For a long time, the way philanthropy worked was simple: Rich people gave their money to museums and churches and opera houses and Harvard. Their names went up on buildings, charities gave them made-up awards, their grandkids went to rehab, the Earth went around the sun.
But philanthropy is changing. Today’s billionaires are less interested in legacy institutions, less obsessed with prestige and perpetuity. Part of this is a function of their age: In 2012, 4 percent of America’s biggest charitable donations were made by people under 50 years old. In 2014, a quarter of them were.
The other factor driving the new philanthropists is how they earned their money in the first place. Last year, six of the 10 largest charitable donations in the United States came from the tech sector, solidifying Silicon Valley’s place as the epicenter of the newer, bigger, disrupty-er philanthropy. There, tech billionaires form “giving circles” to share leads on promising charities, and they hire the same consultants to vet them. They use terms like “hacker philanthropy” and “effective altruism.” These guys—they are mostly guys—believe that they became successful businessmen by upending existing institutions, by scaling simple ideas, by “breaking shit.” And, with few exceptions, that is how they plan to become successful philanthropists, too.
All of this became much more relevant in December, when Mark Zuckerberg and his wife, Priscilla Chan, announced that they were giving 99 percent of their wealth to charity. The total amount they pledged, around $45 billion in Facebook shares at current valuation, exceeds the endowments of the Rockefeller, Ford and Carnegie foundations combined. If Zuckerberg gives away the upper limit of what he announced in December, $1 billion per year for the next three years, he will likely become the world’s second-largest charitable donor after Bill Gates. He is 31 years old.
Zuckerberg’s ability to remake the world in his own image, in his own lifetime, is unprecedented. Andrew Carnegie opened his first library when he was 68, and only managed to get around $5 billion in today’s dollars out the door before he died. John D. Rockefeller, generally considered the most generous industrialist in history, launched his foundation when he was 76, and only gave away around half his fortune. If he wanted to, Zuckerberg could eradicate polio, or de-neglect half a dozen tropical diseases, or fix all the water pipes in Flint, or give $9,000 to every single one of the world’s refugees.
But $45 billion, as a former Bill & Melinda Gates Foundation grantee put it, is “a 1,000-pound gorilla.” You don’t give away that much money without changing the places and institutions and people you give it to, sometimes for the worse.
the hard part about social change “is that it doesn’t scale like a social network.”
Nearly every social advance in history has technology somewhere near the center of it—the aqueduct, the steam train, the birth control pill. And whenever you start asking people about the life-altering potential of Mark Elliot Zuckerberg and the tech-based philanthropy he represents, the first words you’re likely to hear are “The Green Revolution.”
In 1975, nearly three out of five people in Asia lived on less than $1 a day. Rains at the wrong time of year meant the difference between starvation and survival. Then, researchers funded by the Ford and Rockefeller foundations created new crops—varieties that grew taller, needed less water and could be planted year-round. Over the next 30 years, this innovation radically improved the lives of hundreds of millions of people. Rice yields spiked by 1,000 percent. Wheat got cheaper, healthier and more abundant. Norman Borlaug, the scientist who developed the new wheat varieties, won the Nobel Prize.
“Technology,” Toyama says, “is the easiest part of any solution.” The hard part is everything that comes afterward. Take car crashes, which kill more people every year than tuberculosis or pulmonary disease. The technology to prevent these deaths—seat belts, motorcycle helmets—is not rocket science. It’s just that no one has figured out how to make it appeal to the people who need it, especially in the developing world, where 90 percent of these deaths occur.
he should acknowledge that the silver-bullet promise of technology only works at changing the world when it’s combined with political will and popular demand. Until he finds a way to engineer those (please don’t), he should focus on the small ways, at the margins, where technology can improve people’s lives, 8 percent at a time.
In 2001, the Gates Foundation gave PATH and the World Health Organization $70 million, 10 years and a simple objective: Develop a vaccine for meningitis A and make it affordable for every single person who needs it.
Marc LaForce headed the team in charge of bringing the vaccine to market. He says it wasn’t just the scale of the Gates donation that mattered, but its duration. In those days, most grants were capped at two or three years, with check-ins every six months. Years of work could be wiped away if a donor decided progress was moving too slowly and pulled out.
“If you want to do something major,” LaForce says, “you need the ability to go two steps forward, then one step back.”
Plus, the Gates team left LaForce alone. Back then, the foundation only employed about a dozen people who worked out of a small office in a residential neighborhood of Seattle. Staffers spent their time making lists of diseases, ranking them by annual fatalities, then calling around to find out which ones were closest to being cured.
“We didn’t need to be specialists,” says Gordon Perkin, the foundation’s first director of global health. “We just needed to know which organizations had the judgment and the infrastructure, and we gave them money.”
This story doesn’t just illustrate the potential of philanthropy. It also demonstrates that how Zuckerberg gives away his money will be just as important as what he gives it to. Because one way to look at his $45 billion is that it’s a lot of money. Another way to look at it is that it’s about what the United States spends on prisons every six months. Or education every four weeks. Or health care every five days. Even at a scale that large, efficiency matters.
The Gates Foundation, as it’s expanded to more than 1,300 employees, has become prone to the same bloat, the same “expert-itis,” as a former grantee calls it. “They hired Ph.D.s in biotech and all they wanted to do was the science that the grantees were doing.”
It’s hard to overstate just how un-Silicon Valley all of this is. “Money is sitting there to make the world a better place, and to dole it out cautiously is antithetical to why it’s there,” says Freada Kapor Klein, a partner at the Kapor Center for Social Impact, a foundation set up by Mitch Kapor, an early investor in Uber and other unicorns.
Zuckerberg shouldn’t be afraid to fail; he should approach philanthropy like a venture capitalist, testing out ideas to scale up later on. Bypassing legacy institutions is what Silicon Valley CEOs are good at, right? All those consultants must strike them as the charity equivalent of taxi medallions.
What Zuckerberg actually announced last December wasn’t a big fat donation to charity. All he did was establish a limited liability company (LLC) and issue a promise that he would use it for good. Much of the reaction at the time was suspicious, speculating that an LLC was a scheme for Zuckerberg to avoid taxes (which isn’t true) or that it would allow him to spend mountains of money without disclosing how he was doing so (which is).
But the corporate approach actually makes a lot of sense. Under the standard philanthropic model, billionaires set up a foundation and give it a huge endowment. Every year, the foundation has to give away at least 5 percent of its total value. Meanwhile, the other 95 percent gets invested in blue chip stocks, hedge funds, foreign currencies, whatever will keep the total endowment the same size. That’s how foundations like Rockefeller and Ford exist in perpetuity: Do-gooders work on one side of the building finding things to donate to, while bankers work on the other side, making sure there’s more to donate next year.
“This idea that philanthropy is only about nonprofits is an outdated model,” says Paula Goldman, a vice president at the Omidyar Network. Pierre Omidyar, the founder of eBay, was one of the most prominent tech billionaires to merge his investing and grant-making. The foundation still gives donations, but the LLC provides loans and seed capital and invests in things like solar-powered lighting startups, Brazilian test-prep companies and funds that discover Indian entrepreneurs.
Zuckerberg is going even further, giving up on a foundation entirely and putting all of his charity money in a corporate form with no limits on how to spend it. He’s not interested in making his money back. He just wants the flexibility to fund charities or companies or both. Which explains why one of Zuckerberg’s most recent donations wasn’t a donation at all. It was $10 million in seed capital for an education startup called Bridge International Academies, a chain of private elementary schools that wants to deliver education to the world’s poorest students.
The primary appeal of Bridge, especially to investors like Zuckerberg, is the $6 per month it says it charges its students. Operating as a business rather than a charity gives each school an incentive to deliver a decent education and ensures that it’s not going to wither away when development agencies or donors move on to the next idea.
It’s tempting to stop there, to say capitalism perverts philanthropy, full stop, and advise Zuckerberg to just go back and form a foundation. But that’s not right either. One of the most successful private-sector development projects of the last 10 years is M-PESA, the mobile-money system that allows people in Kenya to transfer money via their cell phones. Before the system launched, Kenyans sent money to each other by mail, or by giving envelopes full of cash to bus drivers. Replacing an inefficient, expensive system with a regularized one made everybody better off. That’s not as easy to argue, in the long run, about education.
So, when Zuckerberg hears pitches from companies seeking to solve the world’s problems, he shouldn’t ask them if they have a plan to grow, or an ambition to exist in perpetuity. He should ask himself whether he really wants them to replace the systems that already exist, or simply make them better. Because successful companies don’t just disrupt other companies—they disrupt economies, governments and the people who depend on them. That’s not something that Zuckerberg ever had to worry about, but he has to start.
In 2009, four grad students came up with an audacious idea: Instead of giving poor people the things we think they need—bags of food, stacks of clothing, a pair of goats—what if we gave them enough money to decide for themselves?
They called their charity GiveDirectly, and in 2011 they started doing exactly that. They went to villages in Kenya, found the poorest people living there and transferred $1,000 straight to their cell phones. Later, they came back to ask the villagers what they did with the money. Mostly, it turns out, the villagers spent it on better roofs, better food, paying off debts, starting up businesses. All the stuff the development system used to buy for them—but without any overhead.
In the end, though, Zuckerberg’s greatest impact might be in the model he sets for other philanthropists. The Giving Pledge, which encourages billionaires to donate the majority of their wealth to charity, has attracted more than 142 commitments totaling more than $400 billion. The Founders Pledge has convinced 151 startup executives—most of them look about 19—to devote a portion of their exits to philanthropy. Charitable giving in the United States has nearly quintupled since 1994, and shows no signs of reverting back to opera houses and Harvard.
Five years ago, Google — one of the most public proselytizers of how studying workers can transform productivity — became focused on building the perfect team. In the last decade, the tech giant has spent untold millions of dollars measuring nearly every aspect of its employees’ lives. Google’s People Operations department has scrutinized everything from how frequently particular people eat together (the most productive employees tend to build larger networks by rotating dining companions) to which traits the best managers share (unsurprisingly, good communication and avoiding micromanaging is critical; more shocking, this was news to many Google managers).
The company’s top executives long believed that building the best teams meant combining the best people. They embraced other bits of conventional wisdom as well, like ‘‘It’s better to put introverts together,’’ said Abeer Dubey, a manager in Google’s People Analytics division, or ‘‘Teams are more effective when everyone is friends away from work.’’ But, Dubey went on, ‘‘it turned out no one had really studied which of those were true.’’
In 2012, the company embarked on an initiative — code-named Project Aristotle — to study hundreds of Google’s teams and figure out why some stumbled while others soared. Dubey, a leader of the project, gathered some of the company’s best statisticians, organizational psychologists, sociologists and engineers. He also needed researchers. Rozovsky, by then, had decided that what she wanted to do with her life was study people’s habits and tendencies. After graduating from Yale, she was hired by Google and was soon assigned to Project Aristotle.
Project Aristotle’s researchers began by reviewing a half-century of academic studies looking at how teams worked. Were the best teams made up of people with similar interests? Or did it matter more whether everyone was motivated by the same kinds of rewards? Based on those studies, the researchers scrutinized the composition of groups inside Google: How often did teammates socialize outside the office? Did they have the same hobbies? Were their educational backgrounds similar? Was it better for all teammates to be outgoing or for all of them to be shy? They drew diagrams showing which teams had overlapping memberships and which groups had exceeded their departments’ goals. They studied how long teams stuck together and if gender balance seemed to have an impact on a team’s success.
No matter how researchers arranged the data, though, it was almost impossible to find patterns — or any evidence that the composition of a team made any difference. ‘‘We looked at 180 teams from all over the company,’’ Dubey said. ‘‘We had lots of data, but there was nothing showing that a mix of specific personality types or skills or backgrounds made any difference. The ‘who’ part of the equation didn’t seem to matter.’’
As they struggled to figure out what made a team successful, Rozovsky and her colleagues kept coming across research by psychologists and sociologists that focused on what are known as ‘‘group norms.’’ Norms are the traditions, behavioral standards and unwritten rules that govern how we function when we gather: One team may come to a consensus that avoiding disagreement is more valuable than debate; another team might develop a culture that encourages vigorous arguments and spurns groupthink. Norms can be unspoken or openly acknowledged, but their influence is often profound. Team members may behave in certain ways as individuals — they may chafe against authority or prefer working independently — but when they gather, the group’s norms typically override individual proclivities and encourage deference to the team.
Project Aristotle’s researchers began searching through the data they had collected, looking for norms. They looked for instances when team members described a particular behavior as an ‘‘unwritten rule’’ or when they explained certain things as part of the ‘‘team’s culture.’’ Some groups said that teammates interrupted one another constantly and that team leaders reinforced that behavior by interrupting others themselves. On other teams, leaders enforced conversational order, and when someone cut off a teammate, group members would politely ask everyone to wait his or her turn. Some teams celebrated birthdays and began each meeting with informal chitchat about weekend plans. Other groups got right to business and discouraged gossip. There were teams that contained outsize personalities who hewed to their group’s sedate norms, and others in which introverts came out of their shells as soon as meetings began.
After looking at over a hundred groups for more than a year, Project Aristotle researchers concluded that understanding and influencing group norms were the keys to improving Google’s teams. But Rozovsky, now a lead researcher, needed to figure out which norms mattered most. Google’s research had identified dozens of behaviors that seemed important, except that sometimes the norms of one effective team contrasted sharply with those of another equally successful group. Was it better to let everyone speak as much as they wanted, or should strong leaders end meandering debates? Was it more effective for people to openly disagree with one another, or should conflicts be played down? The data didn’t offer clear verdicts. In fact, the data sometimes pointed in opposite directions. The only thing worse than not finding a pattern is finding too many of them. Which norms, Rozovsky and her colleagues wondered, were the ones that successful teams shared?
Imagine you have been invited to join one of two groups.
Team A is composed of people who are all exceptionally smart and successful. When you watch a video of this group working, you see professionals who wait until a topic arises in which they are expert, and then they speak at length, explaining what the group ought to do. When someone makes a side comment, the speaker stops, reminds everyone of the agenda and pushes the meeting back on track. This team is efficient. There is no idle chitchat or long debates. The meeting ends as scheduled and disbands so everyone can get back to their desks.
Team B is different. It’s evenly divided between successful executives and middle managers with few professional accomplishments. Teammates jump in and out of discussions. People interject and complete one another’s thoughts. When a team member abruptly changes the topic, the rest of the group follows him off the agenda. At the end of the meeting, the meeting doesn’t actually end: Everyone sits around to gossip and talk about their lives.
Which group would you rather join?
In 2008, a group of psychologists from Carnegie Mellon and M.I.T. began to try to answer a question very much like this one. ‘‘Over the past century, psychologists made considerable progress in defining and systematically measuring intelligence in individuals,’’ the researchers wrote in the journal Science in 2010. ‘‘We have used the statistical approach they developed for individual intelligence to systematically measure the intelligence of groups.’’ Put differently, the researchers wanted to know if there is a collective I. Q. that emerges within a team that is distinct from the smarts of any single member.
To accomplish this, the researchers recruited 699 people, divided them into small groups and gave each a series of assignments that required different kinds of cooperation. One assignment, for instance, asked participants to brainstorm possible uses for a brick. Some teams came up with dozens of clever uses; others kept describing the same ideas in different words. Another had the groups plan a shopping trip and gave each teammate a different list of groceries. The only way to maximize the group’s score was for each person to sacrifice an item they really wanted for something the team needed. Some groups easily divvied up the buying; others couldn’t fill their shopping carts because no one was willing to compromise.
What interested the researchers most, however, was that teams that did well on one assignment usually did well on all the others. Conversely, teams that failed at one thing seemed to fail at everything. The researchers eventually concluded that what distinguished the ‘‘good’’ teams from the dysfunctional groups was how teammates treated one another. The right norms, in other words, could raise a group’s collective intelligence, whereas the wrong norms could hobble a team, even if, individually, all the members were exceptionally bright.
But what was confusing was that not all the good teams appeared to behave in the same ways. ‘‘Some teams had a bunch of smart people who figured out how to break up work evenly,’’ said Anita Woolley, the study’s lead author. ‘‘Other groups had pretty average members, but they came up with ways to take advantage of everyone’s relative strengths. Some groups had one strong leader. Others were more fluid, and everyone took a leadership role.’’
As the researchers studied the groups, however, they noticed two behaviors that all the good teams generally shared. First, on the good teams, members spoke in roughly the same proportion, a phenomenon the researchers referred to as ‘‘equality in distribution of conversational turn-taking.’’ On some teams, everyone spoke during each task; on others, leadership shifted among teammates from assignment to assignment. But in each case, by the end of the day, everyone had spoken roughly the same amount. ‘‘As long as everyone got a chance to talk, the team did well,’’ Woolley said. ‘‘But if only one person or a small group spoke all the time, the collective intelligence declined.’’
Second, the good teams all had high ‘‘average social sensitivity’’ — a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions and other nonverbal cues. One of the easiest ways to gauge social sensitivity is to show someone photos of people’s eyes and ask him or her to describe what the people are thinking or feeling — an exam known as the Reading the Mind in the Eyes test. People on the more successful teams in Woolley’s experiment scored above average on the Reading the Mind in the Eyes test. They seemed to know when someone was feeling upset or left out. People on the ineffective teams, in contrast, scored below average. They seemed, as a group, to have less sensitivity toward their colleagues.
In other words, if you are given a choice between the serious-minded Team A or the free-flowing Team B, you should probably opt for Team B. Team A may be filled with smart people, all optimized for peak individual efficiency. But the group’s norms discourage equal speaking; there are few exchanges of the kind of personal information that lets teammates pick up on what people are feeling or leaving unsaid. There’s a good chance the members of Team A will continue to act like individuals once they come together, and there’s little to suggest that, as a group, they will become more collectively intelligent.
In contrast, on Team B, people may speak over one another, go on tangents and socialize instead of remaining focused on the agenda. The team may seem inefficient to a casual observer. But all the team members speak as much as they need to. They are sensitive to one another’s moods and share personal stories and emotions. While Team B might not contain as many individual stars, the sum will be greater than its parts.
Within psychology, researchers sometimes colloquially refer to traits like ‘‘conversational turn-taking’’ and ‘‘average social sensitivity’’ as aspects of what’s known as psychological safety — a group culture that the Harvard Business School professor Amy Edmondson defines as a ‘‘shared belief held by members of a team that the team is safe for interpersonal risk-taking.’’ Psychological safety is ‘‘a sense of confidence that the team will not embarrass, reject or punish someone for speaking up,’’ Edmondson wrote in a study published in 1999. ‘‘It describes a team climate characterized by interpersonal trust and mutual respect in which people are comfortable being themselves.’’
When Rozovsky and her Google colleagues encountered the concept of psychological safety in academic papers, it was as if everything suddenly fell into place. One engineer, for instance, had told researchers that his team leader was ‘‘direct and straightforward, which creates a safe space for you to take risks.’’ That team, researchers estimated, was among Google’s accomplished groups. By contrast, another engineer had told the researchers that his ‘‘team leader has poor emotional control.’’ He added: ‘‘He panics over small issues and keeps trying to grab control. I would hate to be driving with him being in the passenger seat, because he would keep trying to grab the steering wheel and crash the car.’’ That team, researchers presumed, did not perform well.
Most of all, employees had talked about how various teams felt. ‘‘And that made a lot of sense to me, maybe because of my experiences at Yale,’’ Rozovsky said. ‘‘I’d been on some teams that left me feeling totally exhausted and others where I got so much energy from the group.’’ Rozovsky’s study group at Yale was draining because the norms — the fights over leadership, the tendency to critique — put her on guard. Whereas the norms of her case-competition team — enthusiasm for one another’s ideas, joking around and having fun — allowed everyone to feel relaxed and energized.
For Project Aristotle, research on psychological safety pointed to particular norms that are vital to success. There were other behaviors that seemed important as well — like making sure teams had clear goals and creating a culture of dependability. But Google’s data indicated that psychological safety, more than anything else, was critical to making a team work.
‘‘We had to get people to establish psychologically safe environments,’’ Rozovsky told me. But it wasn’t clear how to do that. ‘‘People here are really busy,’’ she said. ‘‘We needed clear guidelines.’’
However, establishing psychological safety is, by its very nature, somewhat messy and difficult to implement. You can tell people to take turns during a conversation and to listen to one another more. You can instruct employees to be sensitive to how their colleagues feel and to notice when someone seems upset. But the kinds of people who work at Google are often the ones who became software engineers because they wanted to avoid talking about feelings in the first place.
Rozovsky and her colleagues had figured out which norms were most critical. Now they had to find a way to make communication and empathy — the building blocks of forging real connections — into an algorithm they could easily scale.
In late 2014, Rozovsky and her fellow Project Aristotle number-crunchers began sharing their findings with select groups of Google’s 51,000 employees. By then, they had been collecting surveys, conducting interviews and analyzing statistics for almost three years. They hadn’t yet figured out how to make psychological safety easy, but they hoped that publicizing their research within Google would prompt employees to come up with some ideas of their own.
Sakaguchi was particularly interested in Project Aristotle because the team he previously oversaw at Google hadn’t jelled particularly well. ‘‘There was one senior engineer who would just talk and talk, and everyone was scared to disagree with him,’’ Sakaguchi said. ‘‘The hardest part was that everyone liked this guy outside the group setting, but whenever they got together as a team, something happened that made the culture go wrong.’’
When asked to rate whether the role of the team was clearly understood and whether their work had impact, members of the team gave middling to poor scores. These responses troubled Sakaguchi, because he hadn’t picked up on this discontent. He wanted everyone to feel fulfilled by their work. He asked the team to gather, off site, to discuss the survey’s results. He began by asking everyone to share something personal about themselves. He went first.
‘‘I think one of the things most people don’t know about me,’’ he told the group, ‘‘is that I have Stage 4 cancer.’’ In 2001, he said, a doctor discovered a tumor in his kidney. By the time the cancer was detected, it had spread to his spine. For nearly half a decade, it had grown slowly as he underwent treatment while working at Google. Recently, however, doctors had found a new, worrisome spot on a scan of his liver. That was far more serious, he explained.
After Sakaguchi spoke, another teammate stood and described some health issues of her own. Then another discussed a difficult breakup. Eventually, the team shifted its focus to the survey. They found it easier to speak honestly about the things that had been bothering them, their small frictions and everyday annoyances. They agreed to adopt some new norms: From now on, Sakaguchi would make an extra effort to let the team members know how their work fit into Google’s larger mission; they agreed to try harder to notice when someone on the team was feeling excluded or down.
There was nothing in the survey that instructed Sakaguchi to share his illness with the group. There was nothing in Project Aristotle’s research that said that getting people to open up about their struggles was critical to discussing a group’s norms. But to Sakaguchi, it made sense that psychological safety and emotional conversations were related. The behaviors that create psychological safety — conversational turn-taking and empathy — are part of the same unwritten rules we often turn to, as individuals, when we need to establish a bond. And those human bonds matter as much at work as anywhere else. In fact, they sometimes matter more.
‘‘I think, until the off-site, I had separated things in my head into work life and life life,’’ Laurent told me. ‘‘But the thing is, my work is my life. I spend the majority of my time working. Most of my friends I know through work. If I can’t be open and honest at work, then I’m not really living, am I?’’
What Project Aristotle has taught people within Google is that no one wants to put on a ‘‘work face’’ when they get to the office. No one wants to leave part of their personality and inner life at home. But to be fully present at work, to feel ‘‘psychologically safe,’’ we must know that we can be free enough, sometimes, to share the things that scare us without fear of recriminations. We must be able to talk about what is messy or sad, to have hard conversations with colleagues who are driving us crazy. We can’t be focused just on efficiency. Rather, when we start the morning by collaborating with a team of engineers and then send emails to our marketing colleagues and then jump on a conference call, we want to know that those people really hear us. We want to know that work is more than just labor.
The paradox, of course, is that Google’s intense data collection and number crunching have led it to the same conclusions that good managers have always known. In the best teams, members listen to one another and show sensitivity to feelings and needs.
‘‘Just having data that proves to people that these things are worth paying attention to sometimes is the most important step in getting them to actually pay attention,’’ Rozovsky told me. ‘‘Don’t underestimate the power of giving people a common platform and operating language.’’
Music Memos offers so many ways of organizing my clips that I’m finding myself recording more just because I can.
For as long as I’ve had my iPhone, I’ve used Apple’s built-in Voice Memos app to record my song ideas, collecting iterations of a riff or melody I don’t want to forget. But Voice Memos is clunky, has no built-in organizational system, and the editing tools are borderline nonexistent.
For me, the songwriting process usually goes something like this: I sit down with an acoustic guitar and play around until I stumble upon something I like. Then I play it on a loop, letting myself get comfortable enough to twist it around and see how it works with other notes and different voicing. Once I can hear a song in the noise, I’ll start singing gibberish lyrics until I come up with a vocal melody that works.
Then I record it, over and over, in Voice Memos.
That’s kind of a problem; I see “New Recording 233” and I sigh. Sure, I could have given the clip a real name, but why bother? I have hundreds upon hundreds of clips, with no way to search or filter them. I occasionally go spelunking in the Voice Memos table view list to discover long-forgotten ideas that I really wish I’d taken the time to flesh out. My entire musical idea system is a ghetto.
If Voice Memos are Post-Its — a quick and dirty tool to make sure I didn’t forget an idea — then Music Memos is a sketchbook. This is where I start the songwriting process, and every part of the app is designed to help facilitate the process and, most shockingly of all, guide me to the next step in fleshing the song out.
This level of organization also makes me want to start recording practice sessions and charting progress. Having that much raw material available and easily searchable also means more clips we can share on Connect, more early listens we can share with our Patreon supporters, and more options for comparison and background content for our podcast. That’s a whole lot of upside for one feature.
Music Memos has so many other tricks up its sleeves that I almost feel like someone at Apple has been reading my dream journal. An app for recording song ideas that uses a robust tagging system is something I’ve personally wanted to build for a long time, but throw in a guitar tuner, chord and tempo detection, exporting to GarageBand, and magical automatic backing instruments, and the dream becomes borderline pornographic.
My experience with the chord detection feature has been mixed, with me watching the app struggle to average out the chords I play with the notes I’m singing. I had the idea to try using it for something else: I’ve been writing a new song, starting with just a vocal melody. Because I’m a self-taught musician with only an intuitive understanding of music theory, this gets a little tricky. Rather that spending time working out what the chords should be, I decided to just sing the melody into Music Memos and see what it suggested.
This is obviously a bit of a mess, but that’s perfectly okay for my purposes. Playing exactly the chords of the vocal melody would be really boring (and on guitar, hard to pull of), but this gives me a great view of the chord set I should be working from. From here, I can start singing over one or two of these chords and work my way out from there. Music Memos has taken one of the most annoying parts of songwriting and made it fun for me. I really can’t overstate how great that feels.
The other major songwriting tool in Music Memos is backing tracks. Record your song the way you normally would, and the app will put drums and/or bass behind it. As with everything else in this app, the controls are dead simple: turn drums on by tapping the drums icon, bass by tapping the bass icon.
My other favorite feature is a subtle one: “Auto”. With this option turned on (again, via a dead-simple button in the main UI), Music Notes does exactly what you’d expect: it sits and listens, and starts recording automatically when it detects that you’re playing a song.
The magic behind this feature is pretty easy to guess: the app listens passively, Siri-style, recording everything, and simply saves the recording starting at the beginning of the waveform. But it’s these little details that add up for me. Since many (if not most) of my clips and recordings are full of dead air at the beginning while I pull up a lyric sheet or get my capo set properly, this is a big win for me. Sure, I could edit by hand, but I don’t, and I never will.
Or, if I just want to show off a snippet of something I’m playing around with, I can send it off to Apple Music Connect, SoundCloud, or YouTube. I couldn’t get Connect sharing to work in my testing — unsurprising if you’ve ever tried to get Apple Music Connect to do anything — but given Connect’s place in the iTunes ecosystem, the day is definitely coming where an artist could write, record, produce, and distribute an entire album using nothing more than their telephone.
Music Memos is less a tool than a toolbox. Each tool works remarkably well for a 1.0 release, and most of them feel like they were designed with my exact needs in mind. The designers could have approached this like recording software, with a series of menus and sub-menus of options, and that would have been more or less fine. But instead Music Memos has the weight and simplicity of spirit of a guitar effect pedal. One button and a handful of dials. Beautiful. My iPhone is only further solidified as an indispensable part of my composing process.
Microphone technology may not be making the same quantum leaps as digital cameras, but putting them to better use is a good start. After all, the best recording studio is the one you have with you.