Podcasts about 4X

AI creatives just hit a new level and the most surprising convert is Playrix. After years of holding back, they've gone all-in on AI across Township, Gardenscapes, and Homescapes. Matej, Jakub, Felix (and Freddie the robot) review 100+ AI creatives to figure out what's actually working right now.The episode is a guided tour through the current AI creative landscape: AppQuantum's Golden Goblins running AI influencers from a creative team scaling toward 100 people, Playrix's full-AI pivot with the recurring "there are no ads in Township" concept (and Felix's repeated insistence that ads are coming anyway), the "getting slapped" husband concept that has spread across the entire 4X category in a single month, freezing-families intros rendered at near-Pixar quality, the pajama guy who quietly took over Last War / Dark War creatives, and the broader collapse of creative production timelines from a week to a single day. It's the clearest snapshot yet of how fast AI creative production is moving — and how quickly a single winning concept now propagates across an entire genre.The throughline: this isn't about whether AI creatives work anymore. It's about who's iterating fastest.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⏱️ TIMESTAMPS00:00 Cold open — Freddie the robot judges the AI creatives02:30 AppQuantum's Golden Goblins and the 100-person creative team04:00 Century portfolio downscaling AI (and near-Pixar quality)07:30 Playrix goes full AI across all three games08:00 "There are no ads in Township" — the recurring concept16:30 Freezing Families gets the full AI intro treatment22:20 The "getting slapped" concept takes over all of Forex33:30 Top Heroes, anime quality, and where this is heading━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━-PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-3bckldvr8-8PXvzciMWdheOzED9hq0SA---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej Lancaric

This episode was sponsored by Cardiff, 365 Cycles & 365 Regroup    LightSpeed VT: https://www.lightspeedvt.com/ Dropping Bombs Podcast: https://www.droppingbombs.com/ Today's Dropping Bombs episode features Ben Berman — the founder who sold his car at 21, launched a bicycle parts company with $2,500 and no experience, and built it into America's largest online bicycle parts retailer by selection.   Ben breaks down the Covid wave that 4X'd his orders overnight, why "hire smart people and get out of their way" is dangerous advice, and the documentation system that finally freed him from his own company after nine years trapped inside it.    If you're making real money but can't step away without the wheels falling off, this episode has a name for your problem and a blueprint for fixing it.   

¡Bienvenidos jugadores y jugadoras! Vuelve el Dado Único, esta vez con una reflexión que a ver si da que hablar: pasamos de "Samuel ha vendido Star Wars Unlimited y dice adiós a los TCG" y acabamos en especulación, toxicidad y si el hobby de los juegos de mesa se está muriendo. ¿Exageración? Bueno, acompáñanos en este precioso viaje de palomiteros a 70€, hipersaldos, Juegos de Pokémon que ya no se juegan, ludotecas de la vergüenza, influencers quemados… ¿Todo mal? Casi. Por suerte, luego llegan las reseñas para salvar el programa: Space Empires 4X (un 4X espacial al que hay que ir con pinzas, cubitos y hojas de cálculo caseras) y Corona del Viejo Rey (Spoiler, a 2 es como funciona). ¡A por ello! 00:00 Inicio y presentación 02:15 Samuel deja Star Wars Unlimited (TCG) 15:59 La muerte del hobby: especulación (Palomiteros, Pokémon, Lego...) 24:58 Cultura del rendimiento e hiperconsumismo (fotos de partidas, retos, ludoteca de la vergüenza) 36:48 Monetización y creadores de contenido quemados 47:57 Consejos para no dejar morir el hobby 53:10 Space Empires 4X 1:19:57 Corona del Viejo Rey 1:30:27 Despedida y cierre

Usilovně jsme se snažili vymyslet, o čem si tentokrát v Lootboxu budeme povídat a napadlo nás, že by nebylo špatné probrat nějaké ty videohry! A tentokrát se jich sešlo vlastně poměrně dost, od zahrabaných myší, dobrých realtime strategií s příšerným UI, přes nějaké ty kartičky až k web-based 4X strategiím. A cestou jsme se zvládli podívat i na pár filmů a zahrát si nějaká ta stolní RPGčka.

Lucy spent 8 years as a corporate lawyer before realizing the traditional career path wasn't going to give her the lifestyle, freedom, or family time she wanted.In this episode, Matt Raad sits down with Lucy to talk about how she and her partner Reefe built an online lifestyle business, 4X'd their income in 18 months, and created a business that lets them work fewer days while being more present with their young boys.You'll hear how they went from running a traditional electrical trade business to building an online asset that generates targeted traffic, attracts corporate advertisers, and creates multiple income streams.Matt also shares the three skills he believes every online business owner needs: sales, networking, and marketing. Plus, why learning the foundations of websites, traffic, and digital assets still matters in the age of AI.If you've ever felt burnt out, stuck in your career, or wondered whether there's another path, Lucy's story is a powerful reminder that your existing knowledge can become a real online asset.Want To Learn How To Digital Skills That Can Replace Your Income and Buy Back Your Lifestyle?You don't need tech skills or prior experience, just the right strategy and a proven plan. Learn how 6-figure earners are buying profitable online businesses (the smart and safe way in 2026): https://www.ebusinessinstitute.com.au/dip

He went from $180K for 14 units… to 7,500 multifamily units… to NOW closing 7 hotels in a single year with $40M of investor capital.   In this short clip from Abundance Mindset, Vinney "Smile" Chopra breaks down:

This week we put the foot in the juice as we talk about the Blues Brothers, Swing Choir, Obsession, wishes, Sherlock Holmes and Mr. Hyde, Monster: Ed Gein, Subnautica 2, Terrifier: The ARTcade Game, Reaper's Crawl, Dungeon Crawler Carl, 4X games, Absolute DC, Manos the Hands of Fate, He-Man, For All Mankind, Oreos, Steam Deck price increase, Stan Lee AI, foot juice, Marvel shakeups, Choose Your Own Adventure, Smugglers Run, Amazing Spider-Man 1000, Transformers: The Movie, The Phantom, and the Warhorse Middle-Earth RPG. So, grab your Machete of Joy, it's time for a GeekShock!

Own Your Results. Own the Returns. Why the Smartest Accredited Investors Are Going All-In on Hotels.   In this episode of Abundance Mindset, Vinney "Smile" Chopra joins co-host Gualter Amarelo for a raw, real, and revelation-packed conversation about what it truly means to OWN your results — in business, in investing, and in life.   Vinney pulls back the curtain on his personal pivot from 7,500+ multifamily units to a laser focus on hospitality — and why he believes hotel rooms are apartment units on steroids.   Here's why: unlike a 12-month lease, a hotel "lease" is signed every single night — with up to five different price points depending on supply, demand, and local events. When a big concert or conference hits your market, your nightly rate can 4X overnight. That's a level of dynamic cash flow multifamily simply cannot match.   In this episode, you'll discover:  

Owner.com is approaching $100M ARR selling to independent restaurants and their GTM team is producing numbers that shouldn't be possible. $150K AEs closing $2M+ ARR per year. Outbound BDRs generating $100K in closed-won ARR per BDR per month. 4X the ARR per rep compared to direct competitors. None of that happens by accident.  In this session, Kyle Norton, CRO at Owner.com, breaks down the exact AI-driven GTM playbook that got them there, including 5 decisions he believes every SaaS company needs to make right now before the gap between AI-native and AI-curious companies becomes impossible to close. What you'll learn: 1. Centralized vs. decentralized AI: why letting a thousand flowers bloom is probably killing your results 2. Build vs. buy: the 5-question framework (hint: buy your infrastructure, build your intelligence) 3. The AI sophistication ladder — Levels 0 through 4, where most companies are stuck, and exactly how to move up 4. The "5 P" prioritization framework for deciding which AI projects to tackle first 5. Agentic vs. assistive: how to think about human-in-the-loop and why chaining too many generative steps is the #1 cause of AI slop 6. Why your personal compounding AI stack is your most underrated competitive asset This isn't theory. This is what $100M ARR in a notoriously difficult SMB market actually looks like when you go all-in on applied AI.

Most sales teams are reactive — waiting for buyers to fill out a form, book a demo, or respond to an email. Tal Peretz, co-founder and CEO of OnFire AI, is building the infrastructure to change that. OnFire monitors millions of public signals across Reddit, Stack Overflow, LinkedIn, Slack, and technical forums to identify high-intent buyers before they ever contact your sales team.In this episode, Tal breaks down how AI is transforming go-to-market for companies selling to technical buyers — CTOs, CISOs, and engineers — who notoriously resist generic outreach and respond only to context-rich, well-timed conversations. Tal shares his journey from engineer to CEO, how he and his co-founder interviewed 275 revenue leaders before writing a line of code, what it's really like to raise a $20M seed round, and the hard-won lessons of learning to sell as a first-time founder. From ICP discovery and outcome-based pricing to the future of AI in sales, this is a masterclass in signal-driven, intent-based revenue growth.Key Takeaways0:00 — Why most sales teams miss buyers who are already signaling intent publicly2:07 — Intro to Tal Peretz: Co-founder & CEO of OnFire AI3:56 — The origin story: 275 revenue leader interviews before building the product4:36 — How OnFire works: Capturing public web signals, de-anonymizing prospects, and delivering real-time context to sales teams6:25 — Why selling to CTOs, CISOs, and engineers is uniquely difficult — and uniquely valuable7:36 — The 50-million-engineer insight: Turning public technical conversations into revenue intelligence10:04 — What true AI ROI looks like: efficiency gains + directly attributed pipeline11:15 — The 4X pipeline result: What customers see in their first quarter with OnFire11:52 — Speed + personalization + human touch: Why all three are required for signal-based outreach13:03 — Raising a $20M seed round and what hypergrowth pressure really means13:47 — What makes a great investor: shared values, chemistry, and true partnership in hard moments15:59 — Managing pressure: Working backwards from a 24-month North Star to break goals into milestones17:07 — Building vs. selling: What was harder in the early days17:59 — An engineer who learned to love sales: How Tal found his passion for closing deals19:21 — The ICP trap: Why selling to everyone early is the most costly mistake a founder makes20:51 — The outbound playbook: Cold calling, LinkedIn, and the "stealth company" message that landed their biggest customers22:10 — The consulting approach: Why leading with curiosity instead of a pitch built their enterprise pipeline24:41 — The three-layer go-to-market machine: Brand, field/events, and outbound working together26:45 — Selling six-figure enterprise deals: Going on-site, acting as a partner, not a vendor28:51 — Staying focused in a crowded AI market: The "build on top of the platform" rule30:02 — Building go-to-market teams as a technical founder: The hardest challenge32:14 — The biggest AI pricing mistake: Why outcome-based pricing is the future35:03 — Sales-led vs. product-led growth: How Tal thinks about when and how to make the shift38:09 — The future of go-to-market: How AI eliminates the 80% of busy work reps do today40:53 — The one thing founders must nail to break through from product to real revenue41:38 — Where to find Tal and OnFire AITweetable Quotes"We monitor the public web for signals — competitors, pain points, product mentions — and surface them to your sales team in real time. Your buyers are already talking. You just have to listen." — Tal Peretz"It's not about quantity. It's about the quality of the data. Act fast, personalize based on the pain point, and always keep the human touch in the loop." — Tal Peretz"We take your existing team and infrastructure and make the pipeline 4X better — not by adding headcount, but by giving them the right signal at the right moment." — Tal Peretz"Every revenue is not good revenue. Nail your ICP first — where you see the biggest pain, the best retention, and the growth potential — then press the pedal." — Tal Peretz"The best investors aren't just writing checks. When something breaks — and something always breaks — that's where you find out if you have a true partner." — Tal Peretz"AI will eat the 80% of the sales rep's day that is busy work. The reps who win will be the ones who know how to leverage those tools and still build real relationships." — Tal Peretz"Outcome-based pricing is the future. Align what your customer pays with the value they actually receive — then you're never fighting about ROI again." — Tal Peretz"We started with outbound and a simple message: 'I'm a stealth founder. I want to learn from your experience.' No pitch. Just curiosity. Our biggest customers today came from that exact message." — Tal PeretzSaaS Leadership Lessons1. Validate the market before you build the product. Tal and his co-founders interviewed 275 revenue leaders before writing a single line of code. They didn't fall in love with a solution — they found the problem first. For early-stage founders, this discipline separates products that get traction from ones that get ignored.2. Your ICP is not a marketing decision — it's a survival decision. Selling to every prospect early feels like progress, but it's a trap. Tal's hard-won insight: identify the customers with the biggest pain, the highest retention potential, and the best growth trajectory early, then build everything around them. Chasing the wrong customers burns runway and muddies your product roadmap.3. Great investors are chosen for the downside, not the upside. When everything is working, any investor looks great. The real test comes when something breaks. Tal defines great investors by shared core values, authentic chemistry, and willingness to engage as a true partner — not just a capital source — when the hard moments arrive.4. Act like a consultant before you act like a vendor. OnFire's biggest enterprise wins came from going on-site, meeting the full revenue team, mapping the customer's strategic goals, and co-designing a plan — before ever talking contract. For founders selling complex, high-ACV solutions, acting as a partner rather than a vendor changes the entire sales dynamic.5. Outcome-based pricing aligns your success with your customer's success. Charging by seat or token puts you in constant translation mode — always proving value. Pricing tied to outcomes (pipeline generated, conversations resolved, deals influenced) makes the value self-evident and creates a partnership, not a vendor relationship. The companies doing this best in AI are winning stickier, larger contracts.6. The future sales rep is an AI orchestrator, not a data processor. Today's reps spend ~80% of their time on research, sourcing, and admin — not selling. AI will progressively eliminate that 80%. The reps who thrive won't be those who resist the change, but those who master AI tooling and redirect all of their energy to the irreplaceable human skill: building trust and closing deals.Guest Resourcestal@onfire.aihttps://onfire.aihttps://www.linkedin.com/in/tal-peretz/instagram.com/peretztalx.com/TalPeretz13Episode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

AppQuantum's Golden Goblins is five years old and still dropping new creatives nobody else is matching. Magic Sword is running 5,000+ new playables per month. Match Villains just flipped the "save the king" formula and is converting harder than ever. And AI creatives are now indistinguishable from human ones in roughly half of the libraries we looked at.Matej Lančarič is joined by Felix Braberg and John Wright (Playable Maker, sitting in for Jakub) to walk through Sensor Tower's April creative trends and figure out what's actually winning right now. The conversation covers the Golden Goblins production machine and why they're still using gates (yes, gates), Match Villains flipping the king-rescue formula into peasant-rescue, the AI invasion of creative libraries, why near-death experience hooks are showing up everywhere, the $40 vs $10 CPI gap between 4X and idle arcade creatives, the brutal volume race (Magic Sword 5K, Royal Match only 300), and the 80/20 iteration rule that separates winners from everyone else.If you ship creatives every week and want to know what's actually working in April 2026, this is the episode.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⏱️ TIMESTAMPS00:00 Cold open — "what signals do you look for anymore?"02:14 Golden Goblins is still dropping new stuff (and using gates)09:13 The 5,000 creatives per month volume race13:55 The 80/20 rule — iteration beats new concepts21:00 "Save the Peasants" — Match Villains flips the king formula30:40 The Royal Match Apple billion-dollar problem35:30 AI creatives are now 50% of some libraries39:13 The $40 vs $10 CPI gap and the Tasty Travels playbook━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Special guest: John WrightJoin our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-3bckldvr8-8PXvzciMWdheOzED9hq0SA---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej Lancaric

Jeden z nas nadrobił właśnie swoją ogromną zaległość. Ten drugi pograł już swego czasu zdecydowanie więcej. W dzisiejszym odcinku zderzamy ze sobą pierwsze wrażenia z takimi już solidnie zamarynowanymi. Zapraszamy do słuchania. Przy okazji czemu by nie polubić naszej stronki na Facebooku i nie zasubskrybować naszego kanału na Youtubie? Zachęcamy. MP3 do POBRANIA

The new shock collar mechanism is attached to the board and since we all lost today's trivia we had 4X shocks at MAX POWER! How much more can we take before we fry Payton like a fish? ALSO ON THE SHOW TODAY, It's an ALL NEW WAR OF THE ROSES! Kyle had a hot take and some listeners called her out on it. BUT FIRST, Kyle got a scam call so Johnjay called them back and could not say Harmonious to save his life (But he did say Hermione) THEN Callie's gym crush turned out to be GAY and Payton's Book Club needs a little extra oomfh because this latest book is only... okay!See omnystudio.com/listener for privacy information.

We're going in for a threepeat – it's Data Centers 3, as we continue with our analysis of the data center industry, the opposition to these hyperscale projects, and the real impacts on social communities and natural ecosystems. We get into the industry playbook of using “counterinsurgency tactics” to undermine opposition to these data centers and the ideological playbook of effective altruists treating these projects like they are just 4X strategy games. Plus how Mr. Wonderful is terraforming Utah by building a massive natural gas power plant to fuel a hyperscale data center right on the shores of the Great Salt Lake. ••• ‘So much worse than I even thought': Utah's ‘hyperscale' data center could create massive heat island near Great Salt Lake https://www.sltrib.com/news/environment/2026/05/07/utahs-data-center-could-create/ ••• The fight against AI datacenters isn't just about tech – it's about democracy https://www.theguardian.com/commentisfree/2026/may/08/ai-datacenters-democracy ••• Rolling the DICE on Data Center Development https://www.earthcharterindiana.org/climate-chronicles/dice ••• Google developers significantly misstate carbon emissions of proposed UK datacentres https://www.theguardian.com/technology/2026/may/09/google-developers-significantly-misstate-carbon-emissions-of-proposed-uk-datacentres Standing Plugs: ••• Order Jathan's book: https://www.ucpress.edu/book/9780520398078/the-mechanic-and-the-luddite ••• Subscribe to Ed's substack: https://substack.com/@thetechbubble ••• Subscribe to TMK on patreon for premium episodes: https://www.patreon.com/thismachinekills Hosted by Jathan Sadowski (bsky.app/profile/jathansadowski.com) and Edward Ongweso Jr. (www.x.com/bigblackjacobin). Production / Music by Jereme Brown (bsky.app/profile/jebr.bsky.social)

A transformation and growth leader at heart, Paul Idziak is a CEO who thrives in complexity and turns bold vision into disciplined execution and scalable results. Like a catalyst for momentum, he does not just grow businesses; he engineers ecosystems where people, process, and performance move in sync. He leads from the front, combining grit with clarity to transform underperforming operations into high-impact, multi-location enterprises. What he brings to the table is a rare blend of private equity acumen, operational rigor, and commercial instinct. He builds strong leadership teams, installs KPI-driven cultures, and creates structures that scale with precision. From due diligence to exit readiness, he aligns strategy with execution, driving profitability, expanding markets, and reducing risk. He operates with urgency, accountability, and a relentless focus on value creation. Over the years, Paul has scaled businesses from the ground up, launching new divisions, expanding across the U.S., Canada, and international markets, and building distributed workforces of 300+ technicians. He has driven 35% revenue CAGR and 110% EBITDA growth, transforming operational performance and positioning companies for successful exits. He has secured tier-1 OEM partnerships, negotiated MSAs, and led high-value projects exceeding $20M while building diversified, resilient customer portfolios. From sourcing more than 100 acquisition targets and supporting approximately $3B in transaction value to executing value creation plans targeting 4X returns, his experience spans the full investment lifecycle. He has improved margins, reduced the cost of poor quality, implemented Lean 6S practices, and built safety cultures, achieving 0 recordables, consistently delivering measurable, repeatable results. His previous experience across Johnson Controls, Siemens, and AWC has further sharpened his leadership approach, strengthening his ability to scale operations, build high-performing teams, and drive consistent enterprise-level impact. What matters most to Paul is building businesses that endure and teams that win long after the strategy is set. He measures success not just by growth, but by the legacy of performance, discipline, and leadership he leaves behind.

On this week's episode of the podcast, I am joined by Phil Black, one of the co-hosts of the Game Economist Cast, a podcast dedicated to game economy design, and a consultant with Game Economist Consulting. Previously, Phil held game economist and analytics roles at Amazon Games, DICE, and Scopely. Phil is also a panelist on the This Week in Games (TWIG) podcast. Phil joins me on this episode to examine the shifting dynamics of the mobile gaming economy, from the consolidation of high-revenue genres to the strategic adoption of AI and direct-to-consumer models. Among other things, we discuss:How the concentration of mobile gaming revenue into puzzle and 4X genres reshapes the competitive landscape for independent developersWhether the integration of hyper-casual mechanics into core loops represents a fundamental evolution of the modern mobile gaming economyWhy the transition from visual AI outputs to deep personalization remains the primary value proposition for future game monetizationWhat the divergence between Android and iOS installation gates reveals about the health of the global mobile marketing ecosystemIf the rise of third-party web shops and direct-to-consumer models can effectively counteract the platform fees of major storesHow the emergence of warbonds and sampling-based monetization strategies signals a shift away from traditional battle pass reward structuresWhen the mobile gaming industry will move beyond post-ATT recovery strategiesThanks to the sponsors of this week's episode of the Mobile Dev Memo podcast:⁠⁠⁠⁠INCRMNTAL⁠⁠⁠. True attribution measures incrementality, always on.Xsolla⁠. With the Xsolla Web Shop, you can create a direct storefront, cut fees down to as low as 5%, and keep players engaged with bundles, rewards, and analytics.⁠Branch⁠. Branch is an AI-powered MMP, connecting every paid, owned, and organic touchpoint so growth teams can see exactly where to put their dollars to bring users in the door and keep them coming backInterested in sponsoring the Mobile Dev Memo podcast? Contact Mobile Dev Memo advertising.The Mobile Dev Memo podcast is available on:YouTubeApple PodcastsSpotify

Most DTC brands are making million-dollar channel decisions based on attribution data that's fundamentally wrong. Olivia Kory — CSO of Haus and the incrementality expert Brett references on stage more than almost anyone — breaks down what it actually takes to know if your ads are working. Spoiler: if you've been writing off YouTube based on MTA, you owe yourself a retest.Inside the episode:Why YouTube's true ROAS is 3.4X what the platform reports — and how Haus's 190-test study across 74 brands proved it (plus why your D2C numbers alone are only half the picture)The right time to start incrementality testing — it's not when you're huge, it's when your business gets complicated enough that turning off ads won't give you a clean answerHow StockX went from barely spending on YouTube to making it their #2 acquisition channel — by running geo holdout tests and acting on the resultsWhy Meta's optimization might be too good — and how brands like Jones Road are improving their iROAS by making changes that look worse in-platformThe surprise winner: AppLovin — Olivia came in skeptical of mobile game ad inventory and got data she didn't expectHaus's new DTC Basics tier — a lower-cost entry point so more brands can stop guessing which channels are actually driving growth Sponsored by OMG Commerce - go to (https://www.omgcommerce.com/contact) and request your FREE strategy session today! Chapters: [00:00] Intro clip — Olivia on treating incrementality as a report card vs. a growth tool[00:22] Introductions & background — Olivia's path from Starcom → TubeMogul → Netflix → Quibi → Sonos → Haus[06:55] What is incrementality? — The randomized controlled trial analogy; geo holdouts vs. click-based attribution[10:45] When should a brand start using incrementality? — The low-to-mid 8-figure inflection point; multi-channel complexity as the signal[15:34] Native platform lift studies (Meta & Google) — Are they worth it? Signal loss, CAPI, iOS 14.5 limitations[17:25] Geo holdout vs. user-level testing — Why Haus was "born out of the ashes of iOS 14.5" and went all-in on geo[19:37] How a Haus geo holdout test actually works — Data ingestion, experiment design, market matching, results[23:20] Actioning on incrementality data — Coaching leadership, making reallocation decisions, improving channel performance over time[26:02] How long should you run a test? — Why 2-week YouTube tests fail; 4–6 week minimums and the role of consideration cycles[27:19] Incrementality as an optimization loop, not a report card — Connor from Ridge, Cody from Jones Road Beauty, and the StockX story[30:42] Key metrics defined — iROAS, iCPA, incrementality factor, and why in-platform ROAS can mislead you[32:47] Branded search — Is it incremental? Simple Modern's 5% read, when Amazon bidding on your terms changes the math[35:57] Treatment window & post-treatment window (PTW) — How Haus structures tests for YouTube, Meta, and CTV; lagged effects explained[39:36] Consideration cycles & post-purchase surveys — Why your path-to-purchase report is probably shorter than reality[41:00] Halo effects: Amazon & retail — Why omnichannel brands that only measure D2C are understating YouTube's impact[41:58] The Haus YouTube study findings — The 3.42x incrementality factor; halo effects that doubled lift when Amazon/retail pulled in; Demand Gen vindicated[44:10] YouTube vs. Meta: how the channels differ incrementally — Meta's short payback window, the "too good at intent" problem, and why YouTube wins on halo effects[46:53] Surprises from the data — YouTube (not surprising to Olivia), AppLovin (very surprising), and why TV results swing wildly based on inventory type[50:16] The biggest levers to improve incrementality — Creative first (30–50% wins), then account structure, traffic composition, and spend level[51:46] A DemandGen campaign running on Gmail — A real audit story and why traffic composition can make a channel look broken when it isn't[53:13] Haus's new DTC Basics tier — A lower-cost entry point to measure D2C and Amazon across core ad channels[54:54] Wrap-up & where to find Olivia — Part two teased around the next Haus YouTube report Connect With Brett: LinkedIn: / thebrettcurry YouTube: / @omgcommerce Website: https://www.omgcommerce.com/ Request a Free Strategy Session: https://www.omgcommerce.com/contact Relevant Links: Olivia's LinkedIn: /olivia-kory-50230812 Past guests on eCommerce Evolution include Ezra Firestone, Steve Chou, Drew Sanocki, Jacques Spitzer, Jeremy Horowitz, Ryan Moran, Sean Frank, Andrew Youderian, Ryan McKenzie, Joseph Wilkins, Cody Wittick, Miki Agrawal, Justin Brooke, Nish Samantray, Kurt Elster, John Parkes, Chris Mercer, Rabah Rahil, Bear Handlon, JC Hite, Frederick Vallaeys, Preston Rutherford, Anthony Mink, Bill D'Allessandro, Stephane Colleu, Jeff Oxford, Bryan Porter and more

We break down the latest creative trends shaping mobile game marketing in 2026.And honestly… things are getting weird.We cover:

Join the eXplorminate crew, and special guest Zeikko (Null Vector Studios, creators of Astro Protocol) as we kickstart our eXamination of the 4X genre.There is a companion video, and article.Astro Protocol is available on Steam.

The average car in Latin America sits idle 90% of the time. Renting one still means queuing at a counter, signing paper forms, and arguing about insurance. For 650 million people, that is still the only option.In this episode, we speak with Juan Manuel Pancic and Juan Andrés Vico — MIT-trained data scientist and tech lead veteran of BlackRock and McDonald's — about TripWip, the peer-to-peer carsharing app turning idle cars into income across Mexico, Argentina, and Uruguay. 23 months in: 130,000 users, 4X year-on-year growth, and Toyota, Kia, and Turo insiders already at the cap table. We also hear from COO and first-ever employee Sabina Balestrino on building a team across three countries.Followed by Investor Talk with #EpicInvestor Luisa Ladrón de Guevara, former Head of Operations Latin America at Uber.Hosted by Maaike Doyer & Hester Spiegel, founders of Epic Angels.

Turn More Leads Into Customers Without More Traffic Most businesses don't need more traffic, they need better conversion. In this video, we break down the 3 hidden revenue killers costing you leads, missed calls, and sales, and how AI is changing the game. From websites that convert 3–4X higher than traditional landing pages to instant follow-up on form submissions, emails, and phone calls, this is the next evolution of digital marketing. If you're not using AI to capture and convert leads faster, your competitors will.   What You'll Learn  How AI-driven websites convert 3–4X more leads than traditional pages Why instant follow-up (calls, texts, emails) is the new competitive edge How to capture and convert more of the leads you already have Ready to stop losing leads? Schedule your strategy call now at Intrycks.com and see how this can work in your business.    

Host Devin Becker sits down with Sam Aune (Gaming Analyst at Sensor Tower) to break down Sensor Tower's State of Gaming 2026 report, which covers the current mobile, console, and PC gaming landscape. The conversation spans genre-level signals and platform shifts, such as why 4X strategy bucked mobile's downward trend to what's driving PC's growth. Devin and Sam unpack recent outliers and inflection points like how creator-focused hits outperformed AAA, Battlefield 6's comeback, the rising importance of cross-platform parity for shooters, and what GTA 6 could do to the broader “social” game landscape. They close with the biggest observed behavior change from 2025 to 2026 and a grounded look at what State of Gaming 2027 might imply for teams planning for the next cycle.Read the full State of Gaming 2026 report: https://sensortower.com/report/state-of-gaming-2026?utm_source=naavik&utm_medium=partner&utm_campaign=stateofgaming&utm_content=report We'd like to thank Heroic Labs for making this episode possible! Thousands of studios have trusted Heroic Labs to help them focus on their games and not worry about gametech or scaling for success. To learn more and reach out, visit https://heroiclabs.com/?utm_source=Naavik&utm_medium=CPC&utm_campaign=Podcast We'd also like to thank Neon – a merchant of record with customizable webshops optimized for conversion – for making this episode possible! Neon is trusted by some of the biggest names in gaming and can help you sell direct without the typical overhead. To learn more, visit https://www.neonpay.com/?utm_source=naavik If you like the episode, please help others find us by leaving a 5-star rating or review! And if you have any comments, requests, or feedback shoot us a note at podcast@naavik.co. Watch the episode: YouTube ChannelFor more episodes and details: Podcast WebsiteFree newsletter: Naavik DigestFollow us: Twitter | LinkedIn | WebsiteSound design by Gavin Mc Cabe.

In this episode of Two and a Half Gamers, we break down the insane launch strategy behind Last Asylum: Plague by 37 Entertainment - a game that mixes onboarding mini-games, massive AI-driven creative production, and aggressive scaling tactics that could define the next phase of the 4X market.From My Perfect Hotel-style onboarding loops to hundreds of AI-generated creatives flooding ad networks, this launch shows how Chinese publishers are building growth machines that are almost impossible to compete with.Get our MERCH NOW: 25gamers.com/shop--------------------------------------PVX Partners offers non-dilutive funding for game developers.Go to: https://pvxpartners.com/They can help you access the most effective form of growth capital once you have the metrics to back it.- Scale fast- Keep your shares- Drawdown only as needed- Have PvX take downside risk alongside you+ Work with a team entirely made up of ex-gaming operators and investors---------------------------------------For an ever-growing number of game developers, this means that now is the perfect time to invest in monetizing direct-to-consumer at scale.Our sponsor FastSpring:Has delivered D2C at scale for over 20 yearsThey power top mobile publishers around the worldLaunch a new webstore, replace an existing D2C vendor, or add a redundant D2C vendor at fastspring.gg.---------------------------------------This is no BS gaming podcast 2.5 gamers session. Sharing actionable insights, dropping knowledge from our day-to-day User Acquisition, Game Design, and Ad monetization jobs. We are definitely not discussing the latest industry news, but having so much fun! Let's not forget this is a 4 a.m. conference discussion vibe, so let's not take it too seriously.Panelists: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jakub Remia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠r,⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Felix Braberg, Matej Lancaric⁠Podcast: Join our slack channel here: https://join.slack.com/t/two-and-half-gamers/shared_invite/zt-3bckldvr8-8PXvzciMWdheOzED9hq0SAChapters00:00 Why the Last Asylum launch is shocking02:10 Fake-ad onboarding becomes real gameplay05:10 My Perfect Hotel mechanics inside a 4X08:40 Star gating progression and retention design12:00 Building pressure systems and event timers15:10 Alliance onboarding and server population tricks18:00 Early performance data and Tier-1 scaling22:00 Comparing launch trajectory vs King Shot26:30 Chinese revenue reality vs Western dashboards30:00 AI production pipeline and creative volume34:00 Creative strategy breakdown and winning concepts39:30 Playables gap and future scaling potential42:30 Final prediction — biggest 4X of 2026?---------------------------------------Matej LancaricUser Acquisition & Creatives Consultant⁠https://lancaric.meFelix BrabergAd monetization consultant⁠https://www.felixbraberg.comJakub RemiarGame design consultant⁠https://www.linkedin.com/in/jakubremiar---------------------------------------Please share the podcast with your industry friends, dogs & cats. Especially cats! They love it!Hit the Subscribe button on YouTube, Spotify, and Apple!Please share feedback and comments - matej@lancaric.me---------------------------------------If you are interested in getting UA tips every week on Monday, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠lancaric.substack.com⁠⁠⁠⁠⁠⁠ & sign up for the Brutally Honest newsletter by Matej LancaricDo you have UA questions nobody can answer? Ask ⁠⁠⁠⁠⁠⁠⁠⁠Matej AI⁠⁠⁠⁠⁠⁠ - the First UA AI in the gaming industry! https://lancaric.me/matej-ai

The SaaS multiples run was long, but it had to come to an end. Or Had it? Navigation: Intro Setting The Scene The Roots — This Didn’t Happen Overnight The Structural Thesis — Why This Isn’t Just A Sell-Off The Private Market Fallout The Bull Case — Is The Market Wrong? Separating The Wheat From The Chaff — Who Survives? Wrap-Up & Key Takeaways Conclusion Our co-hosts: Bertrand Schmitt, Entrepreneur in Residence at Red River West, co-founder of App Annie / Data.ai, business angel, advisor to startups and VC funds, @bschmitt Nuno Goncalves Pedro, Investor, Managing Partner, Founder at Chamaeleon, @ngpedro Our show: Tech DECIPHERED brings you the Entrepreneur and Investor views on Big Tech, VC and Start-up news, opinion pieces and research. We decipher their meaning, and add inside knowledge and context. Being nerds, we also discuss the latest gadgets and pop culture news Subscribe To Our Podcast Introduction Nuno Goncalves PedroWelcome to Episode 75 of Tech DECIPHERED, the SaaS Apocalypse: Why AI Breaks or has Broken or Broke the Software Business Model. In today’s episode, we will talk about what’s been going on in SaaS. SaaS, also known as Software as a Service, as a sector, has just had its worst month since the 2008 financial crisis. Give or take, around 1 trillion in software stock market cap has evaporated this year, and it was triggered in many ways by the rise of a lot of the things we’re seeing, in particular, agentic AI. We’ll talk about it later.One of the key triggers seems to have been the launch of Claude or Claude Cowork. There’s a lot of fears that the model that is taken as SaaS to be the darling of investors, both VCs, private equity funds, and also retail investors, has now evaporated. The sweetheart industry no longer works. Bertrand, what happened to SaaS? What’s happening? Bertrand SchmittSetting The SceneWe are in the middle of what some are calling the SaaSpocalypse. I think that was a coined term early this year. It’s pretty bad. We are recording that March 13th. Definitely January, February of this year, 2026, were really terrible. There is no question about it. Strangely enough, since the start of the war with Iran, there has been a small rebound, so we will see how it goes. But also to give some context, we are still not worse than what happened in 2022. We are still in a better place so far. I would say the difference, there is clearly a focus in terms of SaaS versus tech in general for that down term. Nuno Goncalves PedroWe’ve seen obviously a lot of things happening, right? A lot of announcements. The iShares expanded Tech-Software ETF down 25% year-to-date. Everyone seems to be running into panic, JPMorgan, Goldman Sachs. Basically, Jefferies, I think, as you said, originally termed this the SaaSpocalypse. But definitely, it seems like everyone’s trying to sell stock and saying, “Hey, SaaS is going to die.” We’ve seen a lot of interesting elements to this, we’ll talk about it later, around AI eats software. Software eats the world. AI now eats software. I guess AI eats the world.But the reality is, we’ll discuss it later in the episode, it might be just a lot of stuff that’s reacting to what’s actually happening in the market, that there was a couple of misses in terms of numbers, that the growth of some of the key SaaS players that are driving a lot of the public stock wasn’t that great recently. That adding to some launches like we mentioned, the Claude Cowork launch, et cetera, has led people to say, “Hey, maybe some entire spaces of SaaS don’t make much sense going forward.” Bertrand SchmittActually, I don’t know if you noticed, but I think it was yesterday, it was announced that the CEO of Adobe just resigned. I was shocked how bad they managed the transition to AI. I guess it’s one of the first victims of what has been happening. From my perspective, and I will go deeper, but there is a bit of an overreaction. Claude is amazing as a tool, but the launch of Claude Cowork, a few plugins decimating the market, I think that’s an overreaction in the sense that many of these SaaS companies will be able to actually benefit from AI as well. Or some of the new AI tools really, really depend on the existence of an underlying SaaS layer that’s controlling some processes, some data. So I think we have to be careful about the extremes.At the same time, what is true, the growth rate has been going down for SaaS. If you look in the 2021 to these days, we move maybe from 30-11%, 12% average growth rate. It’s a dramatic difference in growth rate, and you cannot keep the same valuation when your growth rate has been divided by three. I mean, that’s just not possible.I think that there might be some overreaction about what company like Claude can truly achieve. At the same time, the reality is there that while SaaS companies are usually relatively strong companies, the growth rate has diminished, and as a result, so should the valuation.The Roots — This Didn’t Happen OvernightBut maybe we can move deeper about what happened the past 2 years about SaaS. Nuno Goncalves PedroIndeed. Some things going back as much as 2024 when Salesforce had its worst trading day. By then, in 2 decades, and went down by 20% on a rare revenue miss. So some early people, a lot of analysts, see this as an early warning of what was to come. Late last year, a huge shift as the different labs of a bunch of different players started launching agentic solutions, which in some ways started eating into a lot of the functionality, not just of vertical SaaS, but also of horizontal SaaS. As a distinction for some of our listeners who are not familiar with that distinction, vertical SaaS is normally SaaS that’s very specific to a specific industry or sub-industry or specific arena, whereas horizontal SaaS is normally SaaS that doesn’t require much adaptation to work across industries. A good example of that might be HR management systems.But basically, because of some of the early developments in those labs and a lot of the solutions that we started seeing around agentic tools, the market started being less positive on SaaS players and trying to readjust it. Those are the historic moments, 2024, 2025. Then all of a sudden, we see the growth rates of SaaS companies coming down, because obviously this doesn’t only have manifestations in the public equity markets. This has manifestations in clients.People, at this moment in time, we’ll talk about it later, are reconsidering their options. They’re like, “Why should I have a SaaS tool? Should I buy it from another player? Should I have a more holistic solution or an integration with Claude, for example? Should I develop in-house?” We’ll talk at length on what’s in customers’ minds, but customers started changing their views and stop buying some solutions that were out there from the large players that are public equities today. Bertrand SchmittYeah, it’s clear that there has been also just overall industry-wide tendency to try to cut on the SaaS subscriptions. Maybe there was too much interest buying too many software solutions, not rationalizing enough, not being careful about the spend. It makes sense that this has hurt overall SaaS growth rate. At the same time, there has been a transfer from IT spending from SaaS tools to AI, so we create a smaller budget for buying SaaS software.But going back, when you look at the change in revenue multiples, it’s crazy. In 2021, we were close to 20X EV, enterprise value to revenues. Now we are talking about 6-7X entering 2026, and we will see later on it does crunch even more. Right now, we are at 4X revenues. So from 20 to 6 to 4, and that’s the lowest in terms of multiples since 2016. That’s 10 years ago. P/E multiple for what multiples also comprise from close to 40 to close to 20.Talking about Adobe, Adobe trades at 5-year average of 30X, now at 12X. No wonder the CEO resigned. I don’t want to be mean, but I think it’s clear some CEO were very strong leading their companies into a SaaS paradigm, but were not as strong leading their company to a new AI paradigm. I think the markets are going to be brutal. If you are good at showing that you can transition to AI, you’re an important piece of the puzzle for AI, that’s one thing. But if the markets believe your products have not kept up, then it’s truly big trouble.I mean, they are not the only one. Intuit 34% decline in a month. Atlassian, minus 35 in a week. ServiceNow also down a third. They are not the only one, but definitely companies have to show some proof of either the lack of vulnerability in an AI world or their capacity to really move strong to a brand-new AI world. Nuno Goncalves PedroThe Structural Thesis — Why This Isn’t Just A Sell-OffWhat are the structural issues? Why wasn’t this just a sell-off? Why is this structurally a problem? The first thing is really around monetization and business model. SaaS 1.0 or 2.0, however we want to call it, was based on seat-based licensing. Seat-based licensing was the notion that with more employees and more users on the platform, there would be more revenue for the SaaS company. Very simple, very clear, very lucrative.Now, obviously, AI agents don’t occupy seats. An agent can do the work of 10 people, can do the work of 20 people, 30 people, 100 people, whatever it is. Therefore, if I’m a company, and I’m using agents, and not necessarily a human user, I’m not going to buy 10 licenses for the work of 10. I have one license, and it’s used by an agent that basically has access to that tool. That’s the first issue. The first issue is that the seat-based pricing, assuming humans, assuming a certain degree of productivity, et cetera, all of a sudden is under stress. Bertrand SchmittMaybe to highlight some point, not every SaaS company was focused on per-seat pricing. Me, when I led App Annie, we didn’t have a per-seat licensing or pricing at all, so we were focused on value-based pricing. But that’s true that around us, we have seen that quite a lot of your typical SaaS business was run on a per-seat pricing. Anytime there is a market downturn, you pay a dear price for your per-seat pricing. On top of it, these days, as you said, we have AI. In an AI world, the per-seat pricing model breaks down. Nuno Goncalves PedroIndeed. Now people are asking for other kinds of pricing schema, right? Either flat pricing based on certain usage patterns or, for example, outcome-based pricing. So depending on the outcome of what I’m trying to achieve, is it a booking of a sales call, is it something else? Whatever it is, I pay for that. But I do not pay for seats because that doesn’t work anymore.There have been a lot of movements around these licensing agreements and these basic elements. Some have actually now tried to create agentic licensing agreements. It’s like, “Okay, I have licensing agreements now for your agents, not for your end users.” It used to be end user licensing agreements. It’s now agentic licensing agreements. Obviously, there’s a shift.Part of the shift is, I believe people want to be in a measurement scale that is different. They don’t want just to pay for a seat. They want to pay for either specific outcomes that are very clearly measurable or have flat fees across the board on a variety of things. I think we’ll see the emergence of a couple of these business models and these monetization models more significantly. I do think we’re still to see some innovation around some of these monetization models, which will occur over the next probably few years as people are getting used to it. Okay, now it makes more sense for me to pay by this rather than by that.Again, because it’s a disruption, we’re still getting and nailing down what effectively the new monetization models and business models will look like for some of these players, but it still will be served as a service. We’ll come back to that later as well. Agents can do a lot of stuff and whatever, but it’s like agents and AI are software. AI is software, whatever you want to call it. AI is software at its base and its profound meaning and what it does, et cetera. Bertrand SchmittSeat-based pricing, usage-based pricing, yes, it’s too simple. Yes, it has its flaw. But at the same time, when the industry started, it made a lot of sense. That’s easy to manage, easy to control, at least from the SaaS company perspective. But definitely now that the industry is maturing, I can see that rise and the benefit and value of moving to an outcome-based pricing or to a value-based pricing. What I like with that also, it’s more truly win-win for both sides, for the SaaS companies as well as for the customer of the SaaS company. If you are more win-win, more aligned, I think it’s a better situation, more frictionless. I think it would be a big change.Another interesting piece of the puzzle, obviously, of all the changes we’re seeing is that one of the best assumptions in SaaS was you have 80% to 90% gross margin. If you are below 80%, there were serious questions coming your way in terms of what’s wrong with your business model as a SaaS business. Below 80% was blinking yellow light, below 70, blinking red lights. But now, it’s very different because AI-native companies, you’re expecting more a 50-60% gross margin.Obviously, if you’re SaaS companies, you better move fast to more AI-native tools and services. That will impact your margin. When you decrease so much your margins, of course, it will impact your valuation. There is no other way around that. You cannot value the same way a 90% gross margin business and a 50% gross margin business. That’s simply not reasonable. I think that one is part of the change and part of a different way to value companies. It’s very reasonable. Nuno Goncalves PedroThe first two structural issues is, one, obviously the per-seat pricing piece is potentially dying or at least becoming less pervasive in the market, added to these emerging pricing and monetization models that we just discussed, value-based, outcome-based, some usage-based pricing, some hybrid models that are also out there with some base subscriptions and then other kinds of things and tiers on top of it, either usage or outcome-based.The third big structural shift that we are seeing is, and I already alluded to it earlier, this notion of build-versus-buy. In the past, I think the market went fully into buy. In some ways, even beyond the, “I will buy one” solution that solves all the problems, we went into best in class. We went to unbundled buying: I’ll buy the best solutions for what I need in my corporation and enterprise needs.Now we’re getting a shift back into building: I’ll build my own stuff. I think a lot of it is relating to two things. One, there’s coding agents out there like Claude Code, Codex from OpenAI, and a bunch of other coding agents that have emerged. There’s a lot of solutions out there, like we mentioned already, Claude Cowork, that really managed to have agentic solutions into workflows that are deeply embedded into some of the enterprises.At the end of the day, I think there’s a lot more of this notion of, I have all my data in-house. I want to really leverage all the data I have. I don’t want to just use a third-party solution that has generic data. I want to use my data set, I want to use my stuff, and I want to basically fit that into ongoing improvements in terms of workflow.The other piece, I think, what’s happening with IT departments in some large corporations that’s leading to this build mindset rather than this buy mindset is also the notion of maybe we have too many people. How do we really express our productivity if we don’t have solutions that are at the core of our processes? If we have solutions at the core of the processes that we develop ourselves or that we develop in partnership with integrators, et cetera, but using some of these new AI platforms, we also have more visibility on the people that we can let go.Now, I know this is quite negative, but I think this has also been leading to all the layoffs that we’ve been seeing across industries recently, where people are like, “Well, I can just extract productivity.” We’ve seen some of those very visible ones. We were talking about Amazon and what’s happening at Amazon with the layoffs recently. A significant amount of layoffs recently announced.Then some other issues on the other side where apparently the junior engineers that were still working on stuff using Claude and other tools that they were using internally started breaking platforms and breaking systems. Anyway, definitely there’s a lot of that going into this build mindset. I want to have control. I want to make sure I understand where the productivity enhancements are, and that will give me more visibility on the people that I need to keep and the people that I need to let go. Bertrand SchmittI’m not so convinced about this part of the puzzle. I think that for many, AI is a convenient demand, but I’m more thinking that some companies, Amazon included, Microsoft, truly, truly over-hired in 2020, 2021. Yes, they scaled back a bit, 2022, 2023. But I don’t think they ever scaled back to what was reasonable given their needs. So it’s quite convenient to say, “No, it’s not management mistake of efficiency, it’s something new AI, and we have to adjust to that.”What I believe is true, however, is that you cannot fund both at the same time in the sense of you cannot finance an over-bloated workforce, and two, significant extremely large AI investment. At some point, these companies were faced with a choice, and they took a reasonable decision on this to be more efficient with their workforce.But personally, I think that actually the ability to do so much more with AI will make more companies think more about their teams and building things because when suddenly your engineers can be way more efficient, can build way more, the value increases. So you could argue that there is an opportunity for companies to deliver more, and as a result, I can see if you’re a good engineer, then there will be opportunities to build more value, potentially across more companies.So we might see a shift where you have more growth in software-related jobs outside the core top 10 bigger software companies, but growing more widely across your typical S&P 500 and even SMBs who could never afford to really deliver value with typical software engineering. But now suddenly, software engineering equipped with AI can be more dramatic in terms of value for them. Nuno Goncalves PedroI agree this is a scapegoat. I agreed that there’s a lot of posturing as well. If someone can lay off a significant percentage of their… It’s almost like the percentage of people you can lay off becomes your new pattern as a CEO, your new, “Basically, I’m saying right now to the market, I can cut…” I mean, Block, I think, cut off 40% of their workforce.At this point in time, seems a bit dehumanized. I think the tech companies are the worst cases, in particular because AI also does disrupt them a lot in their own processes internally. But it feels to me right now, it’s a little bit this one-upmanship of, “Okay, I can lay off more people than you can, kind of thing.” It’s precisely all the fears that a lot of people have around AI. It’s like you’re dehumanizing work. It’s like at the end of the day, people are still needed to work, et cetera. Bertrand SchmittBut I think Block might be one of these companies that completely over-hired over the past few years and never took the pill to reoptimize the business. Nuno Goncalves PedroI think we mentioned it at a previous episode that there was an estimate at some point in time that… For example, even Google had more than double the number of engineers they needed at any given point in time. So obviously, they did hoard engineering resources in other capacities. But at this point in time, it feels a little bit like up to you since being a software engineer right now is a kiss of death kind of thing. Which is weird because at the same time, we are seeing tremendous reallocation of capital overall in the industry towards infrastructure and platforms, where hyperscalers are at 660-690 billion in infrastructure CapEx for this year alone, and 75% of that being AI, where we are seeing a lot of movements around how do I budget accordingly if I’m a corporation.To your point, I think you made that point earlier, Bertrand, how if I’m the CIO of a company, do I allocate my resources more clearly, in particular, if I’m taking into account that I need to spend more money on AI and AI tooling and AI platforms. Obviously, at the end of the day, the CFOs are still there, and the CFOs are basically saying, “Hey, guys, we went into an unbundled world. We had all these agreements with all these people. I want more concentration.” At the same time, the CEO is telling me we need AI, “So whatever it is, you guys tell me what it is, but we can’t increase our budget for this stuff. We need to decrease it, and there needs to be AI in it.” Obviously, there’s a lot of reallocation also at a micro level within the corporate world. Bertrand SchmittYes, you cannot say it will be more built versus buy. At the same time, we are going to need less engineers to do the build. You see what I mean? Even with AI helping you, building which still cost you more, require more software engineering than just a buy decision. For me, what’s interesting is that not so many of these stories can be true at the same time. You require a next workforce, but at the same time, you’re going to rebuild your whole software stack from zero just because of the AI God that you just brought in from cloud. This is not reasonable, simply not reasonable. Nuno Goncalves PedroI think the thesis is that your top engineer is I think, in particular, the more senior engineers, can now do the job of 10. Therefore, what I am switching in terms of cost, I’m not saying I’m agreeing with the thesis, but the thesis is that. What I’m reallocating in terms of budget is, I’m reallocating towards spend at infrastructure platform level, on tokens, et cetera. That’s basically, I think, the thesis of what we’re seeing happening right now. Bertrand SchmittYes, but if you were just, quote, unquote, buying software, you’re not building software. You didn’t need software engineering to just buy software. Your software engineer that becomes as valuable as 10, yeah, but you had zero if you were just buying software. You see what I mean? Nuno Goncalves PedroNo, IT departments have always had engineers, the larger corporations. Yeah, for sure. Bertrand SchmittIt’s a very different game if you are moving from buying to building. It’s my point, I guess. Nuno Goncalves PedroIt is. Just to be clear, Bertrand, this whole build-versus-buy, the build is going to be done with a lot of use of outsourcing and a lot of use of service providers and a lot of use of integrators, et cetera. This whole bullshit of build-versus-buy, in effect, it’s a misnomer because at the same time, you’re going to have to hire, to your point, you’re going to have to hire companies, et cetera, to help you do this. It’s not magically that you can do it off the existing IT departments that you have. Bertrand SchmittExactly. The question will also be, is your first priority of business to rebuild Salesforce from scratch so that it better fits your internal need as a corporation because you have rebuilt from scratch with AI? I don’t think so. That for me is total overhyped bullshit. Klarna was big on that, this is total BS, quite frankly. Not only it didn’t work, but it makes zero business sense. Zero business sense. You’re not going to rebuild a CRM just for the fun of it while your software engineering could be focused on your core value proposition as a business. If you’re a company just starting, you have processes from scratch, you still don’t have solution, yeah, maybe you could consider that.But even then, is it really your priority versus building your core value proposition? For me, that’s a big question. But what I would expect, however, is that this overall trend mindset and stuff is going to keep the pressure on two software companies in terms of reducing tiers of cost, in terms of delivering more value, in terms of being more aligned to the business, and in terms of overall growth rates that are simply not the same as they used to be. Nuno Goncalves PedroBefore maybe we move to another topic, I think it’s clear, we’ll come back to that later, that there are a lot of overblown elements in this. You can never disregard a couple of very, very core elements. A lot of these software companies have very deep tooling into significant enterprise customers. You can’t just rebuild it from scratch yourself to your point. Not only does it make sense, but you can’t. It would take you years to do it. Good luck to you.Secondly, they have also distribution. They are pervasive in the market. They have sales forces. They have people that are selling out there. They have go-to-market teams. Again, we’ll talk about that in maybe one of our penultimate sections today. But maybe to move forward, we talked a lot about the public equity markets and how there’s been a reckoning by institutional and retail investors, et cetera.The Private Market FalloutBut also there’s been a private market fallout. The first one is very obvious to understand. Private equity firms loaded themselves with SaaS. Some even went after roll-up strategies in SaaS, like bringing a bunch of companies together and trying to attack a market and really getting a significant part of that. Software accounts for roughly 25% of the private credit market, which is incredible. Just that’s private credit alone, significant again. They’re loaded with a bunch of companies that have nowhere to go. They can’t IPO, nobody else is interested in buying them unless it’s for a huge write-off or write-down. That’s the first problem right now that we’re seeing in this fallout, which is the private equity market itself. Not only the buyout market, but also we saw a lot of growth funds loading themselves with private equity stock, with a rather SaaS stock, private SaaS stock.Right now, there’s nowhere for that to go. They’re stuck between rock and a hard place with a lot of solutions that are not growing at the rates they were growing before, with a public market that’s not really interesting right now to IPO in, because as we were mentioning earlier, the multiples have gone downhill dramatically, so it’s not interesting. Basically, it’s a chicken-and-egg issue. I would love to sell this now, but I can’t because I have awful market. I can’t IPO it either, so what do I do with all these assets? That’s the first issue here. Bertrand SchmittIt’s clear that you have to be pretty delusional to think that what’s happening in the software public markets is not impacting the private markets. We don’t know why it will be in six months. In six months, it could keep getting worse in the public markets. Six months, at some point, maybe there is a recognition it went too far in terms of adjustment. It’s always tough. But at the same time, you have to be prudent. For sure, what it means is that if I’m a private equity investor in a SaaS business, you have to be a very, very, very special SaaS company to get more financing these days at good terms.Sometimes it’s a very simple math. If you fundraise at 20X, even 10X, how do you go to get to another round of financing if now your multiples are at 4X? That simply makes absolutely no sense whatsoever. Or you need to have grown into your valuation enough that it’s not crazy anymore. If you raise at 20X, and now you’re in 4X multiple, then you need to have grown 5X in your revenues so that you simply stay at the same valuation, or maybe you have to accept a different valuation. But again, quite frankly, the tough part would be convincing investors that it make any sense to put money in a SaaS business. Nuno Goncalves PedroJust to rub it in, just to make it even worse, the secondary market, which was a great market for exits or partial liquidations, et cetera, is demanding now huge discounts. There’s no way I’m going to buy into a stock if it’s not growing at the same pace. I’m like, “I’m sorry.” I will buy your stock at a significant discount. In some cases, it might be what would be a lesser price per share than your last round or your last two rounds. Not just, I want a discount on what you think you’re worth, but it’s like, I want a discount on your last round.Because there’s liquidity issues also in some parts of the market, we were talking just about the private equity firms, some of these deals will go through. If all of this wasn’t quite enough, we have what’s happening in venture capital, which is very close to my heart, of course, because that’s where I play. If you come to me, it’s like I’m a SaaS player immediately off the game. I’m like, “Really? You’re a SaaS, tell me more.” I was just talking to a player recently, SaaS play, there was nothing around AI in their pitch.It’s not just because you have AI in your pitch that I’m going to give you money, clear, but if you’re doing a SaaS play and there’s no AI in your pitch, I’m like, “Am I missing something?” If it looks very classic, I’m like, “Oh.” There’s been a huge, huge reduction in confidence in the VC space in investing in SaaS. There’s a tremendous hyper focus on AI, and in AI investing, AI apps, platforms, infrastructure by most VC firms at this moment in time. And so at this point in time, if you’re a non-AI SaaS player trying to raise money, where’s your AI play? I think that’s the question you’re going to get. It’s going to be very difficult to raise, very difficult to raise. Bertrand SchmittI agree with you. Myself, I saw that SaaS startups with absolutely no AI in their deck, and I was so shocked. I was like, “Guys, where are you living? Are you living in a parallel universe? Are you living under a rock? What’s going on?” Then they are like, “Yeah, but we’re preparing something like that, I come back and prepare.”But even then, as you say, it’s not just leaving AI in your deck. It’s what are your proof points? What have you delivered? How do you make sure that it’s truly differentiator? And how does it make sense versus a pure AI native companies? How are you going to find the new cloud tools that are going to get out in a few weeks and more or ChatGPT or whatever? You have to have a very different proof point. There is nothing new in the past. It’s how are you going to survive against Google? How are you going to survive against Salesforce? How are you going to survive against Microsoft? So nothing is new.Software universe is changing. There’s always that big guys that can destroy you in a matter of weeks. So the question is more, how are you going to be smart enough not to be killed too easily and to find your way in a space that’s probably moving faster than ever? That is probably the difference is that it’s weeks after weeks, you have big change. I’m pretty sure it didn’t happen in that space before because I’ve seen there, I’ve seen that, and it’s moving faster than ever. But it’s nothing new that there is this big company potentially destroying your business. You have to be smart.I feel in some ways, maybe it’s the 2020s, but people stopped being smart, quite frankly. They just raised easy at very large valuation and think that you just do something sometimes pretty basic in terms of software development and that’s good enough. Your GTM is traditional, and you think you made it, and you deserve some investment. I think you must have seen some of this. I have seen a lot of this. In some ways, it’s good. The market is becoming more discerning. Nuno Goncalves PedroThe Bull Case — Is The Market Wrong?But is the market wrong? Maybe shifting to that, at least my perspective is it’s wrong. It’s not fully wrong, but it’s wrong. There’s a right sizing of multiples, but maybe 4X is not the right multiple either. This whole 20X on actuals and 40X on forward stuff didn’t make any sense. There is an argumentation to say that the market is oversold. All the banks have come forward. Goldman Sachs, JPMorgan, Jeffries, Morgan Stanley. Everyone’s come forward and said there’s been definitely, Bank of America, whatever, there’s been an overselling of stock, a dramatic overselling of stock. There’s been a panic that wasn’t warranted. The price has gone down too dramatically for some of these key players.I think part of it, in some ways, is what we were alluding to earlier, the fact that some of these players have built really important stacks that are fitting their customers in a significant on core processes. You can’t just rip it off and put something new. Magically, it will work. It will be around building things around it rather than building things that replace it. Will there be over the long term potential disruption of some of these players around CRM and other solutions? For sure, we’ll see it.But definitely, some of the existing players, public companies that are large, are here to stay, and they themselves will buy into these markets. They’ll acquire positions into other service providers into toolmakers, into other platforms that allow them to be fully AI-enabled and to make their platforms more AI-enabled. I do think there was a huge amount of overselling. The second thing we already alluded to as well as go-to-market. If I’m selling something to someone, there’s a salesperson involved or there are a couple of salespeople involved, they’re not going anywhere. So in some ways, that relationship building with CIOs, with their teams, with procurement teams, all of that is still there.And a lot of the large SaaS players have been doing this for decades. So they have the surface of attack and go-to-market that will take a long time to build for even some of these startups that are disrupting, so to speak, the market. My view is there has been too much panic and the modes of the large players that are already public, in some cases, haven’t been considered at all. Bertrand SchmittThere’s definitely some truth in that. Another piece of the puzzle is that if SaaS is not growing as fast as it used to be, it’s still growing. Many companies are still very good cash generation machines. Many of these companies are moving to AI full speed, improving their tools, changing how you can search their data, how you can leverage their data. They are very close to the data, so they know best how to deliver value on this data. They can integrate existing AI tools. There are a lot of ways for them to capture part of the value that native AI companies are claiming they will get. I think it’s definitely going to, and we’ll talk more later on. I think there will be a question around how do you differentiate the best SaaS companies from the worst SaaS companies in that context.But maybe I just felt we moved a bit quickly on one big event that’s shaping the software industry, it’s the current crash in private credit. Do you have some thoughts about that? Because what’s happening there is pretty crazy, to be frank. Nuno Goncalves PedroYeah, we’ve seen a lot of these players like KKR and Apollo getting slaughtered. Basically, Blue Owl, TPG, Ares, KKR all fell double this in one day on private credit exposure fears. Overall, Apollo has fell 7% as the date of as we were recording BlackRock, 5%. These guys were walking on water and all of a sudden, there was like, “What happened?” And what happened was private credit exposure. A lot of the concerns in the market is private credit is super sexy, and for those who don’t understand what it means is I’m giving credit to a private company in exchange for something, either warrants in the company or revenue sharing in the future, or I’ll get your revenues in advance from you, or I’ll take, whatever it is. There’s over exposure.There’s this potential logic that all these guys are scaling, all the companies that they give private credit to are scaling. And now there are concerns that there might be some dramatic credit in the market, that some of these companies are actually going to die, they’re going to implode, or they’re not going to really fulfill their covenants in their private credit agreements. Bertrand SchmittIt was hidden in plain sight, but that some of these private credit funds at 25, 35% exposure to software, IT, and SaaS, so a huge chunk in an industry where you bet on the long term revenues and cash flow to pay back your loans, while at the same time there is a discovery that this business may be at risk in the next three, five years or even one year because of AI.I think that was the first big chink in the armor that suddenly the creditworthiness of these companies might not have been evaluated properly. But two, it looks like there is also fraud that has been happening. I was reading stories how three, four people, accounting companies, were valuing and estimating loans for hundreds of SaaS business. Good luck, this is crazy. It looks like there is another layer to that story. Nuno Goncalves PedroWhen there are industries building a lot of wealth or apparent wealth that’s coming a little bit from out of nowhere, the likelihood that there’s fraud and things that were not properly done is, it sadly increases dramatically or exponentially. I think we’re seeing just maybe the first effects of that. Bertrand SchmittI was reading, for instance, that one of these big funds was no haircut across the portfolio, ever seen value that was 100%, whatever. One quarter after that, one of their clients going out of business and they lost everything. In three months, you move from no haircut to 100% haircut, decent enough part of your portfolio. This is crazy for a credit business. Nuno Goncalves PedroIt’s ostrich syndrome. You just put your head under the ground, and you’re like, “Hey, whatever.” I don’t know. Bertrand SchmittYeah, it’s zero mark-to-market in an industry that should be relatively conservative. This is private credit. This is not VC, this is not startup, this is not equity, this is credit, so pretty scary. Another piece was like, some of them were supposedly senior on the debt, but they were not so senior after all, this is insane. You claim seniority, but you don’t have it.My point, I think what’s happening in private credit is maybe it all started with that what’s going on, a lot of software exposure. It’s risky because of AI, but the more investor dig into it, that’s when they started to realize that maybe there is more than just that software issue. I guess, all of this is going to be an issue for software business because if suddenly you cannot get loans anymore or the loans you add, you have to pay them back or when it’s time to pay them off, you cannot renew the loan. There is nobody else to turn yourself to get another loan to replace it. That’s not going to be fun and that’s going to impact your growth rates. That could potentially also even be worse than that, be dramatic for your own business survival. Nuno Goncalves PedroMaybe now switching back to the positive part for the bull case. We think the market’s wrong, not fully, but wrong. The other side is still things move on. We’ve also had the same issues in credits in several industries in the past when markets imploded and credit came back. In some cases, it took a while. In other cases, it came back relatively quickly. One great analogy on making a bull case on why all of this stock that was sold was oversold, there’s too much stock being sold on SaaS and at prices that don’t make any sense is an analogy, precisely, for example, with retail. Amazon was going to destroy everyone their mother in 2010, and it did not. It was going to destroy Walmart. Walmart passed the $1 trillion market cap. Bertrand SchmittNot too bad. Nuno Goncalves PedroSo what happened? They adapted. They had huge advantages. They had huge advantages in terms of their customer base, presence, relationship with their suppliers, with the offerings they had, et cetera. They had huge advantages of economies of scale, and they leverage those advantages. And those advantages ultimately materialized in tremendous increase in revenue, tremendous increase in market capital as well.Amazon has done really well as well. It’s not like Amazon didn’t do well. Again, I think this notion, people sometimes have this difficulty in separating the notion of disruption from the notion of replacement. Disruption doesn’t mean necessarily full replacement. You can disrupt industries, disrupt players in that industry, and still those players will exist 10, 20 years later, and they’ll be much bigger because they adapted. The ones that don’t adapt may be killed.But the disruption doesn’t necessarily mean replacement or killing. It means just that effectively the rules of the game, the business model, which we already talked about, monetization models, the way that capital flows in that industry, et cetera, all of that shifts. It doesn’t mean that necessarily the existing players are not going to exist tomorrow. In some cases, they will exist and they’ll be even stronger tomorrow. Bertrand SchmittI think what’s happening is truly a disruption of the SaaS business model, of the SaaS valuations, of the SaaS analysis, because now you need a new prism to analyze it. What are the markets doing in the meantime? They are just dumping it, waiting for, “Okay, how do we look at it in a different way? Who are going to be the winners and the losers?” For now, we don’t care, they’re all losers. But I think that the next piece of the puzzle for us in this episode, but for the market is, how are we going to separate the wheat from the chaff? Who is going to survive? Who is going to more than just survive? Who is going to thrive in that new industry. Nuno Goncalves PedroThere I feel the ones that survive, there’s a couple of obvious ones we can go into. Two that immediately come to my mind are data infrastructure, the Snowflakes, Databricks of the world, because this is the underpinning of everything that’s happening around AI. I don’t see the data infrastructure fundamentally shifting right now. It might in the future, but right now I don’t see it fundamentally shift. Those guys have, if anything, tailwinds rather than headwinds.Then the other one that’s very obvious to me is cybersecurity, where I think AI is very additive to it rather than just necessarily replacing everything that exists. In some ways, that already been used for a while, certainly by the top players. Definitely, those are two immediate categories and areas that come to mind that have maybe more headwinds and tailwinds where really AI is adding rather than subtracting to it. Bertrand SchmittNo, I totally agree with you concerning data infrastructure, cybersecurity. You could argue if you take cybersecurity, that with the rise of AI attacks, with AI making it easier than ever to generate attacks, you better build up your security. Nuno Goncalves PedroWith AI? No, but you have to have AI on your side defending as well. The only way to defend AI is AI. Bertrand SchmittThat’s my point. Your cybersecurity vendors will become AI-enabled, will leverage AI at scale in order to defend you, else they won’t be able to defend you, just quite frankly. Nuno Goncalves PedroCorrect. Bertrand SchmittThat’s part of the game. Data infrastructure, no questions. Again, I don’t think you want to redo your infrastructure with brand-new tools, brand-new stuff is the current tools are working great and doing the job. Maybe another piece of the puzzle is that vertical SaaS, domain-specific tools, healthcare, manufacturing, if you have proprietary data, regulatory modes, it will be much harder for AI to disrupt quickly. If you are not disrupted quickly, you have more time to readjust your business model, to adjust your business model, to leverage AI to improve your business model.Again, of course, some companies, we have seen with Adobe, for instance, have not proven great skills at adjusting to AI. Not everyone is going to get out as a winner. I think some categories have better chance to actually not just survive, but potentially thrive. Another piece are systems of record. If you are holding proprietary non-scrapable data that AI needs to function, that you have deep switching costs protecting you, you are not going to disappear right away. I think you will probably survive. If you are smart enough, you might be able to even adjust and leverage AI.But I can see some might just stick to their revenues and hold companies hostage and might not innovate a lot. I guess we’ll do well on the short run, but on the medium to long I would definitely more worried. Nuno Goncalves PedroOne point I would like to make is at the end of the day, there’s more than that. The algorithmic methodologies you should use for specific industries, for specific verticals, for specific use cases could vary. We’re still very early in a lot of the application of some of these AI methodologies. We’re not early in the development of the research around them. They’ve been around for decades, but the application of them is still relatively early. I think that’s one of the advantages why vertical SaaS companies and vertical SaaS solutions right now might have an advantage, because the domain in which you’re operating, even algorithmically, is actually different, and you need to really right purpose it for those environments and for those domains.For me, that’s an important point to make. It’s not just any vertical SaaS. I think vertical SaaS, where there’s algorithmic distinctiveness, definitely has a shot at it. Other might not. We just saw a lot of discussions around legal tech and how legal tech got slaughtered with the launch of Claude Cowork, for example. Definitely, it will depend a little bit on the verticals. Bertrand SchmittTake the legal side. There has been some interesting decision recently where basically, if you use AI for legal advice, then this data, this discussion is not privileged. You are at big risk of discovery. There is a lot of issues that if you are working with real lawyers, will not be there. Your data is not discoverable, your discussion stay private, so it cannot be used against you. I think companies have to be very careful and very worried about how some of these tools are being used because it’s creating new risk. Some of these tools are not going to get privileged in the coming few months, I don’t think so.You could argue most of these companies in the first place claim a right to access your data and leverage it. I think that even in legal, it would be interesting to see how it evolved. AI will be able to claim some privilege at some point? Maybe, I don’t know. But on the short run, I can imagine how the legal profession, for instance, will not let it happen too quickly, and how you have to be very careful. It’s great to move fast, but you have to be careful with what is it that you are getting into. Nuno Goncalves PedroLet me guess, the last company you’re going to say or the last type of companies that you’re going to say are like the survive, thrive are AI-first or AI-native companies. Is that correct? Bertrand SchmittYeah, I guess. Yes. They are going to be less disrupted by AI, given that they’re already AI native. Nuno Goncalves PedroThey are AI. Bertrand SchmittWe are going into another territory. Even if you are AI-native, are you going to still get killed by Claude because you don’t have enough technology or ChatGPT because you don’t have enough technology? You are just that basic rapper around another AI tools. Here my perspective and what I share more and more with some entrepreneurs is you have to be careful if you are just an AI native company, but ultimately you are a very AI light in the sense that, yes, you are a native, but you are just reusing other LLMs and stuff, and you have not built any proprietary tech or moat with your data or in your industry. That’s going to be trouble. That’s going to be trouble.I’m not sure the market discriminated well enough at this stage, but I think there will be quickly some premium around, have you built a real technology mode? Are you really in such a situation that you are not going to get killed by a Claude or ChatGPT in a few weeks? I think there will be some discrimination that’s going to happen. Ai native won’t be enough to save you, basically. Nuno Goncalves PedroI think there’s one thing. One is what you’re saying. Is there fundamental technology differentiation and/or product differentiation that will sustain itself as a moat? The second thing is, even if it’s an AI app at a higher level, the reality is the guys that are in the market today, the OpenAIs, the Googles, the Anthropics, etc., they’re not going to address all use cases. There are places where some use cases will still exist. We saw that in the mobile app economy.In some of these use cases, you’d be like, why hasn’t, for example, Apple addressed the need for this kind of solution, whatever, and maybe it took them a decade to do it. Then, when they did it, they almost killed the market. But you have some of these AI apps that I think will still be in the market that will emerge and will address use cases that for some time, for some reason, OpenAI, Anthropic, etc., won’t go after. To Bertrand’s point, and I think importantly, if you’re an entrepreneur, if you’re writing on a very specific use case, and there’s seemingly a high likelihood that any of these players are going to address at some point, you’re not in a sustainable place. You’re not going to be around very long. Bertrand SchmittOr you have to take that initial leadership position and transform it into a deeper technology mode, a business mode. You have to leverage that first mover advantage, maybe, to something deeper than that, something more defensible. Maybe you pivot also in term of industry. You started in industry A, but you realize industry B is really the good one. You have to really optimize your way and not take anything for granted. Nuno Goncalves PedroBertrand, do you remember when it’s like every release of iOS and whatever, we were like, what industry is Apple going to kill now? What are they integrating? There was a period of time where it was literally like every big release, every major release, the yearly one, you’d be like, what industry are they going to kill now? Bertrand SchmittTotally. Totally. I think the same is happening. Definitely, we say AI, but I think some players have been smart enough to zigzag around that onslaught from Apple, from Google. But some will stay put. We think it’s not going to happen to them. Yes, they got into trouble pretty quickly. I think also what we have seen is that a lot of value could be from players who are simply more neutral and independent vis-à-vis a platform. If you need someone in the middle, your three or four mobile platform, or now your three or four LLMs or AI platforms, there might be value you can extract because companies are not… That’s another piece of the puzzle.You don’t want to just depend on Claude. You don’t know in three months, ChatGPT has a better model. You will want to make sure that whatever you are running can adjust to a change of LLM providers, for instance, or tool providers. I think, for instance, one position could be that mutual player, the one gives you the ability to adjust quickly to different technical AI development. We will see. But I think there are different strategies you can go through to make sure you end up not being killed, and that will require smart entrepreneurs. Nuno Goncalves PedroSeparating The Wheat From The Chaff — Who Survives?We talked about who survives, who doesn’t survive. Let me start with one. Or where I think will be categories that will be incredibly under attack, so a lot of players, I think, will disappear or will become very, very small. One obvious for me is anything that relates to the small, medium business markets, so very SMB-focused SaaS, a lot of regional SaaS stuff that has emerged, copycatting in certain markets because the larger players didn’t want to expand in some of those markets.I think a lot of that stuff gets just replaced because a lot of the SMB markets are price sensitive. A lot of these markets are also best effort-driven. It’s like it doesn’t need to be perfect, it just needs to do the basic stuff. Therefore, I see that market as a market that’s going to get, in all honesty, over the next 3-5 years, slaughtered. It’s not going to be rapid death, but some of them are just going to be totally replaced. Bertrand SchmittI agree with you. If you don’t have a big enough moat, if it’s very shallow, if your clients are moving quickly, you can easily switch based on a small price difference. That’s definitely trouble. Nuno Goncalves PedroI’ll let an anecdote just so people I don’t understand. Because people say, but these regional SaaS solutions normally because of their specificities to the markets and stuff like that, whatever. I literally drafted the other day an agreement, a semi-agreement relating to Portuguese law on Claude in Portuguese, from Portugal, not Brazil and Portuguese. It drafted an agreement from scratch based on my prompting, and it took into account specificities of the Portuguese legal system and taxation. Guys, it’s like, this is a freaking consumer tool. Localization of what? The tax regime and whatever? Who gives a shit? It’s like, again, I think that’s the market that definitely will get a pretty significant beating. Bertrand SchmittAnother market for me, we talk about Adobe, but content creation tools. Here, I think there is a dramatic shift in how you use them. Before you use another Photoshop to replace something in a picture, change a slightly picture stuff. Now, you just say, hey, remove this guy from the picture. Hey, replace. Hey, create that picture from scratch. I have five photo IDs, put these guys in context, put them in your meeting room, and go for it. This is such transformational versus how you used to work before that I think some of this industry is getting destroyed.There will be simply no point of using these tools anymore because something else is just 10X better. That is not even a question. You could argue there is still a niche of professionals doing stuff in an always because it guarantees a bit more higher quality or this or that. Sure. But overall, this is getting disrupted big time and the much bigger business might be totally new and totally AI native. Nuno Goncalves PedroI will do a parochial comment. We have two investments in the content creation space, one more on the marketing side and the other one more on the hardcore content creation side. They’re both AI from inception, so they’re both AI native. One of them is called LetsEnhance, the other one is called blaze.ai. I feel it’s true that there’s going to be a lot of replacement of some of the content creation tools in certain markets like consumer and prosumer, driven by the Nano Bananas of the world and all that stuff.But on the top end and in enterprise and all that stuff, we feel that AI native content creation tools are there to be. It’s actually one of the areas of what I would call use cases or AI apps/platforms where I feel being AI native will give you an advantage. Just being a cross-cut play around the market being Anthropic or OpenAI, whatever, actually won’t solve the problem for some of the markets that need to be served in. Bertrand SchmittMakes sense. I agree with you. Maybe more quickly, some point solutions, relatively high risk. If you have a single function tool, then could be easily replaced potentially by an AI agent. We already talk about it. If you are too SMB-focused, that’s not the best segment of the market, typically. Maybe you can have a single test to check if that company is at risk. If you were to replace that tool, can a $20 a month AI agent do this task? If switch it cost are low, then maybe that’s not a good business opportunity. Maybe you should not invest, or you should sell the stock.Again, maybe you have to focus more on regulated niches, hardware dependent, critical private data, solutions where there is already outcome or value-based pricing in place. You have to put some rules and analysis to help you understand, is this business at risk of significant disruption or not? Not all business are the same. As an investor, that might mean that there would be some good opportunities. SaaS businesses that are going to emerge even stronger right now are at a cheap discount. Nuno Goncalves PedroAbsolutely. I think at the end of the day, certain basic workflow tools that are out there to simplify CRM, some very basic ERP modules, anything that’s very, very simple in terms of if this then that, all those tools are also going to be slaughtered relatively soon, sadly. If you’re in that space, maybe time, as Bertrand was saying earlier, to pivot, to go after some fundamental differentiation, or to do something else. You want to conclude, Bertrand? Bertrand SchmittConclusionSure. I guess we could see that from a trade perspective, from an investor perspective. I think it’s creating quite genuinely some opportunities. Some stocks are in the bargain, some of those are value traps, so you better get your investment skills in order. PE, private credit, definitely a lot of risk, not just from AI, I think from basic fraud as well.Secondary market, as you just say, it’s not an easy one. It’s a canary in the coal mine. I think you will agree, but this is before getting between AI native versus everything else these days, especially if you are more early stage. A more established business, it’s a different thing. But right now, just starting a regular SaaS company, that’s a tough one. From an investor perspective, you need to pivot as fast as you can from seed-based pricing, hybrid, outcome-based, value-based pricing. You have to do the move quickly. You don’t want to be pushed when it’s too late.Build-versus-buy is real, and that will only accelerate as coding agents mature. Vertical specialization, proprietary data are strong moat. They were before as well, so it’s nothing new. But I think the importance of having a true moat is more critical than ever. Lots of companies have received investment with not enough moat, and that’s the one getting destroyed in the private and public market. If you have strong matrix, there is a question of when is a good time to exit? I don’t know if the relations will ever come back. I think it truly depends as well on your business, a strategic fit with acquisition opportunities.Anecdotally, I have seen some businesses who look at exit opportunities and now are finding attractive options. It’s not all that dark, I would say. Maybe to answer to the question, do we have a SaaS apocalypse? Yes and no. Some companies are going to end badly, some companies are going to emerge stronger. I think that’s it for today. Thank you, Nino. Nuno Goncalves PedroThank you, Bertrand.

Branding seems to be important, but at the same time, can be hard to pin down. So in this episode, we're making sense of it. You'll hear from brand strategist Rob Genovesi about the three things that made the difference in 4X'ing his revenue, without making big changes. We touch on all three, but spend most of our time on what makes you different and how to express that more effectively so people remember you and choose you. You'll also hear about a few simple and practical shifts, the kind Rob calls "dumb little things," that don't cost anything but can change how people experience your work. Since recording this conversation, I've already implemented two of them and I share what I did in the end of the episode wrap-up. Let me know your key takeaways, as I always love hearing what resonates with you. Do you like what you're hearing? Consider giving it a caffeinated thumbs up. We'd really appreciate it! Need a little (and sometimes big) push to start and stay focused to grow your side hustle? Dive into my online Masterclass: How To Turn Your Thoughts Into Wanted Things. For the full show notes and a link to a free digital copy of Rob's book, Don't Fear The Brand, head on over to the home of Side Hustle Hero. https://www.sidehustlehero.com/186 Connect with Rob: LinkedIn Rob's Build Strong Brands website Connect with Joan: Instagram Facebook About Joan Be on the show! Tell us about your side hustle success story!        

AI game creatives just made another massive leap. In this episode, we break down 127 new creatives across Dark War, Top Heroes, Township and more and the quality jump in just one month is honestly insane.We're talking:• Full anime-quality world model sequences• AI influencers everywhere (grandmas, gamers, street interviews)• 50+ completely different “games” promoted under one title• Movie-level 4X fantasy ads• Hyper-niche creative strategy instead of broad targetingThis is not an incremental improvement. This is a structural shift in how UA works. If you run paid UA, creative production, or mobile marketing… this episode is mandatory.

Shut Up & Sit Down's Tom Brewster joins Intertextual Experience for our 44th episode, perhaps with the show's most adventurous format yet (so adventurous that time stamps honestly wouldn't make much sense–and that's not just me trying to get out of doing the time stamps).In a roguelite/like/who the heck knows the difference (& don't go barking at me; I've watched all the videos that try to differentiate them and then see everyone else still arguing in the comments) structure, we fight the Western canon, make bean wine, create and remix the game we're playing in real time, converse about wanting to share the beautiful parts of our hobbies with others, and just have so many other great moments sprinkled within a pretty wild time. This “roguelike” winds up being “4X” in a sense; it explores exploration, experience, expectations, and extermination–poor bat.If none of that makes sense, you'll just have to listen.If all of that makes sense, you're in the right spot.You know where to find Shut Up & Sit Down, but here's their YouTube channel & here's Tom on Bluesky.In lieu of the various time-stamped sections, attached below is a comprehensive slides presentation that includes the slides for Chip Cat Breach, images of the games within the game, the character creation page, the games mentioned in this episode, and more? Tom does an excellent job of bringing the visual elements of this audio-only show to life. I suggest waiting until after a listen to check out these slides to see how everything matches up in your head, but you're your own person.Click here for slides.-----------------------------------------------------------------------------------------If you like this show, liking it on whatever platform you listen to and writing a review would mean so much! Furthermore, it's as independent as it gets, so any financial support would help with the subscriptions that make this project go smoothly.That can be done by "buying me a coffee" and/or buying a copy of my board game (I like it).All of my socials and support information can be found here: Intertextual Experience Linktree

Text Jason @ Leadership VoyageJason talks more about Gallup's latest on employee engagement, with a focus on how a clear understanding of exceptional can lead to 4X more engagement.Things to try as a manager:Ask your employees for their Top 3 priorities and compare them to your answerObjectively write down what you're judging your people on (hours worked? calls made?), and then write "Exceptional" and start to list the quantifiable things that define itUse the GROW (Goal-Reality-Options-Way Forward) model in your 1:1sAsk what "great" looks like, converse about it, and document it as a behavioral north star for your employeesLeadership Voyage is brought to you by Golden Mean Consulting Group, specializing in the training of new managers.Leadership Voyageemail: StartYourVoyage@gmail.comlinkedin: https://www.linkedin.com/in/jasonallenwick/youtube: https://www.youtube.com/@LeadershipVoyagemusic: by Napoleon (napbak)https://www.fiverr.com/napbakvoice: by Ayanna Gallantwww.ayannagallantVO.com========== Instacart - Groceries delivered in as little as 1 hour. Free delivery on your first order over $35.Disclaimer: This post contains affiliate links. If you make a purchase, I may receive a commission at no extra cost to you.

Zach from Axiom Games LLC and Old Man Gaming stops by to chat about being a TTRPG designer and video game reviewer among the many other hats he wears as a dad with a full time job who still makes time for having fun with family and friends both locally and online. After getting introduced to his sprawling work in the TTRPG space we talk about both his love of wrestling games and of 4X games like Endless Space 2. Then, since he's covered so many games on his YouTube channel, we talked about his top 3 games of last year.0:00:00 - Welcome to the show, Zach!0:23:00 - What Are You Playing? Endless Space 20:39:27 - Top 3 Games of Last Year0:47:48 - Wrap Up/PlugsOld Man Gaming YouTube: https://www.youtube.com/@oldmangaming1061Axiom Games Publishing: https://axiom-games-llc.itch.io/Axiom Games Wrestling TTRPG Publications: https://www.drivethrurpg.com/en/publisher/21843/axiom-games-llcAxiom Games on Bluesky: https://bsky.app/profile/omg-axiomgamesllc.bsky.socialFollow Daniel on Bluesky: https://bsky.app/profile/douibyorthst.bsky.socialFollow the show on Bluesky: https://bsky.app/profile/ttwav.bsky.social

Generating alpha in "boring" businesses often outperforms chasing the latest tech trend. In this solo episode of Mechanics of Money, Sam Silverman breaks down the Private Equity Roll-Up Strategy, specifically how consolidating fragmented industries like commercial paving can generate returns of 3X to 8X.We look past the unglamorous nature of asphalt to reveal the financial mechanics of buying cash-flowing assets at low multiples and exiting at institutional valuations.In this episode, we cover:The Math of Arbitrage: How to buy small operators at 3-4X earnings and exit to institutional buyers at 8X or higher, turning $20M of invested capital into a $56M exit.The "Paving Thesis": Why the $110 billion Bipartisan Infrastructure Law and the essential nature of road maintenance make this a recession-resistant asset class.Economic Moats: Why high equipment costs and supply chain relationships create barriers to entry that protect investor value from new competition.The "Silver Tsunami": How the retirement of baby boomer business owners, over half of the industry, is creating a massive acquisition opportunity for well-capitalized buyers.Execution Risk: The reality of integration failure, culture clashes, and why "simple to explain" does not mean "easy to execute".Links & Resources:Newsletter: Join the Mechanics of Money weekly deep dive: https://www.mechanicsofmoney.coInvest: Invest with Silverman Capital: https://silvermancapital.coAbout the Host: Sam Silverman is the Founder of Silverman Capital, a private equity and real estate investment firm. Mechanics of Money is the audio playbook for high-net-worth individuals moving from "High Earner" to "Sophisticated Allocator."

AI can now identify exactly where your business is losing money. Watch the new AI called "Director of Sales" exponentially grow revenue—without spending a penny more on ads—by systematically finding and fixing bottlenecks in the sales process. In this video, we demonstrate the mathematics of the Sales Process. This is a look at how AI can isolate specific "break points" in your funnel (like a low opt-in rate or missing follow-up) that act as a cap on your income. This is different than just asking ChatGPT for marketing ideas. Most AI guesses based on patterns. This AI analyzes your specific numbers (Traffic, Conversion, LTV) to find the "Lowest Hanging Fruit" that will yield the highest return. You will see the AI audit a sales process, find the part that's leaking money, and then create and help implement a plan that fixes it.

What if the difference between AI mediocrity and breakthrough isn't the tool—it's how you architect your approach? Carter Jensen from The Uncommon Business joins the crew to reveal why most people are stuck "button pushing" while others are unlocking 3X productivity gains. This isn't theory; it's the frontline reality of businesses transforming workflows with the right AI architecture. If you're tired of surface-level AI hype and ready for actionable intelligence on integrating AI into security, compliance, and everyday business operations, this episode delivers. Whether you're Blockbuster or Netflix is up to you.

Happy New Year! You may have noticed that in 2025 we had moved toward YouTube as our primary podcasting platform. As we'll explain in the next State of Latent Space post, we'll be doubling down on Substack again and improving the experience for the over 100,000 of you who look out for our emails and website updates!We first mentioned Artificial Analysis in 2024, when it was still a side project in a Sydney basement. They then were one of the few Nat Friedman and Daniel Gross' AIGrant companies to raise a full seed round from them and have now become the independent gold standard for AI benchmarking—trusted by developers, enterprises, and every major lab to navigate the exploding landscape of models, providers, and capabilities.We have chatted with both Clementine Fourrier of HuggingFace's OpenLLM Leaderboard and (the freshly valued at $1.7B) Anastasios Angelopoulos of LMArena on their approaches to LLM evals and trendspotting, but Artificial Analysis have staked out an enduring and important place in the toolkit of the modern AI Engineer by doing the best job of independently running the most comprehensive set of evals across the widest range of open and closed models, and charting their progress for broad industry analyst use.George Cameron and Micah-Hill Smith have spent two years building Artificial Analysis into the platform that answers the questions no one else will: Which model is actually best for your use case? What are the real speed-cost trade-offs? And how open is “open” really?We discuss:* The origin story: built as a side project in 2023 while Micah was building a legal AI assistant, launched publicly in January 2024, and went viral after Swyx's retweet* Why they run evals themselves: labs prompt models differently, cherry-pick chain-of-thought examples (Google Gemini 1.0 Ultra used 32-shot prompts to beat GPT-4 on MMLU), and self-report inflated numbers* The mystery shopper policy: they register accounts not on their own domain and run intelligence + performance benchmarks incognito to prevent labs from serving different models on private endpoints* How they make money: enterprise benchmarking insights subscription (standardized reports on model deployment, serverless vs. managed vs. leasing chips) and private custom benchmarking for AI companies (no one pays to be on the public leaderboard)* The Intelligence Index (V3): synthesizes 10 eval datasets (MMLU, GPQA, agentic benchmarks, long-context reasoning) into a single score, with 95% confidence intervals via repeated runs* Omissions Index (hallucination rate): scores models from -100 to +100 (penalizing incorrect answers, rewarding ”I don't know”), and Claude models lead with the lowest hallucination rates despite not always being the smartest* GDP Val AA: their version of OpenAI's GDP-bench (44 white-collar tasks with spreadsheets, PDFs, PowerPoints), run through their Stirrup agent harness (up to 100 turns, code execution, web search, file system), graded by Gemini 3 Pro as an LLM judge (tested extensively, no self-preference bias)* The Openness Index: scores models 0-18 on transparency of pre-training data, post-training data, methodology, training code, and licensing (AI2 OLMo 2 leads, followed by Nous Hermes and NVIDIA Nemotron)* The smiling curve of AI costs: GPT-4-level intelligence is 100-1000x cheaper than at launch (thanks to smaller models like Amazon Nova), but frontier reasoning models in agentic workflows cost more than ever (sparsity, long context, multi-turn agents)* Why sparsity might go way lower than 5%: GPT-4.5 is ~5% active, Gemini models might be ~3%, and Omissions Index accuracy correlates with total parameters (not active), suggesting massive sparse models are the future* Token efficiency vs. turn efficiency: GPT-5 costs more per token but solves Tau-bench in fewer turns (cheaper overall), and models are getting better at using more tokens only when needed (5.1 Codex has tighter token distributions)* V4 of the Intelligence Index coming soon: adding GDP Val AA, Critical Point, hallucination rate, and dropping some saturated benchmarks (human-eval-style coding is now trivial for small models)Links to Artificial Analysis* Website: https://artificialanalysis.ai* George Cameron on X: https://x.com/georgecameron* Micah-Hill Smith on X: https://x.com/micahhsmithFull Episode on YouTubeTimestamps* 00:00 Introduction: Full Circle Moment and Artificial Analysis Origins* 01:19 Business Model: Independence and Revenue Streams* 04:33 Origin Story: From Legal AI to Benchmarking Need* 16:22 AI Grant and Moving to San Francisco* 19:21 Intelligence Index Evolution: From V1 to V3* 11:47 Benchmarking Challenges: Variance, Contamination, and Methodology* 13:52 Mystery Shopper Policy and Maintaining Independence* 28:01 New Benchmarks: Omissions Index for Hallucination Detection* 33:36 Critical Point: Hard Physics Problems and Research-Level Reasoning* 23:01 GDP Val AA: Agentic Benchmark for Real Work Tasks* 50:19 Stirrup Agent Harness: Open Source Agentic Framework* 52:43 Openness Index: Measuring Model Transparency Beyond Licenses* 58:25 The Smiling Curve: Cost Falling While Spend Rising* 1:02:32 Hardware Efficiency: Blackwell Gains and Sparsity Limits* 1:06:23 Reasoning Models and Token Efficiency: The Spectrum Emerges* 1:11:00 Multimodal Benchmarking: Image, Video, and Speech Arenas* 1:15:05 Looking Ahead: Intelligence Index V4 and Future Directions* 1:16:50 Closing: The Insatiable Demand for IntelligenceTranscriptMicah [00:00:06]: This is kind of a full circle moment for us in a way, because the first time artificial analysis got mentioned on a podcast was you and Alessio on Latent Space. Amazing.swyx [00:00:17]: Which was January 2024. I don't even remember doing that, but yeah, it was very influential to me. Yeah, I'm looking at AI News for Jan 17, or Jan 16, 2024. I said, this gem of a models and host comparison site was just launched. And then I put in a few screenshots, and I said, it's an independent third party. It clearly outlines the quality versus throughput trade-off, and it breaks out by model and hosting provider. I did give you s**t for missing fireworks, and how do you have a model benchmarking thing without fireworks? But you had together, you had perplexity, and I think we just started chatting there. Welcome, George and Micah, to Latent Space. I've been following your progress. Congrats on... It's been an amazing year. You guys have really come together to be the presumptive new gardener of AI, right? Which is something that...George [00:01:09]: Yeah, but you can't pay us for better results.swyx [00:01:12]: Yes, exactly.George [00:01:13]: Very important.Micah [00:01:14]: Start off with a spicy take.swyx [00:01:18]: Okay, how do I pay you?Micah [00:01:20]: Let's get right into that.swyx [00:01:21]: How do you make money?Micah [00:01:24]: Well, very happy to talk about that. So it's been a big journey the last couple of years. Artificial analysis is going to be two years old in January 2026. Which is pretty soon now. We first run the website for free, obviously, and give away a ton of data to help developers and companies navigate AI and make decisions about models, providers, technologies across the AI stack for building stuff. We're very committed to doing that and tend to keep doing that. We have, along the way, built a business that is working out pretty sustainably. We've got just over 20 people now and two main customer groups. So we want to be... We want to be who enterprise look to for data and insights on AI, so we want to help them with their decisions about models and technologies for building stuff. And then on the other side, we do private benchmarking for companies throughout the AI stack who build AI stuff. So no one pays to be on the website. We've been very clear about that from the very start because there's no use doing what we do unless it's independent AI benchmarking. Yeah. But turns out a bunch of our stuff can be pretty useful to companies building AI stuff.swyx [00:02:38]: And is it like, I am a Fortune 500, I need advisors on objective analysis, and I call you guys and you pull up a custom report for me, you come into my office and give me a workshop? What kind of engagement is that?George [00:02:53]: So we have a benchmarking and insight subscription, which looks like standardized reports that cover key topics or key challenges enterprises face when looking to understand AI and choose between all the technologies. And so, for instance, one of the report is a model deployment report, how to think about choosing between serverless inference, managed deployment solutions, or leasing chips. And running inference yourself is an example kind of decision that big enterprises face, and it's hard to reason through, like this AI stuff is really new to everybody. And so we try and help with our reports and insight subscription. Companies navigate that. We also do custom private benchmarking. And so that's very different from the public benchmarking that we publicize, and there's no commercial model around that. For private benchmarking, we'll at times create benchmarks, run benchmarks to specs that enterprises want. And we'll also do that sometimes for AI companies who have built things, and we help them understand what they've built with private benchmarking. Yeah. So that's a piece mainly that we've developed through trying to support everybody publicly with our public benchmarks. Yeah.swyx [00:04:09]: Let's talk about TechStack behind that. But okay, I'm going to rewind all the way to when you guys started this project. You were all the way in Sydney? Yeah. Well, Sydney, Australia for me.Micah [00:04:19]: George was an SF, but he's Australian, but he moved here already. Yeah.swyx [00:04:22]: And I remember I had the Zoom call with you. What was the impetus for starting artificial analysis in the first place? You know, you started with public benchmarks. And so let's start there. We'll go to the private benchmark. Yeah.George [00:04:33]: Why don't we even go back a little bit to like why we, you know, thought that it was needed? Yeah.Micah [00:04:40]: The story kind of begins like in 2022, 2023, like both George and I have been into AI stuff for quite a while. In 2023 specifically, I was trying to build a legal AI research assistant. So it actually worked pretty well for its era, I would say. Yeah. Yeah. So I was finding that the more you go into building something using LLMs, the more each bit of what you're doing ends up being a benchmarking problem. So had like this multistage algorithm thing, trying to figure out what the minimum viable model for each bit was, trying to optimize every bit of it as you build that out, right? Like you're trying to think about accuracy, a bunch of other metrics and performance and cost. And mostly just no one was doing anything to independently evaluate all the models. And certainly not to look at the trade-offs for speed and cost. So we basically set out just to build a thing that developers could look at to see the trade-offs between all of those things measured independently across all the models and providers. Honestly, it was probably meant to be a side project when we first started doing it.swyx [00:05:49]: Like we didn't like get together and say like, Hey, like we're going to stop working on all this stuff. I'm like, this is going to be our main thing. When I first called you, I think you hadn't decided on starting a company yet.Micah [00:05:58]: That's actually true. I don't even think we'd pause like, like George had an acquittance job. I didn't quit working on my legal AI thing. Like it was genuinely a side project.George [00:06:05]: We built it because we needed it as people building in the space and thought, Oh, other people might find it useful too. So we'll buy domain and link it to the Vercel deployment that we had and tweet about it. And, but very quickly it started getting attention. Thank you, Swyx for, I think doing an initial retweet and spotlighting it there. This project that we released. And then very quickly though, it was useful to others, but very quickly it became more useful as the number of models released accelerated. We had Mixtrel 8x7B and it was a key. That's a fun one. Yeah. Like a open source model that really changed the landscape and opened up people's eyes to other serverless inference providers and thinking about speed, thinking about cost. And so that was a key. And so it became more useful quite quickly. Yeah.swyx [00:07:02]: What I love talking to people like you who sit across the ecosystem is, well, I have theories about what people want, but you have data and that's obviously more relevant. But I want to stay on the origin story a little bit more. When you started out, I would say, I think the status quo at the time was every paper would come out and they would report their numbers versus competitor numbers. And that's basically it. And I remember I did the legwork. I think everyone has some knowledge. I think there's some version of Excel sheet or a Google sheet where you just like copy and paste the numbers from every paper and just post it up there. And then sometimes they don't line up because they're independently run. And so your numbers are going to look better than... Your reproductions of other people's numbers are going to look worse because you don't hold their models correctly or whatever the excuse is. I think then Stanford Helm, Percy Liang's project would also have some of these numbers. And I don't know if there's any other source that you can cite. The way that if I were to start artificial analysis at the same time you guys started, I would have used the Luther AI's eval framework harness. Yup.Micah [00:08:06]: Yup. That was some cool stuff. At the end of the day, running these evals, it's like if it's a simple Q&A eval, all you're doing is asking a list of questions and checking if the answers are right, which shouldn't be that crazy. But it turns out there are an enormous number of things that you've got control for. And I mean, back when we started the website. Yeah. Yeah. Like one of the reasons why we realized that we had to run the evals ourselves and couldn't just take rules from the labs was just that they would all prompt the models differently. And when you're competing over a few points, then you can pretty easily get- You can put the answer into the model. Yeah. That in the extreme. And like you get crazy cases like back when I'm Googled a Gemini 1.0 Ultra and needed a number that would say it was better than GPT-4 and like constructed, I think never published like chain of thought examples. 32 of them in every topic in MLU to run it, to get the score, like there are so many things that you- They never shipped Ultra, right? That's the one that never made it up. Not widely. Yeah. Yeah. Yeah. I mean, I'm sure it existed, but yeah. So we were pretty sure that we needed to run them ourselves and just run them in the same way across all the models. Yeah. And we were, we also did certain from the start that you couldn't look at those in isolation. You needed to look at them alongside the cost and performance stuff. Yeah.swyx [00:09:24]: Okay. A couple of technical questions. I mean, so obviously I also thought about this and I didn't do it because of cost. Yep. Did you not worry about costs? Were you funded already? Clearly not, but you know. No. Well, we definitely weren't at the start.Micah [00:09:36]: So like, I mean, we're paying for it personally at the start. There's a lot of money. Well, the numbers weren't nearly as bad a couple of years ago. So we certainly incurred some costs, but we were probably in the order of like hundreds of dollars of spend across all the benchmarking that we were doing. Yeah. So nothing. Yeah. It was like kind of fine. Yeah. Yeah. These days that's gone up an enormous amount for a bunch of reasons that we can talk about. But yeah, it wasn't that bad because you can also remember that like the number of models we were dealing with was hardly any and the complexity of the stuff that we wanted to do to evaluate them was a lot less. Like we were just asking some Q&A type questions and then one specific thing was for a lot of evals initially, we were just like sampling an answer. You know, like, what's the answer for this? Like, we didn't want to go into the answer directly without letting the models think. We weren't even doing chain of thought stuff initially. And that was the most useful way to get some results initially. Yeah.swyx [00:10:33]: And so for people who haven't done this work, literally parsing the responses is a whole thing, right? Like because sometimes the models, the models can answer any way they feel fit and sometimes they actually do have the right answer, but they just returned the wrong format and they will get a zero for that unless you work it into your parser. And that involves more work. And so, I mean, but there's an open question whether you should give it points for not following your instructions on the format.Micah [00:11:00]: It depends what you're looking at, right? Because you can, if you're trying to see whether or not it can solve a particular type of reasoning problem, and you don't want to test it on its ability to do answer formatting at the same time, then you might want to use an LLM as answer extractor approach to make sure that you get the answer out no matter how unanswered. But these days, it's mostly less of a problem. Like, if you instruct a model and give it examples of what the answers should look like, it can get the answers in your format, and then you can do, like, a simple regex.swyx [00:11:28]: Yeah, yeah. And then there's other questions around, I guess, sometimes if you have a multiple choice question, sometimes there's a bias towards the first answer, so you have to randomize the responses. All these nuances, like, once you dig into benchmarks, you're like, I don't know how anyone believes the numbers on all these things. It's so dark magic.Micah [00:11:47]: You've also got, like… You've got, like, the different degrees of variance in different benchmarks, right? Yeah. So, if you run four-question multi-choice on a modern reasoning model at the temperatures suggested by the labs for their own models, the variance that you can see on a four-question multi-choice eval is pretty enormous if you only do a single run of it and it has a small number of questions, especially. So, like, one of the things that we do is run an enormous number of all of our evals when we're developing new ones and doing upgrades to our intelligence index to bring in new things. Yeah. So, that we can dial in the right number of repeats so that we can get to the 95% confidence intervals that we're comfortable with so that when we pull that together, we can be confident in intelligence index to at least as tight as, like, a plus or minus one at a 95% confidence. Yeah.swyx [00:12:32]: And, again, that just adds a straight multiple to the cost. Oh, yeah. Yeah, yeah.George [00:12:37]: So, that's one of many reasons that cost has gone up a lot more than linearly over the last couple of years. We report a cost to run the artificial analysis. We report a cost to run the artificial analysis intelligence index on our website, and currently that's assuming one repeat in terms of how we report it because we want to reflect a bit about the weighting of the index. But our cost is actually a lot higher than what we report there because of the repeats.swyx [00:13:03]: Yeah, yeah, yeah. And probably this is true, but just checking, you don't have any special deals with the labs. They don't discount it. You just pay out of pocket or out of your sort of customer funds. Oh, there is a mix. So, the issue is that sometimes they may give you a special end point, which is… Ah, 100%.Micah [00:13:21]: Yeah, yeah, yeah. Exactly. So, we laser focus, like, on everything we do on having the best independent metrics and making sure that no one can manipulate them in any way. There are quite a lot of processes we've developed over the last couple of years to make that true for, like, the one you bring up, like, right here of the fact that if we're working with a lab, if they're giving us a private endpoint to evaluate a model, that it is totally possible. That what's sitting behind that black box is not the same as they serve on a public endpoint. We're very aware of that. We have what we call a mystery shopper policy. And so, and we're totally transparent with all the labs we work with about this, that we will register accounts not on our own domain and run both intelligence evals and performance benchmarks… Yeah, that's the job. …without them being able to identify it. And no one's ever had a problem with that. Because, like, a thing that turns out to actually be quite a good… …good factor in the industry is that they all want to believe that none of their competitors could manipulate what we're doing either.swyx [00:14:23]: That's true. I never thought about that. I've been in the database data industry prior, and there's a lot of shenanigans around benchmarking, right? So I'm just kind of going through the mental laundry list. Did I miss anything else in this category of shenanigans? Oh, potential shenanigans.Micah [00:14:36]: I mean, okay, the biggest one, like, that I'll bring up, like, is more of a conceptual one, actually, than, like, direct shenanigans. It's that the things that get measured become things that get targeted by labs that they're trying to build, right? Exactly. So that doesn't mean anything that we should really call shenanigans. Like, I'm not talking about training on test set. But if you know that you're going to be great at another particular thing, if you're a researcher, there are a whole bunch of things that you can do to try to get better at that thing that preferably are going to be helpful for a wide range of how actual users want to use the thing that you're building. But will not necessarily work. Will not necessarily do that. So, for instance, the models are exceptional now at answering competition maths problems. There is some relevance of that type of reasoning, that type of work, to, like, how we might use modern coding agents and stuff. But it's clearly not one for one. So the thing that we have to be aware of is that once an eval becomes the thing that everyone's looking at, scores can get better on it without there being a reflection of overall generalized intelligence of these models. Getting better. That has been true for the last couple of years. It'll be true for the next couple of years. There's no silver bullet to defeat that other than building new stuff to stay relevant and measure the capabilities that matter most to real users. Yeah.swyx [00:15:58]: And we'll cover some of the new stuff that you guys are building as well, which is cool. Like, you used to just run other people's evals, but now you're coming up with your own. And I think, obviously, that is a necessary path once you're at the frontier. You've exhausted all the existing evals. I think the next point in history that I have for you is AI Grant that you guys decided to join and move here. What was it like? I think you were in, like, batch two? Batch four. Batch four. Okay.Micah [00:16:26]: I mean, it was great. Nat and Daniel are obviously great. And it's a really cool group of companies that we were in AI Grant alongside. It was really great to get Nat and Daniel on board. Obviously, they've done a whole lot of great work in the space with a lot of leading companies and were extremely aligned. With the mission of what we were trying to do. Like, we're not quite typical of, like, a lot of the other AI startups that they've invested in.swyx [00:16:53]: And they were very much here for the mission of what we want to do. Did they say any advice that really affected you in some way or, like, were one of the events very impactful? That's an interesting question.Micah [00:17:03]: I mean, I remember fondly a bunch of the speakers who came and did fireside chats at AI Grant.swyx [00:17:09]: Which is also, like, a crazy list. Yeah.George [00:17:11]: Oh, totally. Yeah, yeah, yeah. There was something about, you know, speaking to Nat and Daniel about the challenges of working through a startup and just working through the questions that don't have, like, clear answers and how to work through those kind of methodically and just, like, work through the hard decisions. And they've been great mentors to us as we've built artificial analysis. Another benefit for us was that other companies in the batch and other companies in AI Grant are pushing the capabilities. Yeah. And I think that's a big part of what AI can do at this time. And so being in contact with them, making sure that artificial analysis is useful to them has been fantastic for supporting us in working out how should we build out artificial analysis to continue to being useful to those, like, you know, building on AI.swyx [00:17:59]: I think to some extent, I'm mixed opinion on that one because to some extent, your target audience is not people in AI Grants who are obviously at the frontier. Yeah. Do you disagree?Micah [00:18:09]: To some extent. To some extent. But then, so a lot of what the AI Grant companies are doing is taking capabilities coming out of the labs and trying to push the limits of what they can do across the entire stack for building great applications, which actually makes some of them pretty archetypical power users of artificial analysis. Some of the people with the strongest opinions about what we're doing well and what we're not doing well and what they want to see next from us. Yeah. Yeah. Because when you're building any kind of AI application now, chances are you're using a whole bunch of different models. You're maybe switching reasonably frequently for different models and different parts of your application to optimize what you're able to do with them at an accuracy level and to get better speed and cost characteristics. So for many of them, no, they're like not commercial customers of ours, like we don't charge for all our data on the website. Yeah. They are absolutely some of our power users.swyx [00:19:07]: So let's talk about just the evals as well. So you start out from the general like MMU and GPQA stuff. What's next? How do you sort of build up to the overall index? What was in V1 and how did you evolve it? Okay.Micah [00:19:22]: So first, just like background, like we're talking about the artificial analysis intelligence index, which is our synthesis metric that we pulled together currently from 10 different eval data sets to give what? We're pretty much the same as that. Pretty confident is the best single number to look at for how smart the models are. Obviously, it doesn't tell the whole story. That's why we published the whole website of all the charts to dive into every part of it and look at the trade-offs. But best single number. So right now, it's got a bunch of Q&A type data sets that have been very important to the industry, like a couple that you just mentioned. It's also got a couple of agentic data sets. It's got our own long context reasoning data set and some other use case focused stuff. As time goes on. The things that we're most interested in that are going to be important to the capabilities that are becoming more important for AI, what developers are caring about, are going to be first around agentic capabilities. So surprise, surprise. We're all loving our coding agents and how the model is going to perform like that and then do similar things for different types of work are really important to us. The linking to use cases to economically valuable use cases are extremely important to us. And then we've got some of the. Yeah. These things that the models still struggle with, like working really well over long contexts that are not going to go away as specific capabilities and use cases that we need to keep evaluating.swyx [00:20:46]: But I guess one thing I was driving was like the V1 versus the V2 and how bad it was over time.Micah [00:20:53]: Like how we've changed the index to where we are.swyx [00:20:55]: And I think that reflects on the change in the industry. Right. So that's a nice way to tell that story.Micah [00:21:00]: Well, V1 would be completely saturated right now. Almost every model coming out because doing things like writing the Python functions and human evil is now pretty trivial. It's easy to forget, actually, I think how much progress has been made in the last two years. Like we obviously play the game constantly of like the today's version versus last week's version and the week before and all of the small changes in the horse race between the current frontier and who has the best like smaller than 10B model like right now this week. Right. And that's very important to a lot of developers and people and especially in this particular city of San Francisco. But when you zoom out a couple of years ago, literally most of what we were doing to evaluate the models then would all be 100% solved by even pretty small models today. And that's been one of the key things, by the way, that's driven down the cost of intelligence at every tier of intelligence. We can talk about more in a bit. So V1, V2, V3, we made things harder. We covered a wider range of use cases. And we tried to get closer to things developers care about as opposed to like just the Q&A type stuff that MMLU and GPQA represented. Yeah.swyx [00:22:12]: I don't know if you have anything to add there. Or we could just go right into showing people the benchmark and like looking around and asking questions about it. Yeah.Micah [00:22:21]: Let's do it. Okay. This would be a pretty good way to chat about a few of the new things we've launched recently. Yeah.George [00:22:26]: And I think a little bit about the direction that we want to take it. And we want to push benchmarks. Currently, the intelligence index and evals focus a lot on kind of raw intelligence. But we kind of want to diversify how we think about intelligence. And we can talk about it. But kind of new evals that we've kind of built and partnered on focus on topics like hallucination. And we've got a lot of topics that I think are not covered by the current eval set that should be. And so we want to bring that forth. But before we get into that.swyx [00:23:01]: And so for listeners, just as a timestamp, right now, number one is Gemini 3 Pro High. Then followed by Cloud Opus at 70. Just 5.1 high. You don't have 5.2 yet. And Kimi K2 Thinking. Wow. Still hanging in there. So those are the top four. That will date this podcast quickly. Yeah. Yeah. I mean, I love it. I love it. No, no. 100%. Look back this time next year and go, how cute. Yep.George [00:23:25]: Totally. A quick view of that is, okay, there's a lot. I love it. I love this chart. Yeah.Micah [00:23:30]: This is such a favorite, right? Yeah. And almost every talk that George or I give at conferences and stuff, we always put this one up first to just talk about situating where we are in this moment in history. This, I think, is the visual version of what I was saying before about the zooming out and remembering how much progress there's been. If we go back to just over a year ago, before 01, before Cloud Sonnet 3.5, we didn't have reasoning models or coding agents as a thing. And the game was very, very different. If we go back even a little bit before then, we're in the era where, when you look at this chart, open AI was untouchable for well over a year. And, I mean, you would remember that time period well of there being very open questions about whether or not AI was going to be competitive, like full stop, whether or not open AI would just run away with it, whether we would have a few frontier labs and no one else would really be able to do anything other than consume their APIs. I am quite happy overall that the world that we have ended up in is one where... Multi-model. Absolutely. And strictly more competitive every quarter over the last few years. Yeah. This year has been insane. Yeah.George [00:24:42]: You can see it. This chart with everything added is hard to read currently. There's so many dots on it, but I think it reflects a little bit what we felt, like how crazy it's been.swyx [00:24:54]: Why 14 as the default? Is that a manual choice? Because you've got service now in there that are less traditional names. Yeah.George [00:25:01]: It's models that we're kind of highlighting by default in our charts, in our intelligence index. Okay.swyx [00:25:07]: You just have a manually curated list of stuff.George [00:25:10]: Yeah, that's right. But something that I actually don't think every artificial analysis user knows is that you can customize our charts and choose what models are highlighted. Yeah. And so if we take off a few names, it gets a little easier to read.swyx [00:25:25]: Yeah, yeah. A little easier to read. Totally. Yeah. But I love that you can see the all one jump. Look at that. September 2024. And the DeepSeek jump. Yeah.George [00:25:34]: Which got close to OpenAI's leadership. They were so close. I think, yeah, we remember that moment. Around this time last year, actually.Micah [00:25:44]: Yeah, yeah, yeah. I agree. Yeah, well, a couple of weeks. It was Boxing Day in New Zealand when DeepSeek v3 came out. And we'd been tracking DeepSeek and a bunch of the other global players that were less known over the second half of 2024 and had run evals on the earlier ones and stuff. I very distinctly remember Boxing Day in New Zealand, because I was with family for Christmas and stuff, running the evals and getting back result by result on DeepSeek v3. So this was the first of their v3 architecture, the 671b MOE.Micah [00:26:19]: And we were very, very impressed. That was the moment where we were sure that DeepSeek was no longer just one of many players, but had jumped up to be a thing. The world really noticed when they followed that up with the RL working on top of v3 and R1 succeeding a few weeks later. But the groundwork for that absolutely was laid with just extremely strong base model, completely open weights that we had as the best open weights model. So, yeah, that's the thing that you really see in the game. But I think that we got a lot of good feedback on Boxing Day. us on Boxing Day last year.George [00:26:48]: Boxing Day is the day after Christmas for those not familiar.George [00:26:54]: I'm from Singapore.swyx [00:26:55]: A lot of us remember Boxing Day for a different reason, for the tsunami that happened. Oh, of course. Yeah, but that was a long time ago. So yeah. So this is the rough pitch of AAQI. Is it A-A-Q-I or A-A-I-I? I-I. Okay. Good memory, though.Micah [00:27:11]: I don't know. I'm not used to it. Once upon a time, we did call it Quality Index, and we would talk about quality, performance, and price, but we changed it to intelligence.George [00:27:20]: There's been a few naming changes. We added hardware benchmarking to the site, and so benchmarks at a kind of system level. And so then we changed our throughput metric to, we now call it output speed, and thenswyx [00:27:32]: throughput makes sense at a system level, so we took that name. Take me through more charts. What should people know? Obviously, the way you look at the site is probably different than how a beginner might look at it.Micah [00:27:42]: Yeah, that's fair. There's a lot of fun stuff to dive into. Maybe so we can hit past all the, like, we have lots and lots of emails and stuff. The interesting ones to talk about today that would be great to bring up are a few of our recent things, I think, that probably not many people will be familiar with yet. So first one of those is our omniscience index. So this one is a little bit different to most of the intelligence evils that we've run. We built it specifically to look at the embedded knowledge in the models and to test hallucination by looking at when the model doesn't know the answer, so not able to get it correct, what's its probability of saying, I don't know, or giving an incorrect answer. So the metric that we use for omniscience goes from negative 100 to positive 100. Because we're simply taking off a point if you give an incorrect answer to the question. We're pretty convinced that this is an example of where it makes most sense to do that, because it's strictly more helpful to say, I don't know, instead of giving a wrong answer to factual knowledge question. And one of our goals is to shift the incentive that evils create for models and the labs creating them to get higher scores. And almost every evil across all of AI up until this point, it's been graded by simple percentage correct as the main metric, the main thing that gets hyped. And so you should take a shot at everything. There's no incentive to say, I don't know. So we did that for this one here.swyx [00:29:22]: I think there's a general field of calibration as well, like the confidence in your answer versus the rightness of the answer. Yeah, we completely agree. Yeah. Yeah.George [00:29:31]: On that. And one reason that we didn't do that is because. Or put that into this index is that we think that the, the way to do that is not to ask the models how confident they are.swyx [00:29:43]: I don't know. Maybe it might be though. You put it like a JSON field, say, say confidence and maybe it spits out something. Yeah. You know, we have done a few evils podcasts over the, over the years. And when we did one with Clementine of hugging face, who maintains the open source leaderboard, and this was one of her top requests, which is some kind of hallucination slash lack of confidence calibration thing. And so, Hey, this is one of them.Micah [00:30:05]: And I mean, like anything that we do, it's not a perfect metric or the whole story of everything that you think about as hallucination. But yeah, it's pretty useful and has some interesting results. Like one of the things that we saw in the hallucination rate is that anthropics Claude models at the, the, the very left-hand side here with the lowest hallucination rates out of the models that we've evaluated amnesty is on. That is an interesting fact. I think it probably correlates with a lot of the previously, not really measured vibes stuff that people like about some of the Claude models. Is the dataset public or what's is it, is there a held out set? There's a hell of a set for this one. So we, we have published a public test set, but we we've only published 10% of it. The reason is that for this one here specifically, it would be very, very easy to like have data contamination because it is just factual knowledge questions. We would. We'll update it at a time to also prevent that, but with yeah, kept most of it held out so that we can keep it reliable for a long time. It leads us to a bunch of really cool things, including breakdown quite granularly by topic. And so we've got some of that disclosed on the website publicly right now, and there's lots more coming in terms of our ability to break out very specific topics. Yeah.swyx [00:31:23]: I would be interested. Let's, let's dwell a little bit on this hallucination one. I noticed that Haiku hallucinates less than Sonnet hallucinates less than Opus. And yeah. Would that be the other way around in a normal capability environments? I don't know. What's, what do you make of that?George [00:31:37]: One interesting aspect is that we've found that there's not really a, not a strong correlation between intelligence and hallucination, right? That's to say that the smarter the models are in a general sense, isn't correlated with their ability to, when they don't know something, say that they don't know. It's interesting that Gemini three pro preview was a big leap over here. Gemini 2.5. Flash and, and, and 2.5 pro, but, and if I add pro quickly here.swyx [00:32:07]: I bet pro's really good. Uh, actually no, I meant, I meant, uh, the GPT pros.George [00:32:12]: Oh yeah.swyx [00:32:13]: Cause GPT pros are rumored. We don't know for a fact that it's like eight runs and then with the LM judge on top. Yeah.George [00:32:20]: So we saw a big jump in, this is accuracy. So this is just percent that they get, uh, correct and Gemini three pro knew a lot more than the other models. And so big jump in accuracy. But relatively no change between the Google Gemini models, between releases. And the hallucination rate. Exactly. And so it's likely due to just kind of different post-training recipe, between the, the Claude models. Yeah.Micah [00:32:45]: Um, there's, there's driven this. Yeah. You can, uh, you can partially blame us and how we define intelligence having until now not defined hallucination as a negative in the way that we think about intelligence.swyx [00:32:56]: And so that's what we're changing. Uh, I know many smart people who are confidently incorrect.George [00:33:02]: Uh, look, look at that. That, that, that is very humans. Very true. And there's times and a place for that. I think our view is that hallucination rate makes sense in this context where it's around knowledge, but in many cases, people want the models to hallucinate, to have a go. Often that's the case in coding or when you're trying to generate newer ideas. One eval that we added to artificial analysis is, is, is critical point and it's really hard, uh, physics problems. Okay.swyx [00:33:32]: And is it sort of like a human eval type or something different or like a frontier math type?George [00:33:37]: It's not dissimilar to frontier frontier math. So these are kind of research questions that kind of academics in the physics physics world would be able to answer, but models really struggled to answer. So the top score here is not 9%.swyx [00:33:51]: And when the people that, that created this like Minway and, and, and actually off via who was kind of behind sweep and what organization is this? Oh, is this, it's Princeton.George [00:34:01]: Kind of range of academics from, from, uh, different academic institutions, really smart people. They talked about how they turn the models up in terms of the temperature as high temperature as they can, where they're trying to explore kind of new ideas in physics as a, as a thought partner, just because they, they want the models to hallucinate. Um, yeah, sometimes it's something new. Yeah, exactly.swyx [00:34:21]: Um, so not right in every situation, but, um, I think it makes sense, you know, to test hallucination in scenarios where it makes sense. Also, the obvious question is, uh, this is one of. Many that there is there, every lab has a system card that shows some kind of hallucination number, and you've chosen to not, uh, endorse that and you've made your own. And I think that's a, that's a choice. Um, totally in some sense, the rest of artificial analysis is public benchmarks that other people can independently rerun. You provide it as a service here. You have to fight the, well, who are we to, to like do this? And your, your answer is that we have a lot of customers and, you know, but like, I guess, how do you converge the individual?Micah [00:35:08]: I mean, I think, I think for hallucinations specifically, there are a bunch of different things that you might care about reasonably, and that you'd measure quite differently, like we've called this a amnesty and solutionation rate, not trying to declare the, like, it's humanity's last hallucination. You could, uh, you could have some interesting naming conventions and all this stuff. Um, the biggest picture answer to that. It's something that I actually wanted to mention. Just as George was explaining, critical point as well is, so as we go forward, we are building evals internally. We're partnering with academia and partnering with AI companies to build great evals. We have pretty strong views on, in various ways for different parts of the AI stack, where there are things that are not being measured well, or things that developers care about that should be measured more and better. And we intend to be doing that. We're not obsessed necessarily with that. Everything we do, we have to do entirely within our own team. Critical point. As a cool example of where we were a launch partner for it, working with academia, we've got some partnerships coming up with a couple of leading companies. Those ones, obviously we have to be careful with on some of the independent stuff, but with the right disclosure, like we're completely comfortable with that. A lot of the labs have released great data sets in the past that we've used to great success independently. And so it's between all of those techniques, we're going to be releasing more stuff in the future. Cool.swyx [00:36:26]: Let's cover the last couple. And then we'll, I want to talk about your trends analysis stuff, you know? Totally.Micah [00:36:31]: So that actually, I have one like little factoid on omniscience. If you go back up to accuracy on omniscience, an interesting thing about this accuracy metric is that it tracks more closely than anything else that we measure. The total parameter count of models makes a lot of sense intuitively, right? Because this is a knowledge eval. This is the pure knowledge metric. We're not looking at the index and the hallucination rate stuff that we think is much more about how the models are trained. This is just what facts did they recall? And yeah, it tracks parameter count extremely closely. Okay.swyx [00:37:05]: What's the rumored size of GPT-3 Pro? And to be clear, not confirmed for any official source, just rumors. But rumors do fly around. Rumors. I get, I hear all sorts of numbers. I don't know what to trust.Micah [00:37:17]: So if you, if you draw the line on omniscience accuracy versus total parameters, we've got all the open ways models, you can squint and see that likely the leading frontier models right now are quite a lot bigger than the ones that we're seeing right now. And the one trillion parameters that the open weights models cap out at, and the ones that we're looking at here, there's an interesting extra data point that Elon Musk revealed recently about XAI that for three trillion parameters for GROK 3 and 4, 6 trillion for GROK 5, but that's not out yet. Take those together, have a look. You might reasonably form a view that there's a pretty good chance that Gemini 3 Pro is bigger than that, that it could be in the 5 to 10 trillion parameters. To be clear, I have absolutely no idea, but just based on this chart, like that's where you would, you would land if you have a look at it. Yeah.swyx [00:38:07]: And to some extent, I actually kind of discourage people from guessing too much because what does it really matter? Like as long as they can serve it as a sustainable cost, that's about it. Like, yeah, totally.George [00:38:17]: They've also got different incentives in play compared to like open weights models who are thinking to supporting others in self-deployment for the labs who are doing inference at scale. It's I think less about total parameters in many cases. When thinking about inference costs and more around number of active parameters. And so there's a bit of an incentive towards larger sparser models. Agreed.Micah [00:38:38]: Understood. Yeah. Great. I mean, obviously if you're a developer or company using these things, not exactly as you say, it doesn't matter. You should be looking at all the different ways that we measure intelligence. You should be looking at cost to run index number and the different ways of thinking about token efficiency and cost efficiency based on the list prices, because that's all it matters.swyx [00:38:56]: It's not as good for the content creator rumor mill where I can say. Oh, GPT-4 is this small circle. Look at GPT-5 is this big circle. And then there used to be a thing for a while. Yeah.Micah [00:39:07]: But that is like on its own, actually a very interesting one, right? That is it just purely that chances are the last couple of years haven't seen a dramatic scaling up in the total size of these models. And so there's a lot of room to go up properly in total size of the models, especially with the upcoming hardware generations. Yes.swyx [00:39:29]: So, you know. Taking off my shitposting face for a minute. Yes. Yes. At the same time, I do feel like, you know, especially coming back from Europe, people do feel like Ilya is probably right that the paradigm is doesn't have many more orders of magnitude to scale out more. And therefore we need to start exploring at least a different path. GDPVal, I think it's like only like a month or so old. I was also very positive when it first came out. I actually talked to Tejo, who was the lead researcher on that. Oh, cool. And you have your own version.George [00:39:59]: It's a fantastic. It's a fantastic data set. Yeah.swyx [00:40:01]: And maybe it will recap for people who are still out of it. It's like 44 tasks based on some kind of GDP cutoff that's like meant to represent broad white collar work that is not just coding. Yeah.Micah [00:40:12]: Each of the tasks have a whole bunch of detailed instructions, some input files for a lot of them. It's within the 44 is divided into like two hundred and twenty two to five, maybe subtasks that are the level of that we run through the agenda. And yeah, they're really interesting. I will say that it doesn't. It doesn't necessarily capture like all the stuff that people do at work. No avail is perfect is always going to be more things to look at, largely because in order to make the tasks well enough to find that you can run them, they need to only have a handful of input files and very specific instructions for that task. And so I think the easiest way to think about them are that they're like quite hard take home exam tasks that you might do in an interview process.swyx [00:40:56]: Yeah, for listeners, it is not no longer like a long prompt. It is like, well, here's a zip file with like a spreadsheet or a PowerPoint deck or a PDF and go nuts and answer this question.George [00:41:06]: OpenAI released a great data set and they released a good paper which looks at performance across the different web chat bots on the data set. It's a great paper, encourage people to read it. What we've done is taken that data set and turned it into an eval that can be run on any model. So we created a reference agentic harness that can run. Run the models on the data set, and then we developed evaluator approach to compare outputs. That's kind of AI enabled, so it uses Gemini 3 Pro Preview to compare results, which we tested pretty comprehensively to ensure that it's aligned to human preferences. One data point there is that even as an evaluator, Gemini 3 Pro, interestingly, doesn't do actually that well. So that's kind of a good example of what we've done in GDPVal AA.swyx [00:42:01]: Yeah, the thing that you have to watch out for with LLM judge is self-preference that models usually prefer their own output, and in this case, it was not. Totally.Micah [00:42:08]: I think the way that we're thinking about the places where it makes sense to use an LLM as judge approach now, like quite different to some of the early LLM as judge stuff a couple of years ago, because some of that and MTV was a great project that was a good example of some of this a while ago was about judging conversations and like a lot of style type stuff. Here, we've got the task that the grader and grading model is doing is quite different to the task of taking the test. When you're taking the test, you've got all of the agentic tools you're working with, the code interpreter and web search, the file system to go through many, many turns to try to create the documents. Then on the other side, when we're grading it, we're running it through a pipeline to extract visual and text versions of the files and be able to provide that to Gemini, and we're providing the criteria for the task and getting it to pick which one more effectively meets the criteria of the task. Yeah. So we've got the task out of two potential outcomes. It turns out that we proved that it's just very, very good at getting that right, matched with human preference a lot of the time, because I think it's got the raw intelligence, but it's combined with the correct representation of the outputs, the fact that the outputs were created with an agentic task that is quite different to the way the grading model works, and we're comparing it against criteria, not just kind of zero shot trying to ask the model to pick which one is better.swyx [00:43:26]: Got it. Why is this an ELO? And not a percentage, like GDP-VAL?George [00:43:31]: So the outputs look like documents, and there's video outputs or audio outputs from some of the tasks. It has to make a video? Yeah, for some of the tasks. Some of the tasks.swyx [00:43:43]: What task is that?George [00:43:45]: I mean, it's in the data set. Like be a YouTuber? It's a marketing video.Micah [00:43:49]: Oh, wow. What? Like model has to go find clips on the internet and try to put it together. The models are not that good at doing that one, for now, to be clear. It's pretty hard to do that with a code editor. I mean, the computer stuff doesn't work quite well enough and so on and so on, but yeah.George [00:44:02]: And so there's no kind of ground truth, necessarily, to compare against, to work out percentage correct. It's hard to come up with correct or incorrect there. And so it's on a relative basis. And so we use an ELO approach to compare outputs from each of the models between the task.swyx [00:44:23]: You know what you should do? You should pay a contractor, a human, to do the same task. And then give it an ELO and then so you have, you have human there. It's just, I think what's helpful about GDPVal, the OpenAI one, is that 50% is meant to be normal human and maybe Domain Expert is higher than that, but 50% was the bar for like, well, if you've crossed 50, you are superhuman. Yeah.Micah [00:44:47]: So we like, haven't grounded this score in that exactly. I agree that it can be helpful, but we wanted to generalize this to a very large number. It's one of the reasons that presenting it as ELO is quite helpful and allows us to add models and it'll stay relevant for quite a long time. I also think it, it can be tricky looking at these exact tasks compared to the human performance, because the way that you would go about it as a human is quite different to how the models would go about it. Yeah.swyx [00:45:15]: I also liked that you included Lama 4 Maverick in there. Is that like just one last, like...Micah [00:45:20]: Well, no, no, no, no, no, no, it is the, it is the best model released by Meta. And... So it makes it into the homepage default set, still for now.George [00:45:31]: Other inclusion that's quite interesting is we also ran it across the latest versions of the web chatbots. And so we have...swyx [00:45:39]: Oh, that's right.George [00:45:40]: Oh, sorry.swyx [00:45:41]: I, yeah, I completely missed that. Okay.George [00:45:43]: No, not at all. So that, which has a checkered pattern. So that is their harness, not yours, is what you're saying. Exactly. And what's really interesting is that if you compare, for instance, Claude 4.5 Opus using the Claude web chatbot, it performs worse than the model in our agentic harness. And so in every case, the model performs better in our agentic harness than its web chatbot counterpart, the harness that they created.swyx [00:46:13]: Oh, my backwards explanation for that would be that, well, it's meant for consumer use cases and here you're pushing it for something.Micah [00:46:19]: The constraints are different and the amount of freedom that you can give the model is different. Also, you like have a cost goal. We let the models work as long as they want, basically. Yeah. Do you copy paste manually into the chatbot? Yeah. Yeah. That's, that was how we got the chatbot reference. We're not going to be keeping those updated at like quite the same scale as hundreds of models.swyx [00:46:38]: Well, so I don't know, talk to a browser base. They'll, they'll automate it for you. You know, like I have thought about like, well, we should turn these chatbot versions into an API because they are legitimately different agents in themselves. Yes. Right. Yeah.Micah [00:46:53]: And that's grown a huge amount of the last year, right? Like the tools. The tools that are available have actually diverged in my opinion, a fair bit across the major chatbot apps and the amount of data sources that you can connect them to have gone up a lot, meaning that your experience and the way you're using the model is more different than ever.swyx [00:47:10]: What tools and what data connections come to mind when you say what's interesting, what's notable work that people have done?Micah [00:47:15]: Oh, okay. So my favorite example on this is that until very recently, I would argue that it was basically impossible to get an LLM to draft an email for me in any useful way. Because most times that you're sending an email, you're not just writing something for the sake of writing it. Chances are context required is a whole bunch of historical emails. Maybe it's notes that you've made, maybe it's meeting notes, maybe it's, um, pulling something from your, um, any of like wherever you at work store stuff. So for me, like Google drive, one drive, um, in our super base databases, if we need to do some analysis or some data or something, preferably model can be plugged into all of those things and can go do some useful work based on it. The things that like I find most impressive currently that I am somewhat surprised work really well in late 2025, uh, that I can have models use super base MCP to query read only, of course, run a whole bunch of SQL queries to do pretty significant data analysis. And. And make charts and stuff and can read my Gmail and my notion. And okay. You actually use that. That's good. That's, that's, that's good. Is that a cloud thing? To various degrees of order, but chat GPD and Claude right now, I would say that this stuff like barely works in fairness right now. Like.George [00:48:33]: Because people are actually going to try this after they hear it. If you get an email from Micah, odds are it wasn't written by a chatbot.Micah [00:48:38]: So, yeah, I think it is true that I have never actually sent anyone an email drafted by a chatbot. Yet.swyx [00:48:46]: Um, and so you can, you can feel it right. And yeah, this time, this time next year, we'll come back and see where it's going. Totally. Um, super base shout out another famous Kiwi. Uh, I don't know if you've, you've any conversations with him about anything in particular on AI building and AI infra.George [00:49:03]: We have had, uh, Twitter DMS, um, with, with him because we're quite big, uh, super base users and power users. And we probably do some things more manually than we should in. In, in super base support line because you're, you're a little bit being super friendly. One extra, um, point regarding, um, GDP Val AA is that on the basis of the overperformance of the models compared to the chatbots turns out, we realized that, oh, like our reference harness that we built actually white works quite well on like gen generalist agentic tasks. This proves it in a sense. And so the agent harness is very. Minimalist. I think it follows some of the ideas that are in Claude code and we, all that we give it is context management capabilities, a web search, web browsing, uh, tool, uh, code execution, uh, environment. Anything else?Micah [00:50:02]: I mean, we can equip it with more tools, but like by default, yeah, that's it. We, we, we give it for GDP, a tool to, uh, view an image specifically, um, because the models, you know, can just use a terminal to pull stuff in text form into context. But to pull visual stuff into context, we had to give them a custom tool, but yeah, exactly. Um, you, you can explain an expert. No.George [00:50:21]: So it's, it, we turned out that we created a good generalist agentic harness. And so we, um, released that on, on GitHub yesterday. It's called stirrup. So if people want to check it out and, and it's a great, um, you know, base for, you know, generalist, uh, building a generalist agent for more specific tasks.Micah [00:50:39]: I'd say the best way to use it is get clone and then have your favorite coding. Agent make changes to it, to do whatever you want, because it's not that many lines of code and the coding agents can work with it. Super well.swyx [00:50:51]: Well, that's nice for the community to explore and share and hack on it. I think maybe in, in, in other similar environments, the terminal bench guys have done, uh, sort of the Harbor. Uh, and so it's, it's a, it's a bundle of, well, we need our minimal harness, which for them is terminus and we also need the RL environments or Docker deployment thing to, to run independently. So I don't know if you've looked at it. I don't know if you've looked at the harbor at all, is that, is that like a, a standard that people want to adopt?George [00:51:19]: Yeah, we've looked at it from a evals perspective and we love terminal bench and, and host benchmarks of, of, of terminal mention on artificial analysis. Um, we've looked at it from a, from a coding agent perspective, but could see it being a great, um, basis for any kind of agents. I think where we're getting to is that these models have gotten smart enough. They've gotten better, better tools that they can perform better when just given a minimalist. Set of tools and, and let them run, let the model control the, the agentic workflow rather than using another framework that's a bit more built out that tries to dictate the, dictate the flow. Awesome.swyx [00:51:56]: Let's cover the openness index and then let's go into the report stuff. Uh, so that's the, that's the last of the proprietary art numbers, I guess. I don't know how you sort of classify all these. Yeah.Micah [00:52:07]: Or call it, call it, let's call it the last of like the, the three new things that we're talking about from like the last few weeks. Um, cause I mean, there's a, we do a mix of stuff that. Where we're using open source, where we open source and what we do and, um, proprietary stuff that we don't always open source, like long context reasoning data set last year, we did open source. Um, and then all of the work on performance benchmarks across the site, some of them, we looking to open source, but some of them, like we're constantly iterating on and so on and so on and so on. So there's a huge mix, I would say, just of like stuff that is open source and not across the side. So that's a LCR for people. Yeah, yeah, yeah, yeah.swyx [00:52:41]: Uh, but let's, let's, let's talk about open.Micah [00:52:42]: Let's talk about openness index. This. Here is call it like a new way to think about how open models are. We, for a long time, have tracked where the models are open weights and what the licenses on them are. And that's like pretty useful. That tells you what you're allowed to do with the weights of a model, but there is this whole other dimension to how open models are. That is pretty important that we haven't tracked until now. And that's how much is disclosed about how it was made. So transparency about data, pre-training data and post-training data. And whether you're allowed to use that data and transparency about methodology and training code. So basically, those are the components. We bring them together to score an openness index for models so that you can in one place get this full picture of how open models are.swyx [00:53:32]: I feel like I've seen a couple other people try to do this, but they're not maintained. I do think this does matter. I don't know what the numbers mean apart from is there a max number? Is this out of 20?George [00:53:44]: It's out of 18 currently, and so we've got an openness index page, but essentially these are points, you get points for being more open across these different categories and the maximum you can achieve is 18. So AI2 with their extremely open OMO3 32B think model is the leader in a sense.swyx [00:54:04]: It's hooking face.George [00:54:05]: Oh, with their smaller model. It's coming soon. I think we need to run, we need to get the intelligence benchmarks right to get it on the site.swyx [00:54:12]: You can't have it open in the next. We can not include hooking face. We love hooking face. We'll have that, we'll have that up very soon. I mean, you know, the refined web and all that stuff. It's, it's amazing. Or is it called fine web? Fine web. Fine web.Micah [00:54:23]: Yeah, yeah, no, totally. Yep. One of the reasons this is cool, right, is that if you're trying to understand the holistic picture of the models and what you can do with all the stuff the company's contributing, this gives you that picture. And so we are going to keep it up to date alongside all the models that we do intelligence index on, on the site. And it's just an extra view to understand.swyx [00:54:43]: Can you scroll down to this? The, the, the, the trade-offs chart. Yeah, yeah. That one. Yeah. This, this really matters, right? Obviously, because you can b

Want to get away from the crowds? Want a high mountain lake or stream all to yourself?  The best way to do this is to take a backpacking trip, but you need to prepare more than you would for a car trip or a trip to a lodge. What exactly should you take and what should you leave behind?  What kinds of flies and accessories should you bring? How can you save weight and still have enough gear for a fun fishing trip? Derek Bargaehr [37:36], an experienced fly fisher and backpacker, gives us tips on how to make the most of your next backpacking trip. In the Fly Box this week, we have some questions. A couple of which could only be answered by my co-workers at Orvis so we have responses from both Pete Kutzer, our casting guru and Shawn Brillon, our bamboo rod craftsman. How can I easily estimate how much backing is on my unlabeled reels? A listener relates how some podcast advice on emergers helped him and his son have a successful trip I took a lesson on two-handed casting and it was all done on grass.  Was this wrong? What advice do you have on cleaning the ferrules on bamboo fly rods? Are Orvis bamboo fly rods impregnated? On a tarpon trip, the fish were in deep water so I used a sinking poly leader on my floating line.  Should I have used a full-sinking fly line instead? Is the Albright knot a better knot than the nail knot for attaching a leader to a fly line or backing to a fly line? When connecting pieces of tippet I will normally go up two X sizes, like from 2X to 4X.  Is this wrong? Is it OK to clear a casting lane on a trout stream? What can I do to find bigger trout during the dog days of summer?

Why did drinking feel so motivating? The answer lies in anticipation. Coach Matt explains the neuroscience: Dopamine peaks when a reward is anticipated, not when you take the first sip. This episode reveals how alcohol spikes dopamine up to 4X your normal level, leading to a huge deficit and the "hamster wheel" of drinking. Learn how to consciously cultivate the anticipation of healthier rewards (hobbies, connection, goals) to effectively reprogram your brain, boost your emotional regulation, and move past white-knuckling into effortless freedom.   Download my FREE guide: The Alcohol Freedom Formula For Over 30s Entrepreneurs & High Performers: https://social.alcoholfreelifestyle.com/podcast ★ - Learn more about Project 90: www.alcoholfreelifestyle.com/Project90 ★ - (Accountability & Support) Speak verbally to a certified Alcohol-Free Lifestyle coach to see if, or how, we could support you having a better relationship with alcohol: https://www.alcoholfreelifestyle.com/schedule ★ - The wait is over – My new book "CLEAR" is now available. Get your copy here: https://www.alcoholfreelifestyle.com/clear

The Denver multifamily market just absorbed 9,400 units – about 20% higher than the annual average – while supply continues burning off through 2026. This massive supply wave is creating opportunities for Denver real estate investing 2026 strategies that most investors are missing. Chris Lopez and Richard McGirr, co-founders of Property Llama, break down their 2025 shareholder meeting covering Colorado market divergence, investment strategy shifts, and the company’s evolution into diversified debt fund platforms. With hundreds of Colorado investors served, they reveal what’s working (and what’s not) for Denver real estate investing 2026 and beyond. Market Reality: Single family homes are holding steady with slight declines expected, condos are down 10-20% in recent transactions, and multifamily is trading at 2017 prices with 9 cap returns. Denver ranks in the top 5 hardest-hit metros for rent cuts, with Class A properties offering 3-4 months of concessions that push downward pressure on all rental classes. The supply wave is longer than anticipated, but occupancy is finally trending upward through Q2 2025 data. Cash Flow Strategies for Denver Real Estate Investing 2026: The traditional playbook has fundamentally changed. A room-by-room rental strategy can increase cash flow from $12K to $48K annually on the same property, while selling a rental and investing in 21% debt funds can generate $70K annual income from a property that previously cash flowed $15K. Private lending has emerged as the dominant strategy for Colorado investors seeking 3-4X cash flow increases without tenant management. In This Episode We Cover: Why Denver condos are dropping 10-20% while single family homes hold steady (and what 2026 predictions look like) How the multifamily supply wave created 9 cap opportunities that institutions are now buying The room-by-room rental model that quadruples cash flow (and why most investors won’t do it) Why 1031 exchanges that worked in 2018 now only marginally increase cash flow in 2024 Private lending returns of 12-21% compared to traditional rental property cash flow The active to passive shift happening nationwide (and why single family landlording is ending) How Property Llama found product market fit by focusing exclusively on income funds PL Dynamo 2 fund closure at 99 investor limit and what’s launching Q1 2026 The diversified income fund model with distressed notes, Canadian lending, and commercial opportunities This presentation provides clarity for Denver real estate investing 2026 strategy – whether you’re considering portfolio rebalancing, exploring debt fund diversification, or timing multifamily market entry. Chris and Richard share real client examples, personal portfolio moves (Chris is shifting from 85/15 equity/debt to 50/50), and the due diligence process for upcoming fund launches. Timestamps 00:00 – Welcome & Introduction to Property Llama’s 2025 Event 01:55 – Colorado Single Family vs Condo Market Divergence – Denver Real Estate 2026 Price Trends 02:48 – Colorado Springs Real Estate Trends – Following Denver’s 1-3 Year Lag Pattern 03:55 – Denver Multifamily Supply Wave – Front Range Investment Opportunities Among Crisis 07:14 – Rent Concessions Reality – How Class A Properties Manipulate Colorado Rental Market Data 08:18 – 2026 Market Predictions – Audience Poll on Denver Condo Market Decline & Pricing 13:58 – Room by Room Rental Strategy – 4X Denver Cash Flow Properties Using Co-Living 16:21 – 1031 Exchange Alternatives – Reality Check Comparing 2018 to 2024 Deals 18:00 – Private Lending Real Estate Boom – Active to Passive Investing Shift from Equity to Debt Funds 21:25 – Active to Passive Investing Trend – The End of the Single Family Landlord Era 24:35 – Product Market Fit Journey – How Property Llama Found Focus on Debt Fund Investing Colorado 28:34 – Value-Added Capital Model – Real Estate Portfolio Rebalancing for Debt Funds 31:57 – PL Dynamo 2 Fund Closure – Hitting 99 Investor Limit & Denver Real Estate Investing 2026 Plans 38:00 – Diversified Income Fund Launch – Building Beyond Single Anchor Strategy for Colorado Multifamily Investing Connect with our Hosts Chris Lopez: chris@propertyllama.com Richard McGirr: richard@propertyllama.com Links in Podcast Sign up for the 2026 Portfolio Analysis Mastermind

Linktree: ⁠https://linktr.ee/Analytic⁠Join The Normandy For Additional Bonus Audio And Visual Content For All Things Nme+! Join Here: ⁠https://ow.ly/msoH50WCu0K⁠In this segment of Notorious Mass Effect, host Analytic Dreamz delivers a comprehensive performance and design overview of Whiteout Survival, the blockbuster free-to-play mobile survival strategy game from Century Games.Analytic Dreamz explores the glacial apocalypse setting, core 4X gameplay loop of base-building, resource management, furnace upgrades, hero gacha systems, and intense alliance-based PvP events like State vs. State wars. With over 300 million players worldwide and lifetime revenue exceeding $3 billion in under 2.5 years—including a peak monthly high of $136 million in August 2025—this title stands as a global leader in the SLG genre.Analytic Dreamz examines its aggressive data-driven advertising, rapid growth trajectory, high player ratings of 4.5–4.7 stars, and controversies surrounding pay-to-win mechanics, misleading ads, and monetization pressures.Whether you're a casual survivor or competitive chief, join Analytic Dreamz for an in-depth breakdown of why Whiteout Survival remains a profitable yet divisive mobile phenomenon.Support this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/donationsPrivacy & Opt-Out: https://redcircle.com/privacy

This week in games, the money moves got messy: Dream Games recalibrates its ambitions, ByteDance keeps loading the mobile war chest, and Arc Games chooses freedom (and spreadsheets) by going independent. We take a look at Where Winds Meet and what it really says about China's next wave of global ambitions, unpack South Korea's newest Blizzard play, and ask the uncomfortable question: how are indie studios actually surviving right now? Spoiler: it's not pretty. On the investor side, VC interest in games keeps cooling, but PlayerUnknown Productions thinks lightning can strike twice. Finally, we zoom out to the battlefield of 4X strategy — the genre's current health, the growing wave of female players, and whether a fresh face like Tile Survive can rewrite the rules. 00:00 Welcome00:22 Introduction and Shills04:26 Dream Games Budget Correction14:06 Bite Dance's Mobile Investments19:22 Arc Games Independence20:52 Where Winds Meet: A New Chinese Game29:17 The South Korea Deal and Blizzard's Business Model31:21 Success and Challenges of Indie Game Studios33:38 VC Investments in Gaming: A Tough Landscape36:12 PlayerUnknown Productions and Their New Game47:37 The State of 4X Strategy Games51:24 The Rise of Female Gamers in 4X Strategy55:48 Tile Survive: A New Contender in 4X Strategy59:56 Conclusion and Future Topics

En este episodio me senté con Beto Díaz, co-fundador de Kira Financial, para hablar del futuro del dinero y de cómo están construyendo la infraestructura que va a transformar los pagos en Latinoamérica. Conversamos sobre fintech 3.0, remesas digitales, cómo lograron un retorno de 4X, la importancia de adaptarse y el reto de crear equipos que puedan crecer 100 veces.

As noted earlier, we seem to be having a bit of a space-themed streak at DOS Game Club this year. And it continues with September's game: Ascendancy, a 4X turn-based strategy game from 1995, developed by The Logic Factory. Pick one of the wild and very imaginative alien species featured in this game and get […]

Hey there! I'm Kate from Hello Funnels, and in this episode of The Doing It Online podcast, I'm pulling back the curtain on something that's going to change how you think about funnels forever.I just spent the last six months in deep testing mode—running every possible combination, split-testing ad creative, analyzing conversion data across thousands of funnel visitors.And here's what I discovered: buyer behavior has fundamentally changed.Most sales now happen within the first 1-5 days. Not weeks. Days.If someone doesn't buy from you in that initial window, the chances they'll buy later drop dramatically.Which means if you're still running old-school "long nurture" funnels, you're leaving money on the table.In this episode, I'm sharing the real data, including a hot seat call with a client whose funnel is converting at 9% (more than 4X the industry benchmark) with an average purchase time of less than 24 hours.I'll walk you through why this is happening, what it means for your funnel strategy, and exactly what needs to change if you want your funnels to actually work in today's market.This is the shift that separates funnels that flop from funnels that scale.Let's dive in!Ready to build a funnel designed for today's buyers?Learn more about the Million Dollar Micro Funnel system—the exact framework my clients are using to get 9% conversion rates and sales within 24 hours.

How to 4X your Revenue in 4 Years with Alexis Sikorsky   Most business owners dream about growth, but few know how to scale without chaos. In this episode of Profit Answer Man, Rocky Lalvani talks with Alexis Sikorsky, a strategic advisor who helps founders scale fast and exit strong. Alexis doesn't speak from theory—he built and sold his own Switzerland-based software company, New Access, in a $100M+ private equity deal.   He learned firsthand what it takes to go from exhaustion to exponential growth—and how the right strategy, mindset, and systems can help you 4X your revenue in just four years.   Key Lessons from the Conversation:  Buy, Don't Just Build: Most founders try to grow by grinding harder. Alexis shows why M&A can be a faster, smarter route when done strategically—with the right due diligence and cultural alignment. Don't Confuse Urgent with Important: Entrepreneurs often get stuck fighting fires instead of building vision. The CEO's real job is direction and value creation—not firefighting. Know What You Don't Know: Private equity buyers make money because they see what founders can't. Your blind spots could be worth millions, so get help from people who've done it before. Fire Yourself from the Day-to-Day: If your business can't run without you, it's not scalable—or sellable. Alexis teaches founders to identify the tasks only they can do and delegate the rest. Build a War Chest: Business cycles are inevitable. You need 9–12 months of cash reserves to weather storms and seize opportunities instead of scrambling to survive.   Key Takeaway: What you don't know about your business could be costing you millions. Clarity, cash reserves, and courage to think bigger are what separate sustainable success from burnout.   About Alexis Sikorsky: Alexis Sikorsky is a strategic advisor to founders who are serious about scaling fast and exiting strong. With a nine-figure private equity exit under his belt, Alexis isn't speaking from theory—he's lived the entrepreneurial highs and lows across decades of company building, boardroom negotiation, and international leadership. His flagship book Cashing Out lays out the APEX methodology, a four-part framework (Assess, Plan, Execute, Exit) that demystifies the journey to private equity for founders feeling stuck or overwhelmed by growth and decision fatigue.   Alexis founded, scaled, and sold New Access, a Switzerland-based software company, ultimately closing a $100M+ exit and transitioning into a new chapter as a Special Advisor to ambitious CEOs. Today, through Sikorsky Consulting and KnightScale Partners, he works with growth-stage businesses, typically doing $5M+ in annual revenue, who want to engineer their next chapter or PE exit.   Links: Website: https://www.asikorsky.com/ LinkedIn: https://www.linkedin.com/in/alexis-sikorsky-consulting/ Instagram: https://www.instagram.com/alexissikorsky/   Conclusion: Growth without strategy is just motion. As Alexis shared, success comes when you think like an investor—anticipate risk, build systems, and plan your exit long before you need it.   So, how many seven-figure mistakes are you willing to make? Even six-figure owners can make million-dollar errors without financial clarity. That's why Profit Answer Man exists—to help you keep more of what you earn and build a business that truly serves your life.   #ProfitAnswerMan #BusinessGrowth #PrivateEquity #ScaleYourBusiness #EntrepreneurMindset #MergersAndAcquisitions #CashFlow #ProfitFirst #FinancialFreedom #BusinessStrategy #Leadership   Watch the full episode on YouTube: https://www.youtube.com/@profitanswerman Sign up to be notified when the next cohort of the Profit First Experience Course is available! Profit First Toolkit: https://lp.profitcomesfirst.com/landing-page-page  Relay Bank (affiliate link): https://relayfi.com/?referralcode=profitcomesfirst Profit Answer Man Facebook group: https://www.facebook.com/groups/profitanswerman/ My podcast about living a richer more meaningful life: http://richersoul.com/ Music provided by Junan from Junan Podcast Any financial advice is for educational purposes only and you should consult with an expert for your specific needs.

James Lavish, co-managing partner of the Bitcoin Opportunity Fund and author of The Informationist newsletter, joins Episode 305 of the Julia La Roche Show. In this episode, Lavish explains how the government shutdown has locked nearly $1 trillion in the Treasury General Account, draining liquidity from financial systems and raising concerns about a 2019-style repo crisis as bank reserves fall to dangerous levels. He argues Americans have lost 25% of their purchasing power from 2020 to 2025, and while technology should bring deflation, we instead have persistent 3% inflation because it's necessary to manage $38 trillion in debt through currency debasement. Lavish explains the K-shaped economy where the top 1% gained 8X wealth since 1990 versus 4X for the bottom 50%, noting commercial real estate defaults are spiking and subprime auto lenders are collapsing. When the TGA liquidity eventually floods back into markets, he warns not to mistake it for prosperity—it's currency debasement, which is why he recommends positioning in hard assets like Bitcoin, gold, and real estate. The Fed is trapped between dual mandates with no way out, and while AI stocks may have gotten ahead of themselves risking a market shock, his message is clear: own assets because he's not bullish on the economy, he's bearish on the currency.This episode is brought to you by VanEck. Learn more about the VanEck Rare Earth and Strategic Metals ETF: http://vaneck.com/REMXJuliaLinks: Twitter/X: https://x.com/jameslavish The Informationist: https://jameslavish.substack.com/ The Bitcoin Opportunity Fund: https://www.bitcoinopportunity.fund/ Timestamps: 0:00 - Introduction and welcome1:20 - Big picture macro view: Fed battling dual mandates4:30 - Stagflation risk: prices rising as economy rolls over5:10 - Government shutdown removing liquidity from markets7:19 - Treasury General Account (TGA) explained14:21 - 2019 repo crisis explained21:31 - Current concerns about overnight lending market26:18 - Will Fed do QE again?29:03 - Credit markets29:07 - K-shaped economy explained37:08 - Position for currency deterioration38:28 - Why people think 2% inflation is normal40:11 - Lost 25% purchasing power from 2020 to 202540:41 - Technology should bring deflation, not inflation46:30 - Why we need inflation: $38 trillion debt problem50:59 - What's keeping James up at night55:27 - Closing remarks and contact information

Linktree: ⁠https://linktr.ee/Analytic⁠Join The Normandy For Additional Bonus Audio And Visual Content For All Things Nme+! Join Here: ⁠https://ow.ly/msoH50WCu0K⁠Dive into Segment on Notorious Mass Effect with Analytic Dreamz as we dissect Last War: Survival Game—the 2023 mobile 4X strategy sensation blending zombie apocalypse base building, hero RPG collection, and massive MMO alliance wars. Launched globally by First Fun HK LTD and now published by FUNFLY PTE. LTD., this free-to-play title exploded to over $2B lifetime revenue by Feb 2025, with $1.15B in 2024 alone and peaks of $141M monthly. Android downloads surpass 74M, fueled by viral TikTok/Snapchat ads and Antony Starr's 2024 campaign spiking 12.5M installs. Explore HQ progression to Level 30, resource raids (6 high-reward/day), auto-combat squads, UR heroes like Kimberly and Tesla, and seasonal events (Crimson Plague, Polar Storm). Analytic Dreamz analyzes 4.6★ ratings, P2W whale dominance ($10K+ spends), misleading shooter ads (

Trail boss Duncan Ferris is on the podcast! Duncan Ferris has lived two MTB lives: from 4X racer and World Cup track builder to shaping the UK's most ridden bike park trails at BikePark Wales. In this episode, Duncan takes us from the Bristol BSX scene, to building World Cup 4X tracks like Schladming, Vigo and Villingen and finally chasing down the National Champs jersey after 20 years. We dive deep into trail building psychology, why braking bumps form where they shouldn't, why variety in a crew is essential and how riders unlock creativity on a trail. Plus Duncan shares the BikePark Wales origin story, the dream project that became Vanta with Red Bull and Laurie Greenland, wild tales of dynamite on trail builds and digging up grenades. We hope you enjoy this episode with one of the true legends of UK mountain biking.  The Ride Companion Christmas Ride at BikePark Wales! Episode Sponsors:-  - Hiplok → Head on over to hiplok.com/trc to claim your exclusive offer and keep YOUR bikes YOURS. - Ride more for less with BPWs weekday uplift bundles. You can treat yourself to a bundle of 2,4,6 or 10 uplifts. Valid for use 12 months from the date of purchase so you can rest assured you can rip the UK's biggest bike park as much as you want! bikeparkwales.com/uplift-bundles Please note: Bundles cannot be used to make multiple bookings on the same date. - WORX Tools → 15% off the full range with code THERIDECOMPANION: uk.worx.com - Looking for a new car or van and don't want to deal with dodgy dealers? Check out cargurus.co.uk Get early access & ad-free episodes → https://www.patreon.com/theridecompanion You can also support our long term partners: - Marin Bikes: marinbikes.com/gb - Focus Bikes: focus-bikes.com - HUEL: Get 15% OFF with code 'RIDE' at huel.com/ - Hiplok: https://hiplok.com/the-ride-companion  - Play Fantasy Downhill at The Race Companion: theracecompanion.com instagram.com/theracecompanion - Get 10% off Troy Lee Designs with code 'theridecompanion' at saddleback.avln.me/c/OzduCWvjtcOr - Athletic Greens: Get a FREE 1-year supply of Vitamin D AND 5 FREE travel packs at athleticgreens.com/RIDECOMPANION - Compex: Get 20% off with code ‘THERIDECOMPANION' at compex.com/uk/ - Worx: Get 15% off with code ‘THERIDECOMPANION' at worx.com - LAKA: Get 30 days of FREE insurance with code ‘RIDECOMPANION30' at laka.co - HKT Products: Use code ‘PODCAST' for 10% off the entire site. Follow Olly Wilkins Instagram @odub_23 YouTube @owilkins23 The Ride Companion Instagram @theridecompanion YouTube @TheRideCompanion YouTube clips and BTS channel @moreridecompanion Get official Ride Companion merch, find old episodes and more theridecompanion.co.uk

We're all searching for happiness, but what if we've been looking in all the wrong places? In this deeply personal episode, Gabby shares the single most important practice she's learned in over 20 years of sobriety. It's not about a massive breakthrough; it's about the profound power of small, daily miracles. Gabby opens up about her past struggles with addiction and fear to reveal how she built a foundation of unshakeable faith—one prayer, one sign, and one surrendered fear at a time. Discover the simple yet life-changing practice that can carry you through your darkest moments and prove that the Universe always has your back.To mark 20 years of sobriety Gabby is fundraising for an organization close to her heart—McCall Behavioral Health—who work in her local community to help individuals heal from substance use disorders. Gabby will be matching all donations made. If you feel called to support, you can donate here https://mccallbhn.org/Strengthen your faith with Gabby's FREE free meditation to connect with your Spirit Guides: https://bit.ly/40yZD4E Join Gabby for the Trust the Universe 21-day Challenge and learn how to co-create your dream life with the Universe http://bit.ly/4eTlKZxGet Gabby #1 New York Times bestselling book The Universe Has Your Back https://amzn.to/43Byn7oRecommended practice for this episode: gabby coaching members check out the Surrender to the Universe meditation inside your app. Not a member? Try it out for free http://bit.ly/46tnCWT If you feel you need additional support, please consult this list of safety, recovery and mental health resources. Disclaimer: This podcast is intended to educate, inspire, and support you on your personal journey towards inner peace. I am not a psychologist or a medical doctor and do not offer any professional health or medical advice. If you are suffering from any psychological or medical conditions, please seek help from a qualified health professional.Sponsors:Bobbie is offering an additional 10% off your first order with code: GABBY. Visit hibobbie.com for more details.Head over to iRestore.com and use code GABBY for our show's exclusive discounts on the iRestore EliteHead over to eightsleep.com/deargabby and use the code DEARGABBY to get $350 off your very own Pod 5 Ultra and a 30 day trial.Levels is offering my listeners an additional 2 free months of the Levels annual mebership when you use my link, levels.link/GABBYUse my promo code GABBY and get $20 off at holisticgoddess.com/GABBYThe Fits Everybody collection is available in sizes XXS to 4X. You can shop now at SKIMS.com and SKIMS stores. Select 'podcast' in the survey and be sure to select Dear Gabby in the dropdown menu.Produced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, our guest discussed why retail still outpaces e-commerce 4X. Learn how Amazon-proven brands land on Costco, Target, & Walmart shelves with his insider playbook.   What if your online brand could conquer the retail world, just like it did on Amazon? Join us as we chat with Doug Harding, an expert in navigating the complex transition from online selling to retail dominance. Doug shares invaluable insights into why retail remains a powerhouse, accounting for about 80% of US sales, and how online successes can pave the way for tangible, store-shelf victories. From strategic placement in major retailers like Costco, Walmart, and Target to the essential role of branding and social media in capturing buyer attention, this episode is packed with actionable advice for Amazon sellers ready to make the leap.   We unpack the challenges of retail distribution and explore the sophisticated logistics behind ensuring your product stands out in stores. Doug explains how refining packaging and leveraging distribution partners can smooth the path from online clicks to retail checkout aisles. Discover the financial strategies that can support this shift, including creative financing options like private equity and factoring, which have helped brands like Bertello pizza ovens expand from a Shark Tank pitch to a household name in major retail chains.   For those contemplating retail expansion, we highlight the potential for impressive sales growth and the unique considerations of wholesale cost structures. Our discussion covers the nuances of retail pricing and profit margins, emphasizing the importance of maintaining brand integrity while negotiating store placements. As we explore the opportunities and strategies for retail growth, you'll gain fresh perspectives on why retail is far from dead and how it can be a robust avenue for your business's future success. Tune in for a wealth of wisdom on harnessing retail opportunities and nurturing sustainable business growth. In episode 466 of the AM/PM Podcast, Kevin and Doug discuss: 00:00 - The Power of Retail Expansion for Amazon Sellers 04:30 - Changing Perspectives on Online Retail 07:26 - Navigating Retail Distribution Challenges 15:45 - Retail Pull Strategy Implementation Guidance 17:13 - Maximizing Retail Placement and Distribution 20:56 - Understanding Retail Shelf Placement Strategy 24:55 - Packaging Strategies for Retail Success 31:15 - Retail Logistics and Distribution Challenges 37:17 - Subscription Fees and Dominant Retailers 40:50 - Retail Product Launch and Distribution 42:54 - Shark Tank Product Success Story 50:23 - Retail Margin and Cost Structure 57:42 - Margin Analysis in Retail Sales  1:01:16 - Challenges of Online Advertising 1:03:50 - Exploring Retail Opportunities for Growth

One of our most popular episodes is back—our annual Fall Bucket List: 2025 Edition! Jon and Kris share everything they're looking forward to this season, from timeless traditions to new adventures. Fresh ideas, cozy vibes, and the ultimate guide to making this fall unforgettable. Happy Wednesday__________________________Kristin's Amazon Store FrontJon's Amazon Store FrontJoin all the fun on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow us on Socials:Instagram⁠⁠⁠⁠⁠⁠⁠That's The Point ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kristin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jon⁠⁠⁠⁠⁠⁠⁠TiktokThat's The PointYoutubeKristin's Channel__________________________Head to wayfair.com right now to shop all things home.The Fits Everybody collection is available in sizes XXS to 4X at SKIMS.com and SKIMS stores.Go to shopminnow.com and enter code MEETMINNOW15 at checkout to receive 15% off your first order.Visit Gem.com/THATSTHEPOINT or enter THATSTHEPOINT at checkout for 30% offProduced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Today, Jon and Kristin start with their usual life updates before diving into how to make your home feel cozy and curated. After spending the day antiquing and scoring some amazing finds, they share their go-to tips for hunting down special, timeless pieces that elevate your space and make it truly one-of-a-kind__________________________Kristin's Amazon Store FrontJon's Amazon Store FrontJoin all the fun on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow us on Socials:Instagram⁠⁠⁠⁠⁠⁠⁠That's The Point ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kristin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jon⁠⁠⁠⁠⁠⁠⁠TiktokThat's The PointYoutubeKristin's Channel__________________________Visit GoGeviti.com and use code THATSTHEPOINT for 20% off your first 3 months of membership.Visit thisisneeded.com and use code ThatsThePoint for 20% off your first order.The Fits Everybody collection is available in sizes XXS to 4X at SKIMS.com and SKIMS stores.Get $5 off your next order at magicspoon.com/TTPGo to shopminnow.com and enter code MEETMINNOW15 at checkout to receive 15% off your first order.Produced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Feeling anxious about the rapid rise of AI? In this timely episode, Gabby channels her spirit guides to offer a unique spiritual perspective on the AI revolution. She offers practical tools to help you move from fear to faith and stay grounded in the midst of massive change. Discover how to strengthen your spiritual foundation and navigate the future with a sense of peace. This episode is a reminder that your inner connection is the one thing no technology can ever replace.Show notesGet Gabby's free Magnetic Energy Meditation to attract your desires. https://bit.ly/4cpCDKbJoin Gabby for the Trust the Universe 21-day Challenge! She'll guide you through her proven practices to surrender control and step into a life of joy, purpose and unshakable faith that the Universe has your back. https://bit.ly/44LFhrHRecommended practice inside the gabby coaching membership: Connect to the Universe Meditation. Not a member? Try it out for free here. https://bit.ly/4bomPH7If you feel you need additional support, please consult this list of safety, recovery and mental health resources. Disclaimer: This podcast is intended to educate, inspire, and support you on your personal journey towards inner peace. I am not a psychologist or a medical doctor and do not offer any professional health or medical advice. If you are suffering from any psychological or medical conditions, please seek help from a qualified health professional.Sponsors:Go to cokeurl.com/simplyPOP to find out where you can try Simply Pop!Use code DEARGABBY at checkout for 15% off your entire order at vionicshoes.com when you log into your account. 1 time use only.Bobbie is offering an additional 10% off your first order with code: GABBY. Visit hibobbie.com for more details.The Fits Everybody collection is available in sizes XXS to 4X. You can shop now at SKIMS.com and SKIMS stores. Select 'podcast' in the survey and be sure to select Dear Gabby in the dropdown menu.Produced by Dear MediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.