BIG IDEAS BY NEW ECONOMIES

Ollie Forsyth

Welcome to BIG IDEAS by NEW ECONOMIES - a show where we learn how the most iconic founders have turned crucible moments into global companies. www.neweconomies.co

  1. 1d ago

    Klaviyo

    Subscribe to stay ahead of technology trends. Never miss future editions. Two to three years ago, software companies were still planning product roadmaps in years and quarters. Today, as one of our guests put it, the only question that matters is “what are we shipping this week?” That guest was Chano Fernández, co-CEO of Klaviyo. He joined us to explain how AI agents are reshaping marketing and customer engagement, why taste may be the biggest moat AI companies can build, and what it really takes for a public software company to stay relevant when the ground keeps moving. Chano held senior leadership roles at SAP before becoming co-CEO of Workday, where he helped expand its global and enterprise business. He was then co-CEO of the AI-native talent platform Eightfold, and spent two years on Klaviyo’s board, most recently as interim executive. In January, he became co-CEO alongside co-founder Andrew Bialecki. Andrew focuses fully on Klaviyo’s AI vision and products, while Chano runs go-to-market, operations, and G&A. Klaviyo now serves more than 200,000 customers, with around 9 billion profiles and 250 billion transactions a quarter on the platform. In our in-depth episode, we discuss: * Why email may become less relevant as AI agents start reading it for us, and why Klaviyo’s 14 years of data and infrastructure can’t be replicated with vibe coding * Inside Composer, Klaviyo’s agentic marketing tool: seven or eight sub-agents, and why getting a campaign to come out “80% or 90% right” for a CMO comes down to taste * Where software pricing is heading: from seats to consumption to outcome-based deals, and the 5% to 6% revenue share Chano is seeing emerge across the industry * How 70% to 80% of Klaviyo’s code is now written by AI, why its Friday product meetings show what shipped that week, and why a new product only needs to be 60% to 70% ready at launch * How the co-CEO model works in practice: a written operating agreement, two to three calls a day, and why only a handful of decisions each year really need both of them Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Intro(1:10) How AI is changing marketing(2:24) Is email dead?(5:15) How Klaviyo has evolved(7:10) How Klaviyo stays relevant(10:35) Where business owners are focusing(14:10) What owning your audience means today(15:07) Building AI model partnerships(16:54) Would Klaviyo build its own AI model?(21:32) What great taste looks like(23:23) What great taste is missing(24:32) Are customers worried about pricing?(25:46) Shipping Klaviyo's latest products(28:17) Is the future of software headless?(30:43) An AI co-worker for Klaviyo users?(32:38) Is trust the new moat?(35:05) The role of the product roadmap(36:33) Klaviyo's favorite AI models(38:10) What's missing from the Klaviyo stack(39:22) Is SaaS pricing dead?(44:19) How much code AI writes(47:05) Keeping up with the speed of change(51:10) Will more companies have co-CEOs?(58:17) Running a public company(59:42) Is the market correcting?(1:01:45) Ollie becomes Chano's Chief of Staff Lessons from this episode with Chano 1. Agents will read your emails for you Chano thinks email’s role changes once AI agents start acting on our behalf. “I believe it will become less relevant, just because some of those agents will be the ones consuming many of those emails going forward.” Email won’t disappear. It will stay one of the cheapest and most efficient channels. Chano also expects agents to do more than read: they will use what they know about us to surface better products, offers and services. For brands, the challenge is personalizing at scale, and that is where automation and agents come in. 2. Marketing and service are becoming one Moving Klaviyo from a marketing tool to an AI agent platform wasn’t optional. “The boundaries between marketing and service have become more indistinct.” If a customer has just complained or asked for a refund, the next thing they want is not a promotion. Most brands can’t see that, because their data sits across six or seven disconnected systems with no real-time view. Chano says this is where Klaviyo’s infrastructure gives it an edge: around 9 billion profiles, 250 billion transactions a quarter, and the ability to send 100 million messages in 20 minutes. 3. Taste is the hardest thing to automate Klaviyo’s agent, Composer, builds campaign calendars and campaigns for brands that are short on people or ideas. Getting the output right comes down to taste. “If you’re a chief marketing officer, you want to see the campaign the agent is creating and say: wow, this is really my brand, my image, my language.” Code is fairly deterministic: good code is good code at any company. Brand voice is different for every customer. Klaviyo measures progress through thumbs-up ratings, how many campaigns go live without edits, and direct customer feedback. Each edit a brand makes feeds the next model. 4. Stay model-agnostic and route by task With AI labs shipping updates every few weeks, Chano says no company can afford to lock itself to one model. “It’s important to be as model-agnostic as possible.” Composer runs on at least seven or eight sub-agents, covering email templates, flow templates and campaign taste, and each can sit on a different underlying model. Klaviyo’s engineers test new releases quickly and change the routing when a better model appears. Chano expects model capabilities to become more standardized, so routing well will matter more than picking a winner. 5. Pricing is heading towards outcomes Klaviyo had a head start on the shift away from per-seat pricing. “We were never on seat-based pricing like many other SaaS companies.” Klaviyo’s pricing has always tracked sends and profiles, rising and falling with seasons like Black Friday. Its customer service agent already charges only when it resolves a conversation. Chano sees the industry settling on a base subscription with usage on top. Outcome-based deals come next, where the vendor takes a share of attributed revenue. The benchmark he is seeing is 5% to 6%. 6. Launch at 60% to 70% Around 70% to 80% of Klaviyo’s code is now written by AI, and the planning cadence has changed with it. “Two to three years ago, we were still talking about product roadmaps in years and in quarters, and the conversation today is: what are we shipping this week?” Every Friday, a group of engineers presents what shipped that week. The hard call is when to launch. Customers have less patience than in the old SaaS era, when a product had 12 to 24 months to mature. AI products can also improve dramatically within weeks. Chano’s threshold is a product that is 60% to 70% ready. 7. Co-CEOs only work with trust, complementary skills and low ego Chano was on Klaviyo’s board when the company decided Andrew Bialecki needed to spend all his time on product vision. “It’s not an easy model, but it’s a very effective one.” The two wrote down how they would run the company and speak two to three times a day. Andrew owns product; Chano owns go-to-market and the rest of the business. At first they held joint one-to-ones with leaders, which are no longer needed. Chano’s view is that only a handful of decisions each year, on strategy, culture and organization, need both of them. The rest can be made by either and reversed if needed. 8. Focus on inputs, not the share price With Klaviyo’s stock down 35% this year at the time of this recording, Chano says his answer is to focus on what the company controls. “If you take care of the right inputs, the outputs will take care of themselves.” For Chano, the inputs are innovation, happy and referenceable customers, the right culture, and getting the few big decisions right. He sees the market as trying to separate AI wrappers from companies with durable moats. In his view, that will get easier as more AI-native companies go public, and it could take anywhere from two to six quarters. Where to connect with us Follow Ollie on LinkedIn: https://www.linkedin.com/in/ollieforsyth Follow Chano on LinkedIn: https://www.linkedin.com/in/chanofernandeVist Klaviyo: https://klaviyo.com Previous episodes include See all previous episodes here 👉 Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  2. 2d ago

    TBPN

    Subscribe to stay ahead of technology trends. Never miss future editions. During San Francisco Tech Week a few weeks ago, we co-hosted our first New Media Summit with Yahoo, bringing together media’s biggest names for candid conversations on where the industry goes next. One of our speakers was Dylan Abruscato, President of TBPN, which has had a huge year. He joined us to share how a bootstrapped, 10-person live tech show became one of the most valuable ad slots in Silicon Valley, why live remains “a truly unskippable ad format,” and why reaching a niche audience can be worth more than reaching a big one. Dylan began his career in live TV at SNL and CNBC. He then helped run HQ Trivia through its rise and fall, and built his own live interactive game show before joining TBPN, a daily, three-hour tech variety show hosted by John Coogan and Jordi Hays. Under Dylan, it grew to roughly $25 million in annual revenue with 30 year-long sponsors, led by presenting sponsor Ramp. Six months ago, it was acquired by OpenAI. In our in-depth episode, we discuss: * Why the OpenAI deal had little to do with the show itself, and how the marketing and comms advisory work built into TBPN’s sponsorships became the real reason behind the acquisition * The Super Bowl stunts that preceded the deal: a hyper-local 15-second spot built from over a thousand founder logos, and claudewithads.com * How TBPN copied Formula 1’s sponsorship model: year-long deals, category exclusivity, 100% share of voice, and an ad read to Mark Zuckerberg as he took his seat * Why the show stayed intentionally niche, and why it only takes one or two founders switching platforms for a tech sponsor’s ad to pay for itself * Inside a daily live show: the 6 a.m. gym sessions, the three-hour run of show, last-minute guest booking, and the vibe-coded internal tools that power it all Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Dylan Abruscato(2:20) Why OpenAI acquired TBPN(6:48) What changed after the acquisition(7:43) Will frontier models come on the show?(8:17) What makes TBPN so vibrant(9:32) Prepping for a daily live show(10:46) Why live streaming is hot(12:04) Are niche audiences more valuable?(12:58) How the ad integrations work(15:44) Calling partners post-acquisition(17:10) Building alongside legacy media(18:17) The copycats(19:38) Will another TBPN-style acquisition happen?(20:08) OpenAI’s marketing services(21:02) Thinking about distribution channels(22:10) Dream guests(22:59) TBPN’s next 12 months(23:22) Audience Q&A Lessons from this episode with Dylan 1. The OpenAI deal was for the team, not the show Dylan says most people misread why OpenAI bought a live tech talk show. “I think what a lot of folks got wrong about the deal is that it had very little to do with the show itself and much more to do with the interest in us and the brains behind the operation.” Many of TBPN’s 30 sponsorship deals included marketing and comms advisory work, a tactic the team first used to win sponsors. OpenAI’s offer was simple: keep doing the show, wind down the advisory clients, and make OpenAI the only one. TBPN was profitable, had raised no money and had no plans to sell. Fidji Simo reached out the morning after the Super Bowl. 2. Build the show you’d want to watch The two stunts that came right before the deal both started as ideas the team found funny. “The truth is we’re just building the show that we always wanted to watch.” The first was a hyper-local 15-second ad that aired in San Jose at noon but still counted as a Super Bowl spot. It spelled out TBPN using over a thousand logos of past guests, and those founders shared it everywhere. The second was claudewithads.com, a reskinned Claude that gave one real answer and then rotated through sponsor ad reads. The team’s brainstorming rule is that if an idea makes them laugh, they run with it. 3. Live is the last unskippable ad format Dylan has worked in live TV at SNL, CNBC and HQ Trivia, and has argued for live for a decade. “It’s a truly unskippable ad format, and that’s kind of allowed us to command such high prices.” Almost nothing is watched live anymore. Sports is the exception, and even award shows are now consumed as recaps on X. TBPN’s audience is smaller than HQ’s was, but advertisers value it more because no one can fast-forward. 4. Go intentionally niche TBPN never explains the jargon. Where CNBC would spell out what AWS stands for, John and Jordi just say AWS or GPU, and the audience respects it. “It really only takes one or two founders watching the show to switch to this platform for the advertisement to pay for itself.” A small audience of founders, CFOs and VCs is worth more to a tech sponsor than a mass audience. As an example, John once read a Ramp ad straight to Mark Zuckerberg as he took his seat. 5. Copy Formula 1’s sponsorship model TBPN sold only year-long deals, split into a couple dozen categories with one sponsor per category. “Everyone got 100% share of voice in their category on everything that we did over the course of the year.” Sponsors got the same ad read in every episode, plus the ticker, merch, newsletter links and end cards on tweets. Dylan’s view is that repetition is what counts when a founder is finally ready to switch products. TBPN also never tried to upsell new placements partway through the year. The commercial mindset was there from day one. On the first episode, with one viewer, John did fake ad reads for sponsors the show didn’t have. When TBPN wound down its sponsors after the acquisition, viewers asked for the ad reads back. 6. Chemistry plus a fixed format The show depends on the friendship between its two hosts. “It’s like a true one-of-one friendship, and it’s just so relatable.” John and Jordi meet at the gym at 6 a.m. every day and work out the day’s lead story in the sauna. They go live at 11, and after the show ends they still call each other on the drive home. The format never changes: * Hour one: John and Jordi on the current thing, then live reactions to the timeline. * Hour two: one or two tier-one interviews. * Hour three: a lightning round of startup fundraising news. Most guests are booked last minute. Mag Seven–level names take six months or more of back-and-forth. 7. Be a companion to legacy media, not a competitor TBPN never tried to beat traditional outlets at breaking news. “We always viewed TBPN as a companion show.” When founders or publicists offered scoops, TBPN sent them to the Journal or Bloomberg. The founder would come on a few hours later to break the story down. Every show opens with a hard copy of the Journal. Dylan doesn’t want copycats making another three-hour daily show. He would rather see new formats in tech media, like a late-night tech show or an interactive Shark Tank. Where to connect with us Follow Ollie on X: https://x.com/ollieforsyth Follow Dylan on X: https://x.com/DylanAbruscato Watch and follow TBPN: https://www.tbpn.com Previous episodes include Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  3. Oct 16

    Eric Ries

    Subscribe to stay ahead of technology trends. Never miss future editions. Eric Ries, creator of The Lean Startup and author of Incorruptible, joins us to explain why “financial gravity” pulls even the best companies away from their mission. He explains why the standard best practices founders are handed “are designed to make you lose control of your company.” Eric published The Lean Startup 15 years ago and went on to found the Long-Term Stock Exchange. He co-founded the AI research lab Answer.AI and helped start Virgil, an AI-enabled law firm for early-stage companies. In his latest book, Incorruptible, he studies the companies that have resisted this force, from Costco to Novo Nordisk to Anthropic. In our in-depth episode, we discuss: * How the default “any lawful act or activity” clause in a Delaware charter quietly commits founders to maximizing shareholder value, and why lawyers rarely tell them * The story of Sol Price, FedMart and the origins of Costco, and the “governance fortress” that has protected Costco’s $1.50 hot dog for 40 years * The three mission protective provisions every founder can set up in a matter of weeks: becoming a PBC, adopting a director’s oath, and appointing a mission guardian * The difference between a mission statement and a mission, and what Silicon Valley Bank’s charter revealed after its collapse * Why “it’s always too early until it’s too late,” how to win over the roughly 25 people who must agree to a change, and why Anthropic’s Long-Term Benefit Trust has become a commercial advantage Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Eric Ries(2:14) Eric’s New Book(3:30) How Startups Are Built Today(6:20) Why Companies Self-Destruct(16:37) Who’s to Blame When Companies Fail(21:25) The Blueprint for a High-Performing Company(26:52) How Companies Protect Themselves(33:13) Building Company Ethos and Structure(46:05) Why Companies Get Bullied(50:32) When Is It Too Late to Save a Company?(55:15) The Company Eric Would Have Saved(1:00:05) What Eric Is Excited About Next Lessons from this episode with Eric 1. Why great companies are destroyed by financial gravityEric explains why blaming a greedy investor or a reckless founder misses the real cause of most corporate collapses. * “There is a form of what I call financial gravity that operates on these companies. The ones that collapse, the ones that erupt in scandal, whether I’m talking about Boeing or FTX or Wells Fargo or Silicon Valley Bank, they’re all the same story.” * He compares it to a submarine with a weak hull. Asking whether the water is to blame is the wrong question. The right question is who designed the submarine. * Gravity acts on every company, yet some have lasted 50, 75 or even 500 years. Studying the ones that never collapse is how you learn to build differently. 2. How the firing of Sol Price led to the founding of CostcoSol Price capped his markups at 14% and treated customers as clients he owed a fiduciary duty to. His investors fought him for 20 years, then changed the locks on his office overnight. * “It is so much easier to destroy than to create. They managed to destroy in seven years what had taken Sol more than 20 to build.” * Within seven years of firing him, the investors had completely liquidated FedMart. Two weeks after his dismissal, Sol leased the office upstairs and started the Price Club, which later merged into Costco. * Costco has survived for 40 years because of what Eric calls a governance fortress. That structure lets its directors act as a bulwark against Wall Street pressure. 3. The six words in your charter that hand your company to shareholdersMost founders have never read the first sentence of their own corporate charter. Eric says that sentence decides who the company really serves. * “Since 1986 in the United States, whenever you see ‘any lawful act or activity,’ the courts have decided that actually means maximize shareholder value.” * The phrase sounds open-ended. Legally, it makes your company a financial instrument whose job is to enrich shareholders. * The same duty forced Twitter’s board to sue Elon to complete his deal. It also let Philip Morris take over an inhaled therapeutics company and destroy it. 4. Three mission protective provisions that take weeks, not yearsEric gives founders a short list of protections they can put in place early. Some take only days. * “Welcome to best practices. They are designed to make you lose control of your company by force or by corruption.” * First, become a public benefit corporation, a two-page filing in Delaware. Second, require every director to take an oath to protect the mission, much like the Hippocratic oath. Third, set up a mission guardian, such as a foundation or a purpose trust. * Companies owned by an industrial foundation, such as Novo Nordisk, are five times more likely to reach year 50. Eric’s explanation: “Checks and balances work.” 5. A mission statement is not a missionEric’s own deposits were at Silicon Valley Bank when it collapsed. Afterwards, he looked up what the bank had legally committed to. * “They had no mission at all. The whole thing was a scam.” * The bank’s mission statement promised to move the innovation economy forward. Its charter said “any lawful act or activity.” Even if its interest rate bets had paid off, they would have done nothing for customers. * His test: Google spends billions to make sure its quarterly reports are on time, while “Don’t be evil” was one sentence in the employee handbook. What a company invests in shows what it values. 6. It’s always too early until it’s too lateLawyers and investors often tell founders to deal with governance after product-market fit. Eric says that advice really means never. * “In most companies, there are at most 25 people that need to consent to anything like this.” * Veeva Systems became a public benefit corporation after it was already a $40 billion public company. It spent a year on investor relations and won 90% of the shareholder vote. * Eric’s line for pitching a change to investors: “It is my judgment as the founder of this company that it is essential to our strategy that we win the trust of [some group].” Framing it this way forces people to say exactly what they object to. 7. Why ethics is an accelerantEric uses Anthropic to show that integrity can be a source of commercial strength. * “People talk about ethics like it’s an impediment to business success, but here it is clearly an accelerant.” * Anthropic is governed by the Long-Term Benefit Trust, which appoints directors to its for-profit board. Eric argues this structure is a big part of why enterprises trust it more than its competitors. * Costco, Patagonia, Vanguard, Mondragon and Anthropic have different missions and cultures. The one thing they share is that each rejected the standard best practices for how a company should be structured. Where to connect with us Follow Ollie on X: https://x.com/ollieforsythFollow Eric on X: https://x.com/ericriesEric's new book - Incorruptible: https://www.incorruptible.co Previous episodes include See all previous episodes here 👉 Our partners: Harmonic: Your go-to startup database. Visit Harmonic to learn more. Prelint: Keeps every decision reviewed, recorded, and traceable. Head over to Prelint to learn more. VCX: The public ticker for private tech. Head over to GetVCX.com to learn more. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  4. Oct 8

    Greenhouse

    Subscribe to stay ahead of technology trends. Never miss future editions. Daniel Chait, co-founder and CEO of Greenhouse, joins us to explain why AI has trapped hiring in a “doom loop” where, for the first time he can remember, “neither side is really happy.” He co-founded Greenhouse in 2012 and has built it into one of the world’s leading hiring platforms, used by more than 7,500 companies with a team of over 700. His Dream Job feature lets job seekers flag one application a month as their top choice, and he says those candidates now convert from application to hire at around five times the rate of everyone else. Daniel also shares why 1 to 1.5% of applicants fail an ID check, how North Korean operatives use deepfakes to get through interview rounds, and why he believes starting with an AI interview can remove bias from the most biased stage of hiring. In our in-depth episode, we discuss: * How auto-apply tools and AI résumé filters created the hiring “doom loop,” and why job seekers and employers no longer trust each other * The three-layer “Swiss cheese” model Greenhouse uses to catch fake candidates, and why North Korean infiltrators are so hard to spot once they’re hired * Why an AI interview at the first stage can take racial bias out of résumé screening, and how it cuts around six days from the process * Why Greenhouse’s value has shifted from workflow to data and context, and whether it will come out of this moment as Blockbuster or Netflix * How Daniel decides which bets to place, why “we can do anything, we just can’t do everything,” and what it would mean to hire AI agents through Greenhouse Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps [00:00] Meet Daniel Chait[02:16] The state of hiring right now[04:03] Why employers get 1,000 applications in a day[05:31] The AI doom loop explained[10:31] Why AI résumé filters fail[11:35] The “Jared problem”: how AI amplifies bias[13:32] North Korean spies and fake candidates[16:03] 1 in 100 applicants fail ID checks[16:32] The Swiss cheese model of hiring security[20:34] How deepfake hiring rings get caught[22:30] Why Daniel is still an AI optimist[23:34] Dream Job: the feature with a 5x hire rate[26:05] How COVID, rates and ChatGPT changed hiring[29:02] Are entry-level jobs disappearing?[33:00] Is university still worth it?[35:01] Why durable skills beat technical skills[36:00] Is job hopping a red flag?[37:34] Changing business models [40:00] Staying relevant when anyone can clone software[43:03] Blockbuster or Netflix?[45:31] Greenhouse’s new mission[47:30] Why AI interviews can remove racial bias[51:30] What happens to humans in hiring?[53:32] How AI interviews save 6 days per hire[55:03] From software engineers to agent managers[56:30] Could companies one day hire AI agents?[1:00:02] How Greenhouse places bets[1:03:03] A week as Daniel’s chief of staff[1:04:35] Great leaders have no followers[1:05:35] Leading 700 people as CEO[1:07:02] What keeps Daniel motivated Lessons from this episode with Daniel 1. Why AI has trapped hiring in a doom loopDaniel explains how one unremarkable company got a thousand applications on day one, and why the tools meant to help job seekers have made the system worse for everyone. * “That’s kind of what we’ve referred to as the AI doom loop: this idea that the more each side is using the kind of current generation of AI tools to help themselves, the worse the overall system has gotten for everyone.” * For an in-person manufacturing job in the Midwest, the company was flooded with applicants from overseas and people with graduate degrees looking for tech roles. * Job seekers have put their search on autopilot, so AI applies to thousands of jobs on their behalf. That fills every pipeline with noise, which pushes people to send out even more applications. 2. The Swiss cheese defense against fake candidatesBetween 1% and 1.5% of applicants on Greenhouse fail an ID check. Daniel breaks down the layered security model used to catch them before they get hired. * “In our data, about one to one and a half percent of job applicants hard fail an ID check.” * Every security layer has holes, so you stack them until the holes stop lining up: a voice AI interview first, then digital signals like IP address and device fingerprint, then full ID verification. * Scammers hate a 25-minute voice interview at the start of the process, and most of them drop out right away. 3. The dating app trick that makes candidates five times more likely to get hiredGreenhouse’s Dream Job feature lets job seekers flag one application a month as their top choice. Daniel explains why that scarcity creates a signal that didn’t exist before. * “We’ve had thousands of people get their dream job and they convert from application to hire at about five times the rate of other job applications.” * Because candidates only get one a month, they spend it on a job they actually want and think they can win. * The idea was borrowed from early decision in college admissions and super likes on dating apps. 4. Why Greenhouse’s moat is no longer its softwareCustomers no longer click through Greenhouse’s interface. They connect it to Claude or ChatGPT and do the work there, which changes where the value sits. * “It used to be that the value of Greenhouse was the workflow.” * The value is now in context and data: knowing which moves are legal, which candidates need a response, and which of millions of members are real people actively looking. * “Do you want to emerge from this moment as Blockbuster or Netflix? It’s just that simple.” 5. How AI interviews can design out the bias of résumé screeningDaniel points to a 2003 study showing that résumé review is one of the most biased stages in hiring, and argues that AI can remove that stage altogether. * Identical résumés with only the name changed showed about a 50 percentage point gap in interview invitations, depending on whether the name sounded stereotypically white or Black. * When interviews are scarce, companies gatekeep who gets one. An AI interviewer lets them interview everyone. * “I don’t care what your name is. I don’t care if you were on the women’s volleyball team or the men’s lacrosse team. None of that. It’s just about how do you answer the questions?” 6. What it would mean to hire an AI agent through GreenhouseIn an internal brainstorm, Greenhouse asked what happens when a customer says half of this year’s 100 hires will be agents. Daniel works through the question from first principles. * “What would it mean to put up a job posting for an agent? What would it mean for the agent to apply to that job or be interviewed or get an offer?” * Every step of hiring has a job to do. A job ad tells whoever or whatever can fill a need that the need exists, and an interview tests whether a candidate can actually do the work. * “Some of it’s a little science fiction for now,” but he says anyone in his seat had better be thinking about it. 7. Why great leaders have no followersDaniel shares the leadership lesson from Fred Kofman’s Conscious Business that shapes how he runs a company of 700 people. * Picture runners on a track: the one in front looks like the leader, but the others aren’t chasing him. They’re all running toward the same finish line. * “As a leader, my job is not to get people to follow me. My job is to find the finish line and tell everyone as clearly as I can where it is.” * He doesn’t book sales, answer support tickets or ship code. His job is to set the vision and make it clear to customers, investors and employees. Where to connect with us Follow Ollie on LinkedIn: https://www.linkedin.com/in/ollieforsythFollow Daniel on LinkedIn: https://www.linkedin.com/in/dhchait Visit Greenhouse: https://greenhouse.comOur Partner for this episode is Harmonic - your go-to startup database https://harmonic.ai Previous episodes include See all previous episodes here 👉 Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  5. Oct 1

    Fiverr

    Subscribe to stay ahead of technology trends. Never miss future editions. Micha Kaufman, co-founder and CEO of Fiverr, joins us to explain why anyone who has worked in their profession for more than five years is now “a dinosaur.” He has spent 17 years building Fiverr into a global marketplace connecting millions of freelancers with businesses, and took it public on the New York Stock Exchange in 2019. His internal memo warning staff about AI went viral a year ago, and he says the employees who embraced it are now doing two to three times more than before. Micha also shares why AI has flipped the 80/20 split between human and software work, why an AI that knows everybody “just makes everybody more average,” and why human judgment and accountability will be the biggest moat any professional can have. In our in-depth episode, we discuss: * The asteroid analogy behind Micha’s viral AI memo, and what changed inside Fiverr in the 12 months after he sent it * Why the 20% of work AI can’t do is “infinitely important,” and the risk of outsourcing judgment to machines * Why most AI-generated code is built on poor-quality open source, and what that means for developers whose job was 60–70% copy-pasting * Why Micha wants to stop calling freelancers “freelancers,” and why he believes getting better is each person’s own responsibility * How Micha runs a public company through a period of uncertainty by encouraging impatience, and whether half of Fiverr’s clients could soon be AI agents Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (00:00) Meet Micha Kaufman (02:17) Is the 9-to-5 dead?(05:20) The viral AI memo(05:50) The dinosaur and the asteroid(12:25) Using AI vs really using AI(14:33) The one thing AI lacks: accountability(18:55) Has AI made us lazy?(19:33) How AI flipped the 80/20 of work(21:02) Why AI is a nightmare for designers(23:23) What happens to freelancers doing basic work?(27:32) Why "freelancer" is the wrong word(28:38) Whose job is it to get better?(32:04) Why most AI-generated code is bad(38:32) Why adaptability wins(41:10) Running a public company in uncertain times(43:06) Why Micha encourages impatience(44:02) The hard part of deploying AI(45:19) Fiverr's next 12 months Lessons from this episode with Micha 1. Why every experienced professional is now a dinosaurMicha’s viral memo started with one analogy. He explains why anyone with more than five years in their profession is living through an extinction event, and why the outcome is transformation, not the end. * “If you’ve been doing something as a professional for more than five years, you are a dinosaur. And you are a dinosaur at the moment an asteroid makes impact.” * The crater is only the start. The debris, the darkness, the ice age and the food scarcity that follow are what decide which species survive. * Survival is not about evolution, which takes millions of years. It is about morphing into something new, and some species flourish while others go extinct. [0:05:57 – 0:07:20] 2. The one thing AI will never haveMicha argues that the real line between humans and machines is not intelligence. It is accountability, and he traces the reason back to our mortality. * When ChatGPT gives harmful advice, the chat window still says, “I’m AI and I make many mistakes.” That line sums up AI’s lack of accountability. * Humans are accountable because we are mortal. Reputation is our most precious asset, and keeping it requires accountability. * “Human in the loop” is a buzzword. What it really comes down to is judgment and accountability, and the risk is that people start outsourcing both to software. [0:14:33 – 0:15:57] 3. How AI flipped the 80/20 of workBefore AI, humans did most of the work and software did the rest. Micha explains why that ratio has reversed, and why the part left to humans matters more than ever. * Up until AI, humans did about 80% of the work and software did about 20%. Now it has flipped. * The 80% that AI handles “doesn’t get you anywhere. It just gets you faster.” * The 20% left to humans “is not 20% important. It’s infinitely important.” The danger is starting to outsource that 20% as well. [0:19:36 – 0:21:02] 4. The X factor: why the same AI gives different resultsIf everyone has the same AI tools, where does the advantage come from? Micha argues it comes from the person using them. * Whatever AI produces is the average, because everybody has access to it. Something looking slick doesn’t mean it will work. * “If I prompt Claude Design and you prompt Claude Design, we’re not going to get the same results.” * The difference is design taste and knowing what works. “It’s not Claude Design. It’s me. This is the X factor.” [0:26:10 – 0:27:32] 5. It’s not your CEO’s job to make you betterMicha shares the message he gave his own team, one he admits he might get criticized for. * “Look, it’s not my job to make you better. Not better human beings, not better professionals, not better husbands and wives, not better parents. It’s your job.” * He holds himself to the same rule: it’s not the board’s job or the market’s job to make him better. It’s his own. * Fiverr’s role is to help those who help themselves. “I would do anything to help those who help themselves.” [0:29:05 – 0:30:22] 6. Why most AI-generated code is badCode is being written at blazing speed, but Micha says most of it is poor quality. The reason is built into how AI models learn. * AI is a statistical model. It answers a request from wherever it has the most data, and for code, that is open source. * “The vast majority of open source is terrible.” Repurposing it means code that is insecure, inefficient, full of bugs and weak on edge cases. * Five years ago, about 60 to 70% of a developer’s job was already copy-pasting from open source. AI now does that part. [0:32:35 – 0:34:05] 7. Why Micha encourages impatience with the CEORunning a public company takes long-term patience, but Micha pushes his team to be impatient, including with him. * The framework starts with being impatient with yourself. Only then can you demand the same of others. * If Micha says, “Okay, I’ll think about it,” that’s not a good answer. If he says a week, his team should ask: why not tomorrow? Can we talk about this tonight? * The balance: push hard on what can move fast, but “you can’t define 100 years in a day. It takes time.” [0:42:50 – 0:44:02] Where to connect with us Follow Ollie on X: https://x.com/ollieforsythFollow Micha on X: https://x.com/michakaufmanVisit Fiverr: http://fiverr.comOur partner for today's episode is Harmonic, your go-to startup database: https://harmonic.ai Previous episodes include See all previous episodes here 👉 Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  6. Sep 29

    Fireworks AI

    Subscribe to stay ahead of technology trends. Never miss future editions. Lin Qiao, co-founder and CEO of Fireworks AI, joins us to explain why “a lot of companies will build AI into bankruptcy” this year. She previously led PyTorch at Meta, and her company now powers every major coding company, including Cursor, which it began working with at $2M ARR and has watched grow a thousandfold. Lin also shares why the frontier labs’ bet on general intelligence leaves most companies without a moat, how customizing models on private data can make AI five to 10 times cheaper, and why Fireworks expects to process 100x its current 40 trillion tokens a day within a year. In our in-depth episode, we discuss: * Why product-market fit no longer means a durable business in the AI era, and how companies can scale straight into bankruptcy * The framework for owning your own intelligence: put 10–20% of AI spend into training and cut total cost of ownership by four to eight times * Why the product itself is no longer the moat when coding agents make anything easy to copy, and why your private data is * How Lin runs a company with six additional co-founders, doubling headcount every quarter, on a culture of “extreme ownership” * What Lin learned from Jensen Huang about leadership, and why she believes the future belongs to millions of specialized models rather than a few general ones Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Lin Qiao(2:17) Why Jensen Loves Fireworks(4:15) Jensen's Unique Philosophy(7:33) What "Human Judgement" Really Means(11:06) When Founders Should Stop Interviewing(13:10) Why Join Meta Just to Leave and Build(17:06) Building With 6 Co-Founders(22:00) Managing Co-Founder Conflict(24:47) Why Firms Build on Fireworks(37:21) The Framework for Owning Intelligence(38:40) How Long It Takes to Build an Open Model(43:42) Respecting Privacy in Training(45:57) The Next Moat(48:53) Are Data Centers & GPUs an Opportunity?(50:55) Open vs. Closed Model Strategy(52:34) Ollie Becomes Lin's Chief of Staff(54:18) How Lin Uses AI(56:12) 40 Trillion Tokens a Day Lessons from this episode with Lin 1. Why companies are scaling AI into bankruptcy In the SaaS era, product-market fit and a durable business meant the same thing, because the cost of running software was low. Lin explains why AI has split those two concepts apart. * Once you hit product-market fit in SaaS, you scaled as fast as possible, because your biggest COGS was people. * In AI, your customers can love your product while the cost of serving them exceeds your income. “When you scale, you literally scale into bankruptcy.” * For startups, this means running out of money before the next round. For enterprises, it means the CFO looks at the cost forecast and refuses to approve the rollout. [0:33:30 – 0:35:30] 2. The framework for making AI 10x cheaperEvery company Lin talks to asks the same thing: it sounds like an investment, so how do we justify it? Her answer is to think in terms of total cost of ownership. * Put 10–20% of your overall AI spend into training your own model, and the remaining 80–90% into inference. * Customizing the model cuts inference costs by five to 10 times, and total cost of ownership still falls four to eight times, even after the training investment. * Training isn’t one-off: “We literally launch new models every week,” so the model needs to evolve with the product. [0:37:00 – 0:38:30] 3. Why your data is the only moat left Coding agents have made products easy to build and easy to copy. Lin argues the real moat is the data a company collects from customers using its product. * Every company has unique product taste, judgment and design, but when anyone can build anything, “their uniqueness may not be that unique.” * A company’s customer preferences and feedback are its alpha, and that data will never be shared with anyone else. * Yet that data isn’t being used to create any intelligence. That’s the gap Fireworks calls specialized intelligence. [0:32:00 – 0:33:30] 4. “Are you sure you’re going to start a company with seven of you?” When Lin pitched Benchmark’s Eric Vishria for her first round, he was surprised by the size of her founding team. Most companies have two or three co-founders; Fireworks has seven. * The subtext of his question was that big founding teams usually fall apart through drama and founder dynamics. * Lin’s answer is that the co-founders are “brutally intellectually honest” with each other and share an engineering background, which makes logic their common language. * Four years in, she says they have become each other’s strength and a bigger force than any one of them alone. [0:21:00 – 0:22:00] 5. The Navy SEAL principle behind Fireworks’ cultureFireworks doubled from 150 to 300 people in a single quarter, and Lin still interviews everyone. The trait she looks for is “extreme ownership.” * The army is extremely hierarchical, and everyone stays in their lane. On a battlefield, those boundaries stop mattering: you take responsibility, make the call and watch each other’s backs. * At Fireworks, “no problem is anyone else’s problem.” It doesn’t matter who wrote the code. * It isn’t enough to call out a gap; you have to see it through and fix it yourself. [0:09:00 – 0:10:30] 6. What makes Jensen Huang different from every other CEO Lin believes leadership is not a privilege but judgment, and says Jensen operates unlike any conventional CEO. * She was shocked by how much detail he understands, from capacity allocation to the technical details of every topic. * Traditional companies rely on hierarchy to move information up and down the chain; Jensen simply knows everything across Nvidia. * She predicts AI will make this style of leadership far more common, because information will flow freely without deep hierarchy. [0:05:00 – 0:06:30] 7. How Cursor grew 1,000x, and why knowledge work is next Fireworks started working with Cursor when it was at $2M ARR, and has watched it grow a thousandfold in three years. Lin saw 2025 as the year of coding and this year as the year of knowledge work. * Every major coding company now runs on Fireworks. * Lin describes knowledge work as a bushy tree with coding as the trunk. Every profession, from dentistry to legal, has its own depth. * Each tip of that tree is an AI-native startup building agents for one specific domain, which is why AI applications are now diversifying so quickly. [0:25:30 – 0:27:30] 8. From 40 trillion tokens a day to 100x Fireworks processes 40 trillion tokens a day. Asked where that will be a year from now, Lin weighs two opposing forces. * Demand will definitely go up. * Token efficiency will rise too: smaller models will deliver the same intelligence and need fewer thinking tokens to reach a good result. * Even with those compounding effects, she believes reaching 100 times more tokens within a year is possible. [0:56:00 – 0:57:00] Where to find and connect with us Follow Ollie on X: https://x.com/ollieforsyth Follow Lin on X: https://x.com/lqiao Visit Fireworks: https://fireworks.ai Our partner for today’s episode is Harmonic, your go-to startup database: https://harmonic.ai Previous episodes include See all previous episodes here 👉 Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  7. Sep 26

    Coinbase

    Subscribe to stay ahead of technology trends. Never miss future editions. Rob Witoff, CTO of Coinbase, joins us in his first interview since returning to the crypto company. He explains why 98–99% of Coinbase’s code is now written by agents, how his engineers went from writing code by hand to supervising 3–10 agents each in a single year, and why a Friday-morning “spicy feedback” agent may be giving Coinbase bigger productivity gains than AI in product development. In our in-depth conversation, we discuss: * Why Coinbase engineers are producing 100x the code they did a year ago, and why agent managers of agent managers are coming next * Why it’s now cheaper to build a prototype than to ask for permission, and why a picture is worth a thousand words but a prototype is worth a million * The weekly AI feedback agent every Coinbase employee receives, and the “spicy” section that tells Rob to get out of the weeds * Why crypto is so volatile, and why "it's never as good or as bad as it seems * Will Coinbase become the financial super app? Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Rob Witoff(2:30) Crypto's Understated Opportunity(4:09) Coinbase's Next Opportunity(5:30) Inside Coinbase's CTO Role(8:10) Managing a Third of the Company Under Engineering(10:28) Taste & Judgment in the Modern Era of Engineering(13:33) Inside Coinbase's Engineering Team: Zero to One(15:35) How to Set Up Engineers for Success(18:28) How Rob Sets Up His Agents(20:17) How Disruptive Are Model Updates(22:51) Would Coinbase Ever Launch Its Own AI Model?(26:12) Agentic Trading(26:52) Why Are Markets So Volatile?(29:02) What Is the Super App Opportunity?(31:20) Why Now Is the Moment for Agentic Financial Services(34:38) The Fundamentals of Security(35:38) The Agentic Consumer Experience(41:34) Prediction Markets(43:52) Stablecoins(45:45) Will Stablecoins Be Country-Owned?(47:35) The New Financial Infrastructure(49:52) How Coinbase Structures Internal Feedback(54:16) Ollie Joins as Rob's Chief of Staff(57:41) Has 2026 Been the Inflection Point for AI? Lessons from this episode with Rob 1. Why 98–99% of Coinbase’s code is now written by agents, and why engineers are becoming supervisors of loops and systems A year ago, Coinbase engineers were writing code by hand. Today almost all of it is written by agents, and Rob says he didn’t predict it would change this fast. * Most engineers now supervise three to five agents, and sometimes 10, throughout the day. Rob calls that a very different muscle to exercise. * The next step is supervising whole loops and systems, which shifts the job to deciding what problems should be solved in the first place. * Coinbase is building a “Coinbase brain” that holds its product and architecture strategy and directs its agents and loops. 2. The weekly AI feedback agent that tells Rob to get out of the weeds Every Friday at 9am, everyone at Coinbase gets a full assessment of how they’re working, built from their Slack messages and transcribed meetings. It covers what they’re doing well and where their gaps are. * Rob’s favorite part is the “spicy feedback” section, which asks: what is the really hard feedback you need to hear? * People might not want to say “Rob, get out of my way,” but his agent isn’t afraid to say it. * Lessons that used to take him six months to learn, he now iterates on every week. 3. Why it’s now cheaper to build a prototype than to ask for permission Rob doesn’t want people coming to him or their lead with an idea. He wants them to go and build it, and that applies to 100% of the company, including non-engineers. * During one tax season, people were debating how to fix a confusing tax form. A non-engineer built a fully working prototype, and it immediately became the thing they built. * A picture is worth a thousand words, but a prototype is worth a million. * Once people can see, touch and feel something, it gets far more attention and momentum. 4. How Coinbase builds taste and judgment in engineers Rob says taste in modern engineering means thinking like an entrepreneur about what you’re building and why. That starts with being a daily user of your own product. * Employees can now direct deposit their pay into the Coinbase app, a small feature that makes it easy for everyone to use the product and find its rough edges. * Judgment comes from battle scars: you have to make mistakes to learn the big lessons. * Large companies can’t let staff make mistakes that hit customers, so Coinbase builds safe environments where engineers can break things without customers being affected. 5. Why Coinbase flattened its layers so the engineer doing the work is in the room Large companies have traditionally had many layers, from manager to director to senior director and up. Rob says that is good for coordinating but slow for executing. * Over the last year, Coinbase deliberately removed layers between decision-makers and the people doing the work. * His rule of thumb is that for any big architectural decision, the engineer who will execute it is in the room when it’s discussed. * This removes the games of telephone that have historically made it hard to execute at scale. 6. How to trust an AI agent with your money: read-only first, hard limits second, human sign-off third Rob describes a path that applies to almost any use of agents. Agents start with read-only access and earn more control over time. * Even read-only access can show you how your portfolio is allocated and whether it still matches your risk tolerance. * Once agents can trade on your behalf, they need hard limits on which funds they can move. * Escalating to a human has to be a core part of the workflow, so the agent never makes an assumption you wouldn’t make. Where to find and connect with us Follow Ollie on X: https://x.com/ollieforsyth Follow Rob on X: https://x.com/rwitoff Visit Coinbase: https://coinbase.com/ Our partner for today's episode is Harmonic, your go-to startup database: https://harmonic.ai Previous episodes include See all previous episodes here 👉 If you enjoyed this episode, help sustain our work by clicking ❤️ and 🔄 at the top of this post. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

  8. Sep 21

    Axios

    Subscribe to stay ahead of technology trends. Never miss future editions. Jim VandeHei, Co-Founder and CEO of Axios and Co-Founder of Politico, joins us to explain why he believes the open web will largely die within 2 to 5 years as LLMs become the main way people get their information, why unique, human-created content will soar in value once everyone has access to Einstein-level AI, and how his Simplify framework helps people delete the busy work that eats up roughly 25% of every week. In our in-depth conversation, we discuss: * Why trust scores for media will never work * Why Jim can’t understand how anyone is a solo founder, and how he has worked with his co-founders: Mike Allen and Roy Schwartz for nearly 20 years * Who gets paid when LLMs train on creators’ work: the courts, licensing or subscriptions? * The plan to save local news with AI, one community at a time * Why Axios fires talented people who aren’t good people culturally, and what “killers with humility” means * The Simplify framework: how to stop wasting 25% of your week Watch or listen now across YouTube, Apple Podcasts, Spotify, and X Download the transcript 👇 Timestamps (0:00) Meet Jim VandeHei(2:11) The State of Media Today(4:09) Will We Have Trust Scores?(6:48) Are We in a Better Place Today?(11:08) How Will Creators Be Compensated by LLM Models?(13:32) Timely vs. Timeless Content(17:51) The Opportunity for Creators(19:10) AI Summaries(23:32) How Will Creators Make Money?(25:14) The Opportunity for Local News(31:25) Inside Axios(35:25) How to Navigate Tough Days(39:09) Surrounding Yourself with Great People(47:46) What Makes Dyslexic Founders Tick(49:14) 50% More, with 50% Less(52:22) How to Prioritize Your Life and Work(56:23) Ollie Joins as Jim's Chief of Staff Lessons from this episode with Jim 1. How Mike Allen’s humility shaped Axios’s culture, and why what a leader does is instantly contagious Jim credits his co-founder Mike Allen with the company’s culture. Mike brings cupcakes to events and stays behind to clean up so the cleaning crew doesn’t have to. * If the most famous, powerful person at the company has no ego, nobody else has an excuse to have one. * Whether you lead one person or many, your behavior spreads fast: treat people well and they will treat people well. * Bad behavior spreads the same way, and he says it will move like cancer if you allow it. 2. Why AI companies pay creators nothing today, and why Jim believes they will have to compensate them Jim says AI companies currently believe fair use lets them take whatever they want off the internet, and that legally they might be right. He argues that AI still needs new information, new ideas and human expression, so it will have to pay for them. * He expects the courts to decide over the next one to four years how much power AI companies have to use other people’s work. * Whether the companies are morally right is a separate question from whether they are legally right. * He sees three possible routes to payment: paying people to produce content, licensing deals, or subscriptions built into your personal LLM. 3. Why unique, human-made content will soar in value when everyone has Einstein-level AI Jim says he works under the assumption that within three years, everyone will have access to Einstein-level intelligence on every topic. In that world, he argues, content that is truly unique becomes more valuable, not less. * He compares it to everyone having equal access to a trivia contest when Google is in front of them. * People are wired to want survival and also something other people don’t have, so demand for differentiated content won’t go away. * Commodity content that isn’t vital or useful to anyone will have no value. 4. Why your media diet is your responsibility, and why there is no hero in this story other than you Jim argues that no one can credibly score information for you, so you have to take personal responsibility for what you consume. He treats your media diet like your food diet. * Nobody would agree on who should score media for trust, so it comes down to the individual. * Find at least one source that tries to get closest to the truth in a non-ideological way. * Done properly, he says you can develop almost a bionic brain, with access to the smartest people on any topic. 5. Why you learn more in a year at a startup than in eight years of college Jim advises anyone young to join or start a startup, and describes it as a living experiment built on the assumption that you’re going to fail. * He says you learn more in a month at a startup than in five years of a regular workplace. * Most startups fail, but you learn about yourself, about fear, and about what you want and don’t want in people. * The pain builds emotional intelligence beyond intellectual intelligence, and gives you a more panoramic view of people and the workplace. 6. Why Jim thinks it has never been a better time to be aliveJim says he doesn’t share the pessimism of other people, and believes a lot of them are living in a dark parallel universe. He argues that by most standards, this is the best time in history to be alive. * As a species, he believes we have handled bigger problems than this one and will figure it out. * In his view, most people today are richer, more mobile, more educated, healthier and living longer than at any point in history. * He says that almost everything he lists is not debatable. 7. The 10-minute exercise for deciding what to focus on: write down the three things you’d want to be true about your lifeJim answers how you get down to the two or three things that really matter. He starts with a simple exercise you can do even without reading the book. * Imagine you’re at the end of your life and write down the three things you’d want to say were true. * Use them as a North Star for decisions like taking a job or learning a new skill, then repeat the exercise for your career. * If you’re new to a job, work out the three things you need to do to crush it, then check with your boss that you’re on the same page. Where to find and connect with us Follow Ollie on X: https://x.com/ollieforsythFollow Jim on X: https://x.com/JimVandeHeiVisit Axios: http://axios.com Jim's new book: https://www.smartbrevity.com/simplify Our partner for today's episode - Harmonic: Your go-to startup provider: https://harmonic.ai Previous episodes include See all previous episodes here 👉 If you enjoyed this episode, help sustain our work by clicking ❤️ and 🔄 at the top of this post. Get full access to NEW ECONOMIES at www.neweconomies.co/subscribe

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Welcome to BIG IDEAS by NEW ECONOMIES - a show where we learn how the most iconic founders have turned crucible moments into global companies. www.neweconomies.co

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