Google DeepMind has opened access to Project Genie, an experimental world-building AI tool, as it looks to gather real-world feedback and accelerate progress on the world models it believes are central to the path towards artificial general intelligence.

What Project Genie Is and How It Was Built?

Project Genie is a web-based experimental research prototype developed by Google DeepMind that allows users to generate and explore interactive virtual worlds using text prompts or images. Technically, it is not a standalone model but a front-end experience built on top of several of DeepMind’s most advanced systems.

At its core is Genie 3, DeepMind’s latest general-purpose world model, which generates environments frame by frame in real time as users move through them. This is combined with Nano Banana Pro, an image generation model used to sketch and refine the initial appearance of a world, and Gemini, which handles higher-level reasoning and prompt interpretation. Together, these components allow Project Genie to turn a static description or image into a navigable environment that responds dynamically to user actions.

How Do You Use It?

Practically, users begin by creating what DeepMind calls a “world sketch”. This involves prompting the system with a description of an environment and a character, choosing a first- or third-person perspective, and optionally refining the generated image before entering the world. Once inside, the environment expands in real time as the user moves, with the model simulating basic physics, lighting, and object behaviour. Users can also remix existing worlds, explore curated examples, or download videos of their explorations.

Project Genie was built by DeepMind researchers including Jack Parker-Holder and Shlomi Fruchter, both of whom have been closely involved in the development of Genie 3 and earlier world model research.

DeepMind

Google DeepMind is the name for Google’s dedicated AI research lab, formed through the merger of DeepMind and Google Brain, and is focused on developing general-purpose AI systems. Its long-term stated ambition is to build AI that can reason, plan, and act across the full complexity of the real world, rather than being limited to narrow tasks.

Genie 3 Previewed Back In August 2025

DeepMind first previewed Genie 3 as a research model back in August 2025, positioning it as a major step forward in interactive world simulation. Five months later, the decision to open Project Genie to a wider audience appears to reflect a deliberate transition from closed research testing to broader, real-world experimentation.

In its own recent announcement, Google stated that “the next step is to broaden access through a dedicated, interactive prototype focused on immersive world creation.” Access is currently limited to Google AI Ultra subscribers in the United States aged 18 and over, reinforcing that this is still a controlled research rollout rather than a mass-market launch.

Why Now?

It should be noted here that the timing matters. For example, world models are moving from abstract research concepts into systems that can be directly experienced and evaluated by users. Therefore, by opening access now, DeepMind is hoping to be able to collect feedback, usage patterns, and behavioural data that are difficult to obtain through internal testing alone, while also demonstrating tangible progress in a competitive and fast-moving field.

What Genie Can Do and Who It’s Aimed At

Genie 3 enables real-time interaction at around 24 frames per second, with worlds that remain visually consistent for several minutes. Unlike traditional video generation models that produce a fixed sequence, Genie 3 generates each new frame based on what has already happened and how the user moves, allowing for exploration rather than playback.

Project Genie is actually aimed at several overlapping audiences. For example, in the near term, it is most accessible to creators, researchers, and technically curious users who want to experiment with AI-generated environments. The tool supports whimsical and stylised worlds particularly well, including animated, illustrative, or fantastical settings.

Beyond creative exploration, DeepMind also appears to see some real value in Genie 3 for education, simulation, and research. World models can be used to train and test embodied agents (AI systems designed to act within an environment), including robots or software agents that move and make decisions. Instead of learning in the real world, where training can be expensive, slow, or risky, these agents can practise inside simulated environments. For example, an AI-controlled robot can learn how to navigate difficult terrain or react to unexpected situations without any physical risk or real-world consequences.

DeepMind described world models as systems that “simulate the dynamics of an environment, predicting how they evolve and how actions affect them,” framing Genie 3 as part of a broader capability rather than a single product feature.

How Project Genie Fits Into DeepMind’s AGI Strategy

World models now appear to occupy a central position in DeepMind’s vision for AGI (artificial general intelligence), which are AI systems that can understand, learn, and reason across a wide range of tasks rather than being limited to a single narrow function. The lab has argued that this kind of intelligence requires an internal model of the world that supports planning, prediction, and counterfactual reasoning. In practical terms, this means being able to ask “what happens if” and simulate possible outcomes before acting.

Genie 3 builds on earlier models such as Genie 1 and Genie 2, but adds real-time interaction and longer-horizon consistency. This allows agents to execute longer sequences of actions and pursue more complex goals, which DeepMind sees as essential for general-purpose intelligence.

The company has already demonstrated Genie 3 being used to generate environments for SIMA, its generalist agent for 3D virtual settings. This reinforces that Project Genie is not the end goal, but a way to expose and test the underlying capabilities that future agents will rely on.

The Competitive Landscape and Why Timing Matters

The release of Project Genie comes as competition around world models is intensifying, with several AI labs and startups racing to build systems that go beyond static generation and towards interactive simulation.

For example, Runway has recently introduced its own world model concepts alongside its video tools. Also, World Labs, founded by Fei-Fei Li, has launched Marble as a commercial product aimed at interactive environments. Yann LeCun’s AMI Labs has also signalled a strong focus on world modelling as a foundation for intelligence.

By opening access now, DeepMind is hoping to position itself as a leader not just in theory, but in demonstrable, hands-on systems. This visibility matters for attracting talent, shaping industry standards, and influencing how developers and researchers think about the future of AI simulation.

Limitations, Guardrails, and Why This Is Still a Prototype

Despite its capabilities, Project Genie is explicitly framed as an experimental research prototype. Usage sessions are currently limited to 60 seconds of world generation and navigation, reflecting the heavy computational cost of auto-regressive real-time models.

With this in mind, Google has acknowledged several known limitations. For example, generated worlds may not closely match prompts or real-world physics, characters can be difficult to control, and latency can affect interaction. Some Genie 3 capabilities announced in August, such as promptable world events that change environments mid-exploration, are not yet available in Project Genie.

DeepMind has also been quick to emphasise responsible development. For example, safety guardrails restrict copyrighted content, realistic depictions of certain subjects, and other sensitive material. The company stated that “as with all our work towards general AI systems, our mission is to build AI responsibly to benefit humanity.”

These constraints help explain why Project Genie is not being positioned as a consumer product or game platform, but is currently a testbed designed to surface technical weaknesses and user expectations before wider deployment.

Entertainment Today, Embodied Agents Tomorrow

In the short term, Project Genie’s most obvious use is entertainment and creative experimentation. Its strengths in stylised, animated, and imaginative environments make it well suited to playful exploration and concept development.

However, longer term, DeepMind’s ambitions extend far beyond games. World models offer a scalable way to train embodied agents, including robots and autonomous systems, in simulated environments that mirror the complexity of the real world. This could reduce costs, improve safety, and enable faster iteration across industries such as logistics, manufacturing, and healthcare.

The same technology could also support training, education, and scenario planning, where exploring “what if” situations is valuable.

Business and Industry Implications

For Google, Project Genie is intended to reinforce its position at the frontier of advanced AI research and supports the premium value proposition of its AI Ultra subscription. It also strengthens Google’s influence over how world models are commercialised and evaluated.

For competitors, the move appears to raise the bar for what qualifies as a leading-edge AI system, increasing pressure to demonstrate interactive, real-time capabilities rather than static outputs.

For businesses and developers, Project Genie offers an early glimpse into tools that could reshape simulation, training, design, and creative workflows. At the same time, its limitations highlight that world models are still an emerging technology with unresolved challenges around realism, control, and cost.

For the wider AI market, the release highlights a broader transition from generative content towards generative environments, where interaction and agency matter as much as visual fidelity.

Challenges, and Criticisms

It should be noted that some key challenges remain for world models like Genie 3, particularly around scalability, realism, and controllability. For example, auto-regressive world generation is computationally expensive, which makes long-duration or large-scale simulations difficult to run. Critics have also questioned how quickly these systems can achieve reliable real-world accuracy, especially for safety-critical applications where errors or inconsistencies could have serious consequences.

There are also broader concerns around data use, intellectual property, and the environmental cost of large-scale compute. DeepMind’s cautious, limited rollout reflects an awareness of these issues, even as it pushes the technology forward.

Project Genie, as DeepMind presents it, is not yet a finished destination but a visible step in a much longer journey towards AI systems that can understand and navigate the world in ways that begin to resemble human reasoning.

What Does This Mean For Your Business?

Project Genie shows how world model research is now being tested outside the lab, with DeepMind deliberately exposing early capabilities to real users in order to gather feedback that research alone cannot provide. The limited access, short session lengths, and strict guardrails make it clear that this is about learning and validation rather than product launch.

For UK businesses, the immediate value is not in using Project Genie directly, but in what it signals. Interactive simulation has long-term relevance for training, design testing, robotics, and scenario planning, particularly in sectors where real-world experimentation is expensive or risky. As these models improve, they could become a practical tool for reducing uncertainty before decisions are made in physical environments.

For the wider AI market, the release raises expectations around what advanced AI systems should be able to do. The focus appears to be shifting from static content generation to interaction, consistency, and decision-making over time. Project Genie does not solve those challenges yet, but it does show more clearly how DeepMind is approaching them and increases pressure on competitors pursuing similar world model capabilities.

Tesla confirmed it was winding down parts of its car business as Elon Musk publicly repositioned the company around humanoid robots, artificial intelligence and autonomy rather than electric vehicles alone.

Tesla Drops Model S And X As Focus Shifts Beyond Cars

Tesla has said it will end production of the Model S and Model X and repurpose the manufacturing space at its Fremont, California plant to build its Optimus humanoid robots, marking the clearest signal yet that the company’s future strategy is moving away from premium car models.

Speaking on Tesla’s latest earnings call, Elon Musk said the space currently used to build the two vehicles would be converted into an Optimus production facility, with a long-term ambition of producing up to one million robots a year at the site. He described the change as part of Tesla’s broader shift towards what the company now calls “physical AI”.

The Model S and Model X were once central to Tesla’s rise, helping establish the brand in the early and mid-2010s. In recent years, however, both vehicles had become low-volume products compared with the Model 3 and Model Y, which now account for the majority of Tesla’s car sales.

Tesla said it would continue supporting existing Model S and Model X customers despite the end of production.

Why Tesla’s Core EV Business Came Under Pressure

The strategic change of direction came after a difficult year for Tesla’s automotive business, shaped not only by market conditions but also by growing scrutiny of Elon Musk’s leadership and public profile. The company reported total revenue fell 3 per cent in 2025, its first annual decline in revenue, while vehicle deliveries dropped by about 9 per cent to roughly 1.64 million cars worldwide.

The slowdown was particularly visible at the end of the year, with Tesla saying deliveries fell around 16 per cent year on year in the fourth quarter, reflecting weaker demand, intensifying competition and the impact of reduced government incentives in the United States. Analysts also pointed to rising unease among parts of Tesla’s traditional customer base following Musk’s increasingly high-profile political involvement, which included public support for US President Donald Trump and a senior cost-cutting role in his administration.

During the same period, China-based BYD overtook Tesla as the world’s largest seller of battery electric vehicles by volume, reporting more than 2.25 million BEV sales in 2025, up almost 28 per cent year on year. Chinese manufacturers including BYD, Geely and MG continued to pressure Western carmakers by offering a wider range of lower-priced models, while Tesla faced criticism for a relatively ageing vehicle line-up and a slower pace of major new car launches.

Tesla’s earnings update showed that while automotive revenue weakened, other parts of the business performed more strongly, with energy generation and storage revenue rising about 25 per cent year on year in the fourth quarter and services revenue increasing around 18 per cent, highlighting areas of growth beyond car sales as the company recalibrated its strategy.

How Musk Reframed Tesla’s Future Around Robots

Against that backdrop, Musk has been framing Tesla as an AI and robotics company rather than a car manufacturer. For example, in investor materials, Tesla described 2025 as a pivotal year in its transition from a hardware-led business to one centred on artificial intelligence deployed in the physical world.

Optimus

Optimus, Tesla’s humanoid robot programme first unveiled in 2021, has now become central to that narrative. Tesla said the robot is already performing limited tasks inside its factories, such as sorting objects and handling materials, though it remains far from Musk’s long-term vision of a general-purpose household robot.

In fact, Musk has repeatedly claimed Optimus could eventually perform a wide range of jobs, from factory work to domestic tasks, and has described it as more significant to Tesla’s future than vehicles over time. At the World Economic Forum in January, he said Tesla would probably begin selling humanoid robots to customers by the end of 2027, once safety and reliability reached an acceptable level.

Tesla told investors it plans to reveal a third-generation Optimus design in early 2026, describing it as the first version intended for mass production, with manufacturing expected to begin before the end of that year.

The Financial Stakes Behind The Robot Push

The move towards robotics also carries major financial implications for Tesla and Musk personally. For example, Tesla disclosed it had invested $2bn in Musk’s AI start-up xAI, while also signalling a sharp increase in capital spending, with guidance pointing to more than $20bn of investment in 2026.

That spending is expected to support multiple projects, including Optimus production, robotaxi development, battery manufacturing and AI infrastructure.

It’s worth noting here that Musk’s much publicised record-breaking pay package, approved by shareholders in late 2025, is also closely tied to Tesla delivering new growth drivers beyond car sales. Under the terms of the deal, Musk must significantly increase Tesla’s market value over the next decade, with Optimus and autonomous services positioned as central to that ambition.

Tesla has said its long-term targets include selling up to one million humanoid robots over ten years, a goal Musk has described as achievable if production and costs scale as planned.

Why Humanoid Robots Are A Riskier Bet Than EVs

Despite Tesla’s confidence, many experts view humanoid robots as one of the most difficult challenges in modern engineering. For example, unlike industrial robots designed for controlled environments, humanoids must combine balance, dexterity, perception and decision-making while operating safely around people in unpredictable settings.

Estimates of the potential market vary widely. Analysts at McKinsey have suggested a base-case market for general-purpose robotics of around $370bn by 2040, while other banks have forecast multi-trillion-dollar outcomes over longer timeframes if humanoids become widely adopted.

Supporters argue Tesla has relevant advantages, including experience in mass manufacturing, vertical integration across hardware and software, and expertise in motors and battery systems. Tesla has said those strengths allow it to iterate designs quickly and reduce costs as production scales.

However, critics say that the competitive landscape is far more crowded than when Tesla entered the EV market. For example, more than 90 companies are now developing humanoid robots, including established robotics firms, well-funded startups and technology giants supplying chips and AI platforms.

Questions have also been raised about whether consumer-facing humanoid robots will ever prove practical or affordable at scale, and whether Tesla’s ambitious timelines repeat a pattern seen in previous Musk-led projects, where public targets were missed or delayed.

Political Headwinds And Brand Risk

Tesla’s shift has unfolded alongside growing political and reputational challenges. For example, as noted earlier, Musk’s high-profile political involvement, including DOGE and his support for US President Donald Trump, has polarised public opinion and triggered protests and vandalism at Tesla dealerships in several countries.

Some investors and analysts have actually questioned whether that controversy could affect demand not only for Tesla’s cars, but also for any future consumer robot products, particularly if Optimus is positioned for home use.

Musk has acknowledged scepticism around Tesla’s ambitions but has maintained that the company is pursuing what it believes are the most important long-term technological opportunities, even if progress takes longer than expected.

What Does This Mean For Your Business?

Tesla’s decision to scale back parts of its car business in favour of robotics and AI signals a clear attempt to reset its long-term growth strategy. The move places Optimus and autonomous systems at the centre of Tesla’s future valuation, even though both remain technically complex, capital intensive and commercially unproven at scale.

For investors, suppliers and regulators, this has reframed Tesla less as a cyclical carmaker and more as a long-horizon technology bet, with outcomes likely to hinge on execution rather than vision alone. Success will require Tesla to solve problems in robotics that the wider industry has struggled with for decades, while managing near-term pressure on its automotive revenues and brand.

For UK businesses, the implications are more practical than speculative. For example, if humanoid robots move beyond pilot use in factories, logistics and warehousing, they could reshape labour planning, automation strategies and capital investment decisions over the next decade. At the same time, the uncertainty around timelines and costs reinforces the need for caution, with most analysts expecting meaningful deployment to arrive gradually rather than through rapid disruption.

More broadly, Tesla’s pivot shows how closely modern technology companies are now shaped by leadership choices, political context and investor expectations, not just product roadmaps. Whether Optimus becomes a transformative platform or an overextended ambition, Tesla’s repositioning reflects wider changes in how growth, risk and innovation are being recalibrated across the global technology and manufacturing landscape.

Samsung and WhatsApp are rolling out new security features aimed at reducing everyday privacy risks, including shoulder surfing in public places and cyber attacks targeting user accounts.

Samsung said it will introduce a new privacy layer for Galaxy devices that selectively hides sensitive on-screen content from side angles, while remaining visible to the user. The feature, developed over more than five years, can protect specific areas such as message notifications or passcode entry fields and builds on the company’s Knox security platform.

WhatsApp has also launched a new “Strict Account Settings” mode that groups multiple protections behind a single switch. When enabled, it blocks media and messages from unknown senders, disables link previews, restricts who can add users to groups, and turns on two-step verification and security alerts by default.

For businesses, the updates highlight the importance of reducing simple exposure risks by limiting what can be seen in public, tightening controls on unknown contacts, and enforcing strong default security settings across devices and messaging platforms.

As the environmental and energy costs of Earth-based data centres rise sharply, companies are beginning to test whether space could offer a more sustainable and resilient place to store critical data.

Rising Pressure On Earth-Based Data Centres

The global data centre industry has faced increasing pressure in recent years as demand has accelerated, driven by the expansion of cloud computing, streaming services and artificial intelligence. Management consultancy McKinsey estimates that global data centre demand will grow by between 19 percent and 22 percent each year through to 2030, a pace that is already placing strain on electricity grids, water resources and planning systems in many countries.

Data centres are so important now because they underpin a wide range of essential digital services, from online banking and government platforms to AI model training. As facilities have grown larger and more concentrated, their physical and environmental impact has become more visible. New developments are often located close to urban areas with strong network connectivity and access to power, increasing pressure on local infrastructure and communities.

Energy, Water And Local Resistance

Traditional data centres place some serious sustained demands on electricity networks because servers and cooling systems must operate continuously to prevent overheating. This means that many facilities also rely heavily on water-based cooling, which has become increasingly problematic in regions experiencing drought or long-term water stress.

As a result, new data centre developments in parts of Europe and North America have faced growing opposition or delays, with local authorities and communities raising concerns over water consumption, grid capacity and land use. These pressures are now colliding with national climate targets, as governments attempt to reduce emissions at the same time as demand for digital infrastructure continues to grow faster than efficiency improvements.

Why Some Firms Are Looking Beyond Earth

Against this backdrop, a small but growing group of companies operating at the intersection of space and digital infrastructure are exploring alternatives beyond Earth. The concept is not to replace terrestrial data centres, but to relocate certain types of data storage and processing to space, where resilience and long-term security are prioritised over ultra low latency.

Advances in launch technology, miniaturised electronics and solid state storage have made off planet infrastructure more technically feasible. Lower launch costs and more reliable space systems have enabled companies to begin testing whether space can support limited but valuable digital workloads.

Early Real World Experiments In Space

One of the most advanced efforts is being led by Lonestar Data Holdings, a Florida based company that has already tested a functioning data centre payload in cislunar space and is preparing for further missions around the Moon.

For example, back in February 2025, Lonestar launched its Freedom data centre payload aboard the Athena lunar lander operated by Intuitive Machines, with launch services provided by SpaceX. The payload travelled more than 300,000 kilometres and completed a series of commercial and technical tests designed to demonstrate that secure data storage and limited edge processing can operate reliably beyond Earth.

In a March 2025 press release, Lonestar confirmed that its payload successfully performed file uploads and downloads, encryption and decryption, authentication, and in space data manipulation for government and enterprise customers. The company also reported that power, temperature, CPU memory and telemetry readings remained stable throughout the mission, indicating that the system could operate within expected limits in the space environment.

Testing Sustainability Claims In Practice

Proponents of space based data centres argue that space offers physical characteristics that could reduce environmental impact compared with Earth-based facilities. For example, putting a data centre in space means that solar energy is constant and unobstructed, avoiding the intermittency associated with renewable generation on Earth. Also, heat can be dissipated through radiative cooling into the vacuum of space, thereby reducing the need for water intensive cooling systems.

Lonestar has highlighted these properties as central to its long-term plans. The company says it intends to operate around the Earth Moon L1 Lagrange point, a region of gravitational stability approximately 300,000 kilometres from Earth that allows continuous solar exposure and a relatively stable thermal environment.

In its public materials, Lonestar states that space provides “twenty four hour access to clean free solar energy and natural radiative cooling”, while also noting that physical distance can enhance resilience and security for specific categories of data.

Data Sovereignty Beyond Earth

Data sovereignty has emerged as another key factor driving interest in off planet storage. For example, governments and regulated sectors often require sensitive data to remain under defined legal jurisdictions, a requirement that can be complex in globally distributed cloud environments.

Lonestar argues that existing space law provides a framework for meeting these obligations. Under international treaties, space objects fall under the jurisdiction of the state that licenses or launches them, effectively extending national legal authority beyond Earth.

In its March 2025 announcement, the company said that “leveraging Earth’s largest satellite, the Moon, and the space around it to ensure secure data storage with data sovereignty, security, resiliency and redundancy will become increasingly vital”.

Chris Stott, Lonestar’s executive chair, described the successful in space tests as a foundational moment for the sector, stating, “This is our Kitty Hawk moment. This is where the future begins for this new resilient layer of critical global infrastructure serving us all down here on Earth.”

Independent Studies And Wider Industry Interest

Lonestar’s work actually reflects broader interest across the space and data infrastructure sectors. For example, a European Commission funded feasibility study known as Ascend, led by Thales Alenia Space, concluded in 2024 that orbiting data centres could offer environmental advantages over ground-based facilities under specific conditions.

The study suggested that a constellation delivering around 10 megawatts of computing power could be comparable to a medium sized terrestrial data centre, while avoiding land use and local water consumption. It also noted that the environmental case depends heavily on reducing emissions from launch systems across their full lifecycle.

Technical And Environmental Constraints

Despite growing interest, it must be said that some significant technical and environmental challenges remain. For example, launching hardware into space is still expensive and carbon intensive, even with reusable rockets. Also, once deployed, hardware is difficult or impossible to repair, and radiation exposure poses long-term reliability risks.

Cooling systems must be designed specifically for microgravity, limiting flexibility and upgrade options. Expanding space based data centres beyond niche, high value use cases would, therefore, require large numbers of launches and extensive orbital infrastructure, raising further questions around sustainability and space debris.

An Additional Layer Of Infrastructure

In reality, most proponents position space based data centres as a complementary layer rather than a replacement for terrestrial facilities. The strongest use cases involve disaster recovery, secure backups and long-term preservation of mission critical data, rather than latency sensitive workloads such as real time AI processing.

Lonestar has confirmed customers including the State of Florida and the Isle of Man government, both of which have highlighted resilience and independence from Earth-based risks as key factors. The company has also stated that capacity on its upcoming missions is already fully sold.

What has changed most significantly is that the concept has moved beyond theory. With functioning data storage already demonstrated in cislunar space, attention is now focused on scale, cost, environmental trade offs and how space based infrastructure may fit into wider sustainability strategies for a rapidly expanding digital economy.

What Does This Mean For Your Organisation?

It seems that space based data centres are now moving from conceptual discussion into early operational reality, but they remain a targeted response to specific pressures rather than a universal solution. The sustainability case rests on some clear trade offs. For example, space offers constant solar power, reduced water use and physical separation from climate and geopolitical risks, while also introducing new environmental costs through launches, manufacturing and long-term orbital operations. Whether the balance proves positive at scale will depend on continued reductions in launch emissions, careful limitation of use cases and a realistic assessment of where off planet infrastructure genuinely adds value.

For UK businesses, space based data storage is unlikely to replace domestic or regional data centres, but it may become relevant for organisations with strict resilience, disaster recovery or sovereignty requirements, particularly in regulated sectors such as finance, government and critical national infrastructure. For these users, space offers a potential additional layer of protection rather than a new primary platform, complementing existing cloud and on premises systems rather than displacing them.

For policymakers, regulators and infrastructure planners, the emergence of space based data centres highlights the growing tension between digital growth and environmental limits on Earth. It underlines the need to treat data infrastructure as critical national capacity, subject to the same long-term planning as energy, transport and water. Space is not a shortcut around sustainability challenges, but its growing role reflects how seriously those challenges are now being taken across the global digital economy.

If you use AI to generate lots of images, the waiting can be annoying. This video shows you which (in our tests) came out on top for speed, so you can see for yourself.

[Note – To Watch This Video without glitches/interruptions, It may be best to download it first]

Viewing external calendars in Outlook lets you keep everything in one place, helping you avoid clashes, missed meetings, and constant switching between apps. Here’s how.

How To Add A Google Calendar To Outlook (Unified View)

This method subscribes Outlook to your Google Calendar using an iCal link. It is ideal for visibility rather than editing.

– Open Google Calendar in a web browser.
– In the left-hand calendar list, hover over the calendar you want and select Settings.
– Open Settings and sharing for that calendar.
– Scroll down to Integrate calendar.
– Copy the Secret address in iCal format.
– Open Outlook Calendar (Outlook on the web or the new Outlook app).
– Select Add calendar.
– Select Subscribe from web.
– Paste the iCal link.
– Select Import.

Your Google Calendar will now appear alongside your Outlook calendar and update automatically, although changes must still be made in Google Calendar.

How To Add An Apple (iCloud) Calendar To Outlook

For Apple calendars on Windows, the most reliable option is syncing via iCloud for Windows.

– Download and install iCloud for Windows.
– Sign in using your Apple ID.
– Enable Calendars and Contacts.
– Confirm the option to sync with Microsoft Outlook.
– Open Outlook and check your calendar list.

Your Apple Calendar should now appear inside Outlook and stay in sync.

Things Worth Knowing

– Google Calendar subscriptions in Outlook are usually view-only
– Updates can take a short time to appear after changes
– Most other calendar services will also work if they provide an iCal or ICS subscription link.