What is quantum computing and how does it work?
Quantum computing exploits the puzzling behavior that scientists have been observing for decades in nature’s smallest particles – think atoms, photons or electrons. At this scale, the classical laws of physics ceases to apply, and instead we shift to quantum rules.
While researchers don’t understand everything about the quantum world, what they do know is that quantum particles hold immense potential, in particular to hold and process large amounts of information. Successfully bringing those particles under control in a quantum computer could trigger an explosion of compute power that would phenomenally advance innovation in many fields that require complex calculations, like drug discovery, climate modelling, financial optimization or logistics.
As Bob Sutor, chief quantum exponent at IBM, puts it: “Quantum computing is our way of emulating nature to solve extraordinarily difficult problems and make them tractable,” he tells ZDNet.
What is a quantum computer?
Quantum computers come in various shapes and forms, but they are all built on the same principle: they host a quantum processor where quantum particles can be isolated for engineers to manipulate.
The nature of those quantum particles, as well as the method employed to control them, varies from one quantum computing approach to another. Some methods require the processor to be cooled down to freezing temperatures, others to play with quantum particles using lasers – but share the goal of finding out how to best exploit the value of quantum physics.
What’s the difference between a quantum computer and a classical computer?
The systems we have been using since the 1940s in various shapes and forms – laptops, smartphones, cloud servers, supercomputers – are known as classical computers. Those are based on bits, a unit of information that powers every computation that happens in the device.
In a classical computer, each bit can take on either a value of one or zero to represent and transmit the information that is used to carry out computations. Using bits, developers can write programs, which are sets of instructions that are read and executed by the computer.
Classical computers have been indispensable tools in the past few decades, but the inflexibility of bits is limiting. As an analogy, if tasked with looking for a needle in a haystack, a classical computer would have to be programmed to look through every single piece of hay straw until it reached the needle.
There are still many large problems, therefore, that classical devices can’t solve. “There are calculations that could be done on a classical system, but they might take millions of years or use more computer memory that exists in total on Earth,” says Sutor. “These problems are intractable today.”
How do quantum computers improve on classical devices?
At the heart of any quantum computer are qubits, also known as quantum bits, and which can loosely be compared to the bits that process information in classical computers.
Qubits, however, have very different properties to bits, because they are made of the quantum particles found in nature – those same particles that have been obsessing scientists for many years.
One of the properties of quantum particles that is most useful for quantum computing is known as superposition, which allows quantum particles to exist in several states at the same time. The best way to imagine superposition is to compare it to tossing a coin: instead of being heads or tails, quantum particles are the coin while it is still spinning.
By controlling quantum particles, researchers can load them with data to create qubits – and thanks to superposition, a single qubit doesn’t have to be either a one or a zero, but can be both at the same time. In other words, while a classical bit can only be heads or tails, a qubit can be, at once, heads and tails.
This means that, when asked to solve a problem, a quantum computer can use qubits to run several calculations at once to find an answer, exploring many different avenues in parallel.
So in the needle-in-a-haystack scenario about, unlike a classical machine, a quantum computer could in principle browse through all hay straws at the same time, finding the needle in a matter of seconds rather than looking for years – even centuries – before it found what it was searching for.
What’s more: qubits can be physically linked together thanks to another quantum property called entanglement, meaning that with every qubit that is added to a system, the device’s capabilities increase exponentially – where adding more bits only generates linear improvement.
Every time we use another qubit in a quantum computer, we double the amount of information and processing ability available for solving problems. So by the time we get to 275 qubits, we can compute with more pieces of information than there are atoms in the observable universe. And the compression of computing time that this could generate could have big implications in many use cases.
Why is quantum computing so important?
“There are a number of cases where time is money. Being able to do things more quickly will have a material impact in business,” Scott Buchholz, managing director at Deloitte Consulting, tells ZDNet.
The gains in time that researchers are anticipating as a result of quantum computing are not of the order of hours or even days. We’re rather talking about potentially being capable of calculating, in just a few minutes, the answer to problems that today’s most powerful supercomputers couldn’t resolve in thousands of years, ranging from modelling hurricanes all the way to cracking the cryptography keys protecting the most sensitive government secrets.
And businesses have a lot to gain, too. According to recent research by Boston Consulting Group (BCG), the advances that quantum computing will enable could create value of up to $850 billion in the next 15 to 30 years, $5 to $10 billion of which will be generated in the next five years if key vendors deliver on the technology as they have promised.
What is a quantum computer used for?
Programmers write problems in the form of algorithms for classical computers to resolve – and similarly, quantum computers will carry out calculations based on quantum algorithms. Researchers have already identified that some quantum algorithms would be particularly suited to the enhanced capabilities of quantum computers.
For example, quantum systems could tackle optimization algorithms, which help identify the best solution among many feasible options, and could be applied in a wide range of scenarios ranging from supply chain administration to traffic management. ExxonMobil and IBM, for instance, are working together to find quantum algorithms that could one day manage the 50,000 merchant ships crossing the oceans each day to deliver goods, to reduce the distance and time traveled by fleets.
Quantum simulation algorithms are also expected to deliver unprecedented results, as qubits enable researchers to handle the simulation and prediction of complex interactions between molecules in larger systems, which could lead to faster breakthroughs in fields like materials science and drug discovery.
With quantum computers capable of handling and processing much larger datasets, AI and machine-learning applications are set to benefit hugely, with faster training times and more capable algorithms. And researchers have also demonstrated that quantum algorithms have the potential to crack traditional cryptography keys, which for now are too mathematically difficult for classical computers to break.
What are the different types of quantum computers?
To create qubits, which are the building blocks of quantum computers, scientists have to find and manipulate the smallest particles of nature – tiny parts of the universe that can be found thanks to different mediums. This is why there are currently many types of quantum processors being developed by a range of companies.
One of the most advanced approaches consists of using superconducting qubits, which are made of electrons, and come in the form of the familiar chandelier-like quantum computers. Both IBM and Google have developed superconducting processors.
Another approach that is gaining momentum is trapped ions, which Honeywell and IonQ are leading the way on, and in which qubits are housed in arrays of ions that are trapped in electric fields and then controlled with lasers.
Major companies like Xanadu and PsiQuantum, for their part, are investing in yet another method that relies on quantum particles of light, called photons, to encode data and create qubits. Qubits can also be created out of silicon spin qubits – which Intel is focusing on – but also cold atoms or even diamonds.
Quantum annealing, an approach that was chosen by D-Wave, is a different category of computing altogether. It doesn’t rely on the same paradigm as other quantum processors, known as the gate model. Quantum annealing processors are much easier to control and operate, which is why D-Wave has already developed devices that can manipulate thousands of qubits, where virtually every other quantum hardware company is working with about 100 qubits or less. On the other hand, the annealing approach is only suitable for a specific set of optimization problems, which limits its capabilities.
What can you do with a quantum computer today?
Right now, with a mere 100 qubits being the state of the art, there is very little that can actually be done with quantum computers. For qubits to start carrying out meaningful calculations, they will have to be counted in the thousands, and even millions.
“While there is a tremendous amount of promise and excitement about what quantum computers can do one day, I think what they can do today is relatively underwhelming,” says Buchholz.
Increasing the qubit count in gate-model processors, however, is incredibly challenging. This is because keeping the particles that make up qubits in their quantum state is difficult – a little bit like trying to keep a coin spinning without falling on one side or the other, except much harder.
Keeping qubits spinning requires isolating them from any environmental disturbance that might cause them to lose their quantum state. Google and IBM, for example, do this by placing their superconducting processors in temperatures that are colder than outer space, which in turn require sophisticated cryogenic technologies that are currently near-impossible to scale up.
In addition, the instability of qubits means that they are unreliable, and still likely to cause computation errors. This has given rise to a branch of quantum computing dedicated to developing error-correction methods.
Although research is advancing at pace, therefore, quantum computers are for now stuck in what is known as the NISQ era: noisy, intermediate-scale quantum computing – but the end-goal is to build a fault-tolerant, universal quantum computer.
As Buchholz explains, it is hard to tell when this is likely to happen. “I would guess we are a handful of years from production use cases, but the real challenge is that this is a little like trying to predict research breakthroughs,” he says. “It’s hard to put a timeline on genius.”
What is quantum supremacy?
In 2019, Google claimed that its 54-qubit superconducting processor called Sycamore had achieved quantum supremacy – the point at which a quantum computer can solve a computational task that is impossible to run on a classical device in any realistic amount of time.
Google said that Sycamore has calculated, in only 200 seconds, the answer to a problem that would have taken the world’s biggest supercomputers 10,000 years to complete.
More recently, researchers from the University of Science and Technology of China claimed a similar breakthrough, saying that their quantum processor had taken 200 seconds to achieve a task that would have taken 600 million years to complete with classical devices.
This is far from saying that either of those quantum computers are now capable of outstripping any classical computer at any task. In both cases, the devices were programmed to run very specific problems, with little usefulness aside from proving that they could compute the task significantly faster than classical systems.
Without a higher qubit count and better error correction, proving quantum supremacy for useful problems is still some way off.
What is the use of quantum computers now?
Organizations that are investing in quantum resources see this as the preparation stage: their scientists are doing the groundwork to be ready for the day that a universal and fault-tolerant quantum computer is ready.
In practice, this means that they are trying to discover the quantum algorithms that are most likely to show an advantage over classical algorithms once they can be run on large-scale quantum systems. To do so, researchers typically try to prove that quantum algorithms perform comparably to classical ones on very small use cases, and theorize that as quantum hardware improves, and the size of the problem can be grown, the quantum approach will inevitably show some significant speed-ups.
For example, scientists at Japanese steel manufacturer Nippon Steel recently came up with a quantum optimization algorithm that could compete against its classical counterpart for a small problem that was run on a 10-qubit quantum computer. In principle, this means that the same algorithm equipped with thousands or millions of error-corrected qubits could eventually optimize the company’s entire supply chain, complete with the management of dozens of raw materials, processes and tight deadlines, generating huge cost savings.
The work that quantum scientists are carrying out for businesses is, therefore, highly experimental, and so far there are fewer than 100 quantum algorithms that have been shown to compete against their classical equivalents – which only points to how emergent the field still is.
Who is going to win the quantum computing race?
With most use cases requiring a fully error-corrected quantum computer, just who will deliver one first is the question on everyone’s lips in the quantum industry, and it is impossible to know the exact answer.
All quantum hardware companies are keen to stress that their approach will be the first one to crack the quantum revolution, making it even harder to discern noise from reality. “The challenge at the moment is that it’s like looking at a group of toddlers in a playground and trying to figure out which one of them is going to win the Nobel Prize,” says Buchholz.
“I have seen the smartest people in the field say they’re not really sure which one of these is the right answer. There are more than half a dozen different competing technologies and it’s still not clear which one will wind up being the best, or if there will be a best one,” he continues.
In general, experts agree that the technology will not reach its full potential until after 2030. The next five years, however, may start bringing some early use cases as error correction improves and qubit counts start reaching numbers that allow for small problems to be programmed.
IBM is one of the rare companies that has committed to a specific quantum roadmap, which defines the ultimate objective of realizing a million-qubit quantum computer. In the nearer term, Big Blue anticipates that it will release a 1,121-qubit system in 2023, which might mark the start of the first experimentations with real-world use cases.
What about quantum software?
Developing quantum hardware is a huge part of the challenge, and arguably the most significant bottleneck in the ecosystem. But even a universal fault-tolerant quantum computer would be of little use without the matching quantum software.
“Of course, none of these online facilities are much use without knowing how to ‘speak’ quantum,” Andrew Fearnside, senior associate specializing in quantum technologies at intellectual property firm Mewburn Ellis, tells ZDNet.
Creating quantum algorithms is not as easy as taking a classical algorithm and adapting it to the quantum world. Quantum computing, rather, requires a brand-new programming paradigm that can only be run on a brand-new software stack.
Of course, some hardware providers also develop software tools, the most established of which is IBM’s open-source quantum software development kit Qiskit. But on top of that, the quantum ecosystem is expanding to include companies dedicated exclusively to creating quantum software. Familiar names include Zapata, QC Ware or 1QBit, which all specialize in providing businesses with the tools to understand the language of quantum.
And increasingly, promising partnerships are forming to bring together different parts of the ecosystem. For example, the recent alliance between Honeywell, which is building trapped ions quantum computers, and quantum software company Cambridge Quantum Computing (CQC), has got analysts predicting that a new player could be taking a lead in the quantum race.
What is cloud quantum computing?
The complexity of building a quantum computer – think ultra-high vacuum chambers, cryogenic control systems and other exotic quantum instruments – means that the vast majority of quantum systems are currently firmly sitting in lab environments, rather than being sent out to customers’ data centers.
To let users access the devices to start running their experiments, therefore, quantum companies have launched commercial quantum computing cloud services, making the technology accessible to a wider range of customers.
The four largest providers of public cloud computing services currently offer access to quantum computers on their platform. IBM and Google have both put their own quantum processors on the cloud, while Microsoft’s Azure Quantum and AWS’s Braket service let customers access computers from third-party quantum hardware providers.
What does the quantum computing industry look like today?
The jury remains out on which technology will win the race, if any at all, but one thing is for certain: the quantum computing industry is developing fast, and investors are generously funding the ecosystem. Equity investments in quantum computing nearly tripled in 2020, and according to BCG, they are set to rise even more in 2021 to reach $800 million.
Government investment is even more significant: the US has unlocked $1.2 billion for quantum information science over the next five years, while the EU announced a €1 billion ($1.20 billion) quantum flagship. The UK also recently reached the £1 billion ($1.37 billion) budget milestone for quantum technologies, and while official numbers are not known in China, the government has made no secret of its desire to aggressively compete in the quantum race.
This has caused the quantum ecosystem to flourish over the past years, with new startups increasing from a handful in 2013 to nearly 200 in 2020. The appeal of quantum computing is also increasing among potential customers: according to analysis firm Gartner, while only 1% of companies were budgeting for quantum in 2018, 20% are expected to do so by 2023.
Who is getting quantum-ready now?
Although not all businesses need to be preparing themselves to keep up with quantum-ready competitors, there are some industries where quantum algorithms are expected to generate huge value, and where leading companies are already getting ready.
Goldman Sachs and JP Morgan are two examples of financial behemoths investing in quantum computing. That’s because in banking, quantum optimization algorithms could give a boost to portfolio optimization, by better picking which stocks to buy and sell for maximum return.
In pharmaceuticals, where the drug discovery process is on average a $2 billion, 10-year-long deal that largely relies on trial and error, quantum simulation algorithms are also expected to make waves. This is also the case in materials science: companies like OTI Lumionics, for example, are exploring the use of quantum computers to design more efficient OLED displays.
Leading automotive companies including Volkswagen and BMW are also keeping a close eye on the technology, which could impact the sector in various ways, ranging from designing more efficient batteries to optimizing the supply chain, through to better management of traffic and mobility. Volkswagen, for example, pioneered the use of a quantum algorithm that optimized bus routes in real time by dodging traffic bottlenecks.
As the technology matures, however, it is unlikely that quantum computing will be limited to a select few. Rather, analysts anticipate that virtually all industries have the potential to benefit from the computational speedup that qubits will unlock.
Will quantum computers replace our laptops?
Quantum computers are expected to be phenomenal at solving a certain class of problems, but that doesn’t mean that they will be a better tool than classical computers for every single application. Particularly, quantum systems aren’t a good fit for fundamental computations like arithmetic, or for executing commands.
“Quantum computers are great constraint optimizers, but that’s not what you need to run Microsoft Excel or Office,” says Buchholz. “That’s what classical technology is for: for doing lots of maths, calculations and sequential operations.”
In other words, there will always be a place for the way that we compute today. It is unlikely, for example, that you will be streaming a Netflix series on a quantum computer anytime soon. Rather, the two technologies will be used in conjunction, with quantum computers being called for only where they can dramatically accelerate a specific calculation.
How will we use quantum computers?
Buchholz predicts that, as classical and quantum computing start working alongside each other, access will look like a configuration option. Data scientists currently have a choice of using CPUs or GPUs when running their workloads, and it might be that quantum processing units (QPUs) join the list at some point. It will be up to researchers to decide which configuration to choose, based on the nature of their computation.
Although the precise way that users will access quantum computing in the future remains to be defined, one thing is certain: they are unlikely to be required to understand the fundamental laws of quantum computing in order to use the technology.
“People get confused because the way we lead into quantum computing is by talking about technical details,” says Buchholz. “But you don’t need to understand how your cellphone works to use it.
“People sometimes forget that when you log into a server somewhere, you have no idea what physical location the server is in or even if it exists physically at all anymore. The important question really becomes what it is going to look like to access it.”
And as fascinating as qubits, superposition, entanglement and other quantum phenomena might be, for most of us this will come as welcome news.
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Why Facebook is the AOL of 2021
Once upon a time, roughly thirty years ago, there was a computer network called America Online.
AOL, as it was typically referred to, sent out little diskettes in the mail, and sometimes slipped them into the middle of popular magazines. The diskettes were a way for people to go online. There was already an Internet, but most people didn’t know how to use it or even that it existed.
AOL, and a couple of competitors, Compuserve and Prodigy, offered people online things they could do, such as chat with other people. Mostly, the services helped people to get around the difficult aspects of what are known as Internet protocols. Internet computers need to communicate via connections that require a dedicated communications line, and a so-called IP address, which in turn requires a software program called TCP/IP. Most people’s computers didn’t have any of that.
Instead, the little diskette in the magazine let a person plug their computer into their telephone modem — once they’d bought a modem at the local computer store — and dial up a server computer that would admit them to the world of AOL or, alternatively, to the world of Compuserve or Prodigy. Some people grumbled at how many diskettes were stuck inside magazines, but the diskettes were an effective way to attract new people to sign up and use the service.
Many people spent days and days at a time on AOL and the other services. The services had only one drawback, which was that they were limited. People couldn’t do just whatever they wanted, they could only pick from a small menu of functions, such as chat, that the services provided. And the services didn’t grow or change much, they stayed pretty much the same for years because it wasn’t in their interest to change when the diskettes kept bringing people in.
Most people didn’t mind that the services were limited and didn’t change. People were just excited to be in a place called Cyberspace. Suddenly, they could send a message to someone in a different town, even a different country, even people that they had never met. People could also adopt a secret identity, such as “picklefinger0237,” and the anonymity made interacting even more exciting.
Right about the same time as AOL, a smart person named Tim Berners-Lee, who worked at a prestigious research organization, published some software people could use to connect from their computer to any computer that also had the software. It was the World Wide Web. The software quickly caught the attention of many people and it blew their minds. With a real Internet connection, a person could reach any computer in the world. People saw that they didn’t have to accept the small menu of functions that AOL offered them.
Moreover, the excitement that people felt when they were sending a message to a person in another town now swelled until it became a fervor to see the world. People had a sense the small little place in Cyberspace where they had dwelt was nothing compared to a vast universe just over the garden wall. The excitement pushed even ordinary people to find out how to sign up with a thing called an “Internet Service Provider.” It required people to understand something called “point to point protocol,” which was almost like learning science, but still less annoying than all the diskettes.
As it grew and grew, the World Wide Web became an amazing place in contrast to AOL. People found they could visit articles and whole magazines written by people they’d never met, even from around the world. And there was a constant stream of innovation, with lots of software appearing all the time that made “surfing” the Web amazing.
People even discovered more of the Internet, such as things like “file transfer protocol,” where they could get lots of stuff no one had ever seen in the form of files. Programs such as “finger” let a person see who had been online, which, again, blew people’s minds.
People were so excited by the World Wide Web, they never wanted to go back to AOL or Compuserve or Prodigy. The three services withered. Mostly, people who were older held onto their AOL accounts because they still had an email address linked to AOL and it was a little confusing to try to get a new email address. But over time, with help from the younger generation, even those people were able to shift to using new email services and enjoy the Web.
Soon after people became excited about the Web, business people started to say it was sad that AOL and Compuserve and Prodigy had withered away because they had been a great way to make money for a time.
The business people decided that there should be a way to make something like AOL, even though everyone thought Web sites were amazing and didn’t want to go back. A content company called CNET (a sister site of ZDNet) invented a service called Snap Online. They put out T-shirts telling people it was like having AOL but so much better. They wrote the word Snap with an exclamation point — Snap! — so that it was even more exciting.
The service, though, didn’t make a lot of money, in fact, it cost CNET a lot of money, $101 million dollars through 1999, before CNET sold it to another company called NBC Internet. NBC eventually merged with a cable company called Comcast, and Snap was forgotten.
Other people tried to make another AOL, including a group of the smartest venture capitalists in the world, who spent nearly $50 million to create a site that would be more like meeting real people, called Friendster. It had some success at the beginning because people really wanted to meet not just new people but people they knew. Then people cooled on Friendster, and it got sold — for a lot less money than it had taken to build it — to a Malaysian online payments firm. People mostly forgot about Friendster.
None of those failures deterred business people, and they created new services, including a service called MySpace, where people could put up information about their rock bands.
Finally, some smart people hit on a formula and they created some brand-new places for people to meet.
One of them was called Facebook. People got excited about Facebook because it was a place where they could find real people they knew, just like MySpace, but also because it had some features like AOL, like the game Farmville.
Business people were even more excited because Facebook started to generate a lot of advertising revenue. Advertisers liked Facebook because it not only knew who was talking to whom, it also knew a little bit about the hobbies and interests of people. Advertisers liked that because they could use the information to “target” their ads like never before.
Smart people said that Facebook had what are known as “network effects.” It became more powerful the more people joined it. A scientist deduced the possible reason. It was because Facebook had what’s called a “scale free” network that solved the problem of how to meet up. Most people didn’t know that many people, but everyone knew one or two people who knew a whole lot of people. Those one or two people were the hubs in a social “graph” that allowed even lonely people to meet lots more people, in the same way everyone in Hollywood knew someone who had worked with the famous actor Kevin Bacon on a movie.
As more lonely people met new people — and old friends — via Facebook, Facebook grew and grew. Its revenue swelled from $153 million dollars a year to $2 billion to $18 billion until one day it was making almost $120 billion dollars a year selling advertisements as people did stuff together. Facebook became one of the most powerful entities in the world, worth over a trillion dollars, because it had so many people doing stuff, almost two billion people.
There were just a couple problems with Facebook. Facebook was a lot like AOL. It limited people by telling them with whom they could communicate. And unlike AOL and Compuserve and Prodigy, people couldn’t just be any fun identity they wanted, like picklefinger0237. They had to present themselves as themselves because advertisers liked to know who was talking to whom.
Many people didn’t really mind that they were limited in whom they could talk to. They liked to “build their brand,” they said, by showing off pictures of themselves and talking a lot about themselves. Also, people felt it was fine because just like with AOL, they had a couple other options, including Pinterest and Twitter and LinkedIn and Instagram, and even a new thing called Snap, without the exclamation point. Those were like having Compuserve and Prodigy back in the day.
But a few people got concerned. They noticed that not only did Facebook and services like it limit who could talk, and to whom those people could talk. The concerned people noticed that the services manipulated how people talked to one another, with computer algorithms called “data voodoo dolls.” Even business people became alarmed. They said Facebook had “zucked” people by betraying people’s trust.
One of the bad things was that people no longer had control. They had given so much information about themselves to Facebook and its competitors that it was like those companies owned people when they were in Cyberspace.
The services didn’t seem to do a great job of handling people’s information, either. Even though they wouldn’t let people talk to just anyone they wanted, Facebook and the other services went and sold people’s information to people they didn’t know in far-away countries. And everywhere a person would go on the Internet, Facebook and its competitors would let advertisers keep following them, keeping track of them, which people had never counted on when they joined up.
Concerned thinkers said the new online services were watching everyone’s behavior and shaping it and invading their privacy. The consequences became worse and worse. People had thought they were relating to one another, but they were really screaming at one another like in a school lunchroom food fight.
The reason they were screaming was because the data voodoo dolls and the other algorithmic tools weren’t really bringing people together, they were encouraging repetitive patterns of behavior, like getting people mad by constantly displaying the most inflammatory things people said about anything and everything. It was all for the purpose of sorting people’s behavior into convenient buckets as a way to communicate a clear buying signal to help advertisers.
Even the people who were excited about building their brands had some misgivings. They suspected at times that their identities were not real. They were now simply a figment of an advertising database that constructed an identity for them in order to keep people coming to Facebook and other services. It was almost as if people didn’t exist anymore when they were in Cyberspace.
Then one day, someone smart built a new technology that didn’t require people to sign away their information. Now, people could meet anyone they wanted and talk about whatever they wanted, not just what Facebook or its competitors said was okay. People felt more relaxed, too, because even though there were ads, people could meet up in Cyberspace without every single action they took being used to fuel an advertising machine.
People got excited again, like the first time they found the Web and gave up on AOL.
But there our story ends, because that chapter has not yet been written.
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Motorola thinks it can take business from Apple (with this?)
Recently, I’ve developed an unhealthy fascination with Motorola.
Ever since the company declared itself and its wares to be “agents of change,” I’ve been desperate to witness the change I can believe in.
The company’s first attempt to think different sadly resembled something you’ve seen (far too) many times before.
Still, my eyes became oddly widened when I saw this headline: “Forget the new iPhone 13 — Motorola thinks it just made your next work phone.”
This gave me a shuddery feeling for more than one reason. This is an aggressive alternative to the iPhone 13? This is from Motorola?
And wait, what is a work phone anyway?
Given that the pincer movement between tech and corporate America has forced us to be always on (edge), thanks to mobile technology, how can a work phone really be separate from your usual phone?
It’s cumbersome to carry two phones around — though I know some do — just to listen out for one tune or another to alert you about “work.” Rather than say, “match,” “lover”, or “burgers.”
So I hastily devoured details of what this new Motorola work phone is. It’s called the Motorola Edge 20 Lite Business Edition.
There’s a potentially uncomfortable juxtaposition between “lite” and “business,” so could it be that this phone will alleviate work encumbrances?
I rushed to Motorola’s site, desperate to be moved.
I found these words from our sponsor: “The motorola edge 20 and edge 20 lite Business Edition devices are designed specifically to meet the needs of today’s enterprises. Stay safer and up to date with two Android OS updates and three years of monthly security patches.”
Yes, it really did have Motorola with a small m, which was remarkably modest. And I’m sure monthly security patches are welcome. But it’s just a shame they have to occur every month.
The next sentence was intriguing but may not please all grammarians: “motorola edge 20 and edge 20 lite Business Edition devices are secured by ThinkShield for mobile, a comprehensive set of hardware and software security features, and is Android Enterprise Recommended.”
In essence, then, what makes a business phone a business phone is, according to Motorola, security.
I always worry when any tech company promises security. It seems painfully clear that this is a promise best left as a mobile goal, rather than a nirvana attained.
These Business Edition phones are designed to be sold to businesses in bulk. It’s wise, then, to emphasize the security at their heart.
It does, though, incite another question or two. Why aren’t all phones equally secure? Would it really necessitate a price premium just to give you what you might actually expect as a norm? And talking of price, is the price the other main selling point of the Business Edition against the iPhone 13?
The Edge 20 lite Business Edition may, indeed, be a fine phone. Motorola is, indeed, gaining market share. And many will want it to become more of a competitor in what often seems a very limited race to dominance.
But if you’re going to be a change agent, change something radically. Offer a business phone that won’t work after 7 p.m., for example.
What do you mean no corporation would ever buy that? Aren’t they all about empathy these days?
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AT&T says it has big problems. A T-Mobile salesman showed me how big
I’ve missed going to phone stores.
This may not be a sentence you’ve heard from too many people lately, but I’ve often found talking to those actually selling the phones to be an enlightening experience.
I’ve been an AT&T customer for almost 20 years. Those phones seem especially riveting to me, though I’ve never held one.
Perhaps an AT&T salesperson could inspire me to finally toss my iPhone to the winds.
This Number Is Not Available.
I walked into a reasonably sized AT&T store. There were two customers inside. Very quickly, I was greeted by a saleswoman clutching an iPad.
“What name should I put down?” she asked.
“Chris,” I said.
“Right now, the wait time is thirty minutes,” she replied, very matter-of-factly.
That seemed like an infernally long time, given the sparsely populated store on this weekday afternoon. She didn’t even ask why I was there and simply walked away.
Still, she agreed I could look around. I found the Fold and the Flip, opened them and closed them, and discovered the that the crease on the Fold 3 was markedly visible, while the Flip 3 looked exactly as I’d imagined — utterly charming.
But was I going to spend another 28 minutes in the store? Was I really minded to go back?
I left, with the latest words of AT&T CEO John Stankey swishing around my brain: “Frankly, I’m not satisfied with where the AT&T brand stands right now.”
He worries the company isn’t well positioned for the next 10 years. I worry it’s not well positioned to offer basic customer service right now.
You Want Service? What Sort Of Service?
I wandered away and wondered whether I could get any service at the nearest T-Mobile store. Phone stores can be a little like car dealers, zoned into particular areas.
So I replanted the mask on my face, walked in, stood for perhaps 30 seconds and was approached by a salesperson. This despite the fact that there were four customers in a store that’s smaller than AT&T’s.
“Hi. If I asked you an honest question, would you give me an honest answer?,” I began.
“Sure,” he said.
“Is the T-Mobile coverage better in my area than it used to be?”
“Let’s find out,” he replied.
He then walked me over to the counter and showed me his iPad. He let me type in my address and showed me precisely where the nearest tower is and the strength of the signal.
My house is right on the border between good and not-so-good in signal terms. He was honest enough to not only show it, but not to offer some twisted reasoning for why it was actually guaranteed to be good.
He said that, over the next few years, the signal would improve a lot. And when I made a joke about— there really are so many — he revealed he was a toggler.
“I manually switch from 4G to 5G to see where I can get a better signal,” he said. Which doesn’t sound like the sort of thing most people would be bothered doing.
Still, this was already a very pleasant chat. So I dared to ask about Samsung’s folding phones, the reason I’d gone to the AT&T store.
He took me through a comprehensive explanation of his views on the phones. The Fold 3, he said, still had issues because app designers hadn’t got around to adjusting to the Fold 3’s dimensions. He felt that YouTube just didn’t look great on the phone.
The Flip 3, though, was far more ready for everyday use, he said. The more I stared at it and fiddled with it, the more I liked it.
I could even sink to admitting I wanted it.
Customer Service After My Own Heart.
“The problem is I’m iPhone,” I said. “I just don’t know if I can live with Android.”
“Same,” he replied. “Most of my friends and family have iPhones. If you have just one Android person on a group text, it throws everything.”
The feeling I got as a customer was that, regardless of how many people were in the store, he’d have continued the conversation.
I’d experienced this sort of service attitude at a T-Mobile store before, but I’d imagined it was perhaps a one-off, a single enthusiast. But to maintain this standard of service during and emerging from a pandemic was remarkable.
We chatted a little more. I was (pleasantly) startled to finally find another human being who refuses to put a case on his phone. He proudly showed his shiny silver iPhone 12, perfectly well cared for, while I displayed my slightly more careworn blue iPhone 12.
It’s a rare feat of customer experience when you find yourself unable to buy the product, but desperately wanting to buy something from this person.
Yet, given that working from home has long been my lot in life, I need a decent phone signal at all times and T-Mobile can’t yet guarantee that.
So, Mr. Sankey of AT&T, I know that your phone stores are likely a sad pimple on the chin of your brand perception. I know that you want to take AT&T to “a new place.” One, I imagine, that’s blissfully virtual.
But you might want to learn a thing or two about customer service from T-Mobile. Just as T-Mobile might want to learn a thing or two about security from, well, just about anyone who knows a thing or two about security.
It may well be that all phone brands will soon shut their stores and force customers to shop entirely online.
It may well be that they’ll leave the likes of Best Buy to provide actual physical experience and advice. But customer service remains something that matters to, you know, real humans.
This particular salesman got nothing out of our conversation. I asked him to please hurry the signal improvement near my house. (He said he’d get right on it.)
But, as I walked out of the store, I felt so good about the interaction that it genuinely lifted my day.
Then I looked at my phone and thought: “Hey, it’s still five minutes before anyone at the AT&T store will talk to me.”
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Ninja Van snags $578M in Series E, pulling in Alibaba as new investor
Ninja Van has snagged $578 million in Series E funding, pulling in existing and new investors that include Chinese e-commerce giant Alibaba Group. The latest round comes over a year after it secured $279 million in Series D and amidst talks it is targeting a US public listing.
Singapore-based logistics services operator Ninja Van said Sunday its latest funding round pulled in existing investors Geopost/DPDgroup, B Capital Group, Monk’s Hill Ventures, and Zamrud. Alibaba also participated in this round as a new investor, said Ninja Van.
The funds injection would go towards beefing up its infrastructure and technology systems to support “a sustainable long-term cost structure”, Ninja Van said. It added that the investment also would support the “quality and consistency” of its operations, as well as its micro-supply chain service offerings aimed at helping businesses in Southeast Asia tap e-commerce opportunities.
Launched in 2014, Ninja Van currently has operations across six Southeast Asian markets including Indonesia, Thailand, the Philippines, Malaysia, and Vietnam. The last-mile logistics operator delivers some 2 million parcels daily and works with more than 1.5 million active shippers across the region, including Alibaba’s subsidiary Lazada, Tokopedia, Zalora, and Shopee.
This figure includes unique active shippers that have placed an order with Ninja Van in the past 12 months, Ninja Van said, adding that it delivers to almost 100 million recipients. The company employs more than 61,000 staff and delivery personnel in the region.
Ninja Van Group’s co-founder and CEO Lai Chang Wen said in the statement: “The quality of investors joining us in this round of investment is a clear signal that the market recognises the emerging opportunities for e-commerce logistics in Southeast Asia and how as an entrenched player in the region, Ninja Van is positioned to take a central role in meeting the shifting demands of both businesses and consumers. We remain committed to the success of all our business partners as we move towards the next stages of sustainable growth and continued innovation.”
Lai in July told Financial Times it was “a year away” from a public listing, likely in the US, with discussions with advisors rumoured to have started.
Ninja Van in May 2020 pulled $279 million in its Series D funding round, which it then said would be tapped to drive its presence in the business-to-business (B2B) segment as well as expand its services for small businesses and business-to-consumer (B2C) brands. The round attracted several new and existing investors including, Grab, Golden Gate Ventures Growth Fund, Monk’s Hill Ventures, and B Capital, which is helmed by Facebook’s co-founder Eduardo Saverin.
That same month, Ninja Van said it expected to see another triple-digital volume growth this year in Thailand, where it recorded a 300% climb in shipments in 2020. The company would increase its parcel processing capacity in the country when its new automated sorting facility was scheduled to open later this year. The new site would be able to handle 800,000 parcels a day, supporting next-day delivery across the country including Bangkok and the eastern regions, Ninja Van said, adding that it was hiring another 1,000 employees in Thailand to support another 100 new regional and local distribution centres.
Southeast Asia is projected to be home to 350 million online shoppers by end-2021, up from 310 million last year, according to the latest annual study conducted by Bain & Company and commissioned by Facebook. Online spending also is expected to grow 60% per person this year, pushing total e-commerce sales to expand two-fold to $254 billion in gross merchandise value by 2026. The average consumer in the region was estimated to spend $381 online this year, compared to $238 last year, before hitting a projected $671 in 2026.
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