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With upgraded authorization, soon federal agencies with security requirements at every level will be able to use MongoDB to deploy, run, and scale modern applications in the cloud
NEW YORK, June 30, 2025 /PRNewswire/ – MongoDB, Inc. (NASDAQ:MDB) today announced its commitment to pursuing Federal Risk and Authorization Management Program (FedRAMP) High and Impact Level 5 (IL5) authorizations for MongoDB Atlas for Government workloads, which will expand its eligibility to manage unclassified, yet highly sensitive, U.S. public sector data. With FedRAMP High authorization, even the most critical government agencies looking to adopt cloud and AI technologies—and to modernize aging, inefficient legacy databases—can rely on MongoDB Atlas for Government for secure, fully managed workloads.
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The database market is undergoing significant changes, driven by increasing demands for scale, resilience, and the burgeoning era of AI agents.
Speaking exclusively to AIM, CockroachDB CEO Spencer Kimball stated that the shift towards distributed SQL databases built on a solid PostgreSQL foundation is becoming increasingly crucial for businesses of all sizes, not just tech giants.
The core difference offered by CockroachDB lies in its horizontal scaling capabilities. While it strives to maintain a PostgreSQL-like interface, distributed operations require a different approach.
“Cockroach didn’t reject Postgres. It re-architected it from the ground up to meet the scale, distribution, and the consistency AI demands,” Kimball said.
He further added that scaling 100x on a monolithic architecture is utterly impossible. This, he explained, is where distributed SQL databases like CockroachDB come in, built for “serious scale, like hundreds of terabytes into petabytes” of operational data. “Postgres may be eating the world, but AI needs a database that can digest.”
Kimball said that he is particularly referring to operational databases and not the analytical ones. “It’s about the metadata that tracks the product or service, all the activity, and the high level of concurrent operations that demand strong consistency,” he added.
He explained that both humans and agents would have access to the data. These agents operate at high speed and are continuously active, performing the same tasks multiple times daily or even hourly. They work on behalf of both consumers and businesses, resulting in a steadily increasing volume of traffic.
What’s Next from CockRoach
Kimball sees AI playing a role in observability and support. “AI can move much faster. If you give it the right scenarios and train it, then what could have taken several hours to fix might only take several minutes,” he said.
Vector indexing is another area of focus for CockroachDB. “Customers want nearest-neighbour search in high-dimensional spaces at scale. They want it fast and consistent, even as data changes,” Kimball said.
But he clarified that CockroachDB is not trying to become a general-purpose vector database. Cockroach isn’t trying to compete with OpenSearch, Elastic, or MongoDB on vector search. “If you’re already using CockroachDB for mission-critical relational workloads, you want vector support there. Not everyone needs that, but for our users, it’s essential.”
He further added that they are not trying to win the market for the vector index. “We’re not a vector database. However, it’s a very important modality.”
Moreover, Kimball talked about reducing costs. “Nobody wants to pay 10x more because their workload scales 10x. CockroachDB can improve utilisation with multi-tenancy.” He explained that if a customer has 100 use cases on a large cluster, the peaks and troughs average out, allowing them to move from 10% to 50-60% utilisation.
The company is also working on using cloud cost efficiencies. Kimball said CockroachDB’s architecture allows the use of spot instances, disaggregated storage, and storage tiering. “We believe we can reduce costs by 10 to 16x in the next few years.”
Moat of Cockroach
Kimball said that CockroachDB’s strength is in geographic scale. “We have customers in the EU, the US, and India. If you want to make your service span all of those places, Cockroach has some really interesting capabilities that are different.”
He provided one example from the US sports betting sector. “Customers use Cockroach nodes in multiple states to comply with data locality laws. Data is processed where bets are placed.”
Moreover, he added that CockroachDB is cloud-agnostic and supports hybrid deployments. “Big banks and tech companies use private data centres and all three major clouds. We let customers run the database wherever their business needs it.”
One key challenge, he pointed out, is integrating AI into database operations. “It’s not easy to run distributed systems. When something goes wrong, you want the root cause before a human even looks at it. AI can help.”
On competing with cloud vendors, he noted, “They’re both competitors and partners. Big clouds don’t want to serve self-hosted enterprise customers, and those customers don’t want to be tied to one cloud. CockroachDB fits well there.”
He added that clouds often refer such customers to CockroachDB. “They say, ‘We can’t run this in your data centre, but CockroachDB can.’ That’s why the partnership works.”
As the era of AI agents increases data scale and complexity, CockroachDB is positioning itself to meet those demands through distributed design, cross-cloud flexibility, and AI-enhanced tooling.
Why Postgres
Kimball explained how CockroachDB tries to stay close to the Postgres experience but adapts key behaviours to function at scale in distributed environments.“So well, it tries to look as much like Postgres as possible.”
One clear example was ID generation. Traditional Postgres allows for monotonically increasing sequences, such as auto-incrementing IDs for user records. In monolithic systems, this works smoothly, but things break down at a massive scale.
“In a monolithic system… that counter, it’s all just in one place… But once you say, I want to do 10 million of these concurrently… you don’t want them all going to one node that holds a counter.”
CockroachDB distributes the sequence generation process differently, making it scale-friendly but less linear. “It will look the same as a sequence. But… we have a more distributed mechanism to assign IDs… they’re not just counting 1,2,3,4,5.”
He acknowledged differences between Postgres and MySQL users as well. “Postgres does structured data, too. There’s room for both.
Kimball said that the bigger challenge lies in how the databases are operated, not how they are used by applications. He said that system administrators and DBAs familiar with one will have a steeper learning curve when switching to the other, due to differences in tools, management styles, and best practices.
“If you’re very good as a system administrator or like a DBA using Postgres, then it’s a lot more new stuff to learn.
Kimball said that it often comes down to what teams are already used to operating. “If you’re good at MySQL, moving to distributed MySQL, then TiDB makes sense.” He was referring to TiDB CTO Ed Huang, who said that he believes MySQL will power AI agents.
Journey of the Cockroach
Cockroach Labs was founded in 2015 by ex-Google employees Kimball, Peter Mattis, and Ben Darnell. It draws inspiration from Google’s Bigtable and Spanner databases.
Kimball said that in the early 2000s, systems like Google’s Bigtable avoided SQL not out of dislike, but to keep things simple while focusing on scalability. “It was just easier not to have to do all that stuff and also build something that is elastically scalable and more survivable.”
However, over time, the industry began adding SQL features again. MongoDB added transactions. Google layered SQL on top of Spanner with F1.
“They created a whole new distributed architecture, but they left all of the hard stuff and started adding the hard stuff back on top of it,” said Kimball.
He added that NoSQL systems, such as Cassandra, offer flexibility and scalability but fall short in terms of consistency and schema management. “If you have 50 people working on a complex, mission-critical product… it just becomes impossible.”
By 2015 the CockroachDB team had a clear understanding of their target users which included big banks, major tech firms and other high-stakes organisations.
Instead of building a new SQL dialect, they chose PostgreSQL. “Postgres felt like the cleanest and the most appropriate, and had the most upward velocity momentum.”
MongoDB Announces Commitment to Achieve FedRAMP High and Impact Level 5 Authorizations

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With upgraded authorization, soon federal agencies with security requirements at every level will be able to use MongoDB to deploy, run, and scale modern applications in the cloud
NEW YORK, June 30, 2025 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today announced its commitment to pursuing Federal Risk and Authorization Management Program (FedRAMP) High and Impact Level 5 (IL5) authorizations for MongoDB Atlas for Government workloads, which will expand its eligibility to manage unclassified, yet highly sensitive, U.S. public sector data. With FedRAMP High authorization, even the most critical government agencies looking to adopt cloud and AI technologies—and to modernize aging, inefficient legacy databases—can rely on MongoDB Atlas for Government for secure, fully managed workloads.
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Presented by MongoDB
Technical debt has long been the scourge of IT departments, but today it’s accumulating faster than ever. High-powered computing, technology innovations like AI and speed to market all require modern, scalable solutions. Unfortunately, many businesses are pressing forward with outdated systems and legacy applications, operating under the misconception that addressing technical debt just slows them down, because it demands time and budget the organization thinks it can’t afford to spend. But in today’s landscape, what organizations really can’t afford are the huge hidden costs of legacy applications, which all directly impact performance, security, and innovation.
“Modernization isn’t just about catching up — it’s about building a future-ready foundation for innovation,” says Paul Done, field CTO, modernization at MongoDB. “The true cost of the status quo isn’t just inefficiency — it’s missed opportunities when the market demands agility, making developers use their valuable time to keep legacy architecture running, versus positioning the company for AI with modern infrastructure and applications.”
The mounting costs of technical debt
IT leaders are well aware of the concrete costs that legacy systems entail. For example, the IT team at a top bank reached out to MongoDB when they discovered that out of their team’s $16 million IT budget, $15 million was being spent just on maintaining legacy architecture. That left the bank with only $1 million for innovation.
There are also hidden costs that directly impact performance, security, and innovation. Not all infrastructures are built for the modern, transformative applications that are vital in today’s competitive market. Furthermore, developer productivity is hampered by technology built on outdated code, which makes it difficult for developers to maintain and implement new features, and also lacks the scalability and resilience required to support modern user demands and development practices.
Plus, these systems make organizations significantly more vulnerable to threats because outdated, brittle architecture can be difficult to update or secure. Some companies lack the necessary institutional knowledge or visibility into the underlying legacy code, which also increases vulnerability. And a lot of these systems are simply not compliant, or no longer supported, increasing the inherent new risks that AI and other modern applications can add to a technology stack. Innovation is completely hamstrung, unless businesses address these potential security gaps.
“To overcome these challenges and come up to speed in a fast-paced world, organizations need to adopt flexible, high-performance data platforms,” Done says. “By doing so, they’ll reduce infrastructure complexity and maintenance overhead. Modern databases also help organizations improve security with encryption, compliance tools, and automated updates, and architecture designed for high-performance applications helps them scale. All this accelerates AI adoption by enabling real-time, high-quality data access.”
Assessing the extent and impact of architectural limitations
At a high level, determining when it’s time to modernize is about quantifying cost, risk, and complexity. In dollar terms, it may seem as simple as comparing the expense of maintaining legacy systems versus investing in new architecture. But the true calculation includes hidden costs, like the developer hours lost to patching outdated systems, and the opportunity cost of not being able to adapt quickly to business needs.
True modernization is not a lift-and-shift — it’s a full-stack transformation. That means breaking apart monolithic applications into scalable microservices, rewriting outdated application code into modern languages, and replacing rigid relational data models with flexible, cloud-native platforms that support real-time data access, global scalability, and developer agility.
Many organizations have partnered with MongoDB to achieve this kind of transformation. For example, to ensure they didn’t give up any of their performance, storage capacity or support benefits, Indeed tapped MongoDB to streamline their infrastructure efficiency. In just six months they reduced total costs by 27% — far exceeding the company’s initial goals for its modernization initiative.
Security must also be factored in, assessing how much risk legacy systems add to the organization’s overall security posture. And from an operations and innovation perspective, it’s critical to account for future-forward objectives and overall goals. That’s why Bendigo Bank worked with MongoDB to modernize its core banking technology, leveraging generative AI to modernize the bank’s legacy Agent Delivery System (a retail teller operation) in less than three months. The bank was eager to enable its developers to focus on more meaningful innovation so the bank could remain agile in a fast-moving market.
Overall, Bendigo Bank migrated onto MongoDB Atlas at one-tenth of the cost of a traditional legacy-to-cloud migration. Plus, MongoDB solutions helped reduce the development time required to migrate a core banking application off of a legacy relational database to MongoDB Atlas by up to 70%. With new AI tooling, they automated repetitive developer tasks to accelerate developers’ pace of innovation. For example, AI-powered automations reduced time spent running application test cases from over 80 hours to just five minutes.
But modernization projects are usually a balancing act, and replacing everything at once can be a gargantuan task. Choosing how to tackle the problem comes down to priorities, determining where pain points exist and where the biggest impacts to the business will be. The cost of doing nothing will outrank the cost of doing something.
For instance, Toyota Connected recently experienced reliability issues with the legacy database solution underlying the telemetry-based technology that powers connectivity solutions like Safety Connect in more than 9 million Toyota and Lexus vehicles in North America. The company decided to migrate to Amazon Web Services (AWS) and MongoDB Atlas, an integrated suite of data services centered around a cloud database designed to accelerate and simplify building with data. Safety Connect has attained 99.99% availability and the company aims for that number monthly, according to Toyota Connected’s internal measurements.
“We’re usually going in and tackling some of a company’s biggest, ugliest applications,” Done says. “How you design a solution in this AI era is about finding that right partner who can help evolve not only your applications, but your supporting database to consolidate workloads, reduce complexity, and adapt in a rapid, agile way.”
How modern database solutions enable AI-driven workloads
AI is often a game-changing catalyst — once technical debt is eliminated, a company can embrace all the potential it offers. In order to react instantly and make real-time decisions in things like dynamic pricing, fraud detection, adaptive user experiences, and more, AI solutions depend on fluid, instantly-accessible data. Modern databases can make this happen by consolidating structured and unstructured data to help organizations scale without constraints, and to adapt to AI workloads, massive data volumes, low latency operations, and meet the demands of any AI workload while protecting sensitive information both at rest and in motion.
While modernization is often seen as complex and time-consuming, MongoDB has helped speed up and simplify the modernization process in a repeatable manner for many organizations, providing full-stack modernization at both the data and application layer tailored to a company’s specific architecture. The company’s seamless data model and distributed architecture are built to manage data at scale as new technologies emerge, making it the perfect foundation for AI-powered applications. These solutions make developers at least 50% more productive, with some customers seeing productivity gains as high as 70%, Done says.
For Lombard Odier, a gen AI-assisted modernization initiative with MongoDB enabled the bank to migrate code three times faster than previous migrations; move applications from legacy relational databases to MongoDB twenty times faster; and automate repetitive tasks with AI tooling to accelerate the pace of innovation, reducing project times from days to hours.
The bank’s largest application, PMS (which has thousands of users) manages shares, bonds, exchange-traded funds, and other financial instruments. MongoDB’s ability to scale was key to this system migration, as this system is used to monitor investments, make investment decisions, and generate portfolio statements.
“MongoDB’s AI-powered software-driven approach fully modernizes data and applications at scale in a simplified way,” he explains. “We deliver high-impact results in a short timeframe. We’ve got more than 17 years of experience creating best practices and modern, data-driven applications, so we’re uniquely positioned to understand the ideal end-state of applications for modernization and how to achieve it.”
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- MongoDB (MDB, Financial) is pursuing FedRAMP High and Impact Level 5 (IL5) authorizations.
- Currently trusted by 13 U.S. Federal Cabinet-level agencies and all Department of Defense branches.
- State of Utah implemented MongoDB Atlas for a 25% increase in benefits processing speed.
MongoDB, Inc. (MDB), a leading name in database solutions, has announced its commitment to achieve Federal Risk and Authorization Management Program (FedRAMP) High and Impact Level 5 (IL5) authorizations for its product, MongoDB Atlas for Government. This strategic move will enable MongoDB to manage highly sensitive, unclassified data for the U.S. public sector, expanding its market reach within federal agencies that demand stringent security measures.
The MongoDB Atlas for Government platform, operating currently at a FedRAMP Moderate level, is already a trusted solution for 13 U.S. Federal Cabinet-level agencies, including every branch of the Department of Defense. The platform employs MongoDB’s proprietary Queryable Encryption technology to ensure data protection throughout its lifecycle, which is increasingly critical for federal clients.
Mongodb’s Queryable Encryption is a first-of-its-kind in-use encryption technology, designed to maintain the privacy and security of sensitive data during its entire lifecycle—whether in transit, at rest, or in use. Furthermore, decryption happens only client-side, ensuring additional layers of data security.
A notable success story is the State of Utah, which utilized MongoDB Atlas for Government to manage state benefit eligibility data. The state reported a 25% boost in the speed of benefits calculations, drastically reduced management times, and an improved disaster recovery time, from 58 hours to just 5 minutes.
This move to secure FedRAMP High and IL5 authorizations aligns with MongoDB’s ambition to support critical government sectors such as emergency services, law enforcement, financial, and health systems. Achieving these authorizations will allow MongoDB to serve federal agencies managing the most sensitive unclassified data, positioning it as a formidable competitor for high-value, long-term government contracts.
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MongoDB Announces Commitment to Achieve FedRAMP High and Impact Level 5 Authorizations

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With upgraded authorization, soon federal agencies with security requirements at every level will be able to use MongoDB to deploy, run, and scale modern applications in the cloud
NEW YORK, June 30, 2025 /PRNewswire/ — MongoDB, Inc. (NASDAQ: MDB) today announced its commitment to pursuing Federal Risk and Authorization Management Program (FedRAMP) High and Impact Level 5 (IL5) authorizations for MongoDB Atlas for Government workloads, which will expand its eligibility to manage unclassified, yet highly sensitive, U.S. public sector data. With FedRAMP High authorization, even the most critical government agencies looking to adopt cloud and AI technologies—and to modernize aging, inefficient legacy databases—can rely on MongoDB Atlas for Government for secure, fully managed workloads.
MongoDB Atlas for Government already provides a flexible way for the U.S. public sector to deploy, run, and scale modern applications in the cloud within a dedicated environment built for FedRAMP Moderate workloads. Achieving FedRAMP High and IL5 will allow MongoDB Atlas for Government’s secure, reliable, and high-performance modern database solutions to be used to manage high-impact data, such as in emergency services, law enforcement systems, financial systems, health systems, and any other system where loss of confidentiality, integrity, or availability could have a severe or catastrophic adverse effect on organizational operations, organizational assets, or individuals.
“The federal agencies that manage highly sensitive data involving the protection of life and financial ruin should be using the latest, fastest, and best database technology available,” said Benjamin Cefalo, Senior Vice President of Product Management at MongoDB. “MongoDB is trusted by 13 U.S. Federal Cabinet-level agencies, every branch of the Department of Defense, and a wide range of Intelligence Community partners. Agencies such as the National Oceanic and Atmospheric Administration (NOAA), the Food and Drug Administration (FDA), and the U.S. Department of Health and Human Services (HHS) are building applications powered by Atlas for Government to solve their most challenging data requirements. With FedRAMP High and IL5 authorizations for MongoDB Atlas for Government workloads, they will be able to take advantage of MongoDB’s industry-leading and proprietary Queryable Encryption, multi-cloud flexibility and resilience, high availability with automated backup, data recovery options, and on-demand scaling, and native vector search to facilitate building AI applications.”
MongoDB Atlas for Government already helps hundreds of public sector agencies nationwide develop secure, modern, and scalable solutions. An integral feature of MongoDB Atlas for Government is MongoDB Queryable Encryption. This industry-first, in-use encryption technology enables organizations to encrypt sensitive data that helps organizations protect sensitive data when it is queried and in use on Atlas for Government. With Queryable Encryption, sensitive data remains protected throughout its lifecycle, whether it is in-transit, at-rest, in-use, and in logs and backups. It is only ever decrypted on the client-side.
For example, the State of Utah, which has one of the nation’s fastest-growing populations, chose MongoDB Atlas for Government to store its state benefit eligibility data. To meet a statewide mandate set by the governor of Utah, the State of Utah’s Department of Technology Services needed to migrate its eligibility software out of its physical data center to a FedRAMP-compliant cloud solution. The state government administration recognized it needed a backend database that could reliably handle large documents and deliver results quickly, which led it to identify MongoDB Atlas for Government as an ideal solution. The migration resulted in a 25% increase in speed of benefits calculations and document returns, reduced management time, and a 5-minute point-in-time recovery, compared to a recovery time of up to 58 hours when running on premises.
“It’s much less cumbersome to maintain our databases now that we’re using the fully managed MongoDB Atlas for Government. We tried some other solutions, but they could not match MongoDB,” said Manoj Gangwar, Principal Data Architect at the Department of Technology Services for the State of Utah.
For more information about MongoDB Atlas for Government, visit https://www.mongodb.com/products/platform/atlas-for-government.
About MongoDB
Headquartered in New York, MongoDB’s mission is to empower innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform was built to power the next generation of applications, and MongoDB is the most widely available, globally distributed database on the market. With integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, and simplify complex architectures. Millions of developers and more than 50,000 customers across almost every industry—including 70% of the Fortune 100—rely on MongoDB for their most important applications. To learn more, visit mongodb.com.
Forward-looking Statements
This press release includes certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning MongoDB’s intent to achieve FedRAMP High and Impact Level 5 Authorizations. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “project,” “will,” “would” or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: our customers renewing their subscriptions with us and expanding their usage of software and related services; global political changes; the effects of the ongoing military conflicts between Russia and Ukraine and Israel and Hamas on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; liabilities, reputational harm or other adverse consequences resulting from use of AI in our product offerings and internal operations if they don’t produce the desired benefits; our limited operating history; our history of losses; our potential failure to repurchase shares of our common stock at favorable prices, if at all; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; social, ethical and security issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the effects of social, ethical and regulatory issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (“SEC”), including under the caption “Risk Factors” in our Quarterly Report on Form 10-Q for the quarter ended April 30, 2025, filed with the SEC on June 4, 2025 and other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.
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With several hundred tools and technologies available, developers want the experience to be as streamlined as possible.
Speed, feedback, and fewer tabs—that’s what modern developers really want. At the MongoDB.local event in Bengaluru, the company pitched a vision that meets all three: no more hopping between terminals, GUIs, and browser tabs. Instead, MongoDB is embedding itself right into developer workflows with local-first CLI tools, native IDE integrations, and AI copilots that know their schemas.
MongoDB aims to own the development loop, from test to deployment, and from local development to production-scale AI. It is bundling database power with developer familiarity, resulting in a smoother, faster path to building intelligent apps.
With all the developments the company has mentioned, MongoDB is trying to evolve into much more than a traditional NoSQL database.
Atlas on Your Laptop
At the heart of this revamp is the MongoDB Atlas CLI, a command-line interface for the MongoDB Atlas platform, which now provides a comprehensive developer stack for local machines. Developers can run not only the core database, but also full-text search and AI workloads without requiring cloud access.
Boris Bialek, VP and global field CTO of MongoDB, said on stage, “It’s basically Atlas on your laptop,” highlighting how a single command can spin up a complete environment with everything from time-series queries to AI-powered apps.
Developers can now mirror production environments on local machines, iterate faster, and test features like vector search or semantic recall offline. As Bialek put it, “It’s the perfect build-test-develop imagination system you can have.”
Coding Without Context Switching
MongoDB is also embedding itself deeper into popular IDEs.
“You don’t want to jump the whole time between doing something on the database and doing something on the app. This is one flow by now,” Bialek explained, reinforcing MongoDB’s move to simplify full-stack development.
“We integrated MongoDB into VS Code with GitHub Copilot. So, Copilot is not only supporting MongoDB, but it’s also optimised [for it],” he highlighted.
The integration enables the Copilot to suggest queries, auto-generate parameters, and interact with collections in natural language.
JetBrains users aren’t left out either. IntelliJ now supports native MongoDB integrations, with autocomplete, validation, and even performance tips—all connected to JetBrains’ AI assistant.
He mentioned that the IntelliJ plugin is now in public preview, allowing developers to try it out.
This developer-friendly direction is mirrored by MongoDB’s partner, Microsoft. Azim Uddin, principal cloud solutions architect at Microsoft, told AIM, “MongoDB is one of Microsoft’s prioritised partners…There is a MongoDB extension for Visual Studio.”
Uddin highlighted that the MongoDB extension for VS Code provides developers with an experience similar to that offered by SQL Server Management Studio.
GitHub Copilot also comes into play. “MongoDB has a GitHub Copilot extension,” Uddin confirmed. “People can use MongoDB GitHub Copilot for enhancing productivity with their Copilot.”
While he admitted that no-code-style MongoDB integrations aren’t quite there yet, he noted that “it’s a work-in-progress”, and may evolve as both companies expand their developer tooling.
AI-Aware Copilots and Context-Rich Coding
Underpinning these enhancements is the Model Context Protocol (MCP) support—a new bridge between code, AI, and databases. MCP enables LLMs to query databases intelligently by understanding the schema and query context in real-time.
“Very modern IDEs [like] Windsurf, Cursor. When you’re working with those, you can now use MCP to connect to your MongoDB environment,” Bialek said.
“You can compose excellent queries. You can test things around. And it’s absolutely seamless.”
He continued that users can pose natural language questions such as “Show me the schema of my user collection” or “Who are the most active users in the system?” and even request a query to be built for specific information.
This context-rich approach removes guesswork from AI. It allows developers to issue live queries, get instant previews, and refine results within their IDEs, not in some sandboxed playground.
Uddin, reflecting on hands-on experiments with MongoDB and Azure AI Foundry, said he “definitely sees productivity gains and benefits out of these integrations”.
Even beyond developer tooling, Microsoft is embedding MongoDB deeper into its broader platform. “We are integrating across different stacks,” Uddin said, citing MongoDB’s interoperability with Microsoft Fabric for analytics and enterprise security frameworks. “It is both ways,” he added. “Customers can now bring MongoDB data to Fabric and do analytics on their operational data.”
MongoDB has always been developer-friendly, but now it’s becoming developer-native. With a full local CLI stack, tight IDE integrations, and context-aware copilots, the company is positioning itself not just as a database but as a companion to the entire coding journey.
In an era of impatient product cycles and AI-infused apps, MongoDB’s aim to offer a seamless experience, coupled with partnerships with major tech companies like Microsoft, appears to be a good bet.
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A new benchmark study from observability platform Coroot has shed light on the performance costs of implementing OpenTelemetry in high-throughput Go applications. The findings show that while OpenTelemetry delivers valuable trace-level insights, it introduces notable overhead, increasing CPU usage by approximately 35% and increasing network traffic and latency under load.
Using a simple HTTP service backed by an in-memory database, the study compared baseline performance with full OpenTelemetry instrumentation under identical load conditions (10,000 requests per second). Runs occurring in Docker containers across four Linux hosts revealed several findings: enabling tracing increased CPU usage from 2 to 2.7 cores (roughly 35%), memory usage rose by 5–8 MB, and 99th-percentile latency increased modestly from approximately 10 ms to 15 ms. Additionally, tracing data resulted in approximately 4 MB/s of outbound network traffic, highlighting the resource implications of full request-level telemetry.
The study also contrasted SDK-based tracing with eBPF-based approaches, although caution should be noted, as Coroot sells an eBPF-based observability solution. eBPF, which avoids modifying application code, exhibited lower resource consumption—under 0.3 cores—even under heavy load when only metrics were collected. Coroot concluded that while OpenTelemetry’s SDK offers detailed trace visibility, it comes with measurable overhead that must be weighed against observability needs. They argue that for use cases prioritizing low latency and running with capped resources, an eBPF-based implementation may be a more suitable compromise.
This evaluation sparked conversations in the Go community. A discussion on Hacker News suggested performance gains could be achieved by optimizing SDK internals, such as using faster time functions, replacing mutexes with atomics, or marshaling methodically. On Reddit, users noted that even with zero sampling, significant overhead remains due to context propagation and span management. These perspectives underscore a broader recognition that while OpenTelemetry brings essential insights, it also introduces resource tradeoffs that require careful implementation and tuning.
One user, FZambia, stated the following:
“I was initially skeptical about tracing with its overhead (both resource-wise and instrumentation process-wise) vs properly instrumented app using metrics. As time goes, I have more and more examples when tracing helped to diagnose the issue, investigating incidents. The visualization helps a lot here – cross-team communication simplifies a lot when you have a trace. Still, I see how spans contains so much unnecessary data in tags, and collecting them on every request seems so much work to do while you are not using 99.999% of those spans. Turning on sampling is again controversial – you won’t find span when it’s needed (sometimes it’s required even if the request was successful). So reading such detailed investigations of tracing overhead is really useful, thanks!”
Coroot’s benchmarks provide valuable data showing that OpenTelemetry in Go delivers powerful observability at a measurable cost, with approximately 35% CPU overhead, and increased latency under load. The community response suggests that optimizations are underway, yet teams should still balance the need for trace-level visibility against performance constraints and explore lighter-weight options like eBPF-based metrics when appropriate.

MMS • Daniel Dominguez
Article originally posted on InfoQ. Visit InfoQ

Anthropic has upgraded Claude with new app-building capabilities, allowing users to create, host, and share AI applications directly from text prompts. This functionality, known as Artifacts, enables users to build functional tools like data analyzers, flashcard generators, or study aids by simply describing their ideas. Claude handles the coding behind the scenes, enabling individuals without programming skills to create sophisticated applications.
One of the most notable features of this upgrade is how it shifts the operational costs to end users. When users authenticate with their Claude credentials, the platform ensures that creators don’t have to manage API keys or pay for others’ usage. This means creators can focus on building and sharing their apps without worrying about infrastructure or costs.
Artifacts are now organized into a dedicated workspace, making it easier for users to manage and access their creations. This marks a major shift in how AI interacts with users, moving from simple conversational tasks to enabling the development of fully functional applications with ease.
Anthropic’s push into vibe coding is one of the driving forces behind this upgrade. While Claude’s models have long been a favorite for developers working on coding tasks, this new functionality extends the platform’s appeal to a broader audience.
The shift also represents a growing trend in the AI industry towards democratizing application creation. With this upgrade, Anthropic challenges other AI platforms like OpenAI’s Canvas, which provides similar editing tools but lacks the emphasis on creating shareable, interactive applications. Claude’s focus on seamless app-building also adds a layer of competition in the broader space of AI-driven tools for both developers and non-developers.
Users in X have noted the practical benefits, such as the ability to share apps where usage is counted against the viewer’s subscription, not the creator’s, fostering a cost-effective and collaborative environment.
AI Developer Chris O’Halloran shared:
Wow, I think it will be really for micro apps that are currently being built in v0, lovable etc. Now just need to add a way for creators to get paid when others use Claude.
While Developer Hassan Laasri commented:
Really excited about these updates, especially the dedicated artifacts space. It’s a great step toward making Claude a more interactive workspace. One thing that would make it even more powerful is project-based organization to reduce clutter. It would also be great to have more control over versions, like cleaning up intermediate artifacts I don’t need.
As more users begin to explore the potential of AI-powered apps, this transformation raises important questions about the future of software development. Tools like Claude’s Artifacts are helping to redefine the landscape by providing simple, effective ways for anyone to create, share, and collaborate with AI-powered applications.

MMS • Savannah Kunovsky Jenna Fizel
Article originally posted on InfoQ. Visit InfoQ

Transcript
Olimpiu Pop: Hello, everybody. I’m Olimpiu Pop. And I have in front of me Savannah Kunovsky and Jenna Fizel from IDEO. Well, if you don’t know anything about the company, you can join me as well. I just found a couple of weeks ago that they are the ones who brought the mouse to the shape that is currently in. And thank you for that. And without any further ado, I’ll ask Savannah and Jenna to introduce themselves.
Savannah Kunovsky: Thank you so much for having us and for having us at QCon last month. We had a great time. My name is Savannah Kunovsky. And I’m one of the managing directors of IDEO’s Emerging Tech Lab. IDEO is a global design and innovation firm, and so we work all around the world to use the process of human-centred design and design thinking to help businesses figure out what’s next. And a lot of the work that Jenna and I do in the Emerging Tech Lab is exploring the frontiers of technology, and how to make that technology more useful, and more human, and a lot of the time more interesting. And we like to explore the future of technology, both in the process of design, and the process of how we make things, and also in the outputs of what we create.
And my background is kind of general. I was a software engineer. Towards the beginning of my career, I co-founded a chain of tech schools and was based in Nairobi, Kenya. Lived there and ran that for a couple of years. And I’ve been working in the AI space since before it was cool for over a decade and emerging technology at IDEO for quite some time, too. Jenna?
Jenna Fizel: I am co-MD with Savannah of our Emerging Tech Lab. And we have somewhat similar, but a little bit divergent backgrounds. So, my academic background is in computational geometry and architecture. So, always sat right square in that intersection between design and engineering. I’ve spent a lot of time as a technologist and as a software developer for lots of different kinds of applications, often focused on helping explain complex data to a variety of audiences. And all of my work is really focused on what new technology means in the context of everyday experiences. So, thinking about walking into a room or picking up an everyday object and how those experiences can be changed through new technologies.
Olimpiu Pop: That sounds really cool and quite a nice symbiosis. So, Jenna is identifying new technology and then Savannah is looking how can we better interact with that as humans. I shouldn’t ask this, but where do I apply? It’s really, really cool. So, where do I apply? I’m joking, almost.
Savannah Kunovsky: The answer is through the website.
Jenna Fizel: Yes. We have a careers page. It’s real.
Savannah Kunovsky: And that actually is the best way to come through.
Design is more than just drawing websites and mobile apps [03:14]
Olimpiu Pop: Yes, excellent. Excellent. Usually, people think about, when you say design, they are thinking about, well, broadly speaking, how your website looks like or nowadays, how your mobile app looks like or anything else. But in the conversation that I previously had with Savannah, she just said, “No, it’s much more than that”. So, maybe let’s start by clarifying what does actually design mean in the frame that you already provided?
Jenna Fizel: So, I mean, I think at its highest level, design is about figuring out what questions to ask to understand what people want, what can be made, and how value can be created. So, we often talk about these three aspects of what makes for a good design, being desirability, feasibility and viability, which sort of map to those considerations. But I think in my personal practice, I really think of design and engineering as fairly unified and all about that problem definition at core. And that expresses itself differently, whether you’re talking about designing a website or a mobile app, which are important things that we do. Or if you’re thinking about designing, say a clinical workflow that helps patients and providers spend more time with each other, the kind of base set of considerations can be really similar, even across really divergent outputs.
Savannah Kunovsky: The only thing that I would add is that we say that everything is designed and everything can be designed. And there’s just a matter of how much thoughtfulness and intention was put behind how something was designed. So, if we think about things that we have in our home that just work really well and fit really well into the context of our lives, versus other things that we have where we’re just like, okay, it works well enough, but it’s kind of annoying to use and whatever, I’ll put up with it. We really try to be thoughtful about what we’re creating and what the context it’s going to exist inside of is, so that we’re really solving for the true underlying human needs behind the problems that we’re exploring and trying to solve with what we create.
And specifically, when we work in emerging technology, we are exploring far-out futures a lot of the time, and trying to map what we think will be technologically feasible or what is technologically feasible with what people want and need. And that second part of the statement is the big consideration of the design process, is we can start with an exploration of this company wants to use more AI or they want to go in the direction of robotics. But if we start just with the technology, then we don’t tap into the true needs behind the problem that’s being solved and then the products don’t sell or they aren’t as useful. And so, that second part of the equation is incredibly important to us.
Jenna Fizel: To add a little bit onto that. The closed loop of that system is critically important. So, you can ask yourself strategic questions about what people might want or need. But one of my favorite quotes from one of our founders, Bill Moggridge, is the only way to experience an experience is to experience it. And so, when we design, we’re not just wondering what people might like or need. We’re taking tangible expressions of even future technologies through mixed methods, like simulation and design fiction, to give something to actually respond to feel in some way, so that we can with more confidence understand if that desirability is actually there.
Olimpiu Pop: Okay, nice. Still very exciting what you guys are doing, so my enthusiasm remains with me still. So, just put it simple, you’re just designing the whole thought process into the way how people interact or even the flow with interacting with the given technology. And to make it natural, because that’s what I’m thinking now. Most of the things that we use daily and we appreciate the way how we interact with them, they are just plain simple. They are just extensions of humans and that’s pretty much what you’re doing. You’re ideating and then probably creating some kind of prototype in order to define the interaction. That’s nice.
I’m coming from the technology space. I’m very excited about everything that you’re doing. And as I was sharing in our previous conversation with Savannah, one thing remained stuck in my head from my university. It’s actually my master’s years, when we had a topic that was called ubiquitous computer interaction. And most of the things were far-fetched. And it was almost 20 years ago. But now it seems that a lot of the things are not as science fiction anymore as they used to be, because, for instance, in my current day job, we’re using a lot of 3D printing to just create rapid prototypes, either with resin or filament ones.
How to stay on top of emerging technologies in an extremely fast paced world? [08:14]
And it’s very nice just to be able to look at it. But if I’m thinking about technology, I am thinking about Thoughtworks Technology Radar, but you are even further ahead of that. Do you have a radar like that where you’re just thinking about, okay, this is a technology that is worth looking into? That’s something that we will just leave for one more year to get more pride or something like that? Because obviously, everybody is thinking about AI. Some of them are fearful. The others are very excited. And most of the other people are somewhere in between. They don’t have an opinion about it. So, how do you choose your battles? Because there are so many things happening out there.
Jenna Fizel: Yes. So, there are a few ways this happens. A straightforward one is that we’re a client services company, so we have clients and they have desires. And it’s our job to figure out what those desires are, just the way that it’s our job to figure out what sort of end user desires are as well. But we also have a really vibrant and robust creative community within IDEO. And we definitely use the power of not just being individual designers to spot trends, to see where the excitement is amongst our colleagues in the projects and interests that they pursue outside of our direct client work. We have a variety of structures in place to support this. One of which is a learning group that meets once a week and has now been going on for… gosh, coming up on four years, focused on emerging technology.
So, sometimes we have external expert speakers from our professional and personal networks. Sometimes we have folks within IDEO who are pursuing some interesting technical angle personally. And sometimes we have client project teams come in and share where they are in their process, so that we can all learn and get smarter. And hopefully, see around that corner, which is getting more and more crowded all the time and very full of particular AI startups. So, you can’t keep track of them all. And only I think that through the power of using our collective imagination, can we get any kind of a handle on it at all.
Savannah Kunovsky: Yes. We view technology as building blocks that are stacked on top of each other. So, rather than just focusing on one technology at a time, we think about in the future what is going to be technologically feasible because of the fact that other technologies exist. So, we didn’t go straight from the Stone Age to AI. And we think that mixing together various forms of emerging technology is really interesting, because then you get multiple forms of advanced functionality at the same time. We also work on really varied timelines. So, I have projects that I’m working on right now that are looking at two to three years out and projects that I’m working on right now that are looking at 10 years out. And so, the type of mental equations that you’re doing to think about what might be possible at different timelines is also different.
And sometimes when you are thinking about those more extended timelines, it’s actually about inventing the future, and thinking about where this organization want to put its energy, and its funding, and its momentum to build the future that is going to be exciting for them. And so, sometimes, I guess, always, that’s contextualised in what’s happening in industry, and what trends are we seeing, and where do we think that things are headed. And also, sometimes that future, just because it’s so far out, it just doesn’t exist yet. And we can’t say, ‘ Oh, this company is doing something interesting. ‘ Or we saw this interesting thing in an R & D lab in a university, and so that’s going to be the future.
Sometimes it’s really like, if we squint at what we think might be possible with technology 10 years from now, this company actually is going to go out and invent that thing. And that’s a really fun and interesting place for us to be. And so, we actually have a whole practice at IDEO that is called futuring. And we use the processes of speculative design, so creating concepts that are speculative in nature and futuristic feeling to explore both what the technology might look like in the future, and then also just what life might look in the future.
And so, we’re paying attention to all sorts of social, technological, economic, political vectors of where things are headed and what the world might look like in order to try to almost invent those future worlds. And we obviously take a lot of inspiration from sci-fi, because there have been a lot of amazing authors over the years that have started to create those worlds and set those visions for the tech ecosystem, for better or for worse.
Is our future a “Mad Max” or a “Star Trek” episode? [13:04]
Olimpiu Pop: So, that begs the question: Mad Max, or Star Trek, for dystopia or utopia.
Savannah Kunovsky: This one is straight for Jenna.
Jenna Fizel: Well, I mean, personally, I very much hope for Star Trek. You won’t be surprised to learn that I’ve been a Trekkie since childhood. And I think one of the things that I really love about Star Trek isn’t so much its utopianism, but its focus on competence. Obviously, not uniquely, but kind of unusually amongst very popular narratives. It focuses on a group of professional people doing their jobs mostly well. And I mean, I think my highest aspiration for what my work does is to help in that pursuit. So, do I think we’ll get to a post-scarcity socialist utopia soon? Very, very doubtful for many reasons. However, I do think that design should strive to encourage flourishing in some way.
And I really like how utopian science fiction like Star Trek explores that. While at the same time using the tools of dystopian fiction is really interesting, because it pushes you to consider extremes. And I think that is really critical in the design process, to not just be imagining that sort of happy middle path, but also to be imagining what could go spectacularly wrong or spectacularly well, and making sure that you have room within the systems that you’re designing to accommodate that range of outcomes.
Olimpiu Pop: Okay. Live long and prosper, I suppose. From my point of view, it’ll be a good balance in understanding the need to use our resources carefully. And usually, I would put it in a dystopian frame, but also dream big about what the shiny future could be. There are the utopian ones. And that’s the nice balance, if that can be thought of. So, then I am very jealous of your time budget for your projects, three to 10 years. I think everybody in the software industry only dreams about something like that when people are just negotiating for weeks and months. So, that’s nice. That’s securing the future quite nicely.
But moving back, as you said, your company worked a lot. And as you mentioned, it started in the 1970s, when the mouse was initially built. Just now featuring the past somehow. Are there any situations, any technologies that you said this could have done better in this way or some kind of retrospective, and learning from the past, and the way just to change the design process that you use?
Savannah Kunovsky: Yes. We have many iconic products that are things that people have heard of and used. We worked on the Swiffer, and the View-Master, and the Apple Mouse, and the Palm V, and all of these early iconic products. And the original thinking was taking the process of design and then extending that into a methodology for how to do innovation and how to come up with what’s next. And so, all the time we’re coming up with terrible ideas. And the terrible ideas are actually important for us, because we call them sacrificial concepts. And we take them into testing with potential users and get their reactions to understand how they feel about them. And those terrible ideas take us into what the good ideas actually will be.
And we have definitely predicted things 10 years in the future. And it’s not our job to say here are all of the things and how the world is definitely going to look 10 years in the future. That’s just an impossible thing for us to do. But what we do in our futuring practice is come up with a set of plausible futures, all of which could be possible given the circumstances of future technological breakthroughs, potential resources, and cultural shifts. If those things come true or come somewhat true, then these plausible futures could exist.
And so, we use multiple plausible futures as those anchor points for what might be possible and a lot of time for our recommendations. And not all of them are correct. They’re really well-informed and they’re kind of meant to be guardrails and good anchor points. And as our clients and we go through the process of actually trying to build and discover things, it’s okay if those shift a bit based on what new technological breakthroughs are happening or what the resource needs are all around the world.
Jenna Fizel: I’ll add one more example, because we just got some really exciting news last week, which is that one of our client partners, Teal Health, got FDA approval for the first at-home cervical cancer screening kit. So, this is basically a Pap smear that you can self-administer. The first time this has ever been approved. And we helped with the design of a few aspects, including the unboxing experience and how a person actually comes to learn how to use this new device that they’ve never seen before, because it’s never existed. But also, actually, physically prototyping the mechanism and the physical design of the product itself, which we did during the pandemic when it was quite challenging to get access to prototyping resources.
We used a lot of home 3D printing. So, you mentioned that earlier as something that you’re engaged with as well. And I think this is just a really great example of being able to push just a little bit into the future, go a little bit further to bring something to market, and actually, through a clearance process in a really kind of stunningly short amount of time compared to the usual. I’m just thrilled to see this on the market and out there helping people.
How can you measure the design’s impact? [19:03]
Olimpiu Pop: I can imagine, especially that this has an impact given the aggressiveness with which the cancer is spreading, and it’s only presence. Even me personally, we had a bunch of examples in our family and I know what that means, so kudos for that. And how do you actually measure impact as a company, because you are the managing directors, so this has to be on your agenda as well. It’s obvious the part with the projects where it’s about commercial feasibility, but then we are looking also in impact, because you cannot think about the future without the impact.
Savannah Kunovsky: One of my favorite things is when one of our clients gets promoted because of our work or when new parts of an organization are built because of our work. A lot of the time it depends on how the work is received by the organization. And we try to be really collaborative in the process of design, so we’re not the types of consultants where you give us a challenge, and we go off, and we come back three months later and we’re like, “Here’s the thing”. We’re the type of organization that really want to collaborate with our clients to make sure that whatever it is that we are creating and whatever we’re recommending is contextualized in the needs of the business. And a lot of the time business leaders bring us in because they’re kind of stuck and they don’t necessarily have the capabilities to think outside of their current operating model.
And a lot of the time, businesses are geared up to create one thing or a set of things. And that’s what they’ve been doing and they do really well, but it’s hard for them to get outside of that. And so, we try to work with our clients to have the ability to extend outside of what is currently possible for them, but in a way that still makes sense for their business and makes sense for the types of bets that they want to make.
Will the keyboard become obsolete any time soon? [20:59]
Olimpiu Pop: So, one potential key result for you is promotions per year. So, I’m joking. So, since you started the conversation, one thing was going around in my head over and over again. As we’re talking about the future, we’re talking about the way how things will evolve, but we are still stuck with the keyboard. So, what are you doing here? The next step will be just using our brainwaves to do that? So, basically, what I’m saying is that for me it seems that the evolution of technology is getting at a fast rhythm. If we look in 20 years, 10 years ago, things were slower-paced. Now things are happening so fast. How can you embrace the challenge that you mentioned, where we are doing something appropriately, so that we have a proper interaction? Isn’t it a challenge for you as a company, as a group?
Jenna Fizel: Definitely is, especially because some of that stuff is really highly concentrated in the pre-commercial space basically. And so, we have long-standing collaborations with some academic institutions, for example, the MIT Media Lab, where we’ll often create some speculative work together. And we visit each other and learn about some of those brain computer interfaces, different kinds of haptics, different kinds of XR interfaces. And then we engage in some of that work directly with client organizations as well. And actually, in the realm of new kinds of interactions, we recently wrapped up support of a design sprint, a non-commercial one, for the Apple Vision Pro and medical applications.
So, trying to find clinically meaningful interventions that you could use that hardware and the different kinds of interactions that you can create with that hardware, including… I’m going to use a prop for a second. I know this is a podcast. But including taking everyday objects, tracking them, and using them as stand-ins for pieces of medical equipment that you would need to train with. So, this was, for example, for inserting a catheter into a large vein. Glass was used as a stand-in for an ultrasound.
It was really exciting to lend our space and expertise to that group, allowing us to see how younger designers from the community, non-IDRs, and real clinicians, as well as institutions like Mass General, could come together to understand and imagine new ways of using these technologies for meaningful results. But not necessarily ones that need to be commercially viable yet, allowing us to take that little leap into the future again.
Olimpiu Pop: Well, that’s quite nice. I was just thinking that, I think last year, I was working with a partner of mine. And we are considering doing some kind of consultancy work with people from the US. And then we are just envisioning how nice it would be that rather than going back and forth between Europe and the US, we can just use the new VRs, the Apple Pro in this case. And we’ll just kickstart the interaction in person, and then probably we’ll have a couple of new sessions, and then back and forth.
Provide prototypes to users to understand how they will actually use the technology [24:05]
And then I was just thinking that it’s quite nice to be on the edge, as you are, and then just envision different stuff. But then when they appear on the market, people use them for other stuff as well. So, it feels a lot like an inception. Does it ever happen to you to just look at something that you design and say, “No, that’s not the way how it was intended? Or, it’s more loose than that? It’s like, okay, you just dream big, you come with an interaction and then you learn from what you see people doing differently.
Savannah Kunovsky: I think that’s kind of the point for us actually, is that we create something and then we watch how people interact with it. And we’re like, oh, that’s what you actually need. So, part of our research that we do oftentimes, and especially when we’re designing physical things, IDEO has its roots in industrial design, although we design everything now from supersonic jets to avocado packaging, to digital government services and beyond. But our roots and especially the practice of design research will actually take us sometimes into people’s homes. And we’ll bring sacrificial concepts like I talked about earlier, so early, kind of messy concepts that just sort of illustrate an idea. And those can be things that we 3D printed and actual physical objects. Or I’ve brought in paper plates as a prop when I was working on a project. There are all sorts of ways that you can just simulate what that end experience would be.
And you take those things into people’s homes and you’re like, “Oh, actually, this is way too big to fit in your house”. Or, “Your kid immediately broke it. Okay, great, good to know that this is just not going to work”. Or you go in and you say, “Oh, they’re actually currently doing this thing in this totally other way that we had never seen before”. So, for example, we worked on PillPack, which was, I think, later sold to Amazon. But one of the big insights there was… I guess the end product that we ended up with was these packaged daily packs of pills that you take. If you have a couple of different prescriptions that you’re taking, it has them just clustered in there for you. But one of the big insights that led to that nice, easy to open plastic packaging, was that the team went into an elderly woman’s home and they were like, “Okay, show us your daily routine and how you take your pills”.
And she walked over to this table-top blade, like a grinder blade, electronic blade. And she was like, “Well, my hands are too frail to open up a regular pill bottle, so whenever I need to open up a new pill bottle, I’m using this massive blade to grind off the pill cap”. And we’re like, “Oh, okay, that makes sense”. Those pill bottles don’t actually work for a lot of people who are taking a lot of pills, which is the older adult population. And so, that took us to the form factor of this plastic packaging. So, to your question of what happens when people aren’t using the thing in the way that we expected, we’re like, great, that gives us so much information about what the actual need is and how to design it better. And we kind of really look for that information.
Olimpiu Pop: Okay, that’s cool. Well, because I went to another jeans shop and I asked for talking jeans that I saw them in a presentation. And they just shrugged and they just said, “Okay, dude, I don’t know what pills are you taking, but those don’t exist”. And I say, “Just wait for it”.
Savannah Kunovsky: Every time I give that talk and I talk about the talking pants, I ask for the phone number of the CEO in Levi’s and nobody has given it to me yet. So, anyone listening, if you have the contact number for the CEO of Levi’s, please reach out.
Jenna Fizel: Yes, pants can talk. It’s totally possible.
Olimpiu Pop: Yes. But that was really, really cool. I mean, it was such a nice way. You could’ve just put on the slide that, well, we have to be careful with what we’re doing. We are consuming so much of everything. But then you actually touch on a point that was really cool. Lately, a lot of the, I don’t know what’s the proper term for it, probably vintage clothes, the secondhand clothes are gaining popularity. I’m just thinking about one of the companies in Europe. It became a multi-unicorn just by selling used clothes. And that’s something that is quite nice. But that was quite an interesting concept, where you’re just taking technology, and put it in a proper place. You enhance the experience that a buyer has, and then you’re just thinking about the future as well.
And I can tell you that my daughter was very excited. She’s 10. And she was very excited about the fact and “Oh, are those really talking pants?” No, but it’s just things about the future, because at this point she’s just trying to figure out how she can convince her colleagues to make an impact for the planet. And she’s just brainstorming ideas. So, I’m just thinking that probably you have a huge warehouse where you have all the previous ideas that are stored there just for moving around, right?
Jenna Fizel: Yes. I sometimes call it a museum in a suitcase. Sometimes we put it in a bag and take around some of our more provocative ideas. I want to dive into the pants for a second. So, what we’re talking about here is this speculative piece that we created where we’ve encoded into the context of a large language model-backed chatbot information about a pair of vintage jeans and things about who wore them, how they were created, all of the information that an informed consumer might want to know about before purchasing. As well as some fun things, like what are its opinions about khaki pants. But part of the motivation for this design wasn’t just that large language models had become available. Though that was important. It wasn’t just the social trends of especially younger consumers wanting to shop based on their values more than brand loyalty, but also some regulations.
So, especially in the EU, there are important regulations about the traceability of things like supply chain, labour regulations, and all this other information that now by law needs to be tracked and stored somewhere. And that suddenly created a future world where knowing about your pants is not just a theoretical thing, but actually a sort of mandated, everyday thing. And so, we think about, okay, if that’s the case, what new opportunities are there to take that information and make it engaging, usable, and valuable to folks beyond just that regulatory environment?
And that I think, to Savannah’s point earlier about how we look for signals in our futuring work, that’s really important, because there are some things that will change about the world. And even some of the scarier ones mean that we’ll be living in a different environment in the future. And what sort of secondary consequences do those different worlds have on our daily lives? And maybe one of them is talking pants. We can hope.
How to embrace regulation when you are designing a global product? [31:12]
Olimpiu Pop: Okay. Well, I’m sure you can. But you said the EU word. And I have to ask you, because I’m in the EU. You are on the other side of the pond. And my feeling is that technology companies are not necessarily happy with the EU regulation. And a lot of people are just drawing interesting words towards the EU regulation. At points, I did the same, but now my optic changed a bit when I see also the benefit. For instance, the AI Act is really focusing more or less on the individual. And how do you feel as a design company? Because somehow my feeling is that the intention the EU legislation has and your intention as a design company is pretty much the same thing, to just make sure that the end user gets the most value and in the best place. Do you have customers that say, “We have to get to the EU market, that’s a pain in the back somehow?”
Jenna Fizel: Yes. I think there’s a couple of answers to that. We work for companies all around the world, and so they all live in different regulatory environments. And as designers, one of our responsibility is to understand the constraints of the business. So, we’re pretty often learning about new kinds of regulations or standards, based on either industry or location in the world, or both. I think from a design perspective, understanding different regulatory paradigms is really exciting, because you see this distilled thinking and understanding from at least some representation of a whole society or culture that’s been squeezed through their government and regulatory apparatus. And that’s gold.
Whether or not we’re necessarily designing to be compliant with the EU AI Act, for example, I think I’m personally really inspired by the framework of the different levels of regulation based on the impact of the technology. Whether it’s for entertainment, all the way up through something that might end up creating bio-weapons, and using that as a tiered understanding of harm reduction. That’s very interesting. So, yes, it is a bit nerdy, but I get excited and inspired by different regulations for sure.
Savannah Kunovsky: Also, we have a studio in London. And our studio in London is excited to work on stuff that is in that context and understands it well.
Olimpiu Pop: We don’t talk about London anymore. They decided to leave. So, no, I’m joking. Yes, I bumped in one of your colleagues who is in the London office. Great. I’m going to help you with the future and looking at the future. AI is important, so maybe you would like to look into the AI space and just think about how the people will interact with that, leaving the joke aside. Generative AI opened the box, even though as Savannah put it, AI has been with us for a long time. But now people understand it. And at this point, we are probably at the height of the hype when people are using it for pretty much everything.
How will the interactions with AI evolve? [34:17]
And well, my main concern is with the ecology of that thing, but that’s a whole different conversation. How do you feel about the current interaction that we have with AI? I know that Savannah, during her presentation, had a point on that, that we are at the point where we are between the horse and carriage and the automobile. We don’t call it an automobile yet. We are just calling autonomous horse or whatever. Do you have any thoughts on this?
Savannah Kunovsky: I think that we’re seeing some incredible potential and some incredible capability. And that is in generative AI and in many other parts of the AI space. And I think that we’re in many ways at the beginning of this precipice of having more advanced technology as a part of our day-to-day life. I think that we’re all used to social media algorithms and just having some level of personalization in a lot of the services that we’ve used. So, we’ve gotten the soft launch of these types of services. And there are definitely industries that have been much deeper in this space for quite some time. But from a commercial consumer perspective, I think that it’s becoming more and more explicit for us. And I think that as the devices that we’re using and as the services that we’re using get better, then people just come to expect those types of experiences, those kinds of advanced experiences.
And so, what is normalized happens relatively quickly, but I think that in order for us to get there, we have to be building and designing truly useful technology, and truly useful services that are mapped into what people want and need. And also, into the context of their lives, and their homes, and their work or whatever it is that we’re creating the device for. So, whether the interface is voice-based or brain controlled, or just sensory based on how you’re feeling or whatever kind of the input method is, feels like… I’m like, yes, all of that stuff is super exciting, but it has to be fit for purpose and fit for context. And the technology has to be good, because if it’s not good then people don’t want to use it.
Olimpiu Pop: I think it’s more than just don’t want to use it, because at that point you have a choice. But what I’m thinking now is that there is a startup in the place where I am. And we don’t have many that are that innovative. I’m going back to Star Trek, as we discussed earlier. These guys built a headset, some kind of LiDAR to help blind people. And that was nice, because that empowers people that usually live in a very narrow space due to the constraints that the society builds, willing or unwilling, around us. And then it’s just really opening new doors for a lot of people that otherwise will not have this chance.
And on the other hand, I was just thinking about what you mentioned about the social media algorithms. Well, that’s not necessarily a choice. Now, with the rise of LLMs, I think we have a choice where we can actually, even though it’s a paradox from my point of view, have more time and have better quality. For instance, what I’m trying to do now is to automate most of the things that I would normally do by hand. And obviously, I’d be fine behind my finger, saying that I don’t have enough time, but now LLMs allow you to do that. But on the other hand, I still have the privacy issue. If I simply drop it into ChatGPT or Claude, or whatever, what happens to my data?
Co-Design for deeper user understanding across groups [37:52]
And looking at the way how the world evolves these days, it feels that you want to have your data in a safe space, if possible in a suitcase, as you mentioned the museum in my suitcase. That’s also quite important these days. People are more and more aware. And that pushes me towards the other concern that I was having. A lot of the presentation that you had during your keynote, Savannah, was about the Gen Z generation, and the way how they interact with the technology, because they were constrained to use it more and probably a lot of them prematurely.
I’m just thinking about my kids; my daughter just started school. And then two weeks later, she had to interact with a laptop. It was the first time for her to interact with a computer to do her studies, even though she didn’t know her colleagues. And, actually, a couple of minutes ago, you mentioned older folks. So, how does design vary from this spectrum? I’m not familiar with all the generations. I’m thinking about the boomers, and I know the Gen Zs, and I know the millennials because I’m part of that generation. But how did the interaction change?
Savannah Kunovsky: It’s a super important question, because a lot of the time, the people who are making the technology are from one or two generations, or the people who are designing the technology are from one or two generations. And the people who are using and benefiting from that technology or being harmed by that technology are sometimes from their generation, but also from others. And I think that there’s this thing of different generations liking to rag on each other. It’s like, oh, Gen Z is… Every generation is like the one before it; they’re lazy and whatever. There are all of these just stereotypes that we make up about other generations. And millennials are ragging on boomers. And that’s a thing.
However, if you’re designing products for other generations or I think we can broaden this out into designing products for communities that are just different from your own, using the process of co-design is incredibly important. And an oversimplified definition of what that is is that rather than you just designing something for another group of people and then dropping it on them and being like, “Cool, hope this works. And it’s imbued with all of my values and perspectives on how I think that this technology should serve your community”. Using the process of actually designing with the communities that will be using or benefiting from the technology you’re creating is essential. And getting input and actually giving folks a seat at the table to come with their ideas, and to come with their lived experiences, and perspectives about how that technology might fit into their lives.
So, one of the first things that we did, and specifically IDEO has a play lab where we do toy invention, and we bring the principles of game design and play into everyday objects, including toys, but also extending out into other things. One of the first things that they did when generative AI came out was go to a bunch of Gen Z folks and say, “Hey, how do you actually want this technology to fit into your lives? You’ve been so oversaturated by technology through the pandemic. And that was a really key part of your social upbringing and the formation of how you’re experiencing the world. So, we’re on the cusp of this other technological revolution, and what do you want from it, and how do you want it to fit into your lives? And it’s going to change your work and your social dynamics. And so, how do we build it in healthier ways with you, and for you?”
And the principles that came out of that work were really like, we have had so much technology over-saturating our world that we’re okay with technology, we just want to make sure that it helps us be more human, and stay connected to other people, and connected to ourselves. And those principles I think, are just incredibly important for any form of designing technology. So, there’s this Gen X millennial mentality around tech, that it’s for efficiency and for making things faster and more streamlined. And I don’t want to think, just kind of make the decisions for me and take care of it.
And as much as I think that sometimes it is wonderful, it also takes away some of the friction in our day-to-day life that is actually really important for us in ways that we don’t necessarily expect, and in ways that help us stay connected to ourselves and to other people. So, I think just making sure that we’re getting outside of our own heads, and talking to experts, and talking to people, and working with folks who are going to be potentially using the services that we’re creating is the way to go.
Jenna Fizel: Yes. And I think one thing to add on to that, especially because this is for a more technical audience, is doing that first step of identifying who you actually are designing or building for. Sometimes maybe you’re working at a giant tech company and you have billions of users, and then that can be tricky. But for most folks in most applications, you do have an audience or at least you have an aspiration of meeting a certain set of, I don’t know, behavioral characteristics or place in the world, or some other way that you can define who in particular you’re designing for. And a process I really enjoy is this algorithm bias checklist by the Brookings Institute, that you can run before you start a project. That gets pretty serious and granular about who you’re designing, who your end users actually are, and what your intended impact on those users might be.
And then you can check again after you’ve done some of your work and see, are you living up to your aspiration or have things drifted? And if so, why? And I think using techniques like co-design and having that intentionality when you start can really improve your outcome, not just from a market fit perspective, but actually improve the value that you’re making for the people who use your product or service in the end.
Focus on behaviour patterns rather than traditional personas [44:00]
Olimpiu Pop: And a follow-up question on this. You mentioned just making sure that you know exactly who is the target that you’re addressing. What was going on in my head was at some point I was listening to a design podcast book, I don’t remember exactly what. And they were discussing about personas and how faulty personas can be. And they were providing an example. He lives in a castle, he drives a Rolls-Royce, and he’s in his ’60s and he lived in the UK. And then everybody was like, “Okay, you can either choose Ozzy Osbourne”, or at that point, Prince Charles, currently King Charles. So, that can be really far-fetched. And then I think looking at the way how environments and cities look these days, more than just generational sampling, I think it’s important cultural sampling. How can you manage to get to that particular point of view in such a heterogeneous environment?
Jenna Fizel: Yes, it’s a really good question. And I will say that we relatively rarely use traditional personas in our work. We much more often focus on behavior patterns. So, are we trying to serve people who commute by bicycle or like to spend time with their elders, or sort of like more action-oriented, or behavioral characteristics, rather than demographic ones. Because often a more fruitful site to get inspired when you’re designing. But you’re right, these are not substitutes for understanding your actual in-real-life customers. And that’s why ultimately we basically always talk to individual people while we’re designing.
And even though you can’t talk to every possible customer, you can identify folks who exemplify some of these behavioral characteristics that you’re particularly interested in. You can be inspired directly by Ozzy Osborne or by King Charles, by talking to them and learning a little bit more about their full lives as people and how that might intersect with your design problem. I mean, that’s one of the core tenets of human-centered design, I’d say.
Olimpiu Pop: Great. So, what I hear you saying is not to go after stereotypes like demographics or culture, but rather look at the habit of the product you’re developing. So, go after the tribe, rather than whatever people living in a given city or something like that.
Prioritize observing user actions over listening to stated needs [46:26]
Savannah Kunovsky: Yes. To an extent, we’re looking at behaviors and we’re looking at actions. And there’s also a lot of the time a big difference between what people say versus what they actually do. And so, a great use of emerging technology in the process of design is to be able to create simulated environments or to create digital experiences that replicate some sort of physical, or digital experience that people might be having in a product that you’re creating. And then, actually, you can talk to them about it and hear about their habits. And hear about how the thing might fit into their lives, but then actually prototype that thing and watch what they do and see what happens. Because oftentimes, those two things are actually quite different. There’s something that we talk about, which is the say-do gap. And so, we try to lead with bringing prototypes all the time and actually getting people to walk through or move through those prototypes, so that we can map their behaviors to what we make.
Olimpiu Pop: Let me see if I follow your idea properly and I understand it. So, first of all, you go and discuss with them and you ask them nicely about their intent. And then you go on your IDA to come back with a bunch of prototypes. And you just come and say, “Okay, this is it. Play with it”. And then you’re observing how they’re actually interacting with the prototypes. And that allows you to use the observations, and iterate over what you have, and then narrow down to a more perfected prototype, let’s say. Do you have something like a stage where you’re iterating over the finished product? Like we have currently in the technology space, a very fast iteration, where you have looking like continuous deployment, and observability, and you’re creating that kind of behavior as well?
Jenna Fizel: Yes, definitely. And I guess I’ll maybe nuance your description a little bit. We try to always bring some kind of a prototype, but not a finished one and not one that’s necessarily related to that sort of final outcome. But rather a prototype that lets us ask a question more deeply so that we can learn something about a preference or a behavior that then informs that final design. But when we’re lucky enough, we get to take it all of the way. So, I’ll tell a story from a few years ago now about an AI product. Not generative AI, though. Good old-fashioned AI, about predicting labor and material needs at fast casual restaurants around the City of Chicago.
And we did some of that exploratory research and prototyping first. But eventually, we actually built a machine learning model, a deployed frontend, and installed it in several actual locations that were serving customers around the city. And then had continuous check-ins with the frontline workers and the managers at those locations to evolve the design, until it got to a place where everybody was seeing the kind of information that was appropriate and actionable to them, and that our predictions were working well enough to be useful. And we were actually running the model on one of those old Mac Pros that looked like a black cylinder, basically, which was, by one of the data scientists fraud-named the trash machine.
And so, people would wonder, did the trash machine make the new predictions this morning, or did it fail for some reason? And do we need to restart the trash machine? So, that one just really has stuck in my head, both as a fun and exciting moment for us as designers to go all the way through that process, but also just a really compelling and fast process of doing that iteration, making some code changes, deploying the front end, and then immediately seeing it out in the market within seconds. Which is not so unusual in a lot of software deployment practices these days, especially for web apps, but a little bit different for us than our usual iteration cycle.
Generative AI can help with product co-design, but also as a fast prototyping tool [50:27]
Olimpiu Pop: Okay. Nice. And this leads me to probably my final question. Everybody’s using generative AI. How do you use it? Because it fits for me. What was nice, it was quite easy to build prototypes. It’s a matter of minutes, half an hour to just have something ideating, doing something in practice. Okay, it’s not ideal. It needs a lot of polishing. But how do you do it? I know that Savannah had at least one example in her keynote. But what are the lessons learned? Does it work and what doesn’t?
Savannah Kunovsky: I use it, and I see our teams using it in a lot of different ways, because we have so many designers who are working across different crafts and methods. But I think the general message is that we use it to add rigor to our work and to our research. And so, if there are ways that we can create either more accurate or higher fidelity concepts and prototypes earlier on, or more personalized prototypes earlier on so that we can get more accurate information from our research, then oftentimes we use it in that way.
So, whether that’s vibe coding a prototype or creating a short, scrappy experimental video, or having a bunch of different concepts that we can generate on the fly and pull in based on the preferences of specific people who we might be testing with, all of those are great. And then I think that there are the more obvious day-to-day things that we’ll use it for, where we’re just asking it questions and helping it co-create assets with us, whether that’s written materials or other things.
And at the end of the day, I think that it’s in a place where it’s really useful for some of that scrappier, and more experimental stuff, and sometimes for more later stage polished stuff. But all the time we are coming in with the creative vision, and the creative direction, and trying to use it more as a thought partner to us. But our designers, and their vision, and their agency in the process is the thing that’s the most important. And I think if I could make an ask of generative AI toolmakers, it is to center the human and the process that people are going through as they’re trying to create something. And create experiences in the tools that allow them to more clearly and more easily articulate that vision of what it is that they’re trying to make. And to be in the driver’s seat of creating those assets so that vision comes to life.
Jenna Fizel: I’ll maybe add one more aspect here, which is an aspect of learning. So, I’ll talk about vibe coding, because that’s in many ways closest to my heart. And certainly, I have I think three Cursor projects open right now at various levels of completion. And I use these tools as an extension of my existing skill set. But we have a lot of people who work on digital products at IDEO who don’t necessarily have direct experience with building digital products by putting their hands on a keyboard and writing TypeScript. But we now have this ability, and honestly, this, in some ways, companion who wants you to learn and to know more about processes and expertise that you didn’t get from your schooling or maybe even from your work experience.
And so, we have a few folks, including Neil, a person who works in our play lab, who’ve gone really deep and have built pretty complex pieces of software without that background technical knowledge, but with a ton of curiosity. And a real willingness to put in time and hours to understand the problems, and to understand really basic and foundational ideas, like debugging or building a backlog. And not coming to understand these things by reading articles, but by actually making them in collaboration with these AI tools. And now when I talk to Neil, for example, I can use some of the shorthand and the basic ideas of software development and of digital product design in a way that I couldn’t have a few weeks ago before he started staying up until 5:00 AM to build and rebuild the Slack bot that he’s been working on.
And I don’t want everyone to stay up until 5:00 AM every night, but I think that there’s something to this companionship, this encouragement of learning and exploration that are baked into some of these tools at least. And I would echo Savannah’s sentiment. I hope not only that the designers of these tools help to put the users in the driver’s seat, but also to have that, a nice little, I don’t know, gremlin on your shoulder that encourages you to keep going and keep exploring.
Olimpiu Pop: Thank you. Well, to be honest, rigor and accuracy would’ve not been the two of the words that I would associate with generative AI, at least not in this particular point. But yes, I understand the benefit of… And I’m also using it and my colleagues are using it as well. And just for our closing, I’ll just have to ask you, do you have any plausible scenarios for the technology in the future? I think that was the term that you use, plausible future. So, any, not predictions, but directions that we might expect in the upcoming years maybe?
Savannah Kunovsky: I am excited about more ambient, humane technology in general. I think that there’s the possibility of us having more everyday robotics, and just smarter devices and capabilities. And I think that they could be really cool and really interesting, but I don’t think that they need to be these crazy sci-fi, futuristic or attention-grabbing objects. I think that they can just be super functional and work really well and look really beautiful. And fit nicely into the context of our homes and our lives. And be healthy, and help us stay more connected to other people. So, if I get to put a vision out there, it is that.
Olimpiu Pop: That’s nice. It sounds like an Italian village where everybody’s connected without technology. So, I would second that. Thank you. Thank you, Savannah. Jenna, how about you?
Jenna Fizel: Yes. I think mine’s fairly related. So, there’s often this really strong barrier between the technical interface, I’m puppeting, basically, and then the world around me. And one thing I’m really excited about at the sort of intersection of AI, and robotics, and XR, is that there’s more and more opportunity for our digital interfaces to understand our physical contexts and for our physical context to influence our digital interfaces. And we see that in lots of different ways already through things like wearables, like watches that have been around for many years now, through to things like the Snap Spectacles, which are not a commercial product, but a real AR headset that you can wear around outside.
And I think that this sort of blending and this kind of neither privileging the digital experience, not making you stare into your black rectangle all day, but also acknowledging the value that showing otherwise hidden information can bring to your life as a unified experience is really exciting to me. Maybe not totally new, but I think an experience that we’ve been walking towards for decades and that I think we’re going to see a real acceleration in.
Olimpiu Pop: That sounds like the humans are taking their head out of the rabbit hole, the social media and the internet provide to them and then getting back to the environment around them using the technology in the right space. So, that sounds like an evolution from what we currently have to something closer to what we need to improve our lives in some way. Thank you for your time. It was an absolute pleasure and I’m looking forward to following more of your work. Thank you.
Savannah Kunovsky: Thank you so much for having us.
Jenna Fizel: Yes. Thank you so much.
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