Hexbyte  Tech News  Wired Google I/O 2019: Watch Live Video of the Keynote Right Here

Hexbyte Tech News Wired Google I/O 2019: Watch Live Video of the Keynote Right Here

Hexbyte Tech News Wired

Hexbyte  Tech News  Wired

Michael Short/Bloomberg/Getty Images

Hexbyte  Tech News  Wired

Michael Short/Bloomberg/Getty Images

Do you hear that? It’s the sound of Google executives practicing their lines ahead of Google I/O. The company’s annual developer conference in Mountain View, California, kicks off this Tuesday. The three-day event gives Google a chance to show off its latest work and set the tone for the year to come.

Can’t make it to the Shoreline Amphitheater? You can watch the entire keynote on the event page or on the Google Developers YouTube channel. It begins at 10 am PT (1 pm ET) on May 7 and should last for about 90 minutes. We’ll liveblog the whole thing here on wired.com.

Google I/O is technically a developer’s conference, and there should be plenty of talk about all the fun things developers can build using Google’s latest tools. But it’s also an opportunity to get consumers excited about what’s cooking in Mountain View. Last year, the company used the conference to debut its “digital wellness” initiative and a suite of new visual search tools for Google Lens. It also introduced Duplex, the eerily realistic AI assistant that can make dinner reservations and schedule haircuts like a human would.

This year, expect a parade of Google executives to talk about privacy, artificial intelligence, augmented reality, and more. We’ll likely see the latest version of Android software, and if we’re lucky, maybe even some new hardware.

More Great WIRED Stories

Read More

Hexbyte  Tech News  Wired Spend Part of the $2 Trillion Infrastructure Plan on Robots

Hexbyte Tech News Wired Spend Part of the $2 Trillion Infrastructure Plan on Robots

Hexbyte Tech News Wired

This week, the Democrats and President Trump are talking about a $2 trillion infrastructure plan, a number in line with American Society of Civil Engineers’ estimates for infrastructure needs, but it isn’t clear where the money will come from or if a bipartisan plan will actually move forward.



K. Gretchen Greene is an AI and Governance affiliate researcher at MIT and Harvard, an international AI and AV policy advisor, a lawyer, and a former US national lab mathematician.

The ASCE’s 2017 report card gave America’s infrastructure a D+ with scant progress these last 20 years. “America’s infrastructure is like a third-world country,” said Ray LaHood, transportation secretary under President Obama. If we don’t make a major infrastructure investment, our enormous infrastructure needs will just keep growing. We need good new ideas to make the most of whatever money is approved by the federal government or local governments.

New technologies are threatening jobs but they also offer the possibility of completing projects we otherwise couldn’t afford, minimizing disruption, improving safety and optimizing systems in ways humans working alone could not. Robots and artificial intelligence can help us build the infrastructure we need, here and around the world. In short, the infrastructure robots are coming; in fact, some of them are already here.

In Minnesota, spider-like bridge inspection drones crawl along high abutments and into narrow gaps while hovering drones inspect the undersides of bridge decks. Their access is better, cheaper, and safer with less disruption of traffic. Gas pipe repair robots allow utility crews in Boston, NYC, and Edinburgh, Scotland, to finish a job in a third of the time, without digging up the street at every joint or interrupting service because the robots can safely work inside pressurized lines. In Saudi Arabia and Mexico, water pipe inspection robots are inserted in one fire hydrant, carried by the water flow and captured with a net at another fire hydrant down the line, reporting the locations of leaks a tenth to a third the size old methods could find. In Connecticut, drones are replacing low flying helicopters for power line inspections.

In Oslo, Norway, submarine drones are mapping the landscape of underwater garbage: old tires and toys, plastic bags and the carcasses of abandoned cars, so boats with cranes and human divers can be deployed to clean up the fjords.

In Fukushima, Japan, engineers have embarked on a half century project one expert called more challenging than putting a man on the moon: designing and building robots that can operate in an extremely challenging environment to find, recover, and seal the lost radioactive fuel from the biggest nuclear plant disaster cleanup effort in history.

The early infrastructure robots don’t use much AI. They are remotely controlled or tethered, relaying video to human operators to interpret, carrying tools a human operator can use from a distance, and relying on a human operator to tell them where to go. Their genius lies in their ability to squeeze into small spaces, levitate in the sky, or dive into the water and survive in harsh environments, going places humans can’t go easily, safely, cheaply, or at all.

But the next generation of these machines, it seems clear, will gain more autonomy, adopting computer vision, autonomous vehicle navigation and machine learning technologies. Semi-autonomous drones and robots are in testing and early commercial deployment for inspection of industrial assets, land surveying and sidewalk snow clearing.

It’s not a big step to imagine robots creeping through the gas and water lines all day and night, mapping their own course, quietly fixing leaks, docking at charging and maintenance stations as needed, like a Roomba underground. Above ground robots could patrol the roads, the power grid and the waterways, cleaning up trash and fixing potholes, electrical wires and bridges or reporting what they can’t fix, directing a human crew to the spot.

Machine learning software systems are learning to predict code violations, safety incidents, mechanical failures and natural disasters, directing robotic or human resources to intervene. They are being used for fire code, health code and industrial safety inspection prioritization in Pittsburgh, New York, New Orleans, Boston, Chicago and British Columbia, Canada. Rolls Royce is testing machine learning to predict engine failures. The oil and gas industry is automating the detection of serious pipeline corrosion, adding machine learning to the pipeline robot pigs it has used for decades. British Columbia is trying to predict elevator problems. Pittsburgh is trying to predict landslides on roads.

Robots, sensors and machine learning are being used to direct water to where we want it before it ever hits a pipeline and to reduce pollution. Tech startups in Boston and San Francisco are using sensors and machine learning to create hyperlocal air quality and weather data and predictions. In crowded industrial cities in Guangzhou, China, pollution-detecting airborne drones help law enforcement identify which factory should be punished for emissions. China has used chemical carrying drones to disperse smog and to make rain and is considering the creation of a vast network of fuel burning chambers, planes, drones and artillery, guided by real-time data from satellites, to seed clouds over the Tibetan plateau, the source of most of Asia’s biggest rivers, an area three times the size of Spain.

Advances in robotics, hardware and artificial intelligence have combined to make a new vision possible for how infrastructure maintenance and repair is carried out. More importantly, they offer a vision for how we might be able to afford to do the work we can’t put off forever.

There’s a rising army of robots, ready to serve.

WIRED Opinion publishes pieces written by outside contributors and represents a wide range of viewpoints. Read more opinions here. Submit an op-ed at opinion@wired.com

More Great WIRED Stories

Read More

Hexbyte  News  Computers A comprehensive (and honest) list of UX clichés

Hexbyte News Computers A comprehensive (and honest) list of UX clichés

Hexbyte News Computers

A guide to newcomers.

Hexbyte  News  Computers Go to the profile of Fabricio Teixeira

“You are not your user”

A reminder that you are not designing the product for people like yourself. Often used as a way to encourage more user research in a project.

“If Henry Ford had asked people what they wanted, they would have told him faster horses”

Used as a counter-argument to the previous statement, when you start to realize you won’t have time or money to do enough user research.

“We are testing the design, not your skills”

Disclaimer given to users at the start of a user testing session to make them feel better about being a little stupid.

“Designers should have a seat at the table”

When you are not able to prove your strategic value to the company based on your everyday actions and behaviors, and you have to beg to be invited to important meetings.

“Choices should be limited to 7 plus or minus 2”

A nicer way of saying that choices should range from 5 to 9, without sounding too broad. When in reality every good designer knows choices should range from 1 to 2.

“People don’t want to buy a quarter-inch drill bit; they want a quarter-​inch hole”

Wait, do they really want a hole? Or do they want wireless Bluetooth instead, so no holes are needed whatsoever?

“UX should be a mindset, not a step in the process”

When you realize the deadline is coming close and you haven’t been able to finish your deliverables. Used to try to retroactively convince the PM to extend the project timeline.

“Content is king”

A pretty strong argument to convince everyone to push the deadline because you haven’t received the content that will go on the page you are designing.

“Never underestimate the stupidity of the user”

An efficient way of outsourcing your own responsibility of giving users enough context so they know what to do (a.k.a. being a good designer).

“I’m wondering if this breaks accessibility standards”

Used as last resort when you are running out of arguments to convince other designers their design is not working.

“A user interface is like a joke; if you have to explain it, it’s not that good”

An easy way of killing that onboarding wizard/walkthrough idea your stakeholders are asking for. Watch out for the backfire: others might agree with your argument and blame on you the fact that the product is not that working that well.

“People don’t scroll”

The most offensive statement you can throw at a designer.

“People are used to scrolling; think about the way you use Instagram”

A polite counter-argument to the previous statement. The Instagram example can be replaced by any other feed-based product your interlocutor might be addicted to.

“The fold doesn’t exist”

If you can’t convince them, confuse them.

“UI vs. UX”

Pzajsodiajhsknfksdjbfsdbfkqwehjoqiwejroe. Usually followed by even more cliché analogies of ketchup bottles or unpaved walkways.

“All pages should be accessible in 3 clicks”

Just. Don’t.

“Should designers code?”

A commonly used wild card when the audience is running out of questions in a Q&A session at a design event.

“If you think good design is expensive, you should look at the cost of bad design”

A passive-aggressive way of explaining to clients you will not reduce your price. Usually ineffective.

“You can’t design an experience; experiences are too subjective to be designed”

An argument used by coworkers who are running out of things to say but somehow still want to sound smart.

“Let the users decide”

None of us is going to win this endless argument, so we should take this to c̶o̶u̶r̶t̶ user testing. But I’m still going to prove you wrong at the end.

“No one enters a website through the homepage anymore”

Popular at the peak of the SEO era (2005–2008), the argument was used to cut short endless meetings where a large group of stakeholders is trying to design your homepage by committee.

“The only other industry who names their customers ‘users’ are drug dealers”

Can’t even explain why this one exists. Used a lot when the term UX came about in the early 2000s, is becoming pretty popular again in the “designing for addiction” era.

“When escalators break, they actually become stairs”

Originally used to explain the concept of graceful degradation, the quote started being adopted by developers to convince the product owner that certain bugs do not need to be fixed.

“Mobile users are distracted”

Just a generalization made by someone who still thinks the primary use case for mobile devices is on-the-go, while doing groceries and simultaneously trying to tame a wild giraffe.

“You don’t know what you don’t know”

Honestly, no one knows.

“Leave your ego by the door”

An inspirational quote used before you walk into a user testing session or a collaborative work session with your coworkers. Looks particularly great if written in Helvetica, printed and framed, and hung by the entrance of truly collaborative office spaces.

“Double diamond”

Hey, we need a slide in this deck that represents our design process — can you come up with something that is relatively simple to understand, that will make us look less chaotic than we actually are?

“Users don’t read”

An overly used argument to convince clients and stakeholders to cut copy length in half. If you made this far to this article, you’re living proof that this statement is untrue.

Read More

Hexbyte  News  Computers Taylor Swift, iOS, and the Access Economy: Why the Normal Distribution is Vanishing

Hexbyte News Computers Taylor Swift, iOS, and the Access Economy: Why the Normal Distribution is Vanishing

Hexbyte News Computers

(Note: although I originally wrote this as a stand-alone post, it may make sense to think of it as Part Two of a series, beginning with The Rise of the Access Economy.)

There’s a classic exercise that we often encounter in business school, or elsewhere in higher education: the professor proposes, “I have two tickets to the Taylor Swift concert this Saturday night. All of you should write down on a piece of paper the maximum amount they’d be willing to pay for these tickets, then turn them in, and we’ll look at the distribution of those prices.” The idea is that from there, a promoter should be able to determine the optimal price (or sets of prices) to charge in order to maximize their sales.

Hexbyte  News  Computers t_concert

Now, what typically happens is we see a distribution of numbers get passed in. Some are willing to pay lots, others not so much, and a bunch of people lie somewhere in the middle. We often see these numbers form something like a Gaussian distribution, or as it’s known to the general public, a ‘Normal Distribution’. Why do we see this distribution a lot? And why is it called ‘Normal’?

(Editor’s note: I play fast and loose here with what a ‘Normal’ distribution technically means here. If you’re mathematically inclined, kindly replace the words Normal and Gaussian with ‘Unscientific Lumpy Middle’ and the points should still hold just fine.)

We see this distribution all over the place: in nature, in business, in life. If you’ve taken high school biology, you’ve probably encountered this picture:

Hexbyte  News  Computers heights

There’s a good reason why the normal distribution pops up so much: it represents the sum of some number of component factors. If you’ve played the game Settlers of Catan, you’ll be very familiar with this phenomenon: the outcome of the total roll of the dice, which represents the sum of one die plus the other, follows an approximately normal distribution. If you were rolling five dice instead of two (like in Yahtzee), you’d see this distribution more clearly. If you’re dealing with hundreds or thousands of factors (like human genes), you see normal distributions arise for things like our height. Even when some of these factor are inter-dependent, as is certainly the case with our genetics, we still reliably encounter these distributions: whenever we encounter the sum of many factors, there they are.

Hexbyte  News  Computers dice_sum_probabilities

It should make sense that our stated prices for these Taylor Swift tickets should follow a distribution like this: you could think of it as a sum of many factors. How much did you like 1989? How much disposable income do you have? How far would you have to travel? What are your other options? Would you have friends who would go with you? And so forth. We can generalize this example to our preferences and choices generally: the decisions we make whenever we spend something (money, time, opportunity, etc) represent the sum of many factors. Suppose you’re shopping for a new car: the distribution of new car prices aren’t quite symmetrically Gaussian (there’s a long tail towards the high end), but it’s still Normal-ish. If you’re looking for a car as a young person, you’ll have a certain set of factors you consider – cost, value, practicality, style. As you grow up, these factors will change, but the decision will remain multifactorial in nature – many of your preferences will evolve, but along a similar set of variables. Some of us buy luxury cars, others buy the cheapest that they can, and most of us are in the big fat middle – like rolling two dice and landing somewhere between a 4 and a 10. It won’t happen every time, but it’ll happen most of the time.

There’s just one problem with this principle: it’s vanishing.

Consider these three situations:

1. You’re looking to buy a new smartphone. What does your demand distribution look like? Is it normally distributed? Absolutely not. It looks like this:

Hexbyte  News  Computers what_phone_3

Chances are good that if you’re in the market for a smartphone, you fall into one of two very clear cut categories. Either: a. you’ve decided that your new phone is a very important item in your life, so you’re going to buy the nicest phone available at whatever price. (Usually this means an iPhone, although some flagship Android phones qualify for this category.) Or, b. you’ve decided that just about every phone out there is ‘good enough’ and is more than adequate for your needs, so you’ll go with whatever one costs $0 with your existing wireless contract.

2. You have twenty minutes of free time to spend reading things on the internet. How will you choose to allocate your attention? Odds are, it looks like this:

Hexbyte  News  Computers read_what_3

Chances are good that your next twenty minutes will fall into one of two categories. Either: a. you go to a very specific destination site that you had in mind, with content that you will not find anywhere else and that you know exactly where to find. This could be a high-end news source like the New York Times; it could also be a small, niche site like Stratechery. But either way they’re a destination site – you’ve chosen explicitly that they, and precisely they, are what you’re going to read. Or, b. you don’t choose anything at all; instead you had to Facebook, Twitter, Reddit, etc and just browse through whatever’s posted there. Often it’s content with broad, mass appeal like Buzzfeed or Vox that no one is looking for directly, but many are happy to click and read.

3. You’re a recent college graduate trying to decide where you’ll live. How will you make this decision? Will your options and preferences be normally distributed? Probably not. Odds are, your decision looks like this:

Hexbyte  News  Computers live_where_3

Chances are good that your decision will fall into one of two categories. Either: a. you’re trying to break into the film industry, so you’re overwhelmingly drawn towards LA. Or you’re trying to join a tech startup, so might be drawn to the Bay area. Or for any number of reasons. you just have to live in New York. And you’re willing to put up with all kinds of horrible side effects in order to live in that exact right place. Or, b. You will move anywhere where you get a job, or get into grad school, or where your girlfriend is moving – it’s effectively a single-variable decision.

These aren’t normal distributions at all. They’re totally different – they’re bifurcated distributions where one factor is dominating over all of the other factors. Either you care about X, or you don’t. Either you care about Y, or you don’t. And what used to be the happy middle – the fat part of the normal distribution, where most of the demand is supposed to lie – is all of a sudden quite sparse.

The world seems to be steadily moving in this direction: from one where our demands and preferences are normally distributed to one dominated by these weird, bifurcated, two-tier balances. Why is this happening?

My hunch is this: The world’s shift from Normal, Gaussian distributions of demand towards bifurcated, two-tiered distributions is a natural consequence of our shift from a world governed by scarcity to one governed by abundance. 

In a world governed by scarcity – which is the one we’re used to thinking about, and the one described in our economics textbooks – it makes complete sense for our purchasing and preferences to be multi-factor considerations with normally distributed outcomes. This is because when X is scarce, our default position towards X is ‘potentially interested, if the price / conditions are right’. We consider many factors when determining whether conditions are right, so our preferences and decisions are multifactorial in nature – that’s why we see normal distributions so consistently.

But in a world governed by abundance, it’s all different. When X is abundantly available in many different places and forms, our default position towards any given X is ‘not interested’ unless something specific changes our minds. These aren’t multifactorial decisions – they’re usually dominated by one single factor whose influence trumps everything else. If you care about your smartphone, you’re going to get the nicest one; if you don’t, you’re going to get the cheap one. If you have something specific you want to read, you’re going to read exactly that; otherwise, you’re just going to scroll through Facebook and read whatever. If you care about living in New York, you’re probably ready to make a lot of concessions in order to live there; if you don’t, then there’s no chance. If you’re a Taylor Swift fan, you’re probably willing to pay a lot of money for those tickets; if you aren’t, then there’s a good chance you aren’t willing to pay any money for them because even if they were free, you’d rather do something else on Saturday night anyway. The normal distribution is disappearing: we’re in a new world where either you care about something, or you don’t. The middle ground is vanishing.

So what can we do with this idea?

The first thing to do, in my view, is to look for areas of the world where our preferences are still normally distributed. Then ask: is this because we’re still operating on scarcity-based principles? And if so, are we headed down the road towards abundance-based principles? If so, what can we expect in the future?

Here’s one example: personal banking. Our personal banking requirements are still being served by a big, fat, normally distributed group of companies and services. How come? Is it scarcity-based demand? Well, it certainly isn’t because of actual scarcity. With the birth of online banking and creative partnerships with distribution channels, it’s entirely possible to do your basic banking through any number of online, cheap and functional banking institutions – not to mention the multitude of brick and mortar branches that are all around us, competing for our business.

Some people have genuinely complex banking and credit requirements. Others don’t. And in a world where banking services are abundant – which I believe we’ve entered, at least in the western world – I’m guessing we’re headed towards a similar iOS/Android style bifurcation. At the high end, we’ll see companies and services like American Express – the iOS of cards in my wallet. But what about the low end? Who’s issuing the basic, cheap, functional personal credit – the low-end Android?

Right now it’s other banks. But it doesn’t need to be. Banks are expensive to run (especially those with a big physical presence at brick and mortar branches); they’re built for the normally distributed world with its fat middle. But they’re not built for this new, bifurcated world – many of them can’t compete at the low end or the high end; they’re not sophisticated enough to compete with Chase and Amex, but too operationally expensive to compete with an online-only credit union.

Who could replace them? Maybe someone like Walmart, who already has customer loyalty, the ultimate physical channel, and the resources to make it work? Or maybe, in developing countries whose residents are still unbanked, the wireless carriers? Or maybe emerging payment companies like TenCent, or AliPay, could become effective creditors themselves?

For another example, let’s return to our earlier anecdote about cars. Like I said, automobile production today isn’t a perfect normal distribution – it’s got a bit of a long tail towards the luxury end – but for the most part it’s Gaussian-like. But if we fast forward thirty years, we may see something very different. If the promise of Uber and the driverless car comes true, we could be living in a world where the majority of people do not own their vehicles; they simply use them and enjoy access to them. It’s reasonable to speculate that these vehicles, likely owned in fleets, won’t be fancy – they’ll be vanilla Android cars. But there will probably also be another ‘class’ of vehicles – the iOS cars – for those who still care about owning or using a premium vehicle. These might include luxury cars, special work vehicles, or collectors’ items – the important unifying factor being, these vehicles are owned by people who care about ownership, whereas the vanilla fleet will be used by people who don’t care about fancy ownership – they just need to get places. Either you care about X, or you don’t.

This idea of bifurcated distributions replacing Gaussian distributions maps to the Access Economy idea, which I’ve written about previously, in an interesting way. We may see X Markets of the future that are governed first by a sorting function: Do you really care about X? Do you need your own X, or is access to X enough? Are you a big Taylor Swift fan, and own all her records? (As Taylor wrote in her famous WSJ op-ed last year – part of what makes music special is the contingent of fans who do care, and continue to faithfully buy specific artists’ records even while they stream others’.) Or do you listen to her from time to time on Apple Music, and that’s enough?

Are you sufficiently well off to pay an old-fashioned, prestigious university degree? Or do you simply need access to online course material and learning opportunity? There’s little doubt that our higher education system will see some sort of shakeup in the next few decades, and to me this looks like a potential bifurcation situation: colleges and universities are getting way too expensive across the board; and yet, students who pay top dollar and go to prestigious schools usually do well! And furthermore, the amount of online learning opportunities available are exploding – that’s not the problem either. The issue is the big, fat middle that doesn’t serve anybody particularly well – and is sustainable for the most part because we feel like it’s important. We’ll see how long that lasts. (It could be a while, though.)

Does your company need to lock down a particularly valuable employee to a long-term contract? Or does it simply need access to a steady stream of available talent? For the most part, we don’t get a clear view into how top performing companies structure their compensation from the top all the way down to the bottom – usually we hear about the executive suite and that’s it. But there’s a great exception: professional sports teams, where team salary and cap hit information is usually available. And on these teams, we’ve seen this same thing emerging: either you’re a star, or you’re replacement level. The middle ground is vanishing: either you’re locked up to a multi-year blockbuster deal, or you’re on a one-year, market rate contract that’s pretty indistinguishable from any other. Outside of the sports leagues, where we don’t have such a privileged view, is this happening as well?

We can see, too, that these bifurcations are driven by a shift from a world of scarcity to a world of abundance. Buying an album used to be the only way to listen to your own music – now it streams online, quasi-free, always. Learning material used to be precious and valuable – now it’s abundantly available online, everywhere. Employee retention used to be critical for companies from top to bottom – now freelance and temporary labour is abundant, and only the truly scarce need be locked down. These decisions, which were once multifactorial, are becoming single-variable: either you care about X, or you don’t.

Hexbyte  News  Computers change 2.png

There’s a dark side to this idea: its sinister cousin, economic inequality. We typically celebrate and value an upwardly mobile society with a “healthy middle class”; in other words, a society whose incomes form a Gaussian distribution. But in a world shifting towards one of abundance, where one would hope that rising tides would lift all boats, could this bifurcation be slowly and irreversibly happening as well? Are we heading towards a two-class system: a class of owners and a class of users? I’ll save this question for part three: I still have a lot of personal research to do before I’m able to properly form an opinion here.

In summary, what can we take away from this?

-Normal distributions come from multifactorial situations, where many variables matter.

-In a world governed by scarcity, our decisions and preferences tend to be multifactorial in nature.

-In a world governed by abundance, on the other hand, our decisions are no longer multifactorial. They tend to be dominated by one factor above all others: either X matters to you, or it doesn’t.

-If the world is moving in this direction, we might anticipate other industries and ecosystems that could follow. Education, cars, banking, and employment come to mind as examples; there are many more.

-The fall of the normal distribution maps to the rise of the access economy: ownership and access are not part of the same distribution; they are two halves of a bifurcation.

Read More

Hexbyte – Tech News – Ars Technica | Facebook’s cryptocurrency might work like loyalty points

Hexbyte – Tech News – Ars Technica | Facebook’s cryptocurrency might work like loyalty points

Hexbyte – Tech News – Ars Technica |

Hexbyte - Tech News - Ars Technica |

“I believe that it should be as easy to send money to someone as it is to send a photo,” Facebook CEO Mark Zuckerberg said at the company’s developer conference this week.

David Paul Morris/Bloomberg/Getty Images

If Facebook’s pivot from town square to private living room wasn’t laden with enough irony, here’s a new twist: Big business, it appears, has been invited to join us by the fireplace.

Gregory Barber covers cryptocurrency, blockchain, and artificial intelligence for WIRED.

On Thursday, The Wall Street Journal reported new potential details about Facebook’s long-awaited cryptocurrency plans. The company is reportedly seeking dozens of business partners, including online merchants and financial firms, in an effort to extend the reach of its blockchain-based marketplace. Facebook’s would-be partners are being asked to pitch into an investment fund, valued at $1 billion or more, that would serve as backing for Facebook’s coin and mitigate the wild speculative swings that make cryptocurrencies like bitcoin hard to spend. The pitch, according to the Journal, involves offering merchants lower fees than credit cards.

Some were quick to note that this would reduce Facebook’s ability to make money from payments in the short term. But that may not matter much—if, in the end, Facebook’s crypto effort is really all about getting you to spend more time glued to Facebook.

Facebook appears to be already building out the plumbing to make its marketplace a reality. At its F8 developer conference this week, the word “blockchain” was notably absent. But even as Zuckerberg emphasized the company’s plan to reorganize your Facebook experience around intimate relationships, his update included plenty of ways money would be involved. “I believe that it should be as easy to send money to someone as it is to send a photo,” he said, alluding to “simple and secure payments” as a core feature of his privacy-forward vision. That apparently extends beyond the peer-to-peer payments available on Venmo and Facebook’s own Messenger app. In a series of keynotes, Facebook execs touted a litany of commerce-focused improvements: better checkout for Instagram’s digital mall, donation stickers, and a new tool for small business owners to list items on WhatsApp.

Indeed, WhatsApp appears to sit at the center of Facebook’s commerce efforts—at least to start. At F8, Facebook said WhatsApp Pay, currently on limited trial in India, would expand to additional, unnamed countries later this year. The platform isn’t blockchain-based (for now) and is designed for peer-to-peer payments. But with 80 percent of small businesses in India using WhatsApp to market their goods, some form of payments processing is a natural evolution. In December, Bloomberg reported that the first tests of the crypto coin may occur in India, initially as a way for workers to send money home from overseas.

An added twist from the Journal’s report is the possibility that the coin will be integrated into Facebook’s lucrative ads ecosystem. The scheme, reportedly still under debate within Facebook, would potentially work on both sides of the ads equation: Merchants could use the coins to pay for ads, and users would be rewarded in coins for viewing or interacting with them. That reflects a growing perception—seen recently in efforts like the Brave browser, which compensates users through a token for clicking on ads—that people