ASB GlassFloor makes a glass playing surface for sports arenas that can show animations, track athletes' performance, and repaint court lines with a tap. Pros in Europe are already playing on it.

Share of search is the percentage of search queries for a brand relative to competitors in the same category.

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)

A coiled giant anaconda. They are the largest snake species in Brazil and play a major role in legends including the ‘Boiuna’ and the ‘Cobra Grande.’ CREDIT: Beatriz Cosendey. Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. South America’s lush Amazon region is a biodiversity hotspot, which means that every living thing must find a way to co-exist. Even some of the most feared snakes on the planet–anacondas. In a paper published June 16 in the journal Frontiers in Amphibian and Reptile Science, conservation biologists Beatriz Cosendey and Juarez Carlos Brito Pezzuti from the Federal University of Pará’s Center for Amazonian Studies in Brazil, analyze the key points behind the interactions between humans and the local anaconda populations. Ahead of the paper’s publication, the team at Frontiers conducted this wide-ranging Q&A with Conesday. It has not been altered. Frontiers: What inspired you to become a researcher? Beatriz Cosendey: As a child, I was fascinated by reports and documentaries about field research and often wondered what it took to be there and what kind of knowledge was being produced. Later, as an ecologist, I felt the need for approaches that better connected scientific research with real-world contexts. I became especially interested in perspectives that viewed humans not as separate from nature, but as part of ecological systems. This led me to explore integrative methods that incorporate local and traditional knowledge, aiming to make research more relevant and accessible to the communities involved. F: Can you tell us about the research you’re currently working on? BC: My research focuses on ethnobiology, an interdisciplinary field intersecting ecology, conservation, and traditional knowledge. We investigate not only the biodiversity of an area but also the relationship local communities have with surrounding species, providing a better understanding of local dynamics and areas needing special attention for conservation. After all, no one knows a place better than those who have lived there for generations. This deep familiarity allows for early detection of changes or environmental shifts. Additionally, developing a collaborative project with residents generates greater engagement, as they recognize themselves as active contributors; and collective participation is essential for effective conservation. Local boating the Amazon River. CREDIT: Beatriz Cosendey. F: Could you tell us about one of the legends surrounding anacondas? BC: One of the greatest myths is about the Great Snake—a huge snake that is said to inhabit the Amazon River and sleep beneath the town. According to the dwellers, the Great Snake is an anaconda that has grown too large; its movements can shake the river’s waters, and its eyes look like fire in the darkness of night. People say anacondas can grow so big that they can swallow large animals—including humans or cattle—without difficulty. F: What could be the reasons why the traditional role of anacondas as a spiritual and mythological entity has changed? Do you think the fact that fewer anacondas have been seen in recent years contributes to their diminished importance as an mythological entity? BC: Not exactly. I believe the two are related, but not in a direct way. The mythology still exists, but among Aritapera dwellers, there’s a more practical, everyday concern—mainly the fear of losing their chickens. As a result, anacondas have come to be seen as stealthy thieves. These traits are mostly associated with smaller individuals (up to around 2–2.5 meters), while the larger ones—which may still carry the symbolic weight of the ‘Great Snake’—tend to retreat to more sheltered areas; because of the presence of houses, motorized boats, and general noise, they are now seen much less frequently. A giant anaconda is being measured. Credit: Pedro Calazans. F: Can you share some of the quotes you’ve collected in interviews that show the attitude of community members towards anacondas? How do chickens come into play? BC: When talking about anacondas, one thing always comes up: chickens. “Chicken is her [the anaconda’s] favorite dish. If one clucks, she comes,” said one dweller. This kind of remark helps explain why the conflict is often framed in economic terms. During the interviews and conversations with local dwellers, many emphasized the financial impact of losing their animals: “The biggest loss is that they keep taking chicks and chickens…” or “You raise the chicken—you can’t just let it be eaten for free, right?” For them, it’s a loss of investment, especially since corn, which is used as chicken feed, is expensive. As one person put it: “We spend time feeding and raising the birds, and then the snake comes and takes them.” One dweller shared that, in an attempt to prevent another loss, he killed the anaconda and removed the last chicken it had swallowed from its belly—”it was still fresh,” he said—and used it for his meal, cooking the chicken for lunch so it wouldn’t go to waste. One of the Amazonas communities where the researchers conducted their research. CREDIT: Beatriz Cosendey. Some interviewees reported that they had to rebuild their chicken coops and pigsties because too many anacondas were getting in. Participants would point out where the anaconda had entered and explained that they came in through gaps or cracks but couldn’t get out afterwards because they ‘tufavam’ — a local term referring to the snake’s body swelling after ingesting prey. We saw chicken coops made with mesh, with nylon, some that worked and some that didn’t. Guided by the locals’ insights, we concluded that the best solution to compensate for the gaps between the wooden slats is to line the coop with a fine nylon mesh (to block smaller animals), and on the outside, a layer of wire mesh, which protects the inner mesh and prevents the entry of larger animals. F: Are there any common misconceptions about this area of research? How would you address them? BC: Yes, very much. Although ethnobiology is an old science, it’s still underexplored and often misunderstood. In some fields, there are ongoing debates about the robustness and scientific validity of the field and related areas. This is largely because the findings don’t always rely only on hard statistical data. However, like any other scientific field, it follows standardized methodologies, and no result is accepted without proper grounding. What happens is that ethnobiology leans more toward the human sciences, placing human beings and traditional knowledge as key variables within its framework. To address these misconceptions, I believe it’s important to emphasize that ethnobiology produces solid and relevant knowledge—especially in the context of conservation and sustainable development. It offers insights that purely biological approaches might overlook and helps build bridges between science and society. The study focused on the várzea regions of the Lower Amazon River. CREDIT: Beatriz Cosendey. F: What are some of the areas of research you’d like to see tackled in the years ahead? BC: I’d like to see more conservation projects that include local communities as active participants rather than as passive observers. Incorporating their voices, perspectives, and needs not only makes initiatives more effective, but also more just. There is also great potential in recognizing and valuing traditional knowledge. Beyond its cultural significance, certain practices—such as the use of natural compounds—could become practical assets for other vulnerable regions. Once properly documented and understood, many of these approaches offer adaptable forms of environmental management and could help inform broader conservation strategies elsewhere. F: How has open science benefited the reach and impact of your research? BC: Open science is crucial for making research more accessible. By eliminating access barriers, it facilitates a broader exchange of knowledge—important especially for interdisciplinary research like mine which draws on multiple knowledge systems and gains value when shared widely. For scientific work, it ensures that knowledge reaches a wider audience, including practitioners and policymakers. This openness fosters dialogue across different sectors, making research more inclusive and encouraging greater collaboration among diverse groups. The Q&A can also be read here.

Daniel Pearson , CEO, KnownHostJune 12, 20253 Min ReadBusiness success concept. Cubes with arrows and target on the top.Cyber incidents are expected to cost the US $639 billion in 2025. According to the latest estimates, this dynamic will continue to rise, reaching approximately 1.82 trillion US dollars in cybercrime costs by 2028. These figures highlight the crucial importance of strong cybersecurity strategies, which businesses must build to reduce the likelihood of risks. As technology evolves at a dramatic pace, businesses are increasingly dependent on utilizing digital infrastructure, exposing themselves to threats such as ransomware, accidental data loss, and corruption.  Despite the 3-2-1 backup rule being invented in 2009, this strategy has stayed relevant for businesses over the years, ensuring that the loss of data is minimized under threat, and will be a crucial method in the upcoming years to prevent major data loss.   What Is the 3-2-1 Backup Rule? The 3-2-1 backup rule is a popular backup strategy that ensures resilience against data loss. The setup consists of keeping your original data and two backups.  The data also needs to be stored in two different locations, such as the cloud or a local drive.  The one in the 3-2-1 backup rule represents storing a copy of your data off site, and this completes the setup.  This setup has been considered a gold standard in IT security, as it minimizes points of failure and increases the chance of successful data recovery in the event of a cyber-attack.  Related:Why Is This Rule Relevant in the Modern Cyber Threat Landscape? Statistics show that in 2024, 80% of companies have seen an increase in the frequency of cloud attacks.  Although many businesses assume that storing data in the cloud is enough, it is certainly not failsafe, and businesses are in bigger danger than ever due to the vast development of technology and AI capabilities attackers can manipulate and use.  As the cloud infrastructure has seen a similar speed of growth, cyber criminals are actively targeting these, leaving businesses with no clear recovery option. Therefore, more than ever, businesses need to invest in immutable backup solutions.  Common Backup Mistakes Businesses Make A common misstep is keeping all backups on the same physical network. If malware gets in, it can quickly spread and encrypt both the primary data and the backups, wiping out everything in one go. Another issue is the lack of offline or air-gapped backups. Many businesses rely entirely on cloud-based or on-premises storage that's always connected, which means their recovery options could be compromised during an attack. Related:Finally, one of the most overlooked yet crucial steps is testing backup restoration. A backup is only useful if it can actually be restored. Too often, companies skip regular testing. This can lead to a harsh reality check when they discover, too late, that their backup data is either corrupted or completely inaccessible after a breach. How to Implement the 3-2-1 Backup Rule? To successfully implement the 3-2-1 backup strategy as part of a robust cybersecurity framework, organizations should start by diversifying their storage methods. A resilient approach typically includes a mix of local storage, cloud-based solutions, and physical media such as external hard drives.  From there, it's essential to incorporate technologies that support write-once, read-many functionalities. This means backups cannot be modified or deleted, even by administrators, providing an extra layer of protection against threats. To further enhance resilience, organizations should make use of automation and AI-driven tools. These technologies can offer real-time monitoring, detect anomalies, and apply predictive analytics to maintain the integrity of backup data and flag any unusual activity or failures in the process. Lastly, it's crucial to ensure your backup strategy aligns with relevant regulatory requirements, such as GDPR in the UK or CCPA in the US. Compliance not only mitigates legal risk but also reinforces your commitment to data protection and operational continuity. Related:By blending the time-tested 3-2-1 rule with modern advances like immutable storage and intelligent monitoring, organizations can build a highly resilient backup architecture that strengthens their overall cybersecurity posture. About the AuthorDaniel Pearson CEO, KnownHostDaniel Pearson is the CEO of KnownHost, a managed web hosting service provider. Pearson also serves as a dedicated board member and supporter of the AlmaLinux OS Foundation, a non-profit organization focused on advancing the AlmaLinux OS -- an open-source operating system derived from RHEL. His passion for technology extends beyond his professional endeavors, as he actively promotes digital literacy and empowerment. Pearson's entrepreneurial drive and extensive industry knowledge have solidified his reputation as a respected figure in the tech community. See more from Daniel Pearson ReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

Construction input prices rose 0.2% in May, according to a new Associated Builders and Contractors (ABC) analysis of U.S. Bureau of Labor Statistics’ Producer Price Index data. Last month, nonresidential construction input prices reduced by 0.1%. Overall construction input prices are 1.3% higher than levels from a year ago, and nonresidential construction prices are 1.6% higher. Prices decreased in two of three major energy categories in April. Natural gas prices fell 18.7%, unprocessed energy materials were down 3.5%, and crude petroleum prices increased by 1.3%. Chart credit: Associated Builders and Contractors“Construction materials prices continued to increase at a faster-than-ideal pace in May,” said ABC Chief Economist Anirban Basu. “While input prices are up just 1.3% over the past year, that modest escalation is entirely due to price decreases during the second half of 2024. Costs have increased rapidly since the start of this year, with input prices rising at a 6% annualize...

Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]

These annual rankings were last updated on June 13, 2025. Want to see your firm on next year’s list? Continue reading for more on how you can improve your studio’s ranking. New Zealand is a one-of-a-kind island in the Pacific, famous for its indigenous Maori architecture. The country has managed to preserve an array of historical aboriginal ruins, such as marae (meeting grounds) and wharenui (meeting houses), despite its European colonization during the 19th century. Apart from the country’s ancient ruins, New Zealand is also home to several notable architectural landmarks like the famous Sky Tower piercing the Auckland skyline to the organic forms of the Te Papa Tongarewa Museum in Wellington. Renowned architects like Sir Ian Athfield, whose works blend modernist principles with a deep respect for the natural landscape, have left an indelible mark on the country’s architectural legacy. Being home to a stunning tropical landscape, New Zealand architects have developed eco-friendly residential designs that harness the power of renewable energy as well as visionary urban developments prioritizing livability and connectivity. A notable example is Turanga Central Library in Christchurch, a project that exceeds all eco-friendly design standards and benchmark emissions. Finally, concepts like passive design are increasingly becoming standard practice in architectural circles. With so many architecture firms to choose from, it’s challenging for clients to identify the industry leaders that will be an ideal fit for their project needs. Fortunately, Architizer is able to provide guidance on the top design firms in New Zealand based on more than a decade of data and industry knowledge. How are these architecture firms ranked? The following ranking has been created according to key statistics that demonstrate each firm’s level of architectural excellence. The following metrics have been accumulated to establish each architecture firm’s ranking, in order of priority: The number of A+Awards won (2013 to 2025) The number of A+Awards finalists (2013 to 2025) The number of projects selected as “Project of the Day” (2009 to 2025) The number of projects selected as “Featured Project” (2009 to 2025) The number of projects uploaded to Architizer (2009 to 2025) Each of these metrics is explained in more detail at the foot of this article. This ranking list will be updated annually, taking into account new achievements of New Zealand architecture firms throughout the year. Without further ado, here are the 30 best architecture firms in New Zealand: 30. CoLab Architecture © CoLab Architecture Ltd CoLab Architecture is a small practice of two directors, Tobin Smith and Blair Paterson, based in Christchurch New Zealand. Tobin is a creative designer with a wealth of experience in the building industry. Blair is a registered architect and graduate from the University of Auckland. “We like architecture to be visually powerful, intellectually elegant, and above all timeless. For us, timeless design is achieved through simplicity and strength of concept — in other words, a single idea executed beautifully with a dedication to the details. We strive to create architecture that is conscious of local climate (hunker down in the winter and open up in summer) and the environment (scale and relationship to other buildings and the natural environment).” Some of CoLab Architecture’s most prominent projects include: Urban Cottage, Christchurch, New Zealand The following statistics helped CoLab Architecture Ltd achieve 30th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 1 29. Paul Whittaker © Paul Whittaker Paul Whittaker is an architecture firm based in New Zealand. Its work revolves around residential architecture. Some of Paul Whittaker’s most prominent projects include: Whittaker Cube, Kakanui, New Zealand The following statistics helped Paul Whittaker achieve 29th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 1 28. Space Division © Simon Devitt Photographer Space Division is a boutique architectural practice that aims to positively impact the lives and environment of its clients and their communities by purposefully producing quality space. We believe our name reflects both the essence of what we do, but also how we strive to do it – succinctly and simply. Our design process is inclusive and client focused with their desires, physical constraints, budgets, time frames, compliance and construction processes all carefully considered and incorporated into our designs. Space Division has successfully applied this approach to a broad range of project types within the field of architecture, ranging from commercial developments, urban infrastructure to baches, playhouses and residential homes. Space Divisions team is committed to delivering a very personal and complete service to each of their clients, at each stage of the process. To assist in achieving this Space Division collaborates with a range of trusted technical specialists, based on the specific needs of our client. Which ensures we stay focussed, passionate agile and easily scalable. Some of Space Division’s most prominent projects include: Stradwick House, Auckland, New Zealand The following statistics helped Space Division achieve 28th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 1 27. Sumich Chaplin Architects © Sumich Chaplin Architects Sumich Chaplin Architects undertake to provide creative, enduring architectural design based on a clear understanding and interpretation of a client’s brief. We work with an appreciation and respect for the surrounding landscape and environment. Some of Sumich Chaplin Architects’ most prominent projects include: Millbrook House, Arrowtown, New Zealand The following statistics helped Sumich Chaplin Architects achieve 27th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 1 26. Daniel Marshall Architects © Simon Devitt Photographer Daniel Marshall Architects (DMA) is an Auckland based practice who are passionate about designing high quality and award winning New Zealand architecture. Our work has been published in periodicals and books internationally as well as numerous digital publications. Daniel leads a core team of four individually accomplished designers who skillfully collaborate to resolve architectural projects from their conception through to their occupation. DMA believe architecture is a ‘generalist’ profession which engages with all components of an architectural project; during conceptual design, documentation and construction phases.  We pride ourselves on being able to holistically engage with a complex of architectural issues to arrive at a design solution equally appropriate to its context (site and surrounds) and the unique ways our clients prefer to live. Some of Daniel Marshall Architects’ most prominent projects include: Lucerne, Auckland, New Zealand House in Herne Bay, Herne Bay, Auckland, New Zealand The following statistics helped Daniel Marshall Architects achieve 26th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 2 25. AW Architects © AW Architects Creative studio based in Christchurch, New Zealand. AW-ARCH is committed to an inclusive culture where everyone is encouraged to share their perspectives – our partners, our colleagues and our clients. Our team comes from all over the globe, bringing with them a variety of experiences. We embrace the differences that shape people’s lives, including race, ethnicity, identity and ability. We come together around the drawing board, the monitor, and the lunch table, immersed in the free exchange of ideas and synthesizing the diverse viewpoints of creative people, which stimulates innovative design and makes our work possible. Mentorship is key to engagement within AW-ARCH, energizing our studio and feeding invention. It’s our social and professional responsibility and helps us develop and retain a dedicated team. This includes offering internships that introduce young people to our profession, as well as supporting opportunities for our people outside the office — teaching, volunteering and exploring. Some of AW Architects’ most prominent projects include: OCEAN VIEW TERRACE HOUSE, Christchurch, New Zealand 212 CASHEL STREET, Christchurch, New Zealand LAKE HOUSE, Queenstown, New Zealand RIVER HOUSE, Christchurch, New Zealand HE PUNA TAIMOANA, Christchurch, New Zealand The following statistics helped AW Architects achieve 25th place in the 30 Best Architecture Firms in New Zealand: A+Awards Finalist 1 Total Projects 9 24. Archimedia © Patrick Reynolds Archimedia is a New Zealand architecture practice with NZRAB and green star accredited staff, offering design services in the disciplines of architecture, interiors and ecology. Delivering architecture involves intervention in both natural eco-systems and the built environment — the context within which human beings live their lives. Archimedia uses the word “ecology” to extend the concept of sustainability to urban design and master planning and integrates this holistic strategy into every project. Archimedia prioritizes client project requirements, functionality, operational efficiency, feasibility and programme. Some of Archimedia’s most prominent projects include: Te Oro, Auckland, New Zealand Auckland Art Gallery Toi o Tamaki, Auckland, New Zealand Hekerua Bay Residence, New Zealand Eye Institute , Remuera, Auckland, New Zealand University of Auckland Business School, Auckland, New Zealand The following statistics helped Archimedia achieve 24th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 1 Total Projects 25 23. MC Architecture Studio © MC Architecture Studio Ltd The studio’s work, questioning the boundary between art and architecture, provides engaging and innovative living space with the highest sustainability standard. Design solutions are tailored on client needs and site’s characteristics. Hence the final product will be unique and strongly related to the context and wider environment. On a specific-project basis, the studio, maintaining the leadership of the whole process, works in a network with local and international practices to achieve the best operational efficiency and local knowledge worldwide to accommodate the needs of a big scale project or specific requirements. Some of MC Architecture Studio’s most prominent projects include: Cass Bay House, Cass Bay, Lyttelton, New Zealand Ashburton Alteration, Ashburton, New Zealand restaurant/cafe, Ovindoli, Italy Private Residence, Christchurch, New Zealand Private Residence, Christchurch, New Zealand The following statistics helped MC Architecture Studio Ltd achieve 23rd place in the 30 Best Architecture Firms in New Zealand: Featured Projects 2 Total Projects 19 22. Architecture van Brandenburg © Architecture van Brandenburg Van Brandenburg is a design focused studio for architecture, landscape architecture, urbanism, and product design with studios in Queenstown and Dunedin, New Zealand. With global reach Van Brandenburg conducts themselves internationally, where the team of architects, designers and innovators create organic built form, inspired by nature, and captured by curvilinear design. Some of Architecture van Brandenburg’s most prominent projects include: Marisfrolg Fashion Campus, Shenzhen, China The following statistics helped Architecture van Brandenburg achieve 22nd place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 1 Featured Projects 1 Total Projects 1 21. MacKayCurtis © MacKayCurtis MacKay Curtis is a design led practice with a mission to create functional architecture of lasting beauty that enhances peoples lives. Some of MacKayCurtis’ most prominent projects include: Mawhitipana House, Auckland, New Zealand The following statistics helped MacKayCurtis achieve 21st place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 1 Featured Projects 1 Total Projects 1 20. Gerrad Hall Architects © Gerrad Hall Architects We aspire to create houses that are a joyful sensory experience. Some of Gerrad Hall Architects’ most prominent projects include: Inland House, Mangawhai, New Zealand Herne Bay Villa Alteration, Auckland, New Zealand The following statistics helped Gerrad Hall Architects achieve 20th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 2 Total Projects 2 19. Dorrington Atcheson Architects © Dorrington Atcheson Architects Dorrington Atcheson Architects was founded as Dorrington Architects & Associates was formed in 2010, resulting in a combined 20 years of experience in the New Zealand architectural market. We’re a boutique architecture firm working on a range of projects and budgets. We love our work, we pride ourselves on the work we do and we enjoy working with our clients to achieve a result that resolves their brief. The design process is a collaborative effort, working with the client, budget, site and brief, to find unique solutions that solve the project at hand. The style of our projects are determined by the site and the budget, with a leaning towards contemporary modernist design, utilizing a rich natural material palette, creating clean and tranquil spaces. Some of Dorrington Atcheson Architects’ most prominent projects include: Lynch Street Coopers Beach House, Coopers Beach, New Zealand Rutherford House, Tauranga Taupo, New Zealand Winsomere Cres Kathryn Wilson Shoebox, Auckland, New Zealand The following statistics helped Dorrington Atcheson Architects achieve 19th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 2 Total Projects 14 18. Andrew Barre Lab © Marcela Grassi Andrew Barrie Lab is an architectural practice that undertakes a diverse range of projects. We make buildings, books, maps, classes, exhibitions and research. Some of Andrew Barre Lab’s most prominent projects include: Learning from Trees, Venice, Italy The following statistics helped Andrew Barre Lab achieve 18th place in the 30 Best Architecture Firms in New Zealand: A+Awards Finalist 2 Featured Projects 1 Total Projects 1 17. Warren and Mahoney © Simon Devitt Photographer Warren and Mahoney is an insight led multidisciplinary architectural practice with six locations functioning as a single office. Our clients and projects span New Zealand, Australia and the Pacific Rim. The practice has over 190 people, comprising of specialists working across the disciplines of architecture, workplace, masterplanning, urban design and sustainable design. We draw from the wider group for skills and experience on every project, regardless of the location. Some of Warren and Mahoney’s most prominent projects include: MIT Manukau & Transport Interchange, Auckland, New Zealand Carlaw Park Student Accommodation, Auckland, New Zealand Pt Resolution Footbridge, Auckland, New Zealand Isaac Theatre Royal, Christchurch, New Zealand University of Auckland Recreation and Wellness Centre, Auckland, New Zealand The following statistics helped Warren and Mahoney achieve 17th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 2 Total Projects 5 16. South Architects Limited © South Architects Limited Led by Craig South, our friendly professional team is dedicated to crafting for uniqueness and producing carefully considered architecture that will endure and be loved. At South Architects, every project has a unique story. This story starts and ends with our clients, whose values and aspirations fundamentally empower and inspire our whole design process. Working together with our clients is pivotal to how we operate and we share a passion for innovation in design. We invite you to meet us and explore what we can do for you. As you will discover, our client focussed process is thorough, robust and responsive. We see architecture as the culmination of a journey with you. Some of South Architects Limited’s most prominent projects include: Three Gables, Christchurch, New Zealand Concrete Copper Home, Christchurch, New Zealand Driftwood Home, Christchurch, New Zealand Half Gable Townhouses, Christchurch, New Zealand Kilmore Street, Christchurch, New Zealand The following statistics helped South Architects Limited achieve 16th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 3 Total Projects 6 15. Pac Studio © Pac Studio Pac Studio is an ideas-driven design office, committed to intellectual and artistic rigor and fueled by a strong commitment to realizing ideas in the world. We believe a thoughtful and inclusive approach to design, which puts people at the heart of any potential solution, is the key to compelling and positive architecture. Through our relationships with inter-related disciplines — furniture, art, landscape and academia — we can create a whole that is greater than the sum of its parts. We are open to unconventional propositions. We are architects and designers with substantial experience delivering highly awarded architectural projects on multiple scales. Some of Pac Studio’s most prominent projects include: Space Invader, Auckland, New Zealand Split House, Auckland, New Zealand Yolk House, Auckland, New Zealand Wanaka Crib, Wanaka, New Zealand Pahi House, Pahi, New Zealand The following statistics helped Pac Studio achieve 15th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 3 Total Projects 8 14. Jasmax © Jasmax Jasmax is one of New Zealand’s largest and longest established architecture and design practices. With over 250 staff nationwide, the practice has delivered some of the country’s most well known projects, from the Museum of New Zealand Te Papa Tongarewa to major infrastructure and masterplanning projects such as Auckland’s Britomart Station. From our four regional offices, the practice works with clients, stakeholders and communities across the following sectors: commercial, cultural and civic, education, infrastructure, health, hospitality, retail, residential, sports and recreation, and urban design. Environmentally sustainable design is part of everything we do, and we were proud to work with Ngāi Tūhoe to design one of New Zealand’s most advanced sustainable buildings, Te Uru Taumatua; which has been designed to the stringent criteria of the International Living Future Institute’s Living Building Challenge. Some of Jasmax’s most prominent projects include: The Surf Club at Muriwai, Muriwai, New Zealand Auckland University Mana Hauora Building, Auckland, New Zealand The Fonterra Centre, Auckland, New Zealand Auckland University of Technology Sir Paul Reeves Building , Auckland, New Zealand NZI Centre, Auckland, New Zealand The following statistics helped Jasmax achieve 14th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 3 Total Projects 21 13. Condon Scott Architects © Condon Scott Architects Condon Scott Architects is a boutique, award-winning NZIA registered architectural practice based in Wānaka, New Zealand. Since inception 35 years ago, Condon Scott Architects has been involved in a wide range of high end residential and commercial architectural projects throughout Queenstown, Wānaka, the Central Otago region and further afield. Director Barry Condon (ANZIA) and principal Sarah Scott (FNZIA) – both registered architects – work alongside a highly skilled architectural team to deliver a full design and construction management service. This spans from initial concept design right through to tender management and interior design. Condon Scott Architect’s approach is to view each commission as a bespoke and site specific project, capitalizing on the unique environmental conditions and natural surroundings that are so often evident in this beautiful part of the world. Some of Condon Scott Architects’ most prominent projects include: Sugi House, Wānaka, New Zealand Wanaka Catholic Church, Wanaka, New Zealand Mount Iron Barn, Wanaka, New Zealand Bendigo Terrace House, New Zealand Bargour Residence, Wanaka, New Zealand The following statistics helped Condon Scott Architects achieve 13th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 4 Total Projects 17 12. Glamuzina Paterson Architects © Glamuzina Paterson Architects Glamuzina Architects is an Auckland based practice established in 2014. We strive to produce architecture that is crafted, contextual and clever. Rather than seeking a particular outcome we value a design process that is rigorous and collaborative. When designing we look to the context of a project beyond just its immediate physical location to the social, political, historical and economic conditions of place. This results in architecture that is uniquely tailored to the context it sits within. We work on many different types of projects across a range of scales; from small interiors to large public buildings. Regardless of a project’s budget we always prefer to work smart, using a creative mix of materials, light and volume in preference to elaborate finishes or complex detailing. Some of Glamuzina Paterson Architects’ most prominent projects include: Lake Hawea Courtyard House, Otago, New Zealand Blackpool House, Auckland, New Zealand Brick Bay House, Auckland, New Zealand Giraffe House, Auckland, New Zealand Giraffe House, Auckland, New Zealand The following statistics helped Glamuzina Paterson Architects achieve 12th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 4 Total Projects 5 11. Cheshire Architects © Patrick Reynolds Cheshire Architects does special projects, irrespective of discipline, scale or type. The firm moves fluidly from luxury retreat to city master plan to basement cocktail den, shaping every aspect of an environment in pursuit of the extraordinary. Some of Cheshire Architects’ most prominent projects include: Rore kahu, Te Tii, New Zealand Eyrie, New Zealand Milse, Takanini, New Zealand The following statistics helped Cheshire Architects achieve 11th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 3 Total Projects 3 10. Patterson Associates © Patterson Associates Pattersons Associates Architects began its creative story with architect Andrew Patterson in 1986 whose early work on New Zealand’s unspoiled coasts, explores relationships between people and landscape to create a sense of belonging. The architecture studio started based on a very simple idea; if a building can feel like it naturally ‘belongs,’ or fits logically in a place, to an environment, a time and culture, then the people that inhabit the building will likely feel a sense of belonging there as well. This methodology connects theories of beauty, confidence, economy and comfort. In 2004 Davor Popadich and Andrew Mitchell joined the firm as directors, taking it to another level of creative exploration and helping it grow into an architecture studio with an international reputation. Some of Patterson Associates’ most prominent projects include: Seascape Retreat, Canterbury, New Zealand The Len Lye Centre, New Plymouth, New Zealand Country House in the City, Auckland, New Zealand Scrubby Bay House, Canterbury, New Zealand Parihoa House, Auckland, New Zealand The following statistics helped Patterson Associates achieve 10th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 5 Total Projects 5 9. Team Green Architects © Team Green Architects Established in 2013 by Sian Taylor and Mark Read, Team Green Architects is a young committed practice focused on designing energy efficient buildings. Some of Team Green Architects’ most prominent projects include: Dalefield Guest House, Queenstown, New Zealand Olive Grove House, Cromwell, New Zealand Hawthorn House, Queenstown, New Zealand Frankton House, Queenstown, New Zealand Contemporary Sleepout, Arthurs Point, New Zealand The following statistics helped Team Green Architects achieve 9th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 5 Total Projects 7 8. Creative Arch © Creative Arch Creative Arch is an award-winning, multi-disciplined architectural design practice, founded in 1998 by architectural designer and director Mark McLeay. The range of work at Creative Arch is as diverse as our clients, encompassing residential homes, alterations and renovations, coastal developments, sub-division developments, to commercial projects. The team at Creative Arch are an enthusiastic group of talented professional architects and architectural designers, with a depth of experience, from a range of different backgrounds and cultures. Creative Arch is a client-focused firm committed to providing excellence in service, culture and project outcomes. Some of Creative Arch’s most prominent projects include: Rothesay Bay House, North Shore, New Zealand Best Pacific Institute of Education, Auckland, New Zealand Sumar Holiday Home, Whangapoua, New Zealand Cook Holiday Home, Omaha, New Zealand Arkles Bay Residence, Whangaparaoa, New Zealand The following statistics helped Creative Arch achieve 8th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 5 Total Projects 18 7. Crosson Architects © Crosson Architects At Crosson Architects we are constantly striving to understand what is motivating the world around us. Some of Crosson Architects’ most prominent projects include: Hut on Sleds, Whangapoua, New Zealand Te Pae North Piha Surf Lifesaving Tower, Auckland, New Zealand Coromandel Bach, Coromandel, New Zealand Tutukaka House, Tutukaka, New Zealand St Heliers House, Saint Heliers, Auckland, New Zealand The following statistics helped Crosson Architects achieve 7th place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 1 A+Awards Finalist 2 Featured Projects 4 Total Projects 6 6. Bossley Architects © Bossley Architects Bossley Architects is an architectural and interior design practice with the express purpose of providing intense input into a deliberately limited number of projects. The practice is based on the belief that innovative yet practical design is essential for the production of good buildings, and that the best buildings spring from an open and enthusiastic collaboration between architect, client and consultants. We have designed a wide range of projects including commercial, institutional and residential, and have amassed special expertise in the field of art galleries and museums, residential and the restaurant/entertainment sector. Whilst being very much design focused, the practice has an overriding interest in the pragmatics and feasibility of construction. Some of Bossley Architects’ most prominent projects include: Ngā Hau Māngere -Old Māngere Bridge Replacement, Auckland, New Zealand Arruba, Waiuku, New Zealand Brown Vujcich House Voyager NZ Maritime Museum Omana Luxury Villas, Auckland, New Zealand The following statistics helped Bossley Architects achieve 6th place in the 30 Best Architecture Firms in New Zealand: Featured Projects 6 Total Projects 21 5. Smith Architects © Simon Devitt Photographer Smith Architects is an award-winning international architectural practice creating beautiful human spaces that are unique, innovative and sustainable through creativity, refinement and care. Phil and Tiffany Smith established the practice in 2007. We have spent more than two decades striving to understand what makes some buildings more attractive than others, in the anticipation that it can help us design better buildings. Some of Smith Architects’ most prominent projects include: Kakapo Creek Children’s Garden, Mairangi Bay, Auckland, New Zealand New Shoots Children’s Centre, Kerikeri, Kerikeri, New Zealand Gaia (Earth) Forest Preschool, Manurewa, Auckland, New Zealand Chrysalis Childcare, Auckland, New Zealand House of Wonder, Cambridge, Cambridge, New Zealand The following statistics helped Smith Architects achieve 5th place in the 30 Best Architecture Firms in New Zealand: A+Awards Finalist 1 Featured Projects 6 Total Projects 23 4. Monk Mackenzie © Monk Mackenzie Monk Mackenzie is an architecture and design firm based in New Zealand. Monk Mackenzie’s design portfolio includes a variety of architectural projects, such as transport and infrastructure, hospitality and sport, residential, cultural and more. Some of Monk Mackenzie’s most prominent projects include: X HOUSE, Queenstown, New Zealand TURANGANUI BRIDGE, Gisborne, New Zealand VIVEKANANDA BRIDGE EDITION Canada Street Bridge, Auckland, New Zealand The following statistics helped Monk Mackenzie achieve 4th place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 2 A+Awards Finalist 4 Featured Projects 4 Total Projects 17 3. Irving Smith Architects © Irving Smith Architects Irving Smith Jack (ISJ) has been developed as a niche architecture practice based in Nelson, but working in a variety of sensitive environments and contexts throughout New Zealand. ISJ demonstrates an ongoing commitment to innovative, sustainable and researched based design , backed up by national and international award and publication recognition, ongoing research with both the Universities of Canterbury and Auckland, and regular invitations to lecture on their work. Timber Awards include NZ’s highest residential, commercial and engineering timber designs. Key experience, ongoing research and work includes developing structural timber design solutions in the aftermath of the Canterbury earthquakes. Current projects include cultural, urban, civic and residential projects spread throughout New Zealand, and recently in the United States and France. Some of Irving Smith Architects’ most prominent projects include: SCION Innovation Hub – Te Whare Nui o Tuteata, Rotorua, New Zealand Mountain Range House, Brightwater, New Zealand Alexandra Tent House, Wellington, New Zealand Te Koputu a te Whanga a Toi : Whakatane Library & Exhibition Centre, Whakatane, New Zealand offSET Shed House, Gisborne, New Zealand The following statistics helped Irving Smith Architects achieve 3rd place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 2 A+Awards Finalist 1 Featured Projects 6 Total Projects 13 2. Fearon Hay Architects © Fearon Hay Architects Fearon Hay is a design-led studio undertaking a broad range of projects in diverse environments, the firm is engaged in projects on sites around the world. Tim Hay and Jeff Fearon founded the practice in 1993 as a way to enable their combined involvement in the design and delivery of each project. Together, they lead an international team of experienced professionals. The studio approached every project with a commitment to design excellence, a thoughtful consideration of site and place, and an inventive sense of creativity. Fearon Hay enjoys responding to a range of briefs: Commercial projects for office and workplace, complex heritage environments, public work within the urban realm or wider landscape, private dwellings and detailed bespoke work for hospitality and interior environments. Some of Fearon Hay Architects’ most prominent projects include: Bishop Hill The Camp, Tawharanui Peninsula, New Zealand Matagouri, Queenstown, New Zealand Alpine Terrace House, Queenstown, New Zealand Island Retreat, Auckland, New Zealand Bishop Selwyn Chapel, Auckland, New Zealand The following statistics helped Fearon Hay Architects achieve 2nd place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 2 A+Awards Finalist 3 Featured Projects 8 Total Projects 17 1. RTA Studio © RTA Studio Richard Naish founded RTA Studio in 1999 after a successful career with top practices in London and Auckland. We are a practice that focuses on delivering exceptional design with a considered and personal service. Our work aims to make a lasting contribution to the urban and natural context by challenging, provoking and delighting. Our studio is constantly working within the realms of public, commercial and urban design as well as sensitive residential projects. We are committed to a sustainable built environment and are at the forefront developing carbon neutral buildings. RTA Studio has received more than 100 New Zealand and international awards, including Home of The Year, a World Architecture Festival category win and the New Zealand Architecture Medal. Some of RTA Studio’s most prominent projects include: SCION Innovation Hub – Te Whare Nui o Tuteata, Rotorua, New Zealand OBJECTSPACE, Auckland, New Zealand C3 House, New Zealand Freemans Bay School, Freemans Bay, Auckland, New Zealand ARROWTOWN HOUSE, Arrowtown, New Zealand Featured image: E-Type House by RTA Studio, Auckland, New Zealand The following statistics helped RTA Studio achieve 1st place in the 30 Best Architecture Firms in New Zealand: A+Awards Winner 2 A+Awards Finalist 6 Featured Projects 6 Total Projects 16 Why Should I Trust Architizer’s Ranking? With more than 30,000 architecture firms and over 130,000 projects within its database, Architizer is proud to host the world’s largest online community of architects and building product manufacturers. Its celebrated A+Awards program is also the largest celebration of architecture and building products, with more than 400 jurors and hundreds of thousands of public votes helping to recognize the world’s best architecture each year. Architizer also powers firm directories for a number of AIA (American Institute of Architects) Chapters nationwide, including the official directory of architecture firms for AIA New York. An example of a project page on Architizer with Project Award Badges highlighted A Guide to Project Awards The blue “+” badge denotes that a project has won a prestigious A+Award as described above. Hovering over the badge reveals details of the award, including award category, year, and whether the project won the jury or popular choice award. The orange Project of the Day and yellow Featured Project badges are awarded by Architizer’s Editorial team, and are selected based on a number of factors. The following factors increase a project’s likelihood of being featured or awarded Project of the Day status: Project completed within the last 3 years A well written, concise project description of at least 3 paragraphs Architectural design with a high level of both functional and aesthetic value High quality, in focus photographs At least 8 photographs of both the interior and exterior of the building Inclusion of architectural drawings and renderings Inclusion of construction photographs There are 7 Projects of the Day each week and a further 31 Featured Projects. Each Project of the Day is published on Facebook, Twitter and Instagram Stories, while each Featured Project is published on Facebook. Each Project of the Day also features in Architizer’s Weekly Projects Newsletter and shared with 170,000 subscribers.     We’re constantly look for the world’s best architects to join our community. If you would like to understand more about this ranking list and learn how your firm can achieve a presence on it, please don’t hesitate to reach out to us at editorial@architizer.com. The post 30 Best Architecture and Design Firms in New Zealand appeared first on Journal.