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Trust in the Age of Digital Disruption
What happens when a taxi driver picks up passengers between committing murders? When a stranger sleeps in your spare bedroom? When an algorithm decides your social worth? In her groundbreaking exploration of modern trust dynamics, Rachel Botsman examines how technology is fundamentally rewiring our trust mechanisms. Published in 2017, "Who Can You Trust?" quickly became required reading for business leaders and policymakers navigating our rapidly evolving digital landscape. The book has been praised by figures ranging from the Chief Economist of the Bank of England to the CEO of Tough Mudder as essential for understanding how trust - once flowing upward to institutions - now increasingly flows horizontally between individuals. As traditional institutions crumble under scandals and failures, Botsman reveals how we're experiencing one of the biggest trust shifts in human history, with profound implications for how we live, work, and relate to each other in an increasingly connected world.
2장
The Great Trust Migration: From Institutions to Networks
We're experiencing one of the biggest trust shifts in human history. Trust, once flowing upward to institutions and authorities, is now flowing horizontally between individuals through digital platforms and networks.
This transformation began on September 14, 2008 - the day Lehman Brothers collapsed, marking the beginning of the worst financial crisis since the Great Depression. Coincidentally, this was also Rachel Botsman's wedding day, with senior banking executives receiving urgent messages during her celebration. This wasn't just a financial collapse but the beginning of a profound erosion in institutional trust that continues today.
Since then, trust has been battered by endless scandals while we retreat into media echo chambers that reinforce our beliefs and amplify misinformation. The 2017 Edelman Trust Barometer revealed trust in all major institutions - government, media, business, and NGOs - at all-time lows, with media suffering the biggest blow, now distrusted in 82% of countries surveyed.
This institutional trust wasn't designed for our digital age of radical transparency, where politicians and CEOs operate behind clear glass and trying to hide anything becomes a high-stakes gamble. It wasn't built for platforms where people transact directly, or for a workforce becoming increasingly independent, or for our desire to control everything personally with a click or swipe.
But trust hasn't disappeared - it has shifted. We're now at the start of the third major trust revolution in human history: from local trust (small communities where everyone knew everyone), to institutional trust (through contracts, courts and brands), to distributed trust (flowing horizontally between people and programs).
This new distributed trust enables cooperation between strangers on an unprecedented scale. Jack Ma's Alibaba demonstrates this perfectly - creating a platform where millions of Chinese citizens, culturally predisposed to trust only family and close connections, now conduct billions in transactions with complete strangers. By implementing mechanisms like Alipay (an escrow system where funds are only released after buyer satisfaction) and TrustPass certification (verifying seller identities), Ma enabled what Botsman calls a "trust leap" - that moment when we embrace new ways of doing things that once seemed risky or impossible.
These trust leaps transform behaviors and drive change by bridging the gap between the known and unknown. Like the Maghribi traders of the 11th century who developed reputation-based networks enabling long-distance Mediterranean trade, today's platforms create mechanisms that reduce uncertainty enough for people to take risks and collaborate in new ways.
3장
When Trust Breaks: Institutional Failures and Their Aftermath
The story of Jean Heller, who uncovered the Tuskegee Study scandal in 1972, illustrates the devastating consequences when institutional trust is broken. For forty years, the US Public Health Service deliberately left 399 African American men with syphilis untreated to observe the disease's progression, despite penicillin becoming widely available. This horrific breach created generational mistrust of healthcare among African Americans, with researchers quantifying that this distrust accounts for a 1.4-year decrease in life expectancy among black men over 45-representing over a third of the life-expectancy gap between black and white men.
Similar institutional failures continue today. The 2016 Panama Papers leak exposed how elites worldwide used offshore tax havens to shield their wealth while ordinary citizens bear the burden. When the documents were published, they implicated 29 billionaires, 12 world leaders, and 140 politicians who exploited tax systems while preaching shared sacrifice. This destroyed the tacit understanding that "we're all in this together," confirming why institutional trust is eroding as people feel betrayed while elites thrive unethically.
The numbers tell a stark story. Gallup surveys tracking American confidence in major institutions reveal a dramatic decline over forty years. In the 1970s, approximately 70% of Americans trusted key institutions to do the right thing. By 2016, confidence averaged just 32% across fourteen institutions. Government trust for handling international problems fell from 75% to 49%, while Congress plummeted from 42% to a mere 9%. Similar declines affected banks (60% to 27%), big business (26% to 18%), the church (65% to 41%), and newspapers (39% to 20%).
Three overlapping factors explain this collapse: inequality of accountability (certain people escape punishment while others face penalties); twilight of elites and authority (digital age flattening hierarchies and eroding faith in experts); and segregated echo chambers (living in cultural bubbles deaf to other perspectives).
The banking industry exemplifies this problem. Despite causing catastrophic damage in 2008, only one banker went to jail. A survey by law firm Labaton Sucharow revealed that over half of financial professionals believed their competitors engaged in illegal or unethical behavior, with nearly a quarter admitting they would engage in insider trading if they could escape consequences.
Meanwhile, our information echo chambers amplify distrust. Facebook's algorithm changes prioritize content from friends over traditional media, creating polarizing bubbles with little exposure to contradictory perspectives. During Brexit, Tom Steinberg demonstrated this problem when he couldn't find anyone celebrating the Leave victory on his Facebook feed despite actively searching. People now consider "a person like me" twice as credible as government leaders, creating self-affirming online communities where distrust breeds more distrust.
Trump's 2016 presidential victory emerged directly from this crisis of faith in traditional authorities. His claim to "tell it like it is" represented an intoxicating form of transparency for many, embodying what Stephen Colbert called "truthiness" - ideas that "feel right" despite factual inaccuracies. Similarly, Brexit reflected this distrust when Michael Gove famously declared "people in this country have had enough of experts," highlighting our "post-truth" world where emotional appeals trump objective facts.
4장
Climbing the Trust Stack: How We Learn to Trust the Unfamiliar
For new ideas to gain adoption, people must climb what Botsman calls the "Trust Stack": first trusting the idea itself, then the platform or company, and finally the other person (or machine). Each level requires different forms of certainty before proceeding to the next. The initial climb feels risky, but once completed, our behaviors change quickly and permanently.
BlaBlaCar, a long-distance ridesharing platform that now carries more passengers than Eurostar, demonstrates this process. Founded by Frederic Mazzella in 2006 after noticing countless empty car seats during a holiday journey, the company succeeded by helping users climb this trust stack. The key breakthrough wasn't environmental messaging but implementing advance online payments, which created commitment and reduced cancellations from 35% to under 3%. This enabled millions to take trust leaps that defy childhood warnings about strangers in cars.
New ideas gain trust through making the unfamiliar more familiar - what Botsman calls the "California Roll Principle." Just as the California Roll introduced Americans to sushi by combining raw fish with familiar ingredients like avocado, successful innovations follow the "strangely familiar" approach. Apple uses "skeuomorphism" - design cues from physical objects like calendars and notepads - to help users grasp new digital concepts.
Airbnb demonstrates this brilliantly by helping first-time users understand home-sharing through familiarity. When new users visit the site, they typically search for places in their own hometown first, creating an "ah-huh" moment: "Oh, this is somebody whose house is just near mine... now I get it."
Beyond familiarity, people need to understand benefits before trusting new ideas - the "What's In It For Me?" factor. Edward Jenner's development of vaccination faced fierce resistance despite its life-saving potential. The clergy called it "unchristian," physicians labeled it "unethical," and satirical cartoons depicted cows sprouting from vaccinated people's bodies. This resistance persists today with anti-vaccination movements, showing how doubt can undermine trust regardless of rational evidence.
Self-driving cars present a fascinating paradox in trust development. While Dr. Brian Lathrop of Volkswagen worried about people fearing autonomous vehicles, he discovered something surprising: "People trust the car quickly, almost too easily." After initial anxiety, most users adapt within twenty minutes. The challenge isn't getting people to trust the technology initially, but addressing the "1 percent corner cases" like deer running into roads or navigating crowded parking lots. The WIIFM factor matters most - self-driving cars could save 300,000 lives per decade in the US alone and free up billions of hours wasted in traffic.
Beyond early adopters, "trust influencers" play a critical role in driving mainstream adoption. These are groups who disproportionately influence behavior change and establish new social norms. TransferWise discovered their most powerful trust influencers weren't tech-savvy millennials but British pensioners living abroad who needed to transfer pensions between currencies. Social proof doesn't necessarily require large numbers - it can come from small groups with unique influence power.
5장
Platform Accountability: When Trust Systems Fail
When trust crashes in the digital world, accountability becomes dangerously murky. The Kalamazoo shootings of February 2016 starkly illustrate this problem, when Uber driver Jason Brian Dalton killed six people and seriously injured two others while continuing to pick up passengers between shootings. Despite warning signs - including a terrified passenger who reported Dalton's erratic driving to both 911 and Uber before the first murder - the system failed to respond effectively. Uber later claimed there were "no red flags" and pointed to Dalton's good 4.73-star rating.
This wasn't Uber's first safety incident - drivers worldwide have been arrested for sexual assault, kidnapping, and other crimes. Despite numerous scandals including sexual harassment allegations and a toxic corporate culture, millions still use Uber daily without hesitation, essentially outsourcing their capacity to trust to an algorithm.
Uber's terms of service deny liability for drivers' behavior, classifying them as "independent contractors" while taking up to 25% of fares as a "service fee." Unlike traditional companies where accountability is clearer (as in the Tesco horsemeat scandal where the company took responsibility despite blaming suppliers), platform accountability remains ambiguous. As Tom Goodwin noted, today's largest companies own no assets in their industries: "Uber, the world's largest taxi company, owns no vehicles. Facebook, the world's most popular media owner, creates no content."
Today's platforms fundamentally differ from traditional brands. While we trusted Marriott as a brand, with Airbnb we must trust both the platform itself and the connections between hosts and guests. As Airbnb co-founder Joe Gebbia explains, they're not a technology company but "in the trust business," acting as the "mutual friend" introducing strangers. Their challenge is creating conditions for relationships between people who've never met, then stepping aside.
However, distributed trust isn't always fairly distributed. Harvard researchers found non-black Airbnb hosts could charge approximately 12% more than black hosts, and guests with African-American-sounding names were 16% less likely to be accepted. The #AirbnbWhileBlack hashtag revealed widespread discrimination, showing how personal profiles and photos intended to build trust inadvertently facilitated bias.
Trust requires friction, time, and effort - elements often sacrificed for efficiency in digital platforms. Berkeley professor Coye Cheshire explains that systems have become so complex they're "capable of betrayal," blurring lines between interpersonal trust and system trust. With platforms running on billions of lines of code, we've offloaded cognitive power to "trust engineers" who design social infrastructure to make interactions feel magically seamless. But this acceleration of trust creates vulnerability - whether it's accepting an Uber ride despite safety concerns, impulsively swiping right on dating apps, or sharing news articles without reading them.
Facebook's controversial 2012 "emotional contagion" experiment revealed the platform's ability to manipulate users' emotions by tweaking news feed algorithms. Researchers found that reducing positive content led users to post more negatively, and vice versa - proving emotions spread through social networks. The study sparked outrage not for its explicit findings but because Facebook experimented on 689,003 users without consent. A 2013 survey found 62% of Facebook users were unaware their feeds were algorithmically curated rather than showing all friends' posts chronologically.
Facebook insists it's merely a neutral technology pathway facilitating connections, not a media company - a misconceived and dangerous position given its enormous influence over how misinformation spreads. The questions around Facebook mirror those raised after the Kalamazoo killings: in distributed systems, who will tell the truth, and who bears responsibility when trust is broken?
6장
The Science of Trustworthiness: Signals and Reality
We rely on "trust signals" - clues or symbols that help us decide if someone is trustworthy. These include physical appearance, uniforms, accents, and non-verbal cues. Research by Jon Freeman at NYU shows we make snap judgments about trustworthiness within a tenth of a second based on facial features - people with upturned eyebrows, pronounced cheekbones, and slightly happy expressions are perceived as more trustworthy, though there's no evidence these features correlate with actual trustworthiness.
The author's childhood experience with Doris, a nanny hired through the prestigious magazine The Lady, illustrates how these signals can be misleading. Despite appearing trustworthy - complete with a Salvation Army uniform, references, and a convincing demeanor - Doris turned out to be a criminal involved in a drug ring who used the family's car for an armed robbery. This cautionary tale illustrates philosopher Baroness Onora O'Neill's point that "trust has two enemies: bad character and poor information." The illusion of information about someone can actually be more dangerous than acknowledging our ignorance.
New platforms are creating alternative trust mechanisms. UrbanSitter connects families with babysitters online through "borrowed trust" from social networks. Research by Professor Murnighan showed how powerfully these connections influence trust - merely seeing trusted names subliminally caused participants to exhibit 50% more trust toward strangers in experiments. This explains how figures like Madoff deceived so many - his client list included family members, creating powerful trust signals.
Trustworthiness boils down to three essential traits: competence (skills and capability), reliability (consistency in following through), and honesty (aligned intentions and integrity). Political scientist Russell Hardin frames trust as "encapsulated interest" - a closed loop where each party benefits from maintaining trust. Trust is contextual; we must ask "Do I trust you to do x?" rather than broadly "Do I trust you?"
Traditional CVs offered limited proof of trustworthiness, with studies showing 18% contained outright falsehoods. Today's distributed online profiles create more socially fluid trust signals. On platforms like UrbanSitter, detailed verification processes reject 75% of applicants, while algorithms match parents with sitters using forty different criteria. Reliability is demonstrated through response metrics, while honesty - the hardest trait to verify - is addressed through mandatory video introductions that humanize sitters and provide deeper character insights beyond written profiles.
Modern digital identity verification capabilities are becoming increasingly sophisticated. The Trooly "Instant Trust Rating" system uses just a name and email address to scan public records, watch lists, sex offender registries, social media, and the deep web to build comprehensive trust profiles. The technology classifies people into trust categories, with only 15% achieving "super good" status and 1.5-2% flagged as "super bad." Despite its intrusiveness, Trooly's founder Baveja argues this approach is more ethical and accurate than traditional background checks, which suffer from false positives (wrongly labeling innocent people) and false negatives (missing actual criminals).
7장
The Social Credit System: When Trust Becomes Control
In 2014, China published a plan for a "Social Credit System" that would rate the trustworthiness of its 1.3 billion citizens. This system monitors countless daily activities - purchases, location, social interactions, bill payments - and distills them into a single number that determines eligibility for mortgages, jobs, schools, and even dates. While initially voluntary, by 2020 it will become mandatory for all citizens and companies.
Eight private companies have been licensed to develop social credit scoring systems. Two major players are China Rapid Finance (partnered with Tencent's WeChat) and Alibaba's Ant Financial with its "Sesame Credit" system. Sesame Credit scores citizens from 350 to 950 points based on five factors: credit history, fulfillment capacity, personal information verification, behavior/preferences, and interpersonal relationships.
The system judges character through shopping habits - buying diapers raises your score while purchasing video games lowers it. Even more concerning, your online friends and interactions affect your rating, with "positive energy" about the government boosting scores. This creates a mechanism that not only monitors behavior but actively shapes it, nudging citizens away from government-disapproved activities and speech.
Citizens with high scores receive enticing rewards: loans up to 5,000 yuan at 600 points, car rentals without deposits at 650, VIP airport check-in, larger loans at 666 points, simplified Singapore travel at 700, and fast-tracked European visa applications at 750. These perks have transformed scores into status symbols, with thousands proudly sharing their ratings on social media.
The system even affects dating prospects, with higher scores gaining prominence on dating platforms like Baihe. This gamification of obedience creates powerful social pressure, as friends and family can drag down each other's scores through association. By 2020, when the government's mandatory system launches, penalties will become severe: slower internet, restricted access to restaurants and travel, limited job opportunities, and exclusion from private schools. As the government policy states, it will "allow the trustworthy to roam everywhere under heaven while making it hard for the discredited to take a single step."
This reality eerily parallels dystopian fiction like Gary Shteyngart's novel "Super Sad True Love Story," where people wear devices broadcasting their ratings and personal data, and the "Black Mirror" episode "Nosedive," featuring a society obsessed with five-star ratings. As philosopher Luciano Floridi suggests, we're experiencing a fundamental "de-centering shift" in self-understanding, comparable to the paradigm shifts caused by Copernicus, Darwin, and Freud.
While many Westerners react with visceral alarm to China's Social Credit System, few ask the more pertinent question: could this happen in the West? The reality is we're already moving in this direction. We rate everything from restaurants to doctors, bowel movements to professors. "Yelpers" threaten businesses with poor reviews to get free items. Authors have Amazon scores, Airbnb hosts have cleanliness ratings, and gig workers are constantly evaluated.
Meanwhile, our devices track our every move - Fitbits monitor physical activity, Facebook can identify us without seeing our faces, and we're surrounded by 24.9 connected devices per 100 people in the US. Companies secretly collect our most intimate data - from Bose headphones tracking listening habits to We-Vibe vibrators collecting usage patterns linked to personal email addresses. Digital assistants like Amazon's Echo are always listening, potentially recording conversations that could be subpoenaed for legal proceedings.
As we move toward a future where we're all branded online and data-mined, we urgently need mechanisms for forgiveness. Human beings, with all our imperfections, are more than just numbers. While we may not stop this new era, we must exert our choices and rights now. We need trustworthy mechanisms ensuring ratings and data are used responsibly with our permission. The scoring algorithms must be transparent, despite arguments that disclosure makes systems vulnerable to manipulation.
8장
Blockchain: The Trust Machine
The blockchain - Bitcoin's revolutionary underlying technology - solved the "double-spending problem" that had plagued previous digital currency attempts. This enormous shared digital ledger publicly records every transaction, distributed across thousands of computers worldwide. The blockchain creates an immutable, permanent record that no single entity controls, where ownership can be verified without centralized authority.
Miners maintain this system by solving complex mathematical puzzles to validate transactions, receiving bitcoin rewards for their computational work. The system adjusts difficulty based on solving speed, with rewards halving periodically to control inflation, all part of Satoshi's original design to cap the total supply at 21 million bitcoins by 2140.
Despite Satoshi's vision of a trustless system based on "crypto proof instead of trust," bitcoin faces significant challenges. The anonymous creator's disappearance in 2011 left questions unanswered. The system remains vulnerable to theoretical "51 percent attacks" if mining pools combine forces. Users must trust that their private keys remain secure, as lost keys mean permanently lost bitcoins - as happened to James Howells, who accidentally discarded a hard drive containing 7,500 bitcoins worth millions. Multiple exchanges have suffered thefts, including Mt. Gox's loss of 850,000 bitcoins worth nearly $500 million.
The true innovation isn't bitcoin itself but the underlying blockchain technology - what The Economist called "the trust machine." This distributed public ledger offers a reliable record for any asset transfer, from currencies to contracts, property titles to intellectual rights. As former US Treasury Secretary Larry Summers noted, while bitcoin won't bring a "libertarian paradise," the blockchain technology will be "fundamental to reducing frictions."
Beyond cryptocurrency, blockchain technology offers revolutionary potential for tracking the provenance and authenticity of valuable items. Leanne Kemp founded Everledger in 2015 to digitally certify diamonds on the blockchain, creating a "digital thumbprint" of each diamond using forty unique attributes. This creates an unalterable record for insurers, traders and customers to verify a diamond's entire history, helping combat insurance fraud and the trade of conflict "blood diamonds."
This "World Wide Ledger" concept could transform how we track valuable assets - from fine art and luxury goods to everyday consumer products. Jessi Baker's company Provenance uses it to track fish from catch to plate, while Alibaba and Walmart are implementing blockchain solutions to combat counterfeit foods and ensure supply chain transparency.
Blockchain holds immense promise for emerging markets where weak governance and poor record-keeping create trust deficits. With an estimated 5 billion people worldwide struggling to prove ownership of land, businesses, or vehicles, trillions in "dead capital" remains locked outside the official economy. Companies like Bitfury, ChromaWay and Bitland are working with governments to record property rights on blockchains, creating immutable records that corrupt officials cannot tamper with.
While Satoshi Nakamoto's original vision was to create a system that would eliminate financial middlemen, ironically, the banking industry is racing to adopt blockchain technology. Major financial institutions including Goldman Sachs, Citibank, and JP Morgan have invested over $60 million in Digital Asset Holdings, a company developing blockchain solutions for financial institutions. The appeal for banks is clear: blockchain could reduce trade settlement times from days to minutes, potentially saving banks $15-20 billion annually by 2022.
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Trust in the Age of Intelligent Machines
Our relationship with technology is evolving beyond simple functional reliability. While we've traditionally trusted machines just to do something (like a compass pointing north), we're now trusting them to decide what to do and when to do it. This represents a significant trust leap - from trusting a car to brake when commanded to trusting an autonomous vehicle to decide whether to swerve or stop, raising profound questions about how we trust machines' intentions.
When a robot named Bert dropped an egg while helping humans prepare an omelette, his sad expression, furrowed eyebrows, and apology created an unexpected reaction from human participants. Despite Bert taking 50% longer to complete tasks due to his clumsiness, 15 of 21 participants chose him as their preferred kitchen assistant. This suggests people trust robots that display human-like qualities over those that are more efficient but lack social skills. As cognitive psychologist Frank Krueger explains, people may regain trust in machines that make mistakes if they follow basic social etiquette like saying "I'm sorry."
Humans naturally attribute human-like qualities to non-human entities - naming them, assigning genders, and responding to them emotionally. Research shows this anthropomorphism significantly increases our willingness to trust technology. In a driving simulator study, participants trusted a self-driving car more when it had a female name ("Iris") and voice. Even after a programmed crash, those in the anthropomorphized vehicle were less likely to blame the car.
Programming ethics into machines presents profound challenges. When automation meets autonomy, we need entirely new standards for assessing trustworthiness. As philosopher-turned-diplomat Stephen Cave explains, we must understand how machines make decisions and how robust those processes are. The stakes are high: doctors increasingly rely on AI diagnostics, risking the same "mode confusion" that doomed Air France Flight 447 when pilots couldn't manually fly after autopilot disengaged.
Asimov's famous Three Laws of Robotics fail when robots face genuine ethical dilemmas with no clear answer. The classic "trolley problem" - choosing whether to sacrifice one person to save five - becomes real when autonomous vehicles must decide whether to swerve away from pedestrians at the risk of harming passengers. In MIT's research, most participants theoretically wanted self-driving cars programmed with utilitarian ethics (sacrificing one to save many), but balked at personally buying cars that might sacrifice them.
The next generation won't ask "How will we trust robots?" but "Do we trust them too much?" The real danger isn't insufficient trust but over-reliance on machines that can't communicate their limitations. Robots need the ability to refuse harmful or illegal commands, requiring sophisticated contextual understanding. A surgical robot must recognize when it's uncertain and request human assistance - a form of machine humility that paradoxically makes it more trustworthy.
10장
The Future of Trust
Trust, more than money, makes the world go round. In Kenya, a small business owner named Eric demonstrates this principle when he uses Tala, a mobile lending app, to quickly secure a loan during a crisis. When his cinema's cable service is cut during a Premier League match with 400 paying customers present, Eric uses Tala to borrow money, pay his cable bill, and restore service within minutes.
Tala represents a new approach to financial trust. By analyzing over 10,000 data points from a person's smartphone - from call duration patterns to contact organization habits - the app builds financial identities for the 2.5 billion "unbanked" people globally who lack traditional credit scores. The company has issued loans to more than 300,000 Kenyans with a 90% repayment rate, offering interest rates far lower than predatory loan sharks.
Tala's founder Shivani Siroya created a system that starts with people rather than institutions. The key to Tala's effectiveness is simple: it puts people at the center. As Richard Edelman suggests in response to the "global implosion of trust," organizations must be redesigned to work with people, not just for them. The litmus test is whether people would describe an organization as an "honest, ethical and reliable friend."
The distributed trust landscape includes both inspiring innovators and problematic "digital gods." On one side are entrepreneurs like Gerard Ryle (ICIJ), Leanne Kemp (Everledger), and Savi Baveja (Trooly) who use technology to rebuild broken trust systems. On the other are figures like former Uber CEO Travis Kalanick, who denied responsibility when things went wrong.
In today's distributed trust environment, institutions need not disappear - they must adapt to the new trust landscape by becoming more transparent, inclusive and accountable, putting people first in their design. When institutional systems fail, alternatives will always rise up to take their place.
This revolution is taking place amid rapidly shifting technologies where we're constantly taking trust leaps at a dizzying pace. The challenge is creating trust systems that can adapt to unprecedented change. While distributed trust has enormous potential to help people leap out of low-trust situations, we must acknowledge its vulnerabilities.
Many distributed systems ultimately lead back to centralized power - Amazon, Alibaba and Facebook began as ways to democratize commerce or information but became centralized behemoths controlling sensitive data. China's Social Credit System shows how distributed networks could become tools of government control, while bitcoin mining has concentrated in China, contrary to its globalized ideals.
Trust has evolved through distinct chapters: local, institutional, and now distributed. Like most innovations, distributed trust will be messy and unpredictable. Technology can help us make better choices, but ultimately trust remains a human decision requiring a "trust pause" - a moment to consider if someone or something is worthy of our trust.
