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6 July 2026 at 18:07
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Q&A: How Google plans to reinvent the spreadsheet with AI

13 July 2026 at 12:00

Nearly five decades after the launch of VisiCalc, AI is reshaping one of the world’s most familiar productivity tools — the humble spreadsheet.

While a lot of knowledge workers interact with spreadsheets on a regular basis, many lack the skills and confidence to access more advanced functions.

“For a very long time, spreadsheets forced you to learn spreadsheet syntax and spreadsheet ways of working,” said Eric Birnbaum, director of product management for Google Sheets. “But think about how many people need to use spreadsheets at work and don’t have the skill set to create the kinds of spreadsheets that can be really helpful for them.”

The addition of artificial intelligence can help handle that issue, he said, making the software more accessible to a wide range of office workers. “AI is unlocking the power of spreadsheets, taking on a lot of the difficult work that’s required to use them. That can be incredibly empowering for users,” said Birnbaum.

Google has steadily expanded generative AI (genAI) capabilities in Sheets since launching Duet AI — now Gemini — for Workspace in 2023.

Gemini in Sheets is available at no extra cost to Google Workspace subscribers, though a paid AI Expanded Access add-on – costing $30 per user each month – is required to remove certain usage limits.

Features that have rolled out in recent months include the ability for a Gemini agent in Sheets to carry out multi-step actions such as formatting, analysis and data entry, and, more recently, the ability to create entire spreadsheets from a single prompt. A Fill with Gemini feature builds on the existing AI function, enabling users to automatically populate selected cells by detecting intent from information within a spreadsheet as well as from the web.

Another feature, Sheets Canvas (currently available in alpha), lets users generate interactive apps that update in real-time based on changes to spreadsheet data. This could be a kanban board for a sales pipeline, for instance, or an analytics dashboard that uses Sheets as its back-end data source. 

Google claims users already see a range of benefits from Gemini in Sheets. According to an August 2025 survey of 200 Sheets users conducted by the company, the majority of knowledge workers (89%) said AI features in Sheets save them at least an hour a week, and 88% believe AI features have made them more confident in their data analysis skills. 

The most popular AI use cases include creating spreadsheets and charts, analyzing data, and fixing broken formulas. How widely these features are actually used is unclear; Google declined to provide weekly usage statistics for Gemini in Sheets.

As the company embeds Gemini deeper into Sheets, questions remain about just how much businesses can trust AI tools to handle important business data, as well as what increased automation means for those who spend much of their day wrangling data. 

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Eric Birnbaum, director of product management for Google Sheets.

Google

Computerworld recently talked with Birnbaum about the potential benefits and challenges of the latest evolution of spreadsheet software. This interview has been condensed and edited for clarity.

As businesses become more focused on seeing value from AI investments, what measurable benefits are customers seeing from Gemini in Sheets – for example, time saved or other forms of return on investment? “Spreadsheets remain one of the universal languages for businesses, and we don’t anticipate that’s going to change anytime soon. But by bringing AI into the product where people work, we think it can significantly reduce the technical tax of data: the time spent understanding it, sourcing it, analyzing it, visualizing it.

“Historically, data professionals spent — let’s estimate it at 80% of their time — on the mechanical groundwork, data cleaning, crafting formulas, formatting, troubleshooting, and maybe only 20% of their time on actual strategic decision-making.

“We think that by offloading the manual, time-consuming, error-prone data work to Gemini, we can flip that ratio a bit, so humans can focus on the things that really matter: the high-value questions to ask and the judgment calls and decisions that get made from them. We’re starting to see evidence of that in our own user base and customer base.

“The second point to make is that AI can democratize data analysis to some extent, and make it available to many more users. For so many years, the spreadsheet was a gatekeeper; if you couldn’t speak the rigid language of spreadsheet formulas, you couldn’t extract the value from data. But by introducing these natural language interfaces, AI is separating the analytical capability from the technical literacy. 

“If you can ask the right questions, or describe what you want in plain language, Gemini and Sheets can help you achieve your goals in a spreadsheet, even if you have minimal spreadsheet skills yourself.

“What we’ve seen so far from users and customers is that it’s incredibly empowering for people who might have been scared off by data analysis or dreaded opening spreadsheets in the past. You don’t need to go and wait for a data analyst to help you; you can go and do this work yourself in a spreadsheet.”

The flip side is, how confident can businesses be if more junior employees can take on higher-level analysis tasks by relying on AI? Given the propensity for AI models to hallucinate, to what degree can businesses trust that these tools won’t introduce errors into important business data? “It’s something we spent a ton of time thinking about. We’ve gone to great lengths to build these AI tools to collaborate with you, to show their work, explain what they did, and make sure that you can take over where they leave off.

“Our Sheets agent, for example, lays out a really explicit, transparent plan for you to review and approve before any data manipulation happens. It’s designed to do that in plain natural language in a way that the average user could understand, and then it gives you back that final summary, so you know exactly what it did and where it did it.

“The other thing is that the model is great at explaining things. If you inherit a spreadsheet that has some complex formula that you don’t understand, for example, the model does an amazing job of explaining how it works and what it’s doing.

“In many ways AI is not only making these features more accessible, but helping users feel more confident in the output. Human error is an inherent risk in manual data management, with or without AI. One misplaced comma or broken cell reference can completely corrupt an entire financial model, and it can be completely undetected. 

“Our approach with AI in Sheets is to create this deliberate verification loop. You now have another spreadsheet expert working along with you, reducing the likelihood of these mistakes.”

Even if humans produce errors too, does it ultimately come down to accountability when AI is involved? “Our point of view here is that AI should be partnering with the knowledge worker who’s doing the work here. And everything that we’ve built is designed to be that partner. You might be able to offload tasks to the model, but we’re citing sources, we’re providing plans and explanations. We’re ultimately relying on the user to do that final verification.

“We spend a humongous amount of time focused on quality. We know that for AI to be useful in spreadsheets, it has to be reliable. When we launched Sheets Gemini Agent, for example, we were really proud that we set a state-of-the-art benchmark on the full SpreadsheetBench data set, which at the time exceeded competitors and near-human expert ability. 

“But we know quality is never ‘good enough’ or done. We’re constantly working to improve quality for our users and customers, and for the use cases where they’re relying on AI most.”

What potential do you see for more agentic functionality in Gemini Sheets — for example, bringing in data from other sources, creating recurring reports, or taking more actions independently? “We’re listening closely to customers and users and building what they’re telling us they need. The agent is already capable of doing very complex multistep workflows, and we see users discover that the agent is extremely capable of doing end-to-end spreadsheet tasks. In terms of connectors, in an alpha we have connections available to HubSpot, Salesforce, and Mailchimp. We hope to expand that over time.

“There’s no path to have AI replacing analysts. I think AI is giving analysts more time back to actually do the more valuable parts of their job. Analysts that I work with are way more productive and impactful than they ever were before, because they can push that uninteresting spreadsheet grunt work off to the model, freeing up time for more interesting and impactful work.

“A great example: the visualizations I’m getting back from analysts nowadays are canvases instead of static charts that I can explore myself. It’s way more informative and useful than what I was accustomed to before.”

Looking ahead, do you expect a larger share of spreadsheet work to be carried out by agents, with humans setting goals and reviewing results? What will be the biggest change in how people use spreadsheets with AI? “The tasks are likely to stay similar and the use cases for spreadsheets are likely to continue to be relevant. The biggest change will be the ability to push the uninteresting spreadsheet grunt work off to a model and free up time for the user to do things that are more impactful, more interesting, more meaningful to the business.

‘Spreadsheets have historically been these like static containers where data goes to rest. I think AI can turn spreadsheets into these dynamic, localized software applications. And I do actually think this sort of changes the game for how people might use spreadsheets moving forward. 

“It’s not just a passive grid full of numbers; spreadsheets are evolving to become these live, long-lived, collaborative applications. Employees can build these on the fly, like a basic CRM or supply chain dashboard in seconds. This is an area of investment for us moving forward, and we’re really excited to see how this evolves.”

AI assistants and agents, such as ChatGPT or Claude, might be able to analyze spreadsheet files and business data without users working inside a spreadsheet application. What do you think will keep Sheets central to the workflow, rather than simply making it one part of a wider AI-driven process?“We’re in constant touch with customers and users; it’s clear work is still happening in spreadsheets. I think spreadsheets remain incredibly popular tools. If we can bring the AI capabilities users need directly into the product where they already are, we’ll transform the way they work.  

“I think we can be the front-end for some of this great AI innovation and the products that users are accustomed to today. Everything we build is guided by user feedback: users are telling us right now they want AI to help them do their everyday or more complex tasks, and they’re starting in Sheets today.”

Greenland Ice Sheet Melt: 1500–2200 CE Trends

17 June 2026 at 12:06

The Greenland Ice Sheet: An Unstoppable Catalyst of Global Sea-Level Rise

In the unfolding narrative of our planet’s climate crisis, the Greenland Ice Sheet stands as one of the most formidable agents of change. Since the dawn of satellite observations in 1992, this massive reservoir of ice has relinquished approximately 4,900 gigatons of ice, a staggering volume whose meltwater has contributed about 13 millimeters to the rise in global mean sea level. Such a figure, while already alarming, merely scratches the surface of potential long-term impacts. Even in a hypothetical future devoid of any additional warming, the Greenland Ice Sheet is effectively locked in to contribute an extra ~274 millimeters to global sea levels. This commitment underscores a sobering reality: the ice sheet’s decline is not simply a transient phenomenon but a geological-scale reordering, with the total potential of a 7-meter sea-level rise looming if complete melting ever unfolds.

To truly grasp the implications of the Greenland Ice Sheet’s evolution, scientists have undertaken comprehensive syntheses of its surface melt dynamics extending across centuries—from 1500 to projections as far ahead as 2200 CE. This historical and predictive vantage reveals a striking acceleration in surface melt beginning in the 1990s, characterized by a rapid increase of roughly 1 percent per year. This upsurge has not occurred in isolation. Rather, it is a direct consequence of amplified regional warming patterns coupled with notable alterations in atmospheric circulation dynamics. A phenomenon known as enhanced atmospheric blocking—where persistent high-pressure systems effectively stall weather patterns—has played a pivotal role in this rapid intensification of melt rates.

The consequences of these climate shifts manifest with increasing frequency and severity in extreme melt events. Since 2007, Greenland has experienced multiple unprecedented episodes where melting persists intensively over several consecutive days. These events reach apogees such as the extraordinary case of July 2012, during which almost the entirety of the ice sheet surface succumbed to thawing. Such extremes stress the limits of current predictive models and underscore the urgent need to understand the complex interplay of forcings driving these episodes.

Dominating the seasonal melt cycle is the absorption of shortwave solar radiation. Solar energy, pouring relentlessly onto the ice sheet’s surface during summer months, serves as the primary engine for surface melting. However, this process is far from straightforward or uniform. Turbulent heat fluxes—essentially the turbulent exchange of heat between the ice and atmosphere—alongside cloud cover dynamics and the critical feedback mechanism of albedo, significantly modulate the spatial and temporal variability of melt. Albedo effects refer to the reflectivity of the ice surface: as ice melts and dark patches or meltwater form, less sunlight is reflected back, and more is absorbed, accelerating melt rates in a self-reinforcing loop.

Yet, it is precisely the intricate and interlinked nature of these processes that confounds climate models. Even among today’s state-of-the-art regional climate models, there is a stark divergence in projections of Greenland’s surface melt and mass loss. When subjected to identical climate forcing scenarios, these models yield predictions differing by as much as a factor of two. This discrepancy highlights fundamental uncertainties inherent in simulating the nuanced physics of ice-atmosphere interactions, energy balance complexities, and meltwater behavior within the firn layers—densely packed snow that acts as a sponge holding meltwater before it refreezes or runs off.

A central challenge lies in the accurate representation and integration of these processes within numerical simulations. Extreme melt events, in particular, remain difficult to quantify and predict with confidence. The drivers behind these sporadic but increasingly frequent episodes are multifaceted, involving shifts in jet stream patterns, atmospheric blocking frequencies, and the enigmatic influence of cloud microphysics. Likewise, the fate of meltwater—whether it refreezes in the porous firn layers or contributes to runoff—critically influences the net mass balance of the ice sheet yet remains poorly constrained in many models.

Adding further layers of complexity, coupling between atmospheric forcing and the surface energy balance must accurately capture feedback loops and temporal variability. Discrepancies in these couplings propagate through simulations and exacerbate uncertainties in projections of future melt and associated sea-level rise contributions. Scientists recognize that overcoming these hurdles necessitates expanding observational networks across Greenland, leveraging advanced remote sensing methods combined with in situ monitoring to resolve fine-scale processes.

Furthermore, there is a burgeoning potential for integrating emerging data-driven approaches into this research frontier. Machine learning, artificial intelligence, and data assimilation techniques offer promising means to synthesize vast datasets, identify subtle patterns, and enhance the physical realism of model parameterizations. However, realizing these gains demands concerted efforts in model development, interdisciplinary collaboration, and continued investment in observational infrastructure.

The stakes could hardly be higher. Greenland’s meltwater contributions are a central element of global sea-level rise—one of the most significant threats to coastal communities worldwide. The unprecedented speed and scale of Greenland surface melt ask not only for scientific diligence but also for rapid climate mitigation and adaptation strategies. The Greenland Ice Sheet is both a harbinger and accelerator of change in Earth’s climate system, embodying losses that reverberate far beyond polar latitudes.

Looking to the centuries ahead, the trajectory of Greenland’s surface melt remains riddled with uncertainty but is unmistakably poised on an upward trend in response to continued warming. Understanding and predicting this trajectory with greater precision is not merely an academic endeavor; it is a prerequisite for global climate resilience planning. The Greenland Ice Sheet, with its vast store of frozen freshwater, is a sentinel of deep Earth system transformations, reminding humanity of the interconnectedness of climate, ice, and sea.

Our current epoch demands that we unravel the intricacies of ice sheet melting at a pace matching the climate forces reshaping our planet. The Greenland Ice Sheet’s story, as told from the past into the future, is both a cautionary tale and an invitation to innovate in climate science. Only through enhanced observational capabilities, refined models, and novel integrative approaches can we rise to this challenge. The fate of coastlines, ecosystems, and human settlements worldwide hinge on the answers we find in this colossal frozen archive.

As the Earth warms, the story of Greenland’s shrinking ice transforms from abstract projections into tangible realities. Extreme melt events once considered anomalies have become harbingers of a new normal, with implications cascading through global systems. The urgency of understanding this dynamic, the mechanisms underpinning it, and its future paths cannot be overstated. Greenland, in its ancient calm, increasingly pulses with restless change—bearing witness to and driving one of the most profound climate transformations of our era.

Subject of Research: Greenland Ice Sheet surface melt and its contributions to global sea-level rise.

Article Title: Past, present and future Greenland Ice Sheet surface melt, 1500–2200 CE.

Article References:
Hanna, E., Box, J.E., Fettweis, X. et al. Past, present and future Greenland Ice Sheet surface melt, 1500–2200 CE. Nat Rev Earth Environ (2026). https://doi.org/10.1038/s43017-026-00800-3

Image Credits: AI Generated

DOI: 10.1038/s43017-026-00800-3

Keywords: Greenland Ice Sheet, surface melt, sea-level rise, climate change, atmospheric circulation, atmospheric blocking, albedo feedback, turbulent heat fluxes, climate models, firn processes, meltwater retention, extreme melt events

Widespread Glacier Algal Blooms Grow Across Greenland Ice

16 June 2026 at 16:51

The Greenland Ice Sheet, one of the planet’s largest reservoirs of freshwater, has become the focal point of groundbreaking research revealing a hitherto underestimated phenomenon: pervasive and expanding glacier algal blooms. Recent advanced modeling conducted by scientists Williamson and Tedstone has shed new light on the spatial extent and environmental implications of these microscopic organisms thriving on the ice. This emerging evidence disrupts prior assumptions about glacier ecosystems and poses significant concerns regarding future ice sheet dynamics amid climate warming.

Glacier algae, photosynthetic microorganisms adapted to extreme cold, dark, and nutrient-poor environments, have traditionally been considered minor ecological players in polar regions. However, their capacity to proliferate in biofilms across the ice surface influences the ice’s physical properties profoundly. The team utilized sophisticated climate-ice modeling tools coupling biological growth parameters to simulate algal bloom dynamics across Greenland’s vast ice sheet. This holistic approach enabled quantification of bloom distribution and intensity under current and projected climatic scenarios, opening avenues to assess feedback mechanisms in cryosphere systems.

The researchers addressed key variables including temperature fluctuations, solar radiation, meltwater availability, and nutrient influx that regulate glacier algal growth. By integrating these parameters, their model replicated repeated satellite observations indicating that algal blooms have become ubiquitous rather than occasional phenomena on the ice sheet’s surface. Importantly, this newfound ubiquity corresponds with notable increases in the ice’s albedo reduction, a process by which the darkened ice absorbs significantly more solar energy, accelerating melting rates substantially.

One of the critical breakthroughs of the study is the identification of a positive feedback loop involving glacier algae expansion and ice melt intensification. As temperatures rise, the expanded meltwater ponds create optimal microhabitats, facilitating rapid algal growth. This dark biomass, in turn, reduces reflectance, trapping more heat and thus promoting further meltwater generation—a cascading effect that amplifies ice mass loss. The implications for sea-level rise are profound since even marginal increases in melt rates compound over the Greenland Ice Sheet’s immense area.

The modeling framework also underscores the spatial variability of algal bloom intensity. Coastal fringes and lower elevation zones experience the highest biomass accumulation due to warmer temperatures and longer melt seasons. Conversely, high-altitude sites see more sporadic blooms, limited by colder conditions and shorter windows of liquid water presence. However, the projection models suggest an altitudinal upward expansion of these biological communities as climate warming persists, potentially transforming previously cryotoxic zones into hospitable environments for microbial colonization.

Further analysis revealed complex interactions between algal species composition and their pigmentation, which influence their light-absorption properties. Some glacier algae produce dark pigments like purpurogallin, a bioactive compound that enhances solar radiation absorption, contributing disproportionately to albedo reduction relative to their biomass. This biochemical adaptation illustrates the tightly coupled biological-physical dynamics on ice surfaces and the potential for microbe-driven modulation of glacier energy balance.

The study also incorporates insights from remote sensing technologies, including multispectral satellite instruments capable of detecting subtle color changes on the Greenland Ice Sheet indicative of blooming events. These observational datasets, validated against in-situ measurements, provided empirical support for the model’s predictive accuracy. Such integration of fieldwork and computational modeling represents a cutting-edge methodology in cryoecology, enabling researchers to quantify glacier surface biological phenomena at unprecedented spatial and temporal resolutions.

Climate models incorporating these glacier algal dynamics expose an alarming acceleration in the Greenland Ice Sheet’s mass loss trajectory. Inclusion of biological factors leads to revised melt projections that surpass prior estimates relying solely on physical ice melt processes. This insight challenges the traditional paradigms of cryosphere modeling and calls for a multidisciplinary approach that fully integrates biogeochemical and microbial influences into climate impact assessments.

From an ecological perspective, expanding glacier algal blooms signify a broader shift in polar microbial ecosystems in response to anthropogenic climate change. These vibrant microbial mats not only modify the physical environment but also participate actively in the cycling of nutrients and carbon on the ice surface, potentially altering biogeochemical fluxes at regional and global scales. The expansion of such biota may herald a new era of glacier ecology, where microbial life increasingly shapes cryospheric processes.

The researchers caution that while current models provide a vital baseline, uncertainties remain regarding the precise sensitivity of glacier algae to evolving environmental parameters such as nutrient availability from atmospheric deposition and meltwater chemistry alterations. Future investigations will need to incorporate microbial genomic and metabolic studies to refine growth kinetics and adaptive resilience under multifactorial stress conditions driven by climate variability.

The potential ramifications of glacier algal blooms extend beyond Greenland. Similar processes may be occurring or will develop in ice masses worldwide, including mountain glaciers and Antarctic ice shelves. This raises critical questions about representativeness and transferability of observational and modeling findings across different cryoenvironments. Continued interdisciplinary research is essential to globally map biological-cryosphere interactions and predict their contributions to ice sheet stability and sea-level rise.

Ultimately, the work by Williamson and Tedstone adds a vital piece to the puzzle of how biological life interacts with Earth’s changing cryosphere. Their modeling of ubiquitous and expanding glacier algal blooms challenges us to reconsider ice sheets not merely as inert reservoirs of frozen water, but as dynamic ecosystems intricately entwined with climate processes. As climate warming intensifies, these microscopic organisms emerge as influential agents capable of reshaping the trajectories of some of the planet’s most critical and vulnerable landscapes.

Their findings call for immediate attention from climate scientists, ecologists, and policymakers alike, highlighting the need to integrate biological feedbacks into predictive models and mitigation strategies. Understanding and managing the consequences of expanding glacier algal blooms may prove crucial for preserving polar environments and moderating global sea-level rise in the coming decades.

In essence, Greenland’s ice sheet is not just melting — it is biologically darkening and transforming in ways that complicate our understanding of climate change impacts. The intersection of microbial ecology and glaciology unveiled by this study signals a paradigm shift that underscores the intricate complexity of Earth’s systems and the unforeseen drivers accelerating environmental change.

Subject of Research: Glacier algal blooms on the Greenland Ice Sheet and their impacts on ice melt and albedo reduction.

Article Title: Ubiquitous and expanding glacier algal blooms modelled around the Greenland Ice Sheet.

Article References:
Williamson, C.J., Tedstone, A.J. Ubiquitous and expanding glacier algal blooms modelled around the Greenland Ice Sheet. Commun Earth Environ (2026). https://doi.org/10.1038/s43247-026-03758-8

Image Credits: AI Generated

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