Speaker 1: The ten billion dollars start up training AI to replace
Speaker 1: the white collar workforce. Mercy is promising to replicate most
Speaker 1: professional work. It was also co founded by twenty somethings
Speaker 1: who previously never held a real job. By Tom Foster
Speaker 1: read aloud by Mark Lydorf. When Tasha Kozak, a social
Speaker 1: worker for the Hillsborough County Public Schools in Tampa, met
Speaker 1: the family, they were living in a car. The three
Speaker 1: children's grades had been slipping, The mother was exhausted, the
Speaker 1: father was out of the picture. Kozak began helping them
Speaker 1: connect to housing resources. She checked in with the children
Speaker 1: every couple of days at school. She called the mother
Speaker 1: every three or four. After several months, the family found
Speaker 1: stable housing. The children began to improve in school. I
Speaker 1: saw the mom got her glow back, and she started
Speaker 1: getting more consistent work shifts. Kozak says, moments like that
Speaker 1: are why Kozak does the work. She didn't set out
Speaker 1: to become a social worker, but after taking an elective
Speaker 1: in it in college, she changed her major, and more
Speaker 1: than a decade later, she still calls social work her passion.
Speaker 1: The job, she says, is about listening, connecting, helping in
Speaker 1: a way that feels instantly meaningful. In her view, about
Speaker 1: seventy percent of effective social work is that human relational component.
Speaker 1: The rest is administrative. On many evenings, after finishing her
Speaker 1: full time job with the district, Kozak logs into a
Speaker 1: website called Mercore. The San Francisco startup mercore dot io
Speaker 1: recruits workers in high skill fields doctors, lawyers, investment bankers, journalists,
Speaker 1: social workers, you name it, to help teach artificial intelligence
Speaker 1: systems how to do their work. The company places these
Speaker 1: experts in part time or temporary contract roles to work
Speaker 1: on training projects for major tech companies and AI labs.
Speaker 1: Clients that have included OpenAI, Anthropic, and Meta. It's like
Speaker 1: the Uber of advanced AI training, a gigwork platform for
Speaker 1: white collar and skilled professionals that offers a path for
Speaker 1: them to earn something extra from their expertise at the
Speaker 1: risk of eventually sacrificing their careers to AI. Like many
Speaker 1: of Mercer's contractors, Kozak first heard about the company through
Speaker 1: a LinkedIn job posting. School had just let out for
Speaker 1: the summer in twenty twenty five, so she had spare time,
Speaker 1: and she was curious about the rise of AI. She
Speaker 1: landed an interview and found herself on camera talking to
Speaker 1: an AI agent with a gentle female voice and a
Speaker 1: surprisingly natural conversational style. When Kozak described a specific case
Speaker 1: she'd handled, the agent followed up with targeted prompts, tell
Speaker 1: me more about the parent in that situation, with the
Speaker 1: kind of probing detail she was used to hearing from
Speaker 1: human supervisors. Two weeks later, she was working on her
Speaker 1: first assignments, and today she earns as much money training
Speaker 1: AI for twenty hours a week as she does in
Speaker 1: forty hours helping actual families. Social work is a very
Speaker 1: underpaid occupation, she says. Her work for Mercore is methodical
Speaker 1: and narrow. Kozak is part of a virtual team that
Speaker 1: writes prompts for an AI model to perform specific social
Speaker 1: work tasks based on fictitious case files, For instance, asking
Speaker 1: the AI to produce a social developmental history of an
Speaker 1: elementary school student who needs an individualized education plan. The
Speaker 1: case file includes notes from a parent interview, a student interview,
Speaker 1: a review of student records, and a doctor's report. Another
Speaker 1: team reviews the AI's responses, and still another handles additional
Speaker 1: pieces of the training process. There are hundreds of people
Speaker 1: on the project, Kozak says maybe more. Her team alone
Speaker 1: includes about forty contractors. Hour by hour, highly segmented, task
Speaker 1: by task, they translate professional judgment into training data. Across
Speaker 1: the US, other professionals are doing the same. In San Francisco,
Speaker 1: a doctor of internal medicine named Milania Punacha works fifty
Speaker 1: to sixty hours AGAY week as a full time pediatric
Speaker 1: hospitalist on the night shift and logs into mercor on
Speaker 1: her days off for an additional ten hours or so,
Speaker 1: asking an AI model to interpret lab results and evaluating
Speaker 1: its work. In Baton Rouge, Louisiana, the novelist and screenwriter
Speaker 1: Robin palmer Blanche started evaluating AI generated creative writing for
Speaker 1: Voice and Structure last August. Palmer Blanche, a mother of
Speaker 1: two who struggled to make a living from writing since
Speaker 1: the twenty twenty three Hollywood Writers Strike, is part of
Speaker 1: a large swath of Mercore workers who are under employed
Speaker 1: in their professions and use the platform to patch together income.
Speaker 1: She sometimes finds herself chatting with a Mercore teammate on Slack,
Speaker 1: only to realize there are other novelists doing it too.
Speaker 1: Oh my god, I read that woman's book last year
Speaker 1: and fell in love with it. Mercor has tens of
Speaker 1: thousands of such experts working on its platform, screened using
Speaker 1: those AI run interviews and project specific skills tests and
Speaker 1: practice tasks. In the three years since the company started,
Speaker 1: it's raised almost five hundred million dollars in venture capital
Speaker 1: from a who's who across Silicon Valley in finance, including Benchmark,
Speaker 1: General Catalyst, Peter Teel, Jack Dorsey, and Larry Summers. The
Speaker 1: most recent round of funding in October valued Mercor at
Speaker 1: ten billion dollars, five times what it was thought to
Speaker 1: be worth just a few months earlier. According to the company,
Speaker 1: it's been profitable since its inception. It pays out more
Speaker 1: than two million dollars per day to contractors, and it
Speaker 1: has about three hundred full time employees, largely engineers and
Speaker 1: project managers. The founders, Brendan Foodi, adarsh Hiramat and Suri Amida,
Speaker 1: three early twenties college dropouts who were buddies in high school,
Speaker 1: have become the youngest ever self made billionaires, at least
Speaker 1: on paper. Mercor's rapid rise has also brought controversy, including
Speaker 1: several class action lawsuits now moving through California courts and
Speaker 1: a recent data breach that raised questions about how the
Speaker 1: company handle sensitive information and led one of its clients,
Speaker 1: Meta to indefinitely pause its work with the startup. If
Speaker 1: Mercore and its backers are right, the company is uniquely
Speaker 1: positioned to help AI become the economic force. Silicon Valley
Speaker 1: has been promising. Most of what's been seen from AI
Speaker 1: so far, they argue, has been something of a dazzling
Speaker 1: consumer demo. Going beyond that, making the technology perform reliably
Speaker 1: in professional fields where mistakes carry real consequences is fundamentally
Speaker 1: about feeding the models better training data, says Sundeep Peachew,
Speaker 1: managing partner at the VC firm Felicis Ventures, which led
Speaker 1: Mercor's two most recent funding rounds. The first generation of
Speaker 1: data was from the Internet, he says, and that allowed
Speaker 1: companies to build very general purpose models. But for AI
Speaker 1: to become truly economically useful and not just a toy thing,
Speaker 1: someone has to get humans to tell the model, step
Speaker 1: by step how they actually do their work. At this moment,
Speaker 1: it's hard to understand why people are so willing to
Speaker 1: feed the machines that might one day render them obsolete.
Speaker 1: Anxiety about job loss is everywhere. Job openings in professional
Speaker 1: and business services have fallen by more than a million
Speaker 1: from their post pandemic peak in twenty twenty two, according
Speaker 1: to the Bureau of Labor Statistics. About forty two percent
Speaker 1: of recent college grads are under employed, according to a
Speaker 1: twenty twenty five report from the Federal Reserve Bank of
Speaker 1: New York, and a study last year by the American
Speaker 1: Psychological Association found that fifty four percent of US workers
Speaker 1: are experiencing significant stress about job in security. Mercore's listings
Speaker 1: sometimes ads from the company itself and sometimes from third
Speaker 1: parties using Mercore links that allow them to collect referral fees,
Speaker 1: Saturate LinkedIn, and other job boards, offering a respite from
Speaker 1: all that. Mercore says its average hourly pay is about
Speaker 1: ninety dollars, though the range is wide, from generalists barely
Speaker 1: scratching minimum wage to elite coders or PhDs making two
Speaker 1: hundred and fifty or three hundred dollars per hour. Someone
Speaker 1: working on Mercore Project's full time at ninety dollars per
Speaker 1: hour would earn almost one hundred and ninety thousand dollars
Speaker 1: a year. There are plenty of workers who view Mercore
Speaker 1: as a grim final stop to monetize their expertise before
Speaker 1: professional extinction. Kozak doesn't see it that way, saying her
Speaker 1: reason for doing the work is far more practical. She
Speaker 1: envisions herself off loading social work's tedious thirty percent, the paperwork,
Speaker 1: the reports, so she can spend more time doing the
Speaker 1: parts that can't be automated, coaching someone through a bureaucratic maze,
Speaker 1: gaining a grieving mother's trust. She worries about the potential
Speaker 1: for AI to develop systematic biases, say overlooking cultural differences,
Speaker 1: but sees her training as all the more valuable for
Speaker 1: that reason. Doctor Punache is more explicit about the stakes
Speaker 1: for her own career. I am doing this just because
Speaker 1: I don't want to become obsolete, she says. Medicine is
Speaker 1: evolving rapidly, and AI is going to be part of
Speaker 1: that evolution, whether we want to participate in it or not.
Speaker 1: If history has taught us anything about revolutions in productivity,
Speaker 1: it's that productivity is the tide that lifts all boats.
Speaker 1: Brentan foodie, declares one morning in February. The sandy haired,
Speaker 1: twenty three year old co chief executive officer and co
Speaker 1: founder of Mercor has the standard Techno Optimists conversational habit
Speaker 1: of jumping from earthly matters. In this case the field
Speaker 1: of management consulting to historical analogies and abstract principles. Asked
Speaker 1: how a future Mackenzie consultant replaced by AI would gain
Speaker 1: expertise without the traditional entry level drudgery. Foody zooms out
Speaker 1: to a mainstay Silicon Valley position two hundred years ago,
Speaker 1: when most Americans were farmers, the tractor didn't destroy work.
Speaker 1: It pushed people into different kinds of employment. AI will
Speaker 1: do the same, he says, eliminating some tasks, yes, but
Speaker 1: ultimately creating more value, more opportunity, more progress. History offers
Speaker 1: some support for that view, but the path from one
Speaker 1: kind of work to another has rarely been painless or quick.
Speaker 1: We need to cure cancer and solve climate change and
Speaker 1: go to Mars, Foody says, and I think humans will
Speaker 1: work on a lot of those things once we have
Speaker 1: more productivity in accounting or whatever. These more back office
Speaker 1: functions are from where Foodie sits in a glass walled
Speaker 1: conference room on the thirty third floor of a San
Speaker 1: Francisco skyscraper. The bay extends beneath him in miniature, with
Speaker 1: container ships sliding past and ferries carrying commuters to innumerable
Speaker 1: office buildings. From this height, the physical economy looks less
Speaker 1: like the labor of millions of individuals living full lives
Speaker 1: and more like a table model whose tiny pieces could
Speaker 1: be shuffled with mere keystrokes. Instead of being luddites and
Speaker 1: leaning against the technology, we should instead focus on what
Speaker 1: are the jobs of the future that we need to
Speaker 1: lean into, he says. Footy acquires his rhetorical instincts as
Speaker 1: a competitive debater and perpetual hustler in high school in
Speaker 1: San Jose, where he met Hiramat and Meta. All three
Speaker 1: were raised in tech industry households. Hiramot and Meta are
Speaker 1: the sons of Silicon Valley engineers. Foudie's father founded an
Speaker 1: interactive graphics company, and his mother worked in Meta's real
Speaker 1: estate division. As high schoolers, Foodie earned hundreds of thousands
Speaker 1: of dollars as a consultant for sneaker resellers, while Hiramot
Speaker 1: was geeking out on computer vision research. Meta declined to
Speaker 1: speak to Bloomberg BusinessWeek. By their second year of college,
Speaker 1: Hiramat at Harvard University, FOODI, and Meta at Georgetown. The
Speaker 1: three figured they'd learned enough and applied for a Teal Fellowship,
Speaker 1: which pays kids two hundred thousand dollars to leave college
Speaker 1: and start companies in fields such as cryptocurrency and human longevity.
Speaker 1: I think people overestimate the value you get out of
Speaker 1: a four year degree, but underestimate the value you get
Speaker 1: in one or two years. Hiromot says. The bulk of
Speaker 1: the personal development just comes from living on your own
Speaker 1: for the first time. Knowledge acquisition is even less relevant
Speaker 1: today than it was three years ago, he says, because
Speaker 1: knowledge is just kind of free in chat GPT. The
Speaker 1: knowledge that I want I can just obtain with a
Speaker 1: couple of prompts. The three had just begun to zero
Speaker 1: in on AI training when they started the fellowship in
Speaker 1: early twenty twenty four. The company's original idea was a
Speaker 1: far more sweeping version of LinkedIn. The labor market might
Speaker 1: be the most inefficient marketplace in the world. Foody figured,
Speaker 1: with billions of people looking for work, millions of companies
Speaker 1: looking for talent, and no central clearinghouse connecting the two
Speaker 1: mercor would build a kind of global labor aggregator, where
Speaker 1: everyone everywhere could apply, an interview and be vetted for
Speaker 1: every job, and algorithms would match workers to opportunities with
Speaker 1: near perfect precision. In Foody's telling, even the concept of
Speaker 1: a full time job with a single employer was something
Speaker 1: of a relic. What companies actually need needed were discrete
Speaker 1: units of expertise, tasks that could be broken apart, distributed,
Speaker 1: and completed by whoever in the world happened to be
Speaker 1: best suited to them. To make any of that possible
Speaker 1: would require assembling an extraordinary amount of data about people.
Speaker 1: They began with an AI powered tool for screening software
Speaker 1: engineers that served as a prototype for the far bigger
Speaker 1: system they imagined. They changed focus, though, when AI companies
Speaker 1: began to need not only engineers but also other domain
Speaker 1: experts who could help train and test the models. Mercore
Speaker 1: built a tech platform to host the training itself and
Speaker 1: demand exploded. Venture capitalists eager to fund anything tied to
Speaker 1: the AI boom, started calling Mercoor raised three million dollars
Speaker 1: in early twenty twenty four, led by General Catalyst. It
Speaker 1: raised another round every six or eight months after that.
Speaker 1: Thirty million dollars, one hundred million dollars, then three hundred
Speaker 1: and fifty million dollars last October. Footy Here and Meta
Speaker 1: were also working around the clock, encouraging their growing team
Speaker 1: to adhere to a schedule known as nine to nine
Speaker 1: six in the office from nine am to nine pm
Speaker 1: Monday through Saturday. The regiment first became popular in China
Speaker 1: in the twenty tens as the country built out its
Speaker 1: tech industry. It's now illegal there. Felicia's investor Peachu says
Speaker 1: Foodi and his partners were so hard to pin down
Speaker 1: that he eventually flew with them to Las Vegas on
Speaker 1: a Sunday, the one day they weren't working to drive
Speaker 1: ferraris on a racetrack, just so he could get a
Speaker 1: little uninterrupted time with them on the plane. The founders
Speaker 1: weren't old enough to drink at that time, and none
Speaker 1: of them had ever held a professional job, but the
Speaker 1: hundreds of millions of dollars being plowed into their coffers
Speaker 1: came with a mandate to remake the very nature of
Speaker 1: white collar work in Silicon Valley. That didn't raise any
Speaker 1: alarms about naivete or blind spots. Quite the contrary, Adam DeAngelo,
Speaker 1: a Mercor investor who became Facebook's chief technology officer at
Speaker 1: Day twenty two before co founding Qua, says he asked
Speaker 1: Foody at one point about his work experience. FOODI said
Speaker 1: he'd had an internship one summer perfect. Dangelo said, your
Speaker 1: mind isn't corrupted by the conventional way of doing things.
Speaker 1: We'll be right back with the ten billion dollars start
Speaker 1: up training AI to replace the white collar workforce. Welcome
Speaker 1: back to the ten billion dollars start up training AI
Speaker 1: to replace the white collar workforce. Over the past year,
Speaker 1: criticism of Mercor's platform has become a refrain in online forums,
Speaker 1: news reports, and court filings, Beginning as these things often
Speaker 1: do with anonymous online posts. Reddit in particular contains no
Speaker 1: shortage of grievances from contractors who've cycled through the platform.
Speaker 1: Projects are chaotic, disorganized, and unpredictable. Workers are treated like
Speaker 1: human cattle. The company is building the plane while flying.
Speaker 1: Contractors signed strict confidentiality agreements before beginning work, and many
Speaker 1: fear being removed from its projects if they speak publicly.
Speaker 1: They gripe about slack channels full of motivational chatter and
Speaker 1: rocket ship emoji from project managers, but few concrete answers
Speaker 1: about when work will arrive or advance notice when it stops.
Speaker 1: Like many forms of digital piece work, Mercor's projects are
Speaker 1: closely measured and monitored while they're underway, with software tracking
Speaker 1: productivity and time spent on each assignment. It turns out
Speaker 1: some jobs pay not by the hour, but by the task.
Speaker 1: A frustrated contractor BusinessWeek spoke with, who asked for anonymity
Speaker 1: because he'd signed what a mercoor's non disclosure agreements, has
Speaker 1: been job hunting for almost a year for a full
Speaker 1: time position to make use of his master's degree in physics.
Speaker 1: After he joined Mercoor. His first contract paid thirty dollars
Speaker 1: per task, but he soon learned that the time it
Speaker 1: took for him to complete tasks anywhere from an hour
Speaker 1: to a full day, was often far greater than the
Speaker 1: company's estimates, making the pay too low to justify the work.
Speaker 1: It's a variation of a complaint that services online about
Speaker 1: hourly projects too. Contractors sometimes quietly do part of their
Speaker 1: work off the clock to keep their productivity numbers in
Speaker 1: line with company benchmarks, out of fear of being off boarded.
Speaker 1: Mercy speak for getting booted. A mercoor spokesperson says that
Speaker 1: many experienced contractors prefer task based work because increased efficiency
Speaker 1: can lead to higher effective hourly earnings. Last fall, one
Speaker 1: Mercor contractor dispute spilled into the press when Forbes reported
Speaker 1: that thousands of people working on a large project were
Speaker 1: abruptly locked out without warning. Several hours later, some said
Speaker 1: they were invited back to continue the work, but at
Speaker 1: pay rates roughly a quarter lower than before. Mercore disputed
Speaker 1: the accuracy of the claims and said it was working
Speaker 1: to offer more predictability. The company also alleges that some
Speaker 1: of the people who complain about being let go are
Speaker 1: simply liars. We found cases where people have been doing
Speaker 1: no no work or doing time fraud. Hiramat says, we
Speaker 1: have demonstrable evidence of them committing fraud, but they still
Speaker 1: post about it on Reddit when their contract has been eliminated.
Speaker 1: As criticism of Mercore has grown, lawsuits have followed. One
Speaker 1: of the complaints filed late last year by a finance
Speaker 1: professional named Michael Cox, who worked on Mercore projects, lists
Speaker 1: open Ai as a co defendant and accuses the companies
Speaker 1: of running what it calls a scheme to misclassify workers
Speaker 1: while exercising the kind of control normally associated with an employer.
Speaker 1: According to the filing, Mercoy required Cox to install a
Speaker 1: productivity monitoring software on his computer, creating a level of
Speaker 1: surveillance so intrusive as to trivialize the notion that this
Speaker 1: was a legitimate independent contractor relationship. In another passage, the
Speaker 1: complaint says the company's alleged neglect of employment norms was
Speaker 1: so brazen as to almost by definition, constitute wilful misclassification.
Speaker 1: Mercore and OpenAI haven't publicly responded to the suit, and
Speaker 1: a Mercoors spokesperson says that they don't plan to at
Speaker 1: this time. Two other suits against mercor make similar misclassification claims. Meanwhile,
Speaker 1: if you were a job seeker on LinkedIn last fall,
Speaker 1: Mercy suddenly seemed to be everywhere. Listings offering hundreds of
Speaker 1: dollars an hour to lawyers, doctors, and programmers flooded the platform,
Speaker 1: prompting another wave of social media speculation, this one less
Speaker 1: about working conditions than about whether the whole thing was real.
Speaker 1: Some users began posting on various sites speculating that the
Speaker 1: listings were an elaborate data harvesting scheme in which fake
Speaker 1: applications and AI interviews were designed to collect valuable personal information.
Speaker 1: Mercoor says that this isn't the case, and that it
Speaker 1: uses interviews only to evaluate candidate's skills for jobs on
Speaker 1: the platform, and again the company suggests its critics are
Speaker 1: the scammers. Foodie says the LinkedIn deluge wasn't about Mercoor
Speaker 1: harvesting data, but about a few high volume fraudsters harvesting
Speaker 1: referral fees because it pays users to bring new contractors
Speaker 1: into the fold. There were probably ten people specifically that
Speaker 1: were causing problems, he says. In October, Mercoor banned refers
Speaker 1: from using the name mercor when they post job ads
Speaker 1: for the roles on LinkedIn. Still, postings from third party
Speaker 1: referral outfits have continued. Mercore listings from one such organization,
Speaker 1: a recruiter called Crossing Hurdles, based on the outskirts of
Speaker 1: New Delhi, are a constant presence on LinkedIn. A mercor
Speaker 1: spokesperson tells BusinessWeek that Crossing Hurdles has no official affiliation
Speaker 1: or partnership with Mercy. They post roles on various job
Speaker 1: sites using their Mercore referral link crossing hurdles didn't respond
Speaker 1: to multiple requests for comment. Mercor made another change last fall.
Speaker 1: It moved its third co founder, Meta into a new
Speaker 1: position Chairman of the board, handing his previous role running
Speaker 1: operations to a more seasoned executive, Sundeep Jain, a former
Speaker 1: chief product officer at Uber Technologies. In evoking his previous employer,
Speaker 1: Jane says that the complaints circulating online aren't surprising and
Speaker 1: that early stage marketplaces rarely distribute work evenly. There will
Speaker 1: be some drivers that will be busy all day and
Speaker 1: others that'll be a little bit less busy, he says.
Speaker 1: Communication hiccups, payment disputes, mismatched expectations, those are classic problems
Speaker 1: of a marketplace. But on March thirty, first, the young company,
Speaker 1: growing at breakneck speed and reliant on a handful of
Speaker 1: high profile clients experienced something far worse than a hiccup.
Speaker 1: Mercor disclosed that attackers had infiltrated its systems in a
Speaker 1: sprawling supply chain hack. The breach coming through a corrupted
Speaker 1: open source developer tool called light LLM that thousands of
Speaker 1: companies use. Struck at the core of Mercor's business. As
Speaker 1: much as four terabytes of data, including possibly training data
Speaker 1: and user's personal information, were exposed, according to online posts
Speaker 1: from the hackers. The company announced it was conducting a
Speaker 1: thorough investigation, supported by leading third party forensics experts, but
Speaker 1: the fallout was swift. Along with Meta pausing its projects
Speaker 1: with Mercor, contractors assigned to those projects suddenly found themselves
Speaker 1: without work. In the first week after announcing the breach,
Speaker 1: Mercor was hit with five lawsuits accusing it of failing
Speaker 1: to protect contractor's data. Mercore declined to respond to BusinessWeek's
Speaker 1: questions about the origin and extent of the hack and
Speaker 1: its impact on clients and users. Open Ai Andthropic and
Speaker 1: Google didn't respond to requests for comment on whether they're
Speaker 1: now re evaluating their relationship with Mercor. Beyond the messy
Speaker 1: day to day mechanics of atomizing jobs into training tasks
Speaker 1: is a bigger question. Are the machines actually getting good
Speaker 1: enough to do the work for themselves. In the past year,
Speaker 1: the AI industry has tried to answer that question, mostly
Speaker 1: with a growing ecosystem of evals or evaluation frameworks like
Speaker 1: a standardized test. An eval measures a large language model's
Speaker 1: math ability, reasoning, or factual accuisi, but not typically the
Speaker 1: kinds of specific, judgment heavy tasks that white collar workers
Speaker 1: do all day. Mrcore has developed its own test, a
Speaker 1: project called apex AI Productivity Index that attempts to do
Speaker 1: just that. It measures professional performance and then posts the
Speaker 1: results on the web for anyone to follow as a
Speaker 1: kind of industry leader board. As Foodie put it when
Speaker 1: announcing the initiative last October, AI can pass the bar exam,
Speaker 1: but can it redline a contract? To build apex murcoor
Speaker 1: has worked with some of its most accomplished contractors to
Speaker 1: design short professional scenarios and problems in five fields so far, law, medicine, management, consulting, investment, banking,
Speaker 1: and software engineering. Advisors overseeing the effort include Harvard Law
Speaker 1: professor Cass Sunstein, cardiologist Eric Topel, and former McKenzie Global
Speaker 1: managing partner Dominic Barton. Former Treasury secretary and Mercore investor
Speaker 1: Larry Summers was until recently also affiliated with the project.
Speaker 1: Beneath them is a larger bench of experts, more than
Speaker 1: one hundred lawyers, bankers, consultants and clinicians, who build and
Speaker 1: review the actual test cases, the models do the work,
Speaker 1: and their responses are graded against expert benchmarks to see
Speaker 1: how close they come to professional quality. One case, for instance,
Speaker 1: centers on a fictional wellness company expanding overseas. A Google
Speaker 1: workspace contains the kind of unwieldy digital paper trail a
Speaker 1: junior consultant might inherit on the first day of a project,
Speaker 1: sales data customer surveys, cost projections, and strategy memos scattered
Speaker 1: across spreadsheets and PDFs. Mercor's experts prompt AI models to
Speaker 1: answer strategic questions such as calculating how rising ingredient costs
Speaker 1: might affect pricing or recommending an expansion strategy, and define
Speaker 1: what the correct answer should look like. Then the work
Speaker 1: becomes even more granular, building a checklist or rubric of
Speaker 1: precise criteria the model's output must satisfy. Only once the
Speaker 1: prompt and rubric are complete do the models attempt the task.
Speaker 1: Their answers graded against the rubric line by line, So far,
Speaker 1: the models fall short. The best can produce useful work
Speaker 1: in certain areas, but they are not exactly reliable employees.
Speaker 1: Mercor's own research notes that top systems still struggle on
Speaker 1: complex real world tasks, failing to meet the production bar.
Speaker 1: The results mirror what other attempts to measure AI's real
Speaker 1: world impact have been finding. In March, Anthropic released a
Speaker 1: chart that compared what AI systems appear capable of doing
Speaker 1: with how often they're actually being used on the job.
Speaker 1: The gap was striking, and the chart went viral. White
Speaker 1: collar knowledge work fields looked highly exposed to AI in theory,
Speaker 1: but in practice only a small slice of the work
Speaker 1: was being handled that way in the real world. The
Speaker 1: research drew criticism for its methodology. For one thing, the
Speaker 1: measure of AI's theoretical potential is inherently subjective and doesn't
Speaker 1: count for logistical or legal hurdles to adoption, But the
Speaker 1: basic takeaway that AI still has a long way to
Speaker 1: go was hard to dispute. Meanwhile, Mercor says it seeing
Speaker 1: marked improvement in APEX scores as the AI giants release upgrades.
Speaker 1: A recent chat GPT model tops its current rankings. But
Speaker 1: Opus is the one that has blown us all away,
Speaker 1: says Foodie, referring to Anthropics claud Opus four point six,
Speaker 1: which performed eighteen percent better than its predecessor after just
Speaker 1: a few months, still Even though APEX tries to approximate
Speaker 1: real professional scenarios, forcing agents to navigate complex environments and
Speaker 1: choose the right tools for a given task, it's more
Speaker 1: structured than the open ended work people actually do. Gardner
Speaker 1: analyst vuk Yanisevich says APEX is a credible bridge between
Speaker 1: lab performance and business usefulness, but he cautions that a
Speaker 1: high score on this benchmark does not prove that the
Speaker 1: system can be governed and integrated at scale inside a
Speaker 1: live process. And then there are the human aspects of
Speaker 1: the work that APEX may never be able to measure.
Speaker 1: When doctor Punice examines a patient, for instance, a surprising
Speaker 1: amount of information comes not from lab reports or imaging,
Speaker 1: but from touch. A trained hand on an abdomen can
Speaker 1: detect subtle tension or swelling that doesn't appear in a chart.
Speaker 1: I just don't think AI is going to be able
Speaker 1: to do that, she says. Still, Kosak's notion that only
Speaker 1: thirty percent of her social work will be replaced by
Speaker 1: AI might be wildly optimistic. Investors are certainly hoping so.
Speaker 1: When Jack Dorsey's financial technology conglomerate Block laid off forty
Speaker 1: percent of its workforce in March, ostensibly because of efficiency
Speaker 1: gains from AI. It's stock swored twenty percent. Mass layoff
Speaker 1: announcements at Meta and Amazon have been met with similar
Speaker 1: pops in the stock price. After all, the market optimizes
Speaker 1: for shareholder value, not professional enrichment. Crystallina georgievaaging director of
Speaker 1: the International Monetary Fund, has warned that AI will affect
Speaker 1: roughly forty percent of global jobs in the next few years.
Speaker 1: Foodie argues that the transition will ultimately create new categories
Speaker 1: of work, and what kinds of jobs are those? We
Speaker 1: believe that a large portion of what humans do in
Speaker 1: companies is going to transform to training agents, he says.
Speaker 1: Mercor is already gearing up to cash in on that future.
Speaker 1: Earlier this year, the company elevated Hiramot to co CEO
Speaker 1: and began pushing into a new line of business helping
Speaker 1: corporations deploy agents. Mercor is pitching itself as a sort
Speaker 1: of agent implementation partner, not designing agents itself, but building
Speaker 1: the guardrails to steer that AI in the right way.
Speaker 1: This opportunity, Hiromot says, is enormous, and it could give
Speaker 1: the company a far larger client base than a few
Speaker 1: AI labs, all the Fortune five hundred, all the Fortune
Speaker 1: one thousand could want to integrate models into their own
Speaker 1: work flows, he says, and they're kind of clueless in
Speaker 1: this model. Mercore is suddenly also in the technology consulting
Speaker 1: game two. While Mercor makes a play for an even
Speaker 1: bigger part of the white collar workforce, its contractors sometimes
Speaker 1: find themselves pondering what Poonacha calls very dystopian implications. What
Speaker 1: if patients begin to trust AI over their physicians. What
Speaker 1: if wealthier people continue to have access to human doctors
Speaker 1: and poorer people just get the AI ones. Mercoor's new
Speaker 1: operations head, Jane, meanwhile, is consumed by imagining the limitless
Speaker 1: number of disciplines and workflows still left to automate. Chefs
Speaker 1: and private investigators are already in progress, and any number
Speaker 1: of supposedly AI prooved trades such as plumbing are no
Speaker 1: less exposed than, say, medicine. If there is a ceiling,
Speaker 1: Jane says, we're nowhere near it,