NanoClaw, the open-source AI agent platform created by Gavriel Cohen, is partnering with the containerized development platform Docker to let teams run agents inside Docker Sandboxes, a move aimed at one of the biggest obstacles to enterprise adoption: how to give agents room to act without giving them room to damage the systems around them.

The announcement matters because the market for AI agents is shifting from novelty to deployment. It is no longer enough for an agent to write code, answer questions or automate a task.

For CIOs, CTOs and platform leaders, the harder question is whether that agent can safely connect to live data, modify files, install packages and operate across business systems without exposing the host machine, adjacent workloads or other agents.

That is the problem NanoClaw and Docker say they are solving together.

A security argument, not just a packaging update

NanoClaw launched as a security-first alternative in the rapidly growing “claw” ecosystem, where agent frameworks promise broad autonomy across local and cloud environments. The project’s core argument has been that many agent systems rely too heavily on software-level guardrails while running too close to the host machine.

This Docker integration pushes that argument down into infrastructure.

“The partnership with Docker is integrating NanoClaw with Docker Sandboxes,” Cohen said in an interview. “The initial version of NanoClaw used Docker containers for isolating each agent, but Docker Sandboxes is the proper enterprise-ready solution for rolling out agents securely.”

That progression matters because the central issue in enterprise agent deployment is isolation. Agents do not behave like traditional applications. They mutate their environments, install dependencies, create files, launch processes and connect to outside systems. That breaks many of the assumptions underlying ordinary container workflows.

Cohen framed the issue in direct terms: “You want to unlock the full potential of these highly capable agents, but you don’t want security to be based on trust. You have to have isolated environments and hard boundaries.”

That line gets at the broader challenge facing enterprises now experimenting with agents in production-like settings. The more useful agents become, the more access they need. They need tools, memory, external connections and the freedom to take actions on behalf of users and teams. But each gain in capability raises the stakes around containment. A compromised or badly behaving agent cannot be allowed to spill into the host environment, expose credentials or access another agent’s state.

Why agents strain conventional infrastructure

Docker president and COO Mark Cavage said that reality forced the company to rethink some of the assumptions built into standard developer infrastructure.

“Fundamentally, we had to change the isolation and security model to work in the world of agents,” Cavage said. “It feels like normal Docker, but it’s not.”

He explained why the old model no longer holds. “Agents break effectively every model we’ve ever known,” Cavage said. “Containers assume immutability, but agents break that on the very first call. The first thing they want to do is install packages, modify files, spin up processes, spin up databases — they want full mutability and a full machine to run in.”

That is a useful framing for enterprise technical decision-makers. The promise of agents is not that they behave like static software with a chatbot front end. The promise is that they can perform open-ended work. But open-ended work is exactly what creates new security and governance problems. An agent that can install a package, rewrite a file tree, start a database process or access credentials is more operationally useful than a static assistant. It is also more dangerous if it is running in the wrong environment.

Docker’s answer is Docker Sandboxes, which use MicroVM-based isolation while preserving familiar Docker packaging and workflows. According to the companies, NanoClaw can now run inside that infrastructure with a single command, giving teams a more secure execution layer without forcing them to redesign their agent stack from scratch.

Cavage put the value proposition plainly: “What that gets you is a much stronger security boundary. When something breaks out — because agents do bad things — it’s truly bounded in something provably secure.”

That emphasis on containment rather than trust lines up closely with NanoClaw’s original thesis. In earlier coverage of the project, NanoClaw was positioned as a leaner, more auditable alternative to broader and more permissive frameworks. The argument was not just that it was open source, but that its simplicity made it easier to reason about, secure and customize for production use.

Cavage extended that argument beyond any single product. “Security is defense in depth,” he said. “You need every layer of the stack: a secure foundation, a secure framework to run in, and secure things users build on top.”

That is likely to resonate with enterprise infrastructure teams that are less interested in model novelty than in blast radius, auditability and layered control. Agents may still rely on the intelligence of frontier models, but what matters operationally is whether the surrounding system can absorb mistakes, misfires or adversarial behavior without turning one compromised process into a wider incident.

The enterprise case for many agents, not one

The NanoClaw-Docker partnership also reflects a broader shift in how vendors are beginning to think about agent deployment at scale. Instead of one central AI system doing everything, the model emerging here is many bounded agents operating across teams, channels and tasks.

“What OpenClaw and the claws have shown is how to get tremendous value from coding agents and general-purpose agents that are available today,” Cohen said. “Every team is going to be managing a team of agents.”

He pushed that idea further in the interview, sketching a future closer to organizational systems design than to the consumer assistant model that still dominates much of the AI conversation. “In businesses, every employee is going to have their personal assistant agent, but teams will manage a team of agents, and a high-performing team will manage hundreds or thousands of agents,” Cohen said.

That is a more useful enterprise lens than the usual consumer framing. In a real organization, agents are likely to be attached to distinct workflows, data stores and communication surfaces. Finance, support, sales engineering, developer productivity and internal operations may all have different automations, different memory and different access rights. A secure multi-agent future depends less on generalized intelligence than on boundaries: who can see what, which process can touch which file system, and what happens when one agent fails or is compromised.

NanoClaw’s product design is built around that kind of orchestration. The platform sits on top of Claude Code and adds persistent memory, scheduled tasks, messaging integrations and routing logic so agents can be assigned work across channels such as WhatsApp, Telegram, Slack and Discord. The release says this can all be configured from a phone, without writing custom agent code, while each agent remains isolated inside its own container runtime.

Cohen said one practical goal of the Docker integration is to make that deployment model easier to adopt. “People will be able to go to the NanoClaw GitHub, clone the repository, and run a single command,” he said. “That will get their Docker Sandbox set up running NanoClaw.”

That ease of setup matters because many enterprise AI deployments still fail at the point where promising demos have to become stable systems. Security features that are too hard to deploy or maintain often end up bypassed. A packaging model that lowers friction without weakening boundaries is more likely to survive internal adoption.

An open-source partnership with strategic weight

The partnership is also notable for what it is not. It is not being positioned as an exclusive commercial alliance or a financially engineered enterprise bundle.

“There’s no money involved,” Cavage said. “We found this through the foundation developer community. NanoClaw is open source, and Docker has a long history in open source.”

That may strengthen the announcement rather than weaken it. In infrastructure, the most credible integrations often emerge because two systems fit technically before they fit commercially. Cohen said the relationship began when a Docker developer advocate got NanoClaw running in Docker Sandboxes and demonstrated that the combination worked.

“We were able to put NanoClaw into Docker Sandboxes without making any architecture changes to NanoClaw,” Cohen said. “It just works, because we had a vision of how agents should be deployed and isolated, and Docker was thinking about the same security concerns and arrived at the same design.”

For enterprise buyers, that origin story signals that the integration was not forced into existence by a go-to-market arrangement. It suggests genuine architectural compatibility.

Docker is also careful not to cast NanoClaw as the only framework it will support. Cavage said the company plans to work broadly across the ecosystem, even as NanoClaw appears to be the first “claw” included in Docker’s official packaging. The implication is that Docker sees a wider market opportunity around secure agent runtime infrastructure, while NanoClaw gains a more recognizable enterprise foundation for its security posture.

The bigger story: infrastructure catching up to agents

The deeper significance of this announcement is that it shifts attention from model capability to runtime design. That may be where the real enterprise competition is heading.

The AI industry has spent the last two years proving that models can reason, code and orchestrate tasks with growing sophistication. The next phase is proving that these systems can be deployed in ways security teams, infrastructure leaders and compliance owners can live with.

NanoClaw has argued from the start that agent security cannot be bolted on at the application layer. Docker is now making a parallel argument from the runtime side. “The world is going to need a different set of infrastructure to catch up to what agents and AI demand,” Cavage said. “They’re clearly going to get more and more autonomous.”

That could turn out to be the central story here. Enterprises do not just need more capable agents. They need better boxes to put them in.

For organizations experimenting with AI agents today, the NanoClaw-Docker integration offers a concrete picture of what that box might look like: open-source orchestration on top, MicroVM-backed isolation underneath, and a deployment model designed around containment rather than trust.

In that sense, this is more than a product integration. It is an early blueprint for how enterprise agent infrastructure may evolve: less emphasis on unconstrained autonomy, more emphasis on bounded autonomy that can survive contact with real production systems.

Modules snap into position and leap forward with a bounce over gravel or mud. Each robot is a stand-alone entity, a half-meter chunk made up of two stiff links connected by a central ball. Everything this machine needs to run on its own is inside that ball, including a small circuit board for decision-making, a battery for electricity, and a motor for movement. On its own, one of these little modules can just roll along, perform a sharp turn, or leap into the air, but when three or five are combined, you create bodies with legs that can switch positions at any time. Some of them serve as supports, while others push or strive to balance things out.

Northwestern University researchers got things started by running evolutionary software on a computer. They supplied it these basic modules as raw material and then let it run wild, mixing and matching connections thousands of times to explore how different body shapes would travel through a simulated environment. Who moved the fastest and had the best balance? They went with that shape. They repeatedly made minor changes and chose new victors, none of whom they had come up with themselves. Once they got the best virtual competitors lined up, they assembled the real modules in the same way and conducted some real-world testing.

Unitree G1 Humanoid Robot（No Secondary Development）

• Sleek & Durable Design: Standing at 132cm tall and weighing only approx. 35kg, the G1 is constructed with aerospace-grade aluminum alloy and carbon…
• High Flexibility & Safe Movement: Boasting 23 joint degrees of freedom (6 per leg, 5 per arm), it offers an extensive range of motion. For safety, it…
• Smart Interaction & Connectivity: Powered by an 8-core high-performance CPU and equipped with a depth camera and 3D LiDAR. It supports Wi-Fi 6 and…

These modules can link up almost anywhere, so a leg on one body can transform into a spine or tail on another; they can basically reorganize themselves on the fly. This indicates that these items can work together and solve problems on their own without the need for outside assistance. When they are first released in an open area, they begin moving immediately and can easily navigate uneven terrain such as tree roots or sand patches. One of them will wriggle barely above the ground, another will take small leaps, and a third will spring up with each stride. All of this is accomplished only via the use of sensors within their own joints and bodies to steer and maintain stability.

Once trained, they can perform gymnastics with ease, such as flipping one of the modules onto its back and rolling or twisting until it is upright again. When they jump, they can even spin around in the air before landing and continuing their journey. And when put to the test in real-world outdoor settings, they perform admirably, outperforming fixed robots that typically stall or flip over.

Damage them slightly and they just keep going; you can even cut off a leg (or however many) and the beast will simply redistribute the effort and keep trucking. The severed piece will just roll away by itself, rejoin the group, and snap back into place. If you break the whole thing into separate pieces, each of those little modules can continue to function on its own, rolling or hopping around as if it was never a part of anything larger in the first place.

Great loudspeakers come from all corners of the world, and Denmark has produced more than its fair share of them. Buchardt Audio may not have the century long legacy of some of its Nordic neighbors, but the company has built a loyal following over the past decade with a simple formula: compact loudspeakers that deliver a far bigger, fuller sound than their modest size might suggest.

That approach was cemented with the original S400, which we reviewed in 2021 and quickly became one of the most recommended standmount speakers in its class. Buchardt refined the concept shortly thereafter with the S400 MKII, introducing a revised crossover and higher quality internal components, followed by the S400 MKII Signature with further tuning refinements. For 2026, the Danish brand is pushing the platform forward once again with the announcement of the S400 MK3, the next chapter in a design that has been evolving since its first prototype appeared in 2016.

Buchardt Audio S400 MK3: Larger Cabinet, New Drivers, and a Ground Up Redesign

The S400 MK3 represents a significant redesign of Buchardt’s most recognizable loudspeaker platform. The new model introduces a larger cabinet, newly developed drivers, improved treble resolution, and greater dynamic capability, while aiming to preserve the balanced and natural presentation that helped define the earlier S400 models. According to Buchardt, almost nothing from the previous S400 generation carries over to the MK3, with the exception of the speaker binding posts.

One of the most visible changes with the S400 MK3 is the increase in cabinet size. Buchardt has expanded the internal volume by roughly 18 percent, giving the designers more room to extend low frequency performance and increase overall dynamic headroom. Despite the added volume, the company says the cabinet retains the clean proportions and minimalist aesthetic that made the original S400 such a recognizable design.

S400 MK3 Tweeter: New 26 mm Aluminum Dome with Custom Waveguide

The S400 MK3 features a newly developed 26 mm (1.02-inch) aluminum dome tweeter paired with a redesigned waveguide. Rather than chasing extra brightness or exaggerated top end energy, Buchardt focused on improving refinement and perceived resolution.

The goal was greater clarity and treble detail while maintaining the smooth, natural presentation that has defined the S400 series. According to the company, the new tweeter and waveguide combination helps deliver more precise high frequency dispersion and improved integration with the midbass driver, without introducing listening fatigue.

S400 MK3 Woofer: 7.5-Inch SB Acoustics Driver with Satori PAPYRUS Cone

At the core of the S400 MK3 is a 7.5-inch woofer sourced from SB Acoustics, built around the company’s Satori PAPYRUS paper cone technology. Compared with the driver used in the S400 MKII, the new unit offers 65.5 percent greater displacement headroom, giving the MK3 significantly more room to move air.

The result is increased dynamic capability, stronger bass authority, and more physical impact from a standmount speaker that still maintains relatively compact proportions. Combined with the 18 percent increase in cabinet volume, the new woofer gives the S400 MK3 greater low frequency extension and overall scale than previous versions of the design.

S400 MK3 Bass System: Rear Port Replaces the Passive Radiator

Rather than continuing with the passive radiator used in earlier S400 models, the S400 MK3 adopts a rear mounted portand a traditional bass reflex design. The change allows Buchardt to deliver bass that remains tight, extended, and powerful while taking advantage of the larger cabinet and higher displacement woofer.

The trade off with any ported design is the possibility of minimal port noise under very specific conditions, but in normal listening this should remain largely inaudible. Placement remains relatively flexible for a rear ported speaker; Buchardt recommends leaving at least 5 cm (2 inches) of space between the port and the rear wall to ensure proper airflow and consistent low frequency performance.

S400 MK3 Crossover: Simplified Network with Premium Jantzen Components

Buchardt continues its partnership with Jantzen Audio for the S400 MK3’s crossover network. The updated design uses premium components throughout the signal path but adopts a somewhat simpler topology than the network used in the previous S400 MKII.

The new crossover employs first order filters, a design choice often associated with improved phase coherence and a more natural integration between drivers. While simpler in layout, the network still relies on high quality components to maintain the tonal balance and musical flow that have defined the S400 platform.

Comparison

The Bottom Line 

The Buchardt Audio S400 MK3 continues a design philosophy that Buchardt clearly believes in: compact standmount speakers engineered to deliver the scale, bass authority, and dynamics normally associated with much larger loudspeakers. With its larger cabinet, higher displacement woofer, new waveguided tweeter, and simplified crossover, the MK3 looks like a meaningful evolution of a platform that has been steadily refined since 2016.

At $2,400 USD per pair, the S400 MK3 enters one of the most competitive segments in the loudspeaker market. Models from KEF, Wharfedale, Paradigm, Acoustic Energy, and Chesky Audio are all fighting for the same buyers looking for high performance from relatively compact cabinets. The S400 MK3’s appeal will likely come down to its reputation for delivering big, room filling sound and strong bass output from a standmount design, making it a compelling option for listeners who want serious scale without moving to floorstanding speakers.

Pricing and Availability

The S400 MK3 is available through Buchardt’s website for pre-order, with 75 pairs offered per finish at the introductory price (see below). Estimated delivery is expected to start by late Summer 2026.

• S400 – $2,400 in black/white or $2,574 in real wood veneer (US)
• S400 – €2,300 in black/white or €2,450 in real wood veneer (EU)

Pre-order now at buchardtaudio.com and save €200.

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Smartphone enthusiasts like me will never forget the time when modular phones were about to change everything. Whether it was Google’s canceled Project Ara or the more popular Moto Z series, nothing really panned out as well as the Fairphone, but even it isn’t as mainstream as some other brands. 

So, most users just live with their non-modular smartphones on a day-to-day basis, having already grieved about the idea that once raised plenty of hopes. Well, about that, an upcoming foldable could change that conversation entirely. 

What exactly is Xiaomi cooking?

I’m talking about the Xiaomi Mix Fold 5. Early leaks about it suggest that the company is developing a magnetic modular camera system, wherein users should be able to swap, adjust, or upgrade the magnetic camera modules by attaching a better one, or the one that the user wants (via Gizchina). 

Essentially, the Mix Fold 5’s magnetic camera module would mimic the interchangeable lenses on professional cameras. If the interchangeable interface actually ships, it could be the most exciting camera hardware on a smartphone in years, or one of the most exciting after Xiaomi’s physical zoom ring on the Xiaomi 17 Ultra.

However, I have a rather hot take about it: shipping a modular camera system (with interchangeable lenses) with a foldable could either be a very smart idea, or a rather complicated idea that would keep the exciting tech from the hands of masses. 

Smart idea or a complicated one?

Foldables already come with slightly toned-down camera systems compared to their brand’s conventional flagship (see the differences on the Fold 7 and S26 Ultra). A modular system that sidesteps that limitation entirely could be a clever solution to this problem. On the other hand, foldables don’t sell as much as regular smartphones, and the brand might not get as much attention as the tech deserves. 

The silver lining here is that the Chinese smartphone manufacturer isn’t partnering with a third-party lens or camera equipment manufacturing company. The leaks focus on self-developed modular lenses throughout. 

However, that is the extent of information we have about the purported smartphone. No confirmed specs, pricing, or launch date yet.

Raquel Urtasun has spent 16 years in the self-driving space, long enough to navigate every metaphorical glorious hill and plunging valley. She took the trip from the early “pipe dream” dismissals, to the “we’re this close” certainty, and back again.

The industry is now riding a new wave of optimism and investment, including at Waabi Innovation Inc., the autonomous trucking company that Urtasun founded in 2021. The Spanish-Canadian professor at the University of Toronto, and former chief scientist of Uber’s Advanced Technologies Group, has helped make Waabi a key player. Beginning in fall 2023, theToronto-based startup has been running geofenced cargo routes from Dallas to Houston in a fleet of retrofitted Peterbilt semis, navigating even residential streets in loaded, 36,000-kilogram (80,000-pound) behemoths with a human “safety observer” on board.

In October, the company reached a milestone by integrating its “Waabi Driver” physical-AI system in Volvo’s new VNL Autonomous truck, which the Swedish automaker is building in Virginia. That self-driving solution uses Nvidia’s Drive AGX Thor, an AI-based platform for autonomous and software-defined vehicles.

In January, the Toronto-based startup raised $750 million in its latest funding round to accelerate commercial development in autonomous trucking, and expand its system into the fiercely competitive robotaxi space. Backers include Khosla Ventures, Nvidia, and Volvo.

Urtasun says the Waabi Driver can scale across a full range of vehicles, geographies and environments—although snowstorms can still create a no-go zone for now. It’s powered by what Urtasun calls the industry’s most advanced neural simulator. The verifiable, end-to-end AI model will be a “shared brain” that partners can transplant into cars, trucks, and pretty much anything on wheels. The idea is to grab a chunk of a global autonomous trucking business that McKinsey estimates could be worth more than $600 billion a year by 2035; with autonomous haulers responsible for 15 percent of total U.S. trucking miles as early as 2030.

Backed by an additional $250 million from Uber, Waabi plans to deploy at least 25,000 autonomous taxis through Uber’s ride-hailing service, whose world-dominating reach encompasses 70 countries, about 15,000 cities and more than 200 million monthly users.

Urtasun spoke with IEEE Spectrum about how Waabi is counting on sensors and simulation to prove real-world safety; and why the move to autonomy is a moral imperative that outweighs the disruption for human drivers—whether they’re driving trucks or family sedans. Our conversation was edited for length and clarity.

IEEE Spectrum: Until quite recently, autonomous tech seemed to have hit a wall, at least in the public’s mind. Now investors are flooding the zone again, and companies are all-in. What happened?

Raquel Urtasun: There were a lot of empty promises, or [people] not realizing the complexity of the problem. There was a realization that actually, this problem is harder than people anticipated. It’s also because of the type of technology that was developed at the time, what we call “AV 1.0”. These are hand-engineered systems that need to be brute-forced by humans. You need lots of capital and a massive amount of miles on the road just to get to the first deployment.

What you see with the next generation—AV 2.0 and systems that can reason—is that you finally have a solution that scales. When we started the company, this was a very contrarian view. But today, the breakthroughs in AI have made it clear that this is the next big revolution. It’s not just about more compute; it’s about building a brain that can generalize. That is the “aha moment” the industry is having now.

Even for someone who believes in the tech, seeing a driverless semi-trailer in your rear-view mirror might be unsettling. Now you’ve integrated your tech into the aerodynamic, diesel-powered Volvo VNL Autonomous truck. How do you convince regulators and the public that these trucks belong on the street?

Urtasun: Safety, when you think about carrying 80,000 pounds on this massive rig, is definitely top of mind. We believe the only way to do this safely is with a redundant platform that is fully developed and validated by the OEM, not with a retrofit. The OEM does a special type of truck that has all the redundant steering, power, and braking, so that no matter what happens, there is always a way we can interface and activate that truck in a safe manner. Then we are responsible for the sensors, the compute, and obviously the brain that drives those trucks.

AI’s Impact on Trucking Jobs

One of the biggest points of contention is the displacement of human drivers. As AI disrupts a range of workplaces, how do respond to people who say this will eliminate good-paying, blue-collar jobs?

Urtasun: The way we see this is that everybody who’s a truck driver today, and wants to retire as a truck driver, will be able to do so. This is physical AI; this is not like the digital world where suddenly you can switch immediately to this technology. That adoption and scaling is going to take time. There will also be many jobs created with this technology; remote operations, terminal operations, and other things. You have time to change the form of labor of being on the road, which is for weeks at a time—and it’s a really difficult and dehumanized job, let’s be honest—to something you can do locally. There was an interesting [U.S.] Department of Transportation study that showed because of this gradual adoption, there will be more jobs created than actually removed.

You’ve spoken about a personal motivation behind this. Why do you believe the advantages of autonomy outweigh any growing pains, including the potential for unexpected accidents or even deaths?

Urtasun: There are 2 million deaths on the road globally per year, and nobody’s questioning that. That’s the status quo. If you think the machines have to be perfect to deploy, you are actually sacrificing many humans along the way that you could have saved. Human error in accidents is between 90 percent and 96 percent. Those could be preventable accidents. Some accidents will always be unavoidable; a tire could blow for a machine the same as it could for a human. But the important comparison is how much safer we are. This technology is the answer to many, many things.

Most of the industry is focused on “hub-to-hub” highway driving. But you’ve argued that Waabi’s AI can handle the complexity of local streets.

Urtasun: The rest of the industry has gone with this business model where you need hubs next to the highway. This adds a lot of friction and cost. Thanks to our verifiable end-to-end AI system, we can drive in surface [local] streets. We can do unprotected lefts, traffic lights, and tight turns. These core capabilities enable us to drive all the way to the end customer. We are already hauling commercial loads for customers like Samsung through our Uber Freight partnership.

You’ve mentioned that Waabi doesn’t like to talk about “number of miles” driven as a metric. For an engineering audience, that sounds counterintuitive. How does your “simulation-first” approach replace the need for real-world road time?

Urtasun: In the industry, miles have been used as a proxy for advancement. How many miles does Tesla need to drive to see any of these situations? But we are a simulation-first company. Waabi World can simulate all the sensors, the behaviors of humans, everything. It is the only simulator where you can mathematically prove that testing and driving in simulation is the same as driving in the real world. You can expose the system to billions of simulations in the cloud. This is what allows us to be so capital efficient and fast.

Verifiable AI vs. Black Box Systems

What is the difference between your “interpretable” AI and the “black box” systems we see elsewhere?

Urtasun: We’ve seen an evolution on passenger cars for level– 2+ systems to end-to-end, black box architectures. But those are not verifiable. You cannot validate and verify those systems, which is a massive problem when you think about regulators and OEMs trusting that technology.

What Waabi has built is end-to-end, but fully verifiable. The system is forced to interpret what it is perceiving and use those interpretations for reasoning, so that it can understand the consequences of every action. It is much more akin to how our brain actually works; your “Type 2” thinking, where you start thinking about cause and effect and consequences, and then you typically do a much better choice in your maneuver.

Tesla is famously, and controversially, relying on camera data almost exclusively to run and improve its self-driving systems. You’re not a fan of that approach?

Urtasun: We use multiple sensors: lidar, camera, and radar. That’s very important because failure modes of those sensors are very different and they’re very complementary. We don’t compromise safety to reduce the bill- of- materials cost today.

Those (passenger car) level-2+ systems are not architected for level 4, where there’s no human on board. People don’t necessarily realize there is a huge difference in terms of the bar when there is no human to rely on. It’s not, “Well, if I don’t have a lot of system interventions, I’m almost there.” That’s not a metric. We are native level 4. We decide which areas the system can drive in, and in what conditions. We are building technology that can drive different form factors—trucks or robotaxis—with the same brain.

Editor’s note: This article was updated on 13 March to correct an error in the original post. Contrary to what was stated in the original post, the trucks being driven from Dallas to Houston do have a human observer on board.

In October 2025, Atlassian co-founder and CEO Mike Cannon-Brookes appeared on the 20VC podcast and said something that now reads rather differently. Technology creation, he argued, is ‘not output-bound.’ Atlassian would employ more engineers in five years, not fewer. They would just be more efficient.

On 11 March 2026, Mike Cannon-Brookes sent a memo to staff announcing 1,600 redundancies, approximately 10% of the company’s global workforce, framed as a necessary adaptation to the AI era.

The same week, the company’s shares had lost more than half their value since January, swept up in a sector-wide rout that traders have been calling the ‘SaaSpocalypse’: a sustained selloff in enterprise software stocks driven by investor fears that AI agents could make conventional SaaS tools obsolete.

The stated rationale in the memo was future-facing. Atlassian said the cuts would allow it to ‘self-fund further investment in AI and enterprise sales, while strengthening our financial profile.’ Cannon-Brookes was careful in his framing.

“Our approach is not ‘AI replaces people.’ But it would be disingenuous to pretend AI doesn’t change the mix of skills we need or the number of roles required in certain areas. It does. This is primarily about adaptation. We are reshaping our skill mix and changing how we work to build for the future.”

More than 900 of the affected roles are in software research and development, according to reporting from Implicator.ai.

By geography, North America bears the largest share, 40% of cuts, or around 640 people, followed by Australia at 30% (roughly 480), and India at 16% (around 250). The remainder is spread across Japan, the Philippines, and offices across Europe, the Middle East, and Africa. Atlassian filed a WARN notice in Washington state showing 63 affected workers, the majority of them remote employees in engineering and data science roles.

Departing employees will receive a minimum 16-week separation package, with one additional week per year of service, a pro-rated FY26 bonus, a $1,000 technology payment, and six months of extended healthcare coverage.

The restructuring is expected to cost between $225 million and $236 million in total charges, split between severance ($169–174 million) and office space reductions ($56–62 million), with most costs landing in the company’s third fiscal quarter. Atlassian said the cuts should be largely complete by the end of June 2026.

Alongside the layoffs, Atlassian confirmed that Rajeev Rajan will step down as chief technology officer on 31 March, after nearly four years in the role. Rajan, previously a VP of engineering at Meta and a two-decade veteran of Microsoft, has not publicly commented on the departure.

The company described it as a generational transition: two executives, Taroon Mandhana and Vikram Rao, will split the CTO responsibilities going forward. Mandhana, formerly Atlassian’s head of engineering for AI and products, takes the title of CTO Teamwork; Rao, previously chief trust officer, becomes CTO Enterprise and Chief Trust Officer. Atlassian described both as ‘next generation AI talent.’

The restructuring sits in some tension with Atlassian’s underlying trading performance. The company reported cloud revenue of approximately $1.067 billion in its most recent quarter, up 26% year on year. Remaining performance obligations, a forward indicator of contracted future revenue, stood at roughly $3.814 billion, up 44%.

Its Rovo AI assistant crossed five million monthly active users in February, and the company now counts more than 600 customers generating over $1 million in annual recurring revenue.

Atlassian reaffirmed its full-year financial guidance for the fiscal year ending June 2026 in the same SEC filing that disclosed the cuts.

That combination, strong operational metrics, healthy forward revenue, and 1,600 redundancies, has sharpened scrutiny of what is actually driving these decisions across the industry.

OpenAI CEO Sam Altman described the practice of using AI as justification for cuts made for other reasons as ‘AI washing’ in February, noting that fewer than 1% of 2025 job losses could be directly attributed to artificial intelligence.

Atlassian is not alone in the pattern.

Block cut roughly 4,000 employees last month as Jack Dorsey declared a shift to an ‘intelligence-native’ model, and the stock jumped the day after the announcement. WiseTech Global, another Sydney-based software firm, announced 2,000 cuts over two years. Oracle said AI was enabling it to shrink some development teams.

By early March 2026, tech layoffs globally had surpassed 45,000, according to RationalFX, with AI among the most frequently cited justifications. Whether the technology is genuinely driving workforce changes or serving as a convenient explanation for restructuring decisions shaped primarily by investor pressure is a question that has no clean industry-wide answer, and that Atlassian’s own numbers make harder, not easier, to resolve.

Atlassian has been unprofitable since 2017. Its shares were down around 33% for 2025 before the SaaSpocalypse selloff, and have fallen more than 84% from the peak they reached in 2021 during the pandemic-era surge in cloud-based collaboration tools.

The stock rose around 2% in after-hours trading following the restructuring announcement, the same market logic that rewarded Block, and that Cannon-Brookes might have had in mind when he wrote, with notable precision, about the bar for what ‘great’ looks like for software companies having gone up.

Google is rolling out one of the biggest updates to Google Maps in years, powered by its Gemini AI models.

The changes introduce a new conversational feature called Ask Maps. There is also a redesigned navigation experience that Google says is its largest driving upgrade in more than a decade.

Ask Maps turns the app into something closer to a travel assistant than a traditional map. Instead of searching for individual places, users can ask natural questions such as where to charge a phone nearby without waiting in line. Or, for genuine convenience, users can ask where to find a public tennis court with lights at night. Maps then responds conversationally, showing recommendations directly on the map.

Ever had one of those mornings where your hair simply refuses to cooperate, leaving you wrestling with frizz or uneven strands while the clock keeps ticking before work, school, or a night out?

That is why this current deal is worth noticing, with the ghd original hair straightener & styler now £103.97 instead of its usual £149 retail price, bringing a well-known salon styling tool into far more approachable territory in the Spring Deal event.

The ghd Original drops 30%, making it an easy deal-of-the-day pick

The ghd Original is now over $45 off, ideal for shoppers who want salon‑quality styling without overspending.

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The ghd original hair straightener & styler focuses on consistent styling performance rather than extreme heat, something many people appreciate after dealing with cheaper straighteners that either struggle to smooth hair or damage it.

Instead of pushing temperatures higher, the tool uses single-zone ceramic technology that maintains a steady styling temperature of 185°C across both plates, widely considered the sweet spot for styling hair effectively without unnecessary heat stress.

Its smooth ceramic floating plates glide easily through sections of hair without snagging, which becomes especially helpful when styling quickly before heading out or preparing for an event.

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The ceramic coating also helps leave hair looking smoother with a subtle glossy finish, creating the kind of polished appearance many people associate with freshly styled salon hair.

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Although many people buy this ghd primarily to straighten hair, its rounded barrel design also allows the straightener to twist slightly while styling, making it easy to create soft curls or loose waves.

This versatility can simplify everyday routines considerably, especially for people who prefer keeping their beauty tools minimal rather than juggling several devices.

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The straightener heats up in just thirty seconds, which is particularly helpful during rushed mornings, while an automatic sleep mode switches the device off after thirty minutes of inactivity for added peace of mind.

With the price currently reduced to £103.97 with over £45 off, the ghd original hair straightener & styler becomes a particularly appealing option for anyone wanting dependable styling results without stepping into extremely expensive salon tools.

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