processes. Users gain access to positions across 105+ categories from entry-level to executive roles, alongside career development resources including webinars, resume reviews and skills assessments. Pricing options range from weekly trials to annual subscriptions with a 30-day money-back guarantee. While many users praise the platform for its legitimacy and comprehensive filtering tools, earning high ratings on review sites like Trustpilot, some individuals question whether the subscription fee provides sufficient value compared to free alternatives. Potential limitations include delayed posting of opportunities and varying representation across different industries.
Founded in November 2004 and now operating in over 60 countries with 28 languages, this leading global job search platform serves approximately 390 million visitors monthly worldwide. In the UK alone, it attracts about 34 million monthly visits, with users spending nearly 7 minutes per session and viewing over 8.5 pages on average. The platform maintains more than 610 million jobseeker profiles globally while offering free services for candidates including job searching, application tools, CV uploads, company reviews and salary information. For employers, the business model includes pay-per-click and pay-per-applicant sponsored listings, alongside tools such as Hiring Insights providing salary data and application trends. Since October 2024, visibility for non-sponsored listings has decreased, requiring employers to invest in sponsorship for optimal visibility. Despite this competitive environment requiring strategic budget allocation, the platform remains highly popular due to its comprehensive features and extensive reach.
A meta-directory founded in 2022 by Rodrigo Rocco, this platform aggregates and organises links to over 400 specialised and niche job sites across various industries and regions. Unlike traditional job boards, it does not host listings directly but serves as a discovery tool that redirects users to external platforms where actual applications take place. The service refreshes links approximately every 45 minutes and offers a weekly newsletter. While providing free access and efficient discovery of relevant boards by category or sector, potential users should note that the platform lacks direct job listings, built-in application tracking, or alert systems. It is particularly valuable for professionals exploring highly specialised fields, those wishing to expand beyond mainstream job boards and recruiters seeking to increase their visibility, though beginners might find navigating numerous destination boards somewhat overwhelming.
Founded in Milan by Vito Lomele in 2006 (initially as Jobespresso), this global job aggregator operates in 58 countries and 21 languages. The platform collects between 28 and 35 million job listings monthly from various online sources, attracting approximately 55 million visits and serving over 100 million registered users. The service functions by gathering vacancies from career pages, agencies and job boards, then directing users to original postings when they search. For employers, it offers programmatic recruitment solutions using artificial intelligence and taxonomy to match roles with candidates dynamically, including pay-per-applicant models. While the platform benefits from its extensive global reach and substantial job inventory, its approach of redirecting to third-party sites means the quality and freshness of listings can vary considerably.
Founded in 1993 as Fax-Me Ltd and rebranded in 1995, this pioneering UK job board launched the world's first jobs-by-email service in May 1994. Originally dominating the IT recruitment sector with up to 80% market share in the early 2000s, the platform published approximately 200,000 jobs and processed over 1 million applications monthly by 2010. Currently headquartered in Colchester, Essex, the service maintains a global presence across Europe, North America and Australia, delivering over 1.2 million job-subscription emails daily. The platform employs a proprietary smart matching engine called Alchemy and features manual verification to ensure job quality. While free for jobseekers who can upload CVs and receive tailored job alerts, employers can post vacancies and run recruitment campaigns across various sectors. Although respected for its legacy and niche focus, particularly in technical recruitment, its scale and visibility are more modest compared to larger contemporary platforms.
Founded in 2020 with headquarters in London, Lifelancer operates as an AI-powered talent hiring platform specialising in life sciences, pharmaceutical, biotech, healthcare IT and digital health sectors. The company connects organisations with freelance, remote and international professionals through services including candidate matching and global onboarding assistance. Despite being relatively small, Lifelancer provides distinct features for both hiring organisations and jobseekers. Employers can post positions tailored to specific healthcare and technology roles, utilising AI-based candidate sourcing, while professionals can create profiles to be matched with relevant opportunities. The platform handles compliance and payroll across multiple countries, making it particularly valuable for international teams, though as a young company, it may not yet offer the extensive talent pool of more established competitors in the industry.
The professional networking was core to my search for work and had its uses while doing so. Writing posts and articles did a lot to raise my profile along with reaching out to others, definitely an asset when assessing the state of a freelancing market. The usefulness of the green "Open to Work" banner is debatable because of my freelancing pitch in a slow market. Nevertheless, there was one headhunting approach that might have resulted in something if another offer had not gazumped it. Also, this is not a place to hang around over a weekend with job search moaning filling your feed, though making your interests known can change that. Now that I have paid work, the platform has become a way of keeping up to date in my line of business.
Established in 1994 as The Monster Board, Monster.com became one of the first online job portals, gaining prominence through memorable Super Bowl advertisements. As of June 2025, the platform attracts approximately 4.3 million monthly visits, primarily from the United States (76%), with smaller audiences in India (6%) and the UK (1.7%). The service offers free resources for jobseekers, including resume uploads and career guidance, while employers pay for job postings and additional premium features.
Established in 1999 and headquartered in Richmond, Surrey, PharmiWeb has evolved into Europe's leading pharmaceutical and life sciences platform. The company separated its dedicated job board as PharmiWeb.jobs in 2019, while maintaining industry news and insights on the original portal. With approximately 600,000 registered jobseekers globally and around 200,000 monthly site visits generating 40,000 applications, the platform hosts between 1,500 and 5,000 active vacancies at any time. Jobseekers can access the service completely free, uploading CVs and setting alerts tailored to specific fields, disciplines or locations. Additional recruiter services include CV database access, email marketing campaigns, employer branding and applicant management tools. The platform particularly excels for specialised pharmaceutical, biotech, clinical research and regulatory affairs roles, though its focused nature means it carries fewer listings than mainstream employment boards and commands higher posting costs.
If 2025 was a flashback to the travails of seeking work after completing university education, meeting this name again was another part of that. Founded in May 1960 by Sir Alec Reed, the firm began as a traditional recruitment agency in Hounslow, West London, before launching the first UK recruitment website in 1995. Today, the platform attracts approximately 3.7 million monthly visitors, primarily UK-based users aged 25-34, generating around 80,000 job applications daily. The service offers jobseekers free access to search and apply for roles, job alerts, CV storage, application tracking, career advice articles, a tax calculator, salary tools and online courses. For employers, the privately owned company provides job advertising, access to a database of 18-22 million candidate CVs and specialist recruitment across about 20 industry sectors.
Founded by digital nomad Pieter Levels in 2015, this prominent job board specialises exclusively in 100% remote positions across diverse sectors including tech, marketing, writing, design and customer support. The platform offers free browsing and application for jobseekers, while employers pay fees. Notable features include mandatory salary transparency, global job coverage with regional filtering options and a clean, minimalist interface that works well on mobile devices. Despite hosting over 100,000 remote jobs from reputable companies like Amazon and Microsoft, the platform has limitations including basic filtering capabilities and highly competitive application processes, particularly for tech roles. The simple user experience redirects applications directly to employer pages rather than using an internal system. For professionals seeking remote work worldwide, this board serves as a valuable resource but works best when used alongside other specialised platforms to maximise opportunities.
Founded in 2015 and based in Boulder, Colorado, this platform exclusively focuses on remote work opportunities across diverse industries such as marketing, finance, healthcare, customer support and design. Attracting over 1.5 million monthly visitors, it provides jobseekers with free access to various employment categories including full-time, part-time, freelance and hybrid positions. Beyond job listings, the platform offers a comprehensive resource centre featuring articles, expert insights and best practices from over 108 remote-first companies. Job alerts and weekly newsletters keep users informed about relevant opportunities. While the platform provides strong resources and maintains positive trust ratings of approximately 4.2/5 on Trustpilot, its filtering capabilities are relatively basic compared to competitors. Users might need to conduct additional research as company reviews are not included with job postings. Despite these limitations, the platform serves as a valuable resource for individuals seeking remote work guidance and opportunities.
For jobseekers in the technology and digital sectors, Remotive serves as a specialised remote job board offering approximately 2,000 active positions on its free public platform. Founded around 2014-2015, this service operates with a remote-first approach and focuses on verifying job listings for legitimacy. The platform provides a premium tier called "Remotive Accelerator" which grants users access to over 50,000 additional curated jobs, advanced filtering options based on skills and salary requirements and membership to a private Slack community. While the interface receives praise for its clean design and intuitive navigation, user feedback regarding the paid tier remains mixed, with some individuals noting limitations such as inactive community features and an abundance of US-based or senior-level positions. The platform is particularly valuable for professionals in software development, product management, marketing and customer service who are seeking global remote opportunities.
Originally launched in Canada in 2011 as neuvoo, this global job search engine is now headquartered in Montreal, Quebec, providing access to over 30 million jobs across more than 75 countries. The platform attracts between 12 and 16 million monthly visits worldwide, with approximately 6 percent originating from the UK. Jobseekers can utilise the service without charge, accessing features like salary converters and tax calculators in certain regions to enhance transparency about potential earnings. Employers have the option to post jobs for free in some areas, with additional pay per click sponsored listings available to increase visibility. Despite its extensive coverage and useful tools, user feedback remains mixed, with numerous complaints on review sites regarding outdated listings, unwanted emails and difficulties managing or deleting accounts.
Founded in 2011 and based in New York City, The Muse is an online platform that integrates job listings with career guidance, employer insights and coaching services to support individuals in making informed career decisions. It distinguishes itself by offering detailed employer profiles that include workplace culture, employee perspectives and company values, alongside editorial content on resume writing, interview techniques and career progression. While jobseekers can access core features for free, employers pay to advertise roles and create branded profiles, with additional revenue generated through premium coaching services. The platform appeals to graduates, early-career professionals and those seeking career transitions, prioritising alignment between personal values and workplace environments over simply aggregating job vacancies. Compared to larger job boards, it focuses on storytelling and career development resources, positioning itself as a tool for navigating modern employment trends such as flexible work and diversity initiatives.
Founded in 1999, Totaljobs is a major UK job board currently owned by StepStone Group UK Ltd, a subsidiary of Axel Springer Digital Classifieds. The platform attracts approximately 20 million monthly visits and generates 4-5 million job applications each month, with over 300,000 daily visitors browsing through typically 280,000+ live job listings. As the flagship of a broader network including specialised boards such as Jobsite, CareerStructure and City Jobs, Totaljobs provides jobseekers with search functionality across various sectors, job alerts and career advice resources. For employers and recruiters, the platform offers pay-per-post job advertising, subscription options for CV database access and various employer tools.
Founded in 2011, this is one of the largest purely remote job boards globally, attracting approximately 6 million monthly visitors and featuring over 36,000 remote positions across various categories including programming, marketing, customer support and design. Based in Vancouver, the platform operates with a small remote-first team who vet listings to reduce spam and scams. Employers pay for each standard listing, while jobseekers access the service without charge. The interface is straightforward and categorised by functional area, earning trust from major companies like Google, Amazon and GitHub. However, the platform has limitations including basic filtering capabilities, a predominance of senior-level positions particularly in technology roles and occasional complaints about outdated or misleading posts. The service is most suitable for experienced professionals seeking genuine remote opportunities rather than those early in their careers. Some users report region-restricted application access and positions that offer lower compensation than expected for the required experience level.
Founded in 2014, this job board provides remote work opportunities for digital nomads and professionals across various industries. The platform offers over 30,000 fully remote positions spanning sectors such as technology, marketing, writing, finance and education. Users can browse listings freely, but a Premium subscription grants access to additional jobs, enhanced filters and email alerts. The interface is user-friendly with fast-loading pages and straightforward filtering options. The service primarily features global employment opportunities suitable for location-independent workers. However, several limitations exist: many positions require senior-level experience, particularly in technical fields; the free tier displays only a subset of available listings; filtering capabilities are relatively basic; and job descriptions sometimes lack detail. The platform has received mixed reviews, earning approximately 3.4 out of 5 on Trustpilot, with users noting the prevalence of senior technical roles and questioning the value of the premium subscription. It is most beneficial for experienced professionals comfortable with remote work arrangements, while those seeking entry-level positions might find fewer suitable opportunities.