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How Smart Devices Are Actually Built: An Engineer’s View Pick up any smart device you own. A doorbell that recognizes faces, a watch that reads your heart rhythm, a thermostat that learns when you leave for work. They feel simple. You tap, they respond. That simplicity is a lie. A useful one, but a lie. Behind the clean app and the satisfying click is a stack of engineering decisions that most people never see. And the gap between a device that works for five years and one that dies in eight months almost always traces back to those invisible choices. So let’s look at what actually goes into building the connected gadgets shipping in 2026. Smart starts with the circuit board, not the cloud Most coverage of smart devices jumps straight to AI features and voice assistants. But the foundation is physical. A device is a printed circuit board, a microcontroller, a fistful of sensors, a radio, and a battery, all crammed into a shell that has to survive being dropped, sat on, and left in a hot car. This is where hardware development does its quiet, unglamorous work. Engineers pick a microcontroller based on how much computing the device needs versus how little power it can afford to burn. They route signal traces on the board so a Wi-Fi radio doesn’t drown out a delicate sensor reading. They run the whole thing through thermal testing, drop testing, and certification for FCC and CE marks before it can legally ship. Get this layer wrong, and no amount of clever software saves you. A poorly designed board produces flaky sensor data. Bad antenna placement means the device drops off your network the moment you walk to the next room. These aren’t software bugs. You can’t patch your way out of a physics problem. The companies building good hardware treat the proof-of-concept stage as a real checkpoint. They wire up development boards and modular parts to test the core idea cheaply, before committing to a custom design that costs real money to manufacture. It’s the boring discipline that separates products from expensive paperweights. Firmware is where the device actually thinks Sitting on top of the hardware is firmware. This is the low-level code that tells the chip what to do, when to wake up, how to read a sensor, and when to phone home. People mix up firmware and software all the time, so here’s the clean split. Software runs on your phone or in the cloud and handles the screens you tap. Firmware lives inside the device and controls the hardware directly. Firmware is genuinely hard to write well. The constraints are brutal. A typical IoT microcontroller has a tiny amount of memory, often measured in kilobytes, and it might run on a coin cell that needs to last a year. Every line of code competes for space and power. Then there’s timing. A lot of devices need deterministic, real-time behavior, meaning a sensor reading has to be processed within a fixed window or the whole thing falls apart. A heart monitor that processes a beat “eventually” is useless. The firmware has to guarantee it happens now. If you want the deep version of how this gets built in practice, Yalantis published a solid breakdown of firmware development for embedded IoT devices that covers architecture, power management, and the over-the-air update workflows that keep a device current after it ships. The OTA piece matters more than it sounds. A device that can’t safely update its own firmware is frozen in time the day it leaves the factory. Connectivity is a series of trade-offs Your smart device has to talk to something. Your phone, your router, a cloud server, or all three. Choosing how it talks is one of the most consequential engineering calls in the whole project, and there’s no single right answer. Bluetooth Low Energy sips power and works great for a wearable talking to your phone, but its range is short and it can’t reach the internet on its own. Wi-Fi reaches everything but drains batteries fast. LoRaWAN travels for miles on almost no power, which is perfect for a soil sensor in a field, but it carries tiny amounts of data slowly. Cellular options like NB-IoT and LTE-M let a device work anywhere there’s a signal, with the catch of ongoing data costs and bigger power draw. Engineers usually mix these. A fitness band might use BLE to sync with your phone, and your phone carries the data the rest of the way. An industrial sensor in a remote location might use LoRaWAN to a gateway, which then forwards everything over cellular. The “right” combination depends entirely on power budget, data volume, range, and cost, which is exactly why this decision gets made early and gets revisited often. Sensors and the messy job of trusting them A smart device is only as good as the data it collects. And raw sensor data is messy. Take a simple temperature reading. The sensor drifts over time. It gets warmed by the heat of the chip sitting next to it. It returns noisy values that jitter up and down even when nothing changes. Firmware has to calibrate, filter, and sanity-check all of it before the device acts on a single number. This gets serious fast in regulated fields. A continuous glucose monitor or a medical wearable can’t ship a reading that’s “close enough.” The sensor design, the calibration, and the firmware that validates the data all have to meet standards that consumer gadgets never face. The engineering bar is much higher, and the cost of getting it wrong is measured in patient safety, not customer reviews. For everyday devices the stakes are lower, but the principle holds. Good devices spend a lot of hidden effort turning unreliable physical signals into numbers you can actually trust. Where the AI hype meets the silicon Here’s the part that has changed most recently. A growing share of smart devices now run machine learning models directly on the chip instead of sending everything to the cloud. This is edge computing, and it’s reshaping how devices get built. The appeal is obvious. Processing data on the device means lower latency, since you’re not waiting on a round trip to a server. It means better privacy, because your data never leaves your hand. And it means the device keeps working when your internet goes down. The catch is that running a model on a chip with kilobytes of memory is an engineering puzzle. Models have to be shrunk, quantized, and optimized until they fit in the space available without melting the battery. The face-recognition that runs locally on a modern doorbell is a heavily compressed version of what would run on a server. Squeezing it down to fit is real, specialized work, and it’s increasingly where the competitive difference between two similar gadgets actually lives. Security can’t be the last step For years, connected devices treated security as an afterthought. Ship the product, patch problems later. That approach has aged badly. Outdated firmware is now one of the most common ways attackers break into IoT systems. Research from the security firm ONEKEY found that vulnerable firmware accounts for a large majority of successful attacks on connected devices. Once an attacker is inside one poorly secured gadget on your network, they have a foothold to reach everything else. Building security in from the start means encrypting data both when it’s stored on the device and when it travels to the cloud. It means signing firmware updates so a device only accepts legitimate code, not something an attacker swapped in. And it means designing for recovery, so a compromised device can be safely reset and restored rather than turned into a permanent liability sitting on your shelf. This is the layer consumers never think about and pay the most for when it’s done badly. Why the next generation is harder to build Smart devices are getting more capable, and that capability has a cost that lands squarely on the engineering team. More on-device intelligence. Stricter privacy rules. Longer battery expectations. Tighter security. Regulatory scrutiny that used to apply only to medical gear now creeping toward consumer products too. None of this shows up in the marketing. The ad shows a person tapping a screen and a light turning on. What it doesn’t show is the year of board revisions, firmware rewrites, connectivity tests, and security audits that made that tap reliable. So the next time a smart device just works, give a small nod to the invisible stack underneath. The clean experience on the surface is the product of a lot of unglamorous engineering refusing to cut corners. That refusal is the whole difference between a gadget you trust and one you return. #Smart #Devices #Built #Engineers #Viewengineering,smart devices

How Smart Devices Are Actually Built: An Engineer’s View

Pick up any smart device you own. A doorbell that recognizes faces, a watch that reads your heart rhythm, a thermostat that learns when you leave for work. They feel simple. You tap, they respond.

That simplicity is a lie. A useful one, but a lie.

Behind the clean app and the satisfying click is a stack of engineering decisions that most people never see. And the gap between a device that works for five years and one that dies in eight months almost always traces back to those invisible choices. So let’s look at what actually goes into building the connected gadgets shipping in 2026.

Smart starts with the circuit board, not the cloud

Most coverage of smart devices jumps straight to AI features and voice assistants. But the foundation is physical. A device is a printed circuit board, a microcontroller, a fistful of sensors, a radio, and a battery, all crammed into a shell that has to survive being dropped, sat on, and left in a hot car.

This is where hardware development does its quiet, unglamorous work. Engineers pick a microcontroller based on how much computing the device needs versus how little power it can afford to burn. They route signal traces on the board so a Wi-Fi radio doesn’t drown out a delicate sensor reading. They run the whole thing through thermal testing, drop testing, and certification for FCC and CE marks before it can legally ship.

Get this layer wrong, and no amount of clever software saves you. A poorly designed board produces flaky sensor data. Bad antenna placement means the device drops off your network the moment you walk to the next room. These aren’t software bugs. You can’t patch your way out of a physics problem.

The companies building good hardware treat the proof-of-concept stage as a real checkpoint. They wire up development boards and modular parts to test the core idea cheaply, before committing to a custom design that costs real money to manufacture. It’s the boring discipline that separates products from expensive paperweights.

Firmware is where the device actually thinks

Sitting on top of the hardware is firmware. This is the low-level code that tells the chip what to do, when to wake up, how to read a sensor, and when to phone home. People mix up firmware and software all the time, so here’s the clean split. Software runs on your phone or in the cloud and handles the screens you tap. Firmware lives inside the device and controls the hardware directly.

Firmware is genuinely hard to write well. The constraints are brutal. A typical IoT microcontroller has a tiny amount of memory, often measured in kilobytes, and it might run on a coin cell that needs to last a year. Every line of code competes for space and power.

Then there’s timing. A lot of devices need deterministic, real-time behavior, meaning a sensor reading has to be processed within a fixed window or the whole thing falls apart. A heart monitor that processes a beat “eventually” is useless. The firmware has to guarantee it happens now.

If you want the deep version of how this gets built in practice, Yalantis published a solid breakdown of firmware development for embedded IoT devices that covers architecture, power management, and the over-the-air update workflows that keep a device current after it ships. The OTA piece matters more than it sounds. A device that can’t safely update its own firmware is frozen in time the day it leaves the factory.

Connectivity is a series of trade-offs

Your smart device has to talk to something. Your phone, your router, a cloud server, or all three. Choosing how it talks is one of the most consequential engineering calls in the whole project, and there’s no single right answer.

Bluetooth Low Energy sips power and works great for a wearable talking to your phone, but its range is short and it can’t reach the internet on its own. Wi-Fi reaches everything but drains batteries fast. LoRaWAN travels for miles on almost no power, which is perfect for a soil sensor in a field, but it carries tiny amounts of data slowly. Cellular options like NB-IoT and LTE-M let a device work anywhere there’s a signal, with the catch of ongoing data costs and bigger power draw.

Engineers usually mix these. A fitness band might use BLE to sync with your phone, and your phone carries the data the rest of the way. An industrial sensor in a remote location might use LoRaWAN to a gateway, which then forwards everything over cellular. The “right” combination depends entirely on power budget, data volume, range, and cost, which is exactly why this decision gets made early and gets revisited often.

Sensors and the messy job of trusting them

A smart device is only as good as the data it collects. And raw sensor data is messy.

Take a simple temperature reading. The sensor drifts over time. It gets warmed by the heat of the chip sitting next to it. It returns noisy values that jitter up and down even when nothing changes. Firmware has to calibrate, filter, and sanity-check all of it before the device acts on a single number.

This gets serious fast in regulated fields. A continuous glucose monitor or a medical wearable can’t ship a reading that’s “close enough.” The sensor design, the calibration, and the firmware that validates the data all have to meet standards that consumer gadgets never face. The engineering bar is much higher, and the cost of getting it wrong is measured in patient safety, not customer reviews.

For everyday devices the stakes are lower, but the principle holds. Good devices spend a lot of hidden effort turning unreliable physical signals into numbers you can actually trust.

Where the AI hype meets the silicon

Here’s the part that has changed most recently. A growing share of smart devices now run machine learning models directly on the chip instead of sending everything to the cloud. This is edge computing, and it’s reshaping how devices get built.

The appeal is obvious. Processing data on the device means lower latency, since you’re not waiting on a round trip to a server. It means better privacy, because your data never leaves your hand. And it means the device keeps working when your internet goes down.

The catch is that running a model on a chip with kilobytes of memory is an engineering puzzle. Models have to be shrunk, quantized, and optimized until they fit in the space available without melting the battery. The face-recognition that runs locally on a modern doorbell is a heavily compressed version of what would run on a server. Squeezing it down to fit is real, specialized work, and it’s increasingly where the competitive difference between two similar gadgets actually lives.

Security can’t be the last step

For years, connected devices treated security as an afterthought. Ship the product, patch problems later. That approach has aged badly.

Outdated firmware is now one of the most common ways attackers break into IoT systems. Research from the security firm ONEKEY found that vulnerable firmware accounts for a large majority of successful attacks on connected devices. Once an attacker is inside one poorly secured gadget on your network, they have a foothold to reach everything else.

Building security in from the start means encrypting data both when it’s stored on the device and when it travels to the cloud. It means signing firmware updates so a device only accepts legitimate code, not something an attacker swapped in. And it means designing for recovery, so a compromised device can be safely reset and restored rather than turned into a permanent liability sitting on your shelf.

This is the layer consumers never think about and pay the most for when it’s done badly.

Why the next generation is harder to build

Smart devices are getting more capable, and that capability has a cost that lands squarely on the engineering team. More on-device intelligence. Stricter privacy rules. Longer battery expectations. Tighter security. Regulatory scrutiny that used to apply only to medical gear now creeping toward consumer products too.

None of this shows up in the marketing. The ad shows a person tapping a screen and a light turning on. What it doesn’t show is the year of board revisions, firmware rewrites, connectivity tests, and security audits that made that tap reliable.

So the next time a smart device just works, give a small nod to the invisible stack underneath. The clean experience on the surface is the product of a lot of unglamorous engineering refusing to cut corners. That refusal is the whole difference between a gadget you trust and one you return.

#Smart #Devices #Built #Engineers #Viewengineering,smart devices

Tech-news ,

Pick up any smart device you own. A doorbell that recognizes faces, a watch that reads your heart rhythm, a thermostat that learns when you leave for work. They feel simple. You tap, they respond.

That simplicity is a lie. A useful one, but a lie.

Behind the clean app and the satisfying click is a stack of engineering decisions that most people never see. And the gap between a device that works for five years and one that dies in eight months almost always traces back to those invisible choices. So let’s look at what actually goes into building the connected gadgets shipping in 2026.

Smart starts with the circuit board, not the cloud

Most coverage of smart devices jumps straight to AI features and voice assistants. But the foundation is physical. A device is a printed circuit board, a microcontroller, a fistful of sensors, a radio, and a battery, all crammed into a shell that has to survive being dropped, sat on, and left in a hot car.

This is where hardware development does its quiet, unglamorous work. Engineers pick a microcontroller based on how much computing the device needs versus how little power it can afford to burn. They route signal traces on the board so a Wi-Fi radio doesn’t drown out a delicate sensor reading. They run the whole thing through thermal testing, drop testing, and certification for FCC and CE marks before it can legally ship.

Get this layer wrong, and no amount of clever software saves you. A poorly designed board produces flaky sensor data. Bad antenna placement means the device drops off your network the moment you walk to the next room. These aren’t software bugs. You can’t patch your way out of a physics problem.

The companies building good hardware treat the proof-of-concept stage as a real checkpoint. They wire up development boards and modular parts to test the core idea cheaply, before committing to a custom design that costs real money to manufacture. It’s the boring discipline that separates products from expensive paperweights.

Firmware is where the device actually thinks

Sitting on top of the hardware is firmware. This is the low-level code that tells the chip what to do, when to wake up, how to read a sensor, and when to phone home. People mix up firmware and software all the time, so here’s the clean split. Software runs on your phone or in the cloud and handles the screens you tap. Firmware lives inside the device and controls the hardware directly.

Firmware is genuinely hard to write well. The constraints are brutal. A typical IoT microcontroller has a tiny amount of memory, often measured in kilobytes, and it might run on a coin cell that needs to last a year. Every line of code competes for space and power.

Then there’s timing. A lot of devices need deterministic, real-time behavior, meaning a sensor reading has to be processed within a fixed window or the whole thing falls apart. A heart monitor that processes a beat “eventually” is useless. The firmware has to guarantee it happens now.

If you want the deep version of how this gets built in practice, Yalantis published a solid breakdown of firmware development for embedded IoT devices that covers architecture, power management, and the over-the-air update workflows that keep a device current after it ships. The OTA piece matters more than it sounds. A device that can’t safely update its own firmware is frozen in time the day it leaves the factory.

Connectivity is a series of trade-offs

Your smart device has to talk to something. Your phone, your router, a cloud server, or all three. Choosing how it talks is one of the most consequential engineering calls in the whole project, and there’s no single right answer.

Bluetooth Low Energy sips power and works great for a wearable talking to your phone, but its range is short and it can’t reach the internet on its own. Wi-Fi reaches everything but drains batteries fast. LoRaWAN travels for miles on almost no power, which is perfect for a soil sensor in a field, but it carries tiny amounts of data slowly. Cellular options like NB-IoT and LTE-M let a device work anywhere there’s a signal, with the catch of ongoing data costs and bigger power draw.

Engineers usually mix these. A fitness band might use BLE to sync with your phone, and your phone carries the data the rest of the way. An industrial sensor in a remote location might use LoRaWAN to a gateway, which then forwards everything over cellular. The “right” combination depends entirely on power budget, data volume, range, and cost, which is exactly why this decision gets made early and gets revisited often.

Sensors and the messy job of trusting them

A smart device is only as good as the data it collects. And raw sensor data is messy.

Take a simple temperature reading. The sensor drifts over time. It gets warmed by the heat of the chip sitting next to it. It returns noisy values that jitter up and down even when nothing changes. Firmware has to calibrate, filter, and sanity-check all of it before the device acts on a single number.

This gets serious fast in regulated fields. A continuous glucose monitor or a medical wearable can’t ship a reading that’s “close enough.” The sensor design, the calibration, and the firmware that validates the data all have to meet standards that consumer gadgets never face. The engineering bar is much higher, and the cost of getting it wrong is measured in patient safety, not customer reviews.

For everyday devices the stakes are lower, but the principle holds. Good devices spend a lot of hidden effort turning unreliable physical signals into numbers you can actually trust.

Where the AI hype meets the silicon

Here’s the part that has changed most recently. A growing share of smart devices now run machine learning models directly on the chip instead of sending everything to the cloud. This is edge computing, and it’s reshaping how devices get built.

The appeal is obvious. Processing data on the device means lower latency, since you’re not waiting on a round trip to a server. It means better privacy, because your data never leaves your hand. And it means the device keeps working when your internet goes down.

The catch is that running a model on a chip with kilobytes of memory is an engineering puzzle. Models have to be shrunk, quantized, and optimized until they fit in the space available without melting the battery. The face-recognition that runs locally on a modern doorbell is a heavily compressed version of what would run on a server. Squeezing it down to fit is real, specialized work, and it’s increasingly where the competitive difference between two similar gadgets actually lives.

Security can’t be the last step

For years, connected devices treated security as an afterthought. Ship the product, patch problems later. That approach has aged badly.

Outdated firmware is now one of the most common ways attackers break into IoT systems. Research from the security firm ONEKEY found that vulnerable firmware accounts for a large majority of successful attacks on connected devices. Once an attacker is inside one poorly secured gadget on your network, they have a foothold to reach everything else.

Building security in from the start means encrypting data both when it’s stored on the device and when it travels to the cloud. It means signing firmware updates so a device only accepts legitimate code, not something an attacker swapped in. And it means designing for recovery, so a compromised device can be safely reset and restored rather than turned into a permanent liability sitting on your shelf.

This is the layer consumers never think about and pay the most for when it’s done badly.

Why the next generation is harder to build

Smart devices are getting more capable, and that capability has a cost that lands squarely on the engineering team. More on-device intelligence. Stricter privacy rules. Longer battery expectations. Tighter security. Regulatory scrutiny that used to apply only to medical gear now creeping toward consumer products too.

None of this shows up in the marketing. The ad shows a person tapping a screen and a light turning on. What it doesn’t show is the year of board revisions, firmware rewrites, connectivity tests, and security audits that made that tap reliable.

So the next time a smart device just works, give a small nod to the invisible stack underneath. The clean experience on the surface is the product of a lot of unglamorous engineering refusing to cut corners. That refusal is the whole difference between a gadget you trust and one you return.

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What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?  

The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details. 

The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there. 

This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.  

Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.  

Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?  

“In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.” 
 
Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.  

There are around 3,600 AI startups in London, which, together, have raised around $12.1 billion out of the $14.8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me. 
 
That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.  

Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.  

Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a $3.3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.  

“The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.” 

This former notorious red-light district is now one of the world’s top AI hubs | TechCrunch What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?   The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details.  The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there.  This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.   Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.   Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?   “In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.”  Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.   There are around 3,600 AI startups in London, which, together, have raised around .1 billion out of the .8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me.  That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.   Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.   Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a .3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.   “The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.”  Image Credits:Phoenix Court Top founders want to stay Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said.  Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.   “Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said.  Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia.  Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said.  Image Credits:Synthesia Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.   Unsurprisingly, London’s AI boom is also causing a talent war.U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up.  “The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.” When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK
Image Credits:Phoenix Court

Top founders want to stay

Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said. 
 
Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.  

“Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said. 
 
Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia. 

Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said. 

Image Credits:Synthesia

Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.  

Unsurprisingly, London’s AI boom is also causing a talent war.

U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up. 

“The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.”

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK">This former notorious red-light district is now one of the world’s top AI hubs | TechCrunch What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?   The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details.  The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there.  This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.   Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.   Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?   “In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.”  Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.   There are around 3,600 AI startups in London, which, together, have raised around .1 billion out of the .8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me.  That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.   Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.   Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a .3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.   “The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.”  Image Credits:Phoenix Court Top founders want to stay Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said.  Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.   “Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said.  Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia.  Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said.  Image Credits:Synthesia Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.   Unsurprisingly, London’s AI boom is also causing a talent war.U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up.  “The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.” When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK

European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.  

Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.  

Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?  

“In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.” 
 
Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.  

There are around 3,600 AI startups in London, which, together, have raised around $12.1 billion out of the $14.8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me. 
 
That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.  

Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.  

Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a $3.3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.  

“The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.” 

This former notorious red-light district is now one of the world’s top AI hubs | TechCrunch What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?   The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details.  The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there.  This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.   Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.   Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?   “In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.”  Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.   There are around 3,600 AI startups in London, which, together, have raised around .1 billion out of the .8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me.  That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.   Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.   Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a .3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.   “The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.”  Image Credits:Phoenix Court Top founders want to stay Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said.  Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.   “Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said.  Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia.  Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said.  Image Credits:Synthesia Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.   Unsurprisingly, London’s AI boom is also causing a talent war.U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up.  “The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.” When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK
Image Credits:Phoenix Court

Top founders want to stay

Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said. 
 
Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.  

“Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said. 
 
Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia. 

Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said. 

Image Credits:Synthesia

Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.  

Unsurprisingly, London’s AI boom is also causing a talent war.

U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up. 

“The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.”

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK">This former notorious red-light district is now one of the world’s top AI hubs | TechCrunch

What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?  

The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details. 

The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there. 

This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.  

Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.  

Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?  

“In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.” 
 
Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.  

There are around 3,600 AI startups in London, which, together, have raised around $12.1 billion out of the $14.8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me. 
 
That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.  

Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.  

Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a $3.3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.  

“The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.” 

This former notorious red-light district is now one of the world’s top AI hubs | TechCrunch What every U.K. AI startup wants to know these days is, how can I get office space in King’s Cross?   The area is so hot that a VC firm allegedly recently won a deal by promising a founder office space in the neighborhood. “We stop at nothing to win deals [for] and to support” founders, “including helping them source office space when needed,” the firm told me when asked about the rumor, declining to confirm or deny any details.  The neighborhood’s popularity began back in 2016 when DeepMind — then newly acquired by Google — moved in. Soon after, a flood of AI startups followed, wanting to be around the Google DeepMind magic. Today, they hope to take advantage of the cluster of AI talent there.  This has transformed King’s Cross into one of the world’s top AI hubs, rivaled only by San Francisco and Beijing. Around London, it’s known by the sobriquet “Knowledge Quarter,” as it’s home to names like OpenAI, Meta, Isomorphic Labs, Cusp AI, Wayne, Recursive, and, a little farther down the road, Synthesia and Anthropic. The European Technology Network (ETN) just moved into a glossy new office nearby, while University College London sits around the corner.   Mixed in with the new developments are trendy food spots like Hoppers and BAO. Hop a train from King’s Cross, and founders can be in Cambridge in 45 minutes to source talent or can be in Paris in two hours to strike a deal.   Who would have guessed that a little more than 20 years ago, this was one of the seediest areas in London?   “In the ’80s, crack and heroin made the area a major narcotics market,” Hussein Kanji, an investor at Hoxton Ventures, said, recalling syringes in tree trunks and gangs patrolling the streets. “In 1982, the local church was occupied by the English Collective of Prostitutes for 12 straight days.” Then, in the early 2000s, a real estate developer had a dream and, well, “now it is the AI hotbed of the United Kingdom,” Kanji said. “What a change.”  Around 18 months ago, his portfolio company BioCorteX moved from the neighborhood Holborn to the Jellicoe building in King’s Cross, hoping to be near the action. “Lots going on in London right now,” Nik Sharma, co-founder of BioCorteX, told me. “Lots of hyperscalers moving in.” That includes, reportedly, Jeff Bezos’ AI company Prometheus, which is also said to be in talks to move into the Jellicoe.   There are around 3,600 AI startups in London, which, together, have raised around .1 billion out of the .8 billion raised in the city since late July, according to Dealroom. Since the start of June, AI-related startups have leased more than 1 million square feet of office space in London, according to the real estate firm Knight Frank. With that, prime rents in King’s Cross have risen 18% over the past three years, Chris Dunn, a commercial insight associate at the firm, told me.  That percentage represents only the largest leases encompassing at least 10,000 square feet, like the ones OpenAI and Prometheus are signing. The shorter deals go for even more, he said, and now the vacancy rate for conventional office space is just 0.9%. “Demand has outstripped supply,” he continued.   Today, one of the big topics of the area is sovereignty. It was a wake-up call for many when Anthropic shut off access to Mythos and Fable this summer, leaving some in the ecosystem to conclude: “We’d better look after ourselves,” Saul Klein, co-founder of the VC firm Phoenix Court, told me.   Phoenix Court is located in the King’s Cross area and has three portfolio companies in the vicinity, including Olix (which just announced a .3 billion valuation), Early Health and CoMind. Robin Klein, co-founder of the firm, said the shutdown of Fable and Mythos access was a “small but sharp reminder that Europe can’t simply rent its AI capabilities and capacity; it needs to build and hold some of its own.” King’s Cross, he said, is where much of this building is actually happening.   “The bigger question,” he continued, “is whether the U.K. builds the infrastructure, compute, energy, capital, to make this self-reliance durable, rather than just hosting outposts of U.S. labs.”  Image Credits:Phoenix Court Top founders want to stay Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said.  Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.   “Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said.  Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia.  Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said.  Image Credits:Synthesia Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.   Unsurprisingly, London’s AI boom is also causing a talent war.U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up.  “The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.” When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK
Image Credits:Phoenix Court

Top founders want to stay

Simon Kohl, founder of Latent Labs, has offices in King’s Cross and San Francisco. The London office, at the moment, is growing faster, and he’s more bullish than ever on the ecosystem, he said. “The mood right now feels less like London trying to catch up and more like London becoming one of the default places to start a serious AI company,” he said. 
 
Look around and you are likely to see Wayve testing its autonomous cars. Founded in 2017 by co-founder Alex Kendall, the unicorn is one of London’s biggest success stories.  

“Ten years ago, building a frontier AI company from London felt like an unusual choice,” Kendall told me. “Now it feels like an obvious one.” Wayve moved into King’s Cross in 2018 looking for a space that could double as a garage — “a rare combination in Central London,” Kendall said. He has watched the ecosystem mature around him — and it’s now evident that a startup can stay in London, raise serious capital, hire world-class AI talent, and remain globally competitive, he said. 
 
Down the street from Anthropic’s new 158,000-square-foot office is the AI agent builder Sierra and the AI video platform Synthesia. 

Laura Gonzalez Florez, Synthesia’s chief of staff and head of people, says the company moved into its glossy new office building a year ago to accommodate its growing team. They were drawn to the area for the same reason as everyone else: “It’s very close to the airport … very close to where a lot of investors are,” she said. 

Image Credits:Synthesia

Around two-thirds of Synthesia’s engineers are remote, Gonzalez Florez said, letting the company tap into an affordable, international, and diverse talent pool and helping it scale faster. “From London, we can hire and work, without any problem, people from anywhere, from Slovenia to Portugal,” she said.  

Unsurprisingly, London’s AI boom is also causing a talent war.

U.K. AI job postings have skyrocketed in the past few years, per data from PwC. When Anthropic announced it moved into town earlier this year, it listed, for example, a salary range of £260,000 to £630,000 for a machine learning research engineer when the average salary in London for the same role is around £102,000. Some founders in the U.K., like those in Silicon Valley, are being forced to raise more and bigger rounds to keep up. 

“The real test is whether more globally significant AI companies are founded, funded, and scaled from the U.K., while continuing to attract the world’s best talent to build them here,” Zain Ali, founder of the King’s Cross-based AI legal firm Centuro, told me. “If that continues to happen, King’s Cross won’t just be an AI hub. It’ll become one of the U.K.’s most important strategic assets.”

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

#Thisformernotorious #redlight #districtis #nowone #worlds #top #hubs #TechCrunchUK
Why Remote Server Access Is a Common Attack Vector

Every cloud server exposes at least one remote access point – usually SSH on port 22 – and that port is scanned constantly by bots looking for weak credentials. Password-based login is the most common way a system can be attacked by brute force, and it is quite common with a standard setup that the system is left in a state where an admin/user of that system – “root user” – can log in directly by guessing the user ID and the password combination. Add in shared team credentials, forgotten firewall rules from old projects, and staging servers left publicly reachable, and it’s clear why misconfigured remote access – not zero-day exploits – is behind most cloud server compromises. The fix is simple, though you’ll need to do some setup yourself instead of trusting the default setting.

How to lock down SSH access in the right way

The best way to secure remote access is through a series of small improvements rather than one major overhaul. The following is a real working method:

  1. Switch to SSH key authentication, then turn off password login completely in the same location: sshd_config. Keys are far harder to brute-force than passwords.
  2. Disable root login over SSH and require a non-root user with sudo privileges instead; this is a security advantage that even without any change, one key compromising would limit the impact.
  3. Change the default SSH port away from 22 to cut down on automated scanning noise (not a security measure on its own, but it reduces log clutter).
  4. Restrict access by IP using your firewall or security group, allowing SSH only from known office or VPN IP ranges rather than the entire internet.
  5. Add a VPN as a connection layer for anyone accessing servers from outside a trusted network – a VPN like Planet VPN’s free VPN service can encrypt the connection between a remote worker’s laptop and the server before SSH traffic ever leaves their device, which matters especially on public wi-fi or shared networks.

Every step you take closes the doors that the attacker may open slightly, and when you combine everything, you would have a door to a series of locked gates.

Why Do I Need a Bastion Host?

When it comes to teams working with many servers, deploying a bastion host (jump box) may be a clean and effective way to solve the long-term problem. With this method, you only expose SSH to the bastion, and the other servers accept it only when it’s from the bastion’s internal IP. This centralizes logging and makes auditing access far simpler.

Using a virtual private network (VPN) and a bastion host together is perfectly fine – and many solutions use both: a safe way to access the private network via a VPN, followed by use of a bastion host to restrict which servers can be accessed. Some teams may decide to forgo the bastion because they don’t have the manpower to manage one, and so use a combination of a VPN and a set of firewall rules that still quite a bit lowers the exposure, but without the extra effort.

Mistakes Leading to Leaving Cloud Servers Open

The Mistakes That Cause Leaving Cloud Servers Open: Even the safest development teams may have slip-ups from time to time. One of the most frequent is leaving staged, or test servers with relaxed firewall rules equivalent to those of the production environment – hackers don’t concern themselves with which environment they first land on. Another frequent error is reusing the same SSH key on several servers and clients, causing one hacked laptop to compromise all servers that key has access to.

Another common human error is not removing or changing access when someone leaves the team, so stale credentials remain valid and accessible. At last, putting trust in the strategy of “security through obscurity” – meaning one thinks that setting up the unusual port or hiding the hostname from the public is actually sufficient security – creates a wrong impression of security. This is mostly true; in reality, modern-day attackers scan all ports anyway, and there is no way to hide from them.

System Administrators Guide to Secure Remote Access 

Prohibit SSH password logins totally and depend exclusively on key-based authentication

  • Surely no one wants to type in the password every time; because of this, use only key-based authentication 
  • Deter the user from logging in as root by default and instead use the sudo command to do root tasks
  • A firewall or security group rule can provide great help with IP address-based restrictions on SSH logins 
  • For access that comes from untrusted sources or external networks, use a secure tunnel like a vpn or bastion host
  • Periodically change SSH keys and inspect access lists
  • Maintain records of login attempts and detect brute-force attacks as quickly as possible

Frequently Asked Questions

What is the principal means by which cloud servers suffer break-ins through remote access?

By far the biggest reason why this can happen is unauthorized password-based SSH logins that have been cracked by brute force. The scenario is most likely to develop where the administrator allows root login via password and leaves the default port open.

Sufficient security measures for the server via SSH keys: Do you think it is safe to rely only on key authentication? 

It is true that SSH keys drastically decrease the danger of a successful brute-force attack. However, if there is only key authentication on the server, there are still risks – the server administrator can always enable root login or disable the IP-based login restrictions. So it is recommended to always have these three in the server configuration: disabled root login, restricted access to known IPs, and rotating the keys regularly as the main components of meaningful protection. 

Is it really necessary to set up a separate VPN network while I am able to connect via SSH keys? 

A VPN gives extra security by first encrypting your computer traffic before it reaches your network, where the SSH connection will be used. This is mostly important when working on untrusted networks – like public wi-fi – where there is a threat of a potential hacker in your local network looking at your data.

Bastion host vs VPN as methods of accessing the server? 

The bastion host works by funneling SSH sessions from many users through a single, carefully monitored server point while the rest of the network (and mostly the servers) remains protected from the internet. At the same time, a vpn will fully encrypt the communication between the user and the corporate network or servers. The combination is very common among larger security teams as a part of the defense-in-depth principle. 

How often should SSH keys be rotated? 

The ideal period to change your SSH keys depends on the security practices of your organization, but generally a good practice is to change SSH keys between 90 and 180 days, or in the case that the user who had access with the key leaves or is no longer able to be contacted, such as when a team member leaves.

Why is only changing the port number for SSH enough to secure that port?

Changing the port reduces automated scanning noise in your logs but isn’t a real security control on its own — port scanners check all ports, so it should never replace key-based auth and firewall rules.

#Secure #Remote #Access #Cloud #ServerCloud,remote access">How to Secure Remote Access to Your Cloud Server in 2026 Protection against unauthorized remote logins to your cloud server is a matter of SSH key-based authentication, a strict security policy on the firewall, two-factor authentication, and a secure communication channel – no individual component will give adequate protection by itself. The major risk point in breaches is not the server but the open door to remote login. To patch that vulnerability, you need a series of countermeasures, not a magical setting. Why Remote Server Access Is a Common Attack Vector Every cloud server exposes at least one remote access point – usually SSH on port 22 – and that port is scanned constantly by bots looking for weak credentials. Password-based login is the most common way a system can be attacked by brute force, and it is quite common with a standard setup that the system is left in a state where an admin/user of that system – “root user” – can log in directly by guessing the user ID and the password combination. Add in shared team credentials, forgotten firewall rules from old projects, and staging servers left publicly reachable, and it’s clear why misconfigured remote access – not zero-day exploits – is behind most cloud server compromises. The fix is simple, though you’ll need to do some setup yourself instead of trusting the default setting. How to lock down SSH access in the right way The best way to secure remote access is through a series of small improvements rather than one major overhaul. The following is a real working method: Switch to SSH key authentication, then turn off password login completely in the same location: sshd_config. Keys are far harder to brute-force than passwords. Disable root login over SSH and require a non-root user with sudo privileges instead; this is a security advantage that even without any change, one key compromising would limit the impact. Change the default SSH port away from 22 to cut down on automated scanning noise (not a security measure on its own, but it reduces log clutter). Restrict access by IP using your firewall or security group, allowing SSH only from known office or VPN IP ranges rather than the entire internet. Add a VPN as a connection layer for anyone accessing servers from outside a trusted network – a VPN like Planet VPN’s free VPN service can encrypt the connection between a remote worker’s laptop and the server before SSH traffic ever leaves their device, which matters especially on public wi-fi or shared networks. Every step you take closes the doors that the attacker may open slightly, and when you combine everything, you would have a door to a series of locked gates. Why Do I Need a Bastion Host? When it comes to teams working with many servers, deploying a bastion host (jump box) may be a clean and effective way to solve the long-term problem. With this method, you only expose SSH to the bastion, and the other servers accept it only when it’s from the bastion’s internal IP. This centralizes logging and makes auditing access far simpler. Using a virtual private network (VPN) and a bastion host together is perfectly fine – and many solutions use both: a safe way to access the private network via a VPN, followed by use of a bastion host to restrict which servers can be accessed. Some teams may decide to forgo the bastion because they don’t have the manpower to manage one, and so use a combination of a VPN and a set of firewall rules that still quite a bit lowers the exposure, but without the extra effort. Mistakes Leading to Leaving Cloud Servers Open The Mistakes That Cause Leaving Cloud Servers Open: Even the safest development teams may have slip-ups from time to time. One of the most frequent is leaving staged, or test servers with relaxed firewall rules equivalent to those of the production environment – hackers don’t concern themselves with which environment they first land on. Another frequent error is reusing the same SSH key on several servers and clients, causing one hacked laptop to compromise all servers that key has access to. Another common human error is not removing or changing access when someone leaves the team, so stale credentials remain valid and accessible. At last, putting trust in the strategy of “security through obscurity” – meaning one thinks that setting up the unusual port or hiding the hostname from the public is actually sufficient security – creates a wrong impression of security. This is mostly true; in reality, modern-day attackers scan all ports anyway, and there is no way to hide from them. System Administrators Guide to Secure Remote Access  Prohibit SSH password logins totally and depend exclusively on key-based authentication Surely no one wants to type in the password every time; because of this, use only key-based authentication  Deter the user from logging in as root by default and instead use the sudo command to do root tasks A firewall or security group rule can provide great help with IP address-based restrictions on SSH logins  For access that comes from untrusted sources or external networks, use a secure tunnel like a vpn or bastion host Periodically change SSH keys and inspect access lists Maintain records of login attempts and detect brute-force attacks as quickly as possible Frequently Asked Questions What is the principal means by which cloud servers suffer break-ins through remote access? By far the biggest reason why this can happen is unauthorized password-based SSH logins that have been cracked by brute force. The scenario is most likely to develop where the administrator allows root login via password and leaves the default port open. Sufficient security measures for the server via SSH keys: Do you think it is safe to rely only on key authentication?  It is true that SSH keys drastically decrease the danger of a successful brute-force attack. However, if there is only key authentication on the server, there are still risks – the server administrator can always enable root login or disable the IP-based login restrictions. So it is recommended to always have these three in the server configuration: disabled root login, restricted access to known IPs, and rotating the keys regularly as the main components of meaningful protection.  Is it really necessary to set up a separate VPN network while I am able to connect via SSH keys?  A VPN gives extra security by first encrypting your computer traffic before it reaches your network, where the SSH connection will be used. This is mostly important when working on untrusted networks – like public wi-fi – where there is a threat of a potential hacker in your local network looking at your data. Bastion host vs VPN as methods of accessing the server?  The bastion host works by funneling SSH sessions from many users through a single, carefully monitored server point while the rest of the network (and mostly the servers) remains protected from the internet. At the same time, a vpn will fully encrypt the communication between the user and the corporate network or servers. The combination is very common among larger security teams as a part of the defense-in-depth principle.  How often should SSH keys be rotated?  The ideal period to change your SSH keys depends on the security practices of your organization, but generally a good practice is to change SSH keys between 90 and 180 days, or in the case that the user who had access with the key leaves or is no longer able to be contacted, such as when a team member leaves. Why is only changing the port number for SSH enough to secure that port? Changing the port reduces automated scanning noise in your logs but isn’t a real security control on its own — port scanners check all ports, so it should never replace key-based auth and firewall rules. #Secure #Remote #Access #Cloud #ServerCloud,remote access

  1. free VPN service can encrypt the connection between a remote worker’s laptop and the server before SSH traffic ever leaves their device, which matters especially on public wi-fi or shared networks.

Every step you take closes the doors that the attacker may open slightly, and when you combine everything, you would have a door to a series of locked gates.

Why Do I Need a Bastion Host?

When it comes to teams working with many servers, deploying a bastion host (jump box) may be a clean and effective way to solve the long-term problem. With this method, you only expose SSH to the bastion, and the other servers accept it only when it’s from the bastion’s internal IP. This centralizes logging and makes auditing access far simpler.

Using a virtual private network (VPN) and a bastion host together is perfectly fine – and many solutions use both: a safe way to access the private network via a VPN, followed by use of a bastion host to restrict which servers can be accessed. Some teams may decide to forgo the bastion because they don’t have the manpower to manage one, and so use a combination of a VPN and a set of firewall rules that still quite a bit lowers the exposure, but without the extra effort.

Mistakes Leading to Leaving Cloud Servers Open

The Mistakes That Cause Leaving Cloud Servers Open: Even the safest development teams may have slip-ups from time to time. One of the most frequent is leaving staged, or test servers with relaxed firewall rules equivalent to those of the production environment – hackers don’t concern themselves with which environment they first land on. Another frequent error is reusing the same SSH key on several servers and clients, causing one hacked laptop to compromise all servers that key has access to.

Another common human error is not removing or changing access when someone leaves the team, so stale credentials remain valid and accessible. At last, putting trust in the strategy of “security through obscurity” – meaning one thinks that setting up the unusual port or hiding the hostname from the public is actually sufficient security – creates a wrong impression of security. This is mostly true; in reality, modern-day attackers scan all ports anyway, and there is no way to hide from them.

System Administrators Guide to Secure Remote Access 

Prohibit SSH password logins totally and depend exclusively on key-based authentication

  • Surely no one wants to type in the password every time; because of this, use only key-based authentication 
  • Deter the user from logging in as root by default and instead use the sudo command to do root tasks
  • A firewall or security group rule can provide great help with IP address-based restrictions on SSH logins 
  • For access that comes from untrusted sources or external networks, use a secure tunnel like a vpn or bastion host
  • Periodically change SSH keys and inspect access lists
  • Maintain records of login attempts and detect brute-force attacks as quickly as possible

Frequently Asked Questions

What is the principal means by which cloud servers suffer break-ins through remote access?

By far the biggest reason why this can happen is unauthorized password-based SSH logins that have been cracked by brute force. The scenario is most likely to develop where the administrator allows root login via password and leaves the default port open.

Sufficient security measures for the server via SSH keys: Do you think it is safe to rely only on key authentication? 

It is true that SSH keys drastically decrease the danger of a successful brute-force attack. However, if there is only key authentication on the server, there are still risks – the server administrator can always enable root login or disable the IP-based login restrictions. So it is recommended to always have these three in the server configuration: disabled root login, restricted access to known IPs, and rotating the keys regularly as the main components of meaningful protection. 

Is it really necessary to set up a separate VPN network while I am able to connect via SSH keys? 

A VPN gives extra security by first encrypting your computer traffic before it reaches your network, where the SSH connection will be used. This is mostly important when working on untrusted networks – like public wi-fi – where there is a threat of a potential hacker in your local network looking at your data.

Bastion host vs VPN as methods of accessing the server? 

The bastion host works by funneling SSH sessions from many users through a single, carefully monitored server point while the rest of the network (and mostly the servers) remains protected from the internet. At the same time, a vpn will fully encrypt the communication between the user and the corporate network or servers. The combination is very common among larger security teams as a part of the defense-in-depth principle. 

How often should SSH keys be rotated? 

The ideal period to change your SSH keys depends on the security practices of your organization, but generally a good practice is to change SSH keys between 90 and 180 days, or in the case that the user who had access with the key leaves or is no longer able to be contacted, such as when a team member leaves.

Why is only changing the port number for SSH enough to secure that port?

Changing the port reduces automated scanning noise in your logs but isn’t a real security control on its own — port scanners check all ports, so it should never replace key-based auth and firewall rules.

#Secure #Remote #Access #Cloud #ServerCloud,remote access">How to Secure Remote Access to Your Cloud Server in 2026

Protection against unauthorized remote logins to your cloud server is a matter of SSH key-based authentication, a strict security policy on the firewall, two-factor authentication, and a secure communication channel – no individual component will give adequate protection by itself. The major risk point in breaches is not the server but the open door to remote login. To patch that vulnerability, you need a series of countermeasures, not a magical setting.

Why Remote Server Access Is a Common Attack Vector

Every cloud server exposes at least one remote access point – usually SSH on port 22 – and that port is scanned constantly by bots looking for weak credentials. Password-based login is the most common way a system can be attacked by brute force, and it is quite common with a standard setup that the system is left in a state where an admin/user of that system – “root user” – can log in directly by guessing the user ID and the password combination. Add in shared team credentials, forgotten firewall rules from old projects, and staging servers left publicly reachable, and it’s clear why misconfigured remote access – not zero-day exploits – is behind most cloud server compromises. The fix is simple, though you’ll need to do some setup yourself instead of trusting the default setting.

How to lock down SSH access in the right way

The best way to secure remote access is through a series of small improvements rather than one major overhaul. The following is a real working method:

  1. Switch to SSH key authentication, then turn off password login completely in the same location: sshd_config. Keys are far harder to brute-force than passwords.
  2. Disable root login over SSH and require a non-root user with sudo privileges instead; this is a security advantage that even without any change, one key compromising would limit the impact.
  3. Change the default SSH port away from 22 to cut down on automated scanning noise (not a security measure on its own, but it reduces log clutter).
  4. Restrict access by IP using your firewall or security group, allowing SSH only from known office or VPN IP ranges rather than the entire internet.
  5. Add a VPN as a connection layer for anyone accessing servers from outside a trusted network – a VPN like Planet VPN’s free VPN service can encrypt the connection between a remote worker’s laptop and the server before SSH traffic ever leaves their device, which matters especially on public wi-fi or shared networks.

Every step you take closes the doors that the attacker may open slightly, and when you combine everything, you would have a door to a series of locked gates.

Why Do I Need a Bastion Host?

When it comes to teams working with many servers, deploying a bastion host (jump box) may be a clean and effective way to solve the long-term problem. With this method, you only expose SSH to the bastion, and the other servers accept it only when it’s from the bastion’s internal IP. This centralizes logging and makes auditing access far simpler.

Using a virtual private network (VPN) and a bastion host together is perfectly fine – and many solutions use both: a safe way to access the private network via a VPN, followed by use of a bastion host to restrict which servers can be accessed. Some teams may decide to forgo the bastion because they don’t have the manpower to manage one, and so use a combination of a VPN and a set of firewall rules that still quite a bit lowers the exposure, but without the extra effort.

Mistakes Leading to Leaving Cloud Servers Open

The Mistakes That Cause Leaving Cloud Servers Open: Even the safest development teams may have slip-ups from time to time. One of the most frequent is leaving staged, or test servers with relaxed firewall rules equivalent to those of the production environment – hackers don’t concern themselves with which environment they first land on. Another frequent error is reusing the same SSH key on several servers and clients, causing one hacked laptop to compromise all servers that key has access to.

Another common human error is not removing or changing access when someone leaves the team, so stale credentials remain valid and accessible. At last, putting trust in the strategy of “security through obscurity” – meaning one thinks that setting up the unusual port or hiding the hostname from the public is actually sufficient security – creates a wrong impression of security. This is mostly true; in reality, modern-day attackers scan all ports anyway, and there is no way to hide from them.

System Administrators Guide to Secure Remote Access 

Prohibit SSH password logins totally and depend exclusively on key-based authentication

  • Surely no one wants to type in the password every time; because of this, use only key-based authentication 
  • Deter the user from logging in as root by default and instead use the sudo command to do root tasks
  • A firewall or security group rule can provide great help with IP address-based restrictions on SSH logins 
  • For access that comes from untrusted sources or external networks, use a secure tunnel like a vpn or bastion host
  • Periodically change SSH keys and inspect access lists
  • Maintain records of login attempts and detect brute-force attacks as quickly as possible

Frequently Asked Questions

What is the principal means by which cloud servers suffer break-ins through remote access?

By far the biggest reason why this can happen is unauthorized password-based SSH logins that have been cracked by brute force. The scenario is most likely to develop where the administrator allows root login via password and leaves the default port open.

Sufficient security measures for the server via SSH keys: Do you think it is safe to rely only on key authentication? 

It is true that SSH keys drastically decrease the danger of a successful brute-force attack. However, if there is only key authentication on the server, there are still risks – the server administrator can always enable root login or disable the IP-based login restrictions. So it is recommended to always have these three in the server configuration: disabled root login, restricted access to known IPs, and rotating the keys regularly as the main components of meaningful protection. 

Is it really necessary to set up a separate VPN network while I am able to connect via SSH keys? 

A VPN gives extra security by first encrypting your computer traffic before it reaches your network, where the SSH connection will be used. This is mostly important when working on untrusted networks – like public wi-fi – where there is a threat of a potential hacker in your local network looking at your data.

Bastion host vs VPN as methods of accessing the server? 

The bastion host works by funneling SSH sessions from many users through a single, carefully monitored server point while the rest of the network (and mostly the servers) remains protected from the internet. At the same time, a vpn will fully encrypt the communication between the user and the corporate network or servers. The combination is very common among larger security teams as a part of the defense-in-depth principle. 

How often should SSH keys be rotated? 

The ideal period to change your SSH keys depends on the security practices of your organization, but generally a good practice is to change SSH keys between 90 and 180 days, or in the case that the user who had access with the key leaves or is no longer able to be contacted, such as when a team member leaves.

Why is only changing the port number for SSH enough to secure that port?

Changing the port reduces automated scanning noise in your logs but isn’t a real security control on its own — port scanners check all ports, so it should never replace key-based auth and firewall rules.

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