Dr. Niklas: Venture Grade

"In my first two, three weeks of putting up flyers, we got about three, four thousand users."

I talk to Kaia, founder of the nonprofit ShareVita, about building community tools that get people off their phones and onto their neighbors' doorsteps. Kaia started at BCG, built out the emerging health tech research division at PitchBook, ran a venture studio's ideation team, and then went nonprofit: Share Skippy lets neighbors borrow and walk each other's dogs, and Rideshare Tahoe carpools skiers from the Bay and Reno to the slopes.

We get into growth with zero marketing budget, trust and safety without background checks, why "transactional" isn't a dirty word, the chaotic founder brain, and the co-founder prenup she makes every founding team write before they start.

In this episode:

- From BCG to a Nonprofit: Why shareholder value stopped being enough.

- The Dog That Started It: One Buy Nothing post for a sick friend's dog, and the response that turned into Share Skippy.

- 4,000 Users From Flyers: Growth without a budget: posters, elderly centers, ski resorts and the local paper.

- Carpooling Only Works for Skiing: Why US carpooling fails, and why Tahoe is the exception.

- Trust Without Background Checks: Social verification, public first meetings, license plates and reviews.

- The Mom Test: How PitchBook research habits shape what she asks users.

- Transactional Is Fine: Why mutual benefit without money is the basis of most relationships.

- The Chaotic Founder Brain: Why she needs to feel like she'll fail every day.

- The Co-Founder Prenup: Buyout discounts, firing clauses, and why you write it while you're still happy.

- What Success Looks Like: KPIs, user emails, and building the whole thing with Claude having never opened a terminal.

🎧 Full episode on all podcast platforms

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#DN #CommunityTech #ShareSkippy #ShareVita #RideshareTahoe #Nonprofit #SoloFounder #CoFounders #TheMomTest #SocialImpact #Startups

Timestamps:

0:00 Intro

0:12 From BCG and PitchBook to a nonprofit

1:36 The dog that started Share Skippy

3:30 Financing a nonprofit: grants, donations, interns

5:10 Growth without a marketing budget

7:58 Designing tools that bring people together offline

10:31 Trust and safety without background checks

12:30 The Travelmate app I never launched

14:18 What PitchBook research taught her: The Mom Test

16:30 Transactional isn't a dirty word

18:25 The chaotic founder brain

20:06 Should you be a founder? Jobs with ownership

22:11 Solo founding, interns and YC's 20% solo founders

26:16 The co-founder prenup

28:42 How she measures success

29:54 Advice for purpose-driven community tools

"I bought the $200 plan and put it on OpenClaw, which was not allowed at the time. Anthropic found out. The funny thing is, they actually refunded the money."

I talk to Sean Chiu, a builder who moved from Taiwan to the US two years ago and landed in the middle of San Francisco's hackathon scene. Today he works on BuilderBase, bringing SF-style hackathons to London, Paris, Berlin, New York, and back home to Taipei.

We get into what makes SF different (ten events a day on Luma), the context problem that AI still hasn't solved, why his event-matching side project stayed a side project, the OpenClaw story that got him caught by Anthropic, whether a new generation of classifier models could be the next step after LLMs, and whether founders abroad should still buy the plane ticket.

In this episode:

- Taiwan to SF: Three high schools in four years, then the Bay, where "everything popped off."

- 10 Events a Day: Why talent density in SF is a number you can see on Luma.

- Context Is Still Unsolved: LinkedIn and WhatsApp can't talk to your agents, and we're back to copy-pasting.

- The $200 Plan Story: Running Claude through OpenClaw, getting caught, and getting refunded.

- Beyond LLMs: Classifier-style models, why transformers really won, and why nobody spots the ChatGPT moment in advance.

- Hackathons as Community: Building with someone for a day beats scanning their LinkedIn QR code.

- Is SF Worth the Ticket?: When to fly in, when to stay home, and why friends beat transactions.

- 30 Days Into AI: Skip the textbook, start with what you already care about.

🎧 Full episode on all podcast platforms

💬 Is San Francisco still worth the plane ticket for founders outside the US? Let us know in the comments!

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#DN #SanFrancisco #Hackathons #BuilderBase #SeanChiu #OpenClaw #AIAgents #BuildInPublic #Community #Startups

Timestamps:

0:00 Intro

0:17 From Taiwan to LA to the Bay

1:00 What makes SF different: 10 events a day

2:24 Too many GitHub projects, and learning to focus

4:06 What's still hard: context across your apps

5:07 Voice in, text out: my ideal AI workflow

6:29 Matching people before the event

8:21 Why it stayed a side project

9:17 The $200 Claude plan, OpenClaw, and the refund

11:25 What OpenClaw was actually good for

13:09 Harnesses and the next breakthrough

13:59 Why transformers really won

16:41 Nobody sees the ChatGPT moment coming

18:34 BuilderBase: SF-style hackathons for every city

22:11 Is SF worth the plane ticket?

24:01 Make friends, not transactions

25:09 GetMinds, synthetic personas, and AI in biotech

28:48 30 days to get into AI

"The only people that made a lot of money were the people who wrote the seed check. They made 55 times their money. The last three rounds got 1X."

The first DN Briefing: every four weeks, Martin Tobias (Incisive Ventures) and I take the biggest stories in startups, tech, and venture and ask what they actually mean for founders and early-stage investors.

This time: Character AI goes bankrupt, Oura pulls its IPO on pricing day, SpaceX buys Cursor with public stock, Bending Spoons turns Airtable into instant market cap, and Anthropic offers its biggest-ever discount on prepaid credits ahead of an IPO.

In this episode:

- Character AI: Vibes, consumer, and a thin wrapper. Why Martin called zero from day one.

- Oura's Pulled IPO: When 63% of the offering is insiders selling, public buyers become bag holders.

- SpaceX's Public Currency: All-primary IPO, a tiny float, and Cursor bought with stock.

- The Bending Spoons Arbitrage: Buy at 2.5-3x revenue, get valued at 8x. Will it last?

- Airtable's 55x vs 1x: Late-stage private marks meet public market reality.

- Exits at Every Scale: Roll-ups, PE, IPOs, and dead startups selling their GitHub repos as training data.

- Anthropic's IPO: Prepaid credits, stacked books, and a compute-constrained world.

- Where the Moats Are Now: Vertical B2B software, and the $4.5 trillion of labor that SaaS can finally reach.

🎧 Full episode on all podcast platforms

💬 Would you buy into an IPO where most of the money goes to early investors? Let us know in the comments!

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#DN #DNBriefing #VentureCapital #MartinTobias #IncisiveVentures #IPO #Anthropic #SpaceX #BendingSpoons #Startups

Timestamps:

0:00 Seed got 55x, late rounds got 1x

0:19 Intro

0:40 Character AI files for bankruptcy

4:06 Oura pulls its IPO

9:19 SpaceX, public currency, and the Cursor deal

12:15 The Bending Spoons playbook

12:59 Private marks vs public reality: the Airtable exit

16:24 Invest early, invest long, solve a real problem

18:35 Can the roll-up premium last?

22:22 Exits at every scale, even for failed startups

25:50 Will Anthropic go public this year?

28:48 Compute constraints and the trillion-dollar capex

30:33 Elon, the fastest data center builder

31:25 Dario, job loss, and AI politics

36:28 Which moats remain in the age of AI

37:23 Why vertical B2B software, from Replit to Claude Code

"Your data has always been used for ads, for big data, for selling to other companies. And the user benefits the least. It doesn't have to be like that."

I talk to Ahmet and Ömer Delibaş, two brothers building Klara, a "digital brain" for Android that captures everything you do on your phone, organizes it, and gives it back to you as searchable memory, habits, and a daily digest. One brother is a software engineer who spent years watching user data flow to ad networks and data centers. The other is a dentist who builds on the side. Together they spent four years turning an idea into a product.

We get into the privacy model that most AI apps won't touch (fully local if you want, anonymized and deleted after five days if you turn AI on), how a chaotic accessibility feed becomes a clean memory database, the ADHD users who showed up unplanned, and the Reddit playbook that got them 1,000 pre-registrations before launch and 1,000 downloads in week one.

In this episode:

- Your Digital Brain: What Klara captures, what it throws away, and why your biological brain is the model.

- Users Benefit the Least: The founding thesis, and why the AI wave made it buildable.

- Five-Day Deletion: Local-only mode, anonymized AI calls, and leaving nothing behind when a user quits.

- The ADHD Surprise: Users the founders never planned for, and "fall in love with the users, not the product."

- 2K MAU, 10% MoM, Zero Ads: Why they refuse to buy growth before the product deserves it.

- The Reddit Playbook: 1,000 pre-registrations in one month, 1,000 downloads in week one, and posting about more than Klara.

- Great Product, Terrible Business: My Wunderjob lesson on churn, and why subscriptions beat ads for privacy products.

- Focus on Hard Things: Why everyone builds AI agents, why memory is harder, and why distribution still beats product.

🎧 Full episode on all podcast platforms

💬 Would you let an app record everything on your phone if it deleted it after five days? Let us know in the comments!

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#DN #DigitalBrain #Klara #PrivacyFirst #AIMemory #AndroidApp #BuildInPublic #Reddit #IndieHackers #Startups

Timestamps:

0:00 Intro

0:10 What Clara is: your digital brain

1:06 The engineer's path: years of watching user data go the wrong way

3:30 The dentist who builds: why two brothers started a company

4:41 Android first, other platforms later

5:17 The privacy model: local-only, anonymized, deleted in five days

9:28 How memory works: turning accessibility chaos into a database

13:14 Text only for now, and where AI devices are heading

13:51 The ADHD users nobody planned for

15:55 Building in public: X, Reddit, Telegram, in-app surveys

17:12 Wunderjob: a great product that was a terrible business

18:54 2K monthly active users, 10% growth, no ads yet

20:25 Subscription vs ads, and why big companies didn't start with ads

22:41 The Reddit playbook: 1,000 pre-registrations before launch

26:04 Advice for two-person teams: ship, distribute, focus on hard things

"People will go to a website because they're going to enjoy the experience there, not because the information they need is there."

I talk to Chus, a Barcelona-based art director and UX/UI designer with 15+ years across Madrid ad agencies, an Amsterdam mobile startup, a gaming corporate, a trading platform, and now a Dutch insurer, about his side mission to make the internet weird again. By day he designs AI tools for insurance agents at Nationale-Nederlanden. By night he builds hyper-realistic folder icons and a portfolio that runs like a 90s operating system.

We get into why the Flash era was the best the web ever looked, how conversion obsession and design systems flattened everything, why he uses Lovable constantly but never ships the Lovable look, and his exact workflow from ChatGPT tech-stack advice to plan mode to telling the AI to stop adding emojis.

In this episode:

- Make the Internet Weird Again: Why AI answering your questions means websites now have to be worth visiting for the experience.

- The Flash Era: What 2004 got right, and how accessibility and conversion obsession turned the web flat and minimal.

- Bootstrap, Tailwind, Lovable: The same trap three times. Out-of-the-box defaults are why every AI site looks identical.

- Never Ship the Lovable Look: Using AI for the code you can't write, then replacing every visual default with your own.

- Agency vs Corporate: 15 years from late-night Madrid briefings to laser-focused insurance product teams, and why both can work.

- AI in Insurance: How a regulated Dutch insurer studies China's AI moves and builds agent tools without breaking the rules.

- The 140 Folders: From a post on X to a Lovable-built gallery and mini-shop, step by step.

- Intentional Design: A Comic Sans can work if it's on purpose. Limit the AI early, upload your own fonts and icons.

- Advice for Non-Designers: Learn the design movement behind what you call "nice" before you prompt anything.

🎧 Full episode on all podcast platforms

💬 Was the Flash-era internet actually better, or is it just nostalgia? Let us know in the comments!

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#DN #WebDesign #Lovable #UXDesign #MakeTheInternetWeirdAgain #ArtDirector #AIDesign #VibeCoding #DesignSystems #Startups

Timestamps:

0:00 Intro

0:39 Madrid ad agencies: briefings, late nights, the golden rule

3:00 The jump to a mobile startup in Amsterdam

4:38 Learning how developers think without learning to code

6:00 Gaming, agencies, Barcelona, and the trading platform

7:00 Nacar design studio and the biggest corporate job yet

8:50 Make the internet weird again

9:55 Why the Flash era was the best the web ever looked

11:10 Nobody visits websites for information anymore

12:04 Design systems and why every website looks the same

13:31 Airbnb, Shopify, and the designers fighting back

14:45 Bootstrap, Lovable, and the out-of-the-box trap

16:12 Niklas's brokerage app: when a redesign kills the UX

18:58 Is this the NFT bubble again?

20:14 Local AI, open weights, and where the craft goes

22:43 Firefly, prompting, and why a designer now feels like a coder

26:35 AI inside a regulated insurer

29:21 The 140 folders: from an X post to a Lovable shop

32:50 Plan mode first, small prompts, stop the emojis

35:38 Advice for non-designers who want to build something nice

"I haven't met anybody who's working less after the release of AI. If anything, deadlines got shorter."

I talk to Alexander Solod, an AI developer who has been building with generative models since a GPT-2-written paper blew his mind in college, about where AI actually stands right now: open weight models running at home, on-device chips, the interface problem nobody has solved, and what AI is really doing to jobs.

We get into why Alex runs Gemma 4 on a Mac Studio for his personal agents, the "Airbnb for compute" idea of renting out your idle hardware for tokens, why radiologists are still booming ten years after AI was supposed to replace them, and the only career advice that survives automation: get into a position where you make decisions.

In this episode:

- Open Weights at Home: Why the future is small on-device models for daily work, with frontier models reserved for the hard stuff.

- The Benchmark Problem: Why "better than the average human at using a computer" tells you almost nothing.

- Filter and Enzyme: LLMs won't discover relativity, but they filter the world's information and collapse weeks of work into hours.

- Airbnb for Compute: Renting out your idle Mac Studio for tokens, and why solar at home makes it obvious.

- Chat Was V1: Voice in, visuals out, generative UI, and why the AI interface is still unsolved.

- Ambient AI Scribes: The easiest healthcare win so far, and why radiology still hasn't been automated.

- Nobody Works Less: The paradox of AI productivity, and why the layoffs blamed on AI are really pandemic overhiring.

- The Job That's Safe: A machine can never be held responsible, so get into a position with stakes and decisions.

🎧 Full episode on all podcast platforms

💬 Are you working less since AI, or more? Let us know in the comments!

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#DN #OpenWeightModels #OnDeviceAI #AlexanderSolod #AIJobs #HealthcareAI #AIAgents #DecentralizedCompute #LocalLLM #Startups

Timestamps:

0:00 Intro

0:25 The GPT-2 paper that pulled Alex into AI

1:12 OpenAI Gym and the hide-and-seek video

3:28 Open weight models: running Gemma 4 on a Mac Studio

4:04 Nobody knows the limit of intelligence

4:53 Is "better than the average human" a real benchmark?

6:49 Could an LLM have discovered relativity?

8:39 LLMs as filter and enzyme

9:54 On-device AI and Apple's neural engine bet

11:36 Airbnb for compute: renting out your idle hardware

13:35 Chat was V1: the unsolved AI interface

15:43 How far away are brain interfaces?

16:52 Predictive healthcare and the radiology myth

19:28 Giving clinicians their time back

20:48 Regulation, trust, and security in healthcare AI

22:24 Ambient AI scribes: the easiest win so far

23:47 Whoop data and democratizing Bryan Johnson

26:05 Nobody is working less since AI

27:23 Are the layoffs really AI, or pandemic overhiring?

30:31 How to prepare: actually use the agents

32:08 Best advice Alex ever received: keep momentum

33:01 Hot take: the AI interface is still wide open

"People think TPM is gonna be the job that gets replaced first. In reality, it's gonna be the hardest thing to replace."

I talk to Jake Kim, founder of Serro AI, about agentic technical program management and why the horizontal coordination role everyone expects AI to kill first is actually the one it can't. Jake was an engineering manager at Apple, where his team's software sat on the critical path to every iPhone and iPad launch, and where he watched world-class TPMs hold the whole machine together.

We get into the temporal knowledge graphs that give AI agents organizational memory, the client with 2 TPMs, 300 people, and 75 recurring reports, the R&D tax documentation wedge that pays for the product on day one, and the founder lesson Jake learned right before running out of money.

In this episode:

- The Job AI Can't Replace: Why vertical agents (coding, design, product) make the horizontal coordinator more essential, not less.

- Agentic TPM: Technical program management, not manager. Extending human TPMs' reach to the agents downstream.

- Temporal Knowledge Graphs: Why programs need a memory of decisions, tradeoffs, and dependencies over time, not just a snapshot.

- 2 TPMs, 300 People, 75 Reports: The middle-management squeeze nobody's tooling has touched.

- The R&D Tax Wedge: The audit-ready documentation report worth millions in credits that gets Serro in the enterprise door.

- Per-Program Pricing: Why Serro charges $100 per tracked program with unlimited seats, and what that alignment does in enterprise sales.

- Don't Be Shy About Your Vision: The lesson Jake learned right before running out of money, and why humble framing nearly killed the company.

- Advice for TPMs: Why the role should feel confident, not threatened, and how to extend your influence to the agent layer.

🎧 Full episode on all podcast platforms

💬 Is middle management really the hardest thing for AI to replace, or the first to go? Let us know in the comments!

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#DN #AgenticTPM #KnowledgeGraphs #JakeKim #SerroAI #AIAgents #EngineeringManagement #EnterpriseAI #Startups #TPM

Timestamps:

0:00 Intro

0:15 What agentic TPM actually is

1:37 From Apple engineering manager to founder

3:12 Why AI employees haven't worked so far

4:46 Why agentic TPM is technically hard (programs vs projects)

8:22 Integrating with the company's tools

9:21 2 TPMs, 300 people, 75 recurring reports

13:15 Temporal knowledge graphs explained

14:28 How the product works: Gantt charts, drift alerts, MCP

16:00 Two years of pivots and what they taught him

17:50 The vision lesson: don't be shy right before running out of money

19:44 The R&D tax documentation wedge (and PwC)

22:38 Per-program pricing and the death-of-SaaS question

25:58 Why not just be a knowledge graph company?

27:54 The AI-native org two to three years

"A good assistant takes about three days for one blueprint. I'm using AI, and it's 30 seconds."

I talk to Vinh, solo founder of HD Parking Lot, about the construction marketplace he's building after a $25,000 junk-removal quote pushed him into the most overlooked trillion-dollar industry in tech. Vinh spent 20+ years as a software engineer before a rough patch (losing his mom, getting laid off) turned into a junk-removal side hustle, which turned into discovering how broken the contractor-homeowner relationship actually is.

We get into why Angi and Thumbtack sell your information to 20 contractors and then abandon you, the AI blueprint reader that compresses three days of estimation work into 30 seconds, and why the boring industries nobody wants to touch are where the real opportunities hide.

In this episode:

- The $25K Junk Quote: The cleanup bill that turned a laid-off engineer into a junk removal operator, and then a construction founder.

- The Lead-Selling Trap: Why Angi and Thumbtack make homeowners repeat themselves to 20 contractors, and what a marketplace that doesn't sell leads looks like.

- The AI Blueprint Reader: Materials, labor hours, schedule, and regulations from an uploaded blueprint in 30 seconds instead of 3 days.

- Boring Industry Gold: Why construction is a trillion-dollar industry tech people won't touch, and why that's exactly the opportunity.

- The Full Lifecycle Bet: Directory, project bidding, materials marketplace, calculators, and community in one platform that stays past the handshake.

- The Visualizer: The free tool that repaints your room from a photo in 30 seconds, built to settle an argument with his wife.

- Don't Do Paper Research: Why one day living inside the process you want to automate beats a hundred customer interviews.

- Kitesurfing as Focus Training: Why you can't ride the wind while worrying about your startup, and what that teaches about adaptability.

🎧 Full episode on all podcast platforms

💬 Would you rather build in the crowded AI market, or own a boring trillion-dollar industry? Let us know in the comments!

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#DN #ConstructionTech #AIBlueprints #Vinh #HDParkingLot #BoringIndustries #SoloFounder #BuildingInPublic #Startups #PropTech

Timestamps (RAW — re-time after edit):

0:00 Intro

0:35 From junk removal side hustle to construction tech

4:14 The lead-selling trap: how Angi and Thumbtack actually work

8:33 The $25K junk quote that started everything

11:32 Why boring industries beat flashy AI startups

13:03 The AI blueprint reader: 3 days to 30 seconds

14:40 The Visualizer: repaint your room from a photo

18:37 The full feature walkthrough: directory, bidding, materials, calculators

26:26 Why he builds in public (the Marc Lou inspiration)

30:16 Marketing as a solo founder with an SEO background

33:32 Building a global company with an equity-based team

44:14 The one advice: hands-on research, not paper research

46:51 Kitesurfing, focus, and adaptability

"Kids don't just consume technology, they create with it."

I talk to Feifei Qiu, founder of Kindlewood Studios, about the dragon story her four-year-old told at the dinner table that turned into a learning platform, and why storytelling is the wedge into teaching kids reading, writing, and creativity. Feifei spent years as a product manager at Microsoft before leaving to build Kindlewood for her own kids, and 200+ young storytellers later, the thesis is holding.

We get into the viral loop of imagination (one kid's story inspires five more), how she designs AI that keeps kids in the driver's seat instead of writing for them, and the product lessons she carried over from enterprise PM work.

In this episode:

- The Viral Loop of Imagination: How one dragon story at a play date turned every kid in the room into a storyteller.

- Kids as Creators: Why children are born to create, and what happens when you give them the tool to express it.

- AI in the Passenger Seat: How Kindlewood's writing coach asks open-ended questions instead of writing the story, keeping kids as the authors.

- The Persona Split: Why the reading app and the creator experience had to be completely separate products, a lesson from watching her three-year-old.

- From Microsoft PM to Founder: What enterprise product thinking transfers to a kids' startup, and what you have to unlearn.

- Demanding Customers Are the Best Customers: The PM lesson that the hardest customers to win become the most loyal.

- Steady Over Viral: Why she's deliberately growing slow, with 9 school partnerships and workshops where kids don't want to leave.

- "Is It Me the Reason You Got a Job?": The five-year-old's question that sums up the whole company.

🎧 Full episode on all podcast platforms

💬 Should kids use AI to create, or does it risk replacing their creativity? Let us know in the comments!

🔔 Please like and subscribe! Every subscriber helps our channel grow.

#DN #EdTech #KidsLearning #FeifeiQiu #Kindlewood #Storytelling #AIinEducation #Startups #Parenting #Creativity

Timestamps:

0:00 Intro

0:51 Why build for the youngest learners

2:30 From China to Seattle to Microsoft

3:55 Big tech vs founding: what actually changed

7:12 Product thinking she carried from Microsoft

11:29 What you have to unlearn after big tech

14:12 Why the name Kindlewood

16:16 The dragon story that started everything

21:04 Traction: 200+ young creators, 9 school partnerships

24:37 Ten years of overnight success

26:04 The viral loop of imagination

28:07 AI in education: kids stay in the driver's seat

31:36 How vibe coding changed her builder workflow

34:25 "Is it me the reason you got a job?"

"It's cheaper to upgrade to an expensive model."

I talk to Joakim William Hauge, co-founder of Monetize, about "runtime economics" (the new category he's carving out for AI governance) and why CFOs are locked out of the AI dashboards their engineers use every day. Joakim spent years in quantitative finance and risk management before turning that framework onto the non-deterministic runtime behavior of autonomous AI systems.

He's building Unitflow, an enterprise governance layer launching soon, and already shipped Circuit Breaker on GitHub as a validation vehicle. The wedge: use runtime telemetry to translate token spend into income-statement language the CFO can actually act on.

In this episode:

- Runtime Economics: The new category Joakim is defining, using runtime telemetry to understand AI value creation and risks, not just cost optimization.

- The CFO Gap: Why the executives signing off on AI budgets are locked out of the dashboards their engineers actually use.

- Why Expensive Models Are Cheaper: The counter-intuitive result of testing, that upgrading to a more expensive model can reduce total spend by killing recursive retry loops.

- Cost, Value, Risk: The three-pillar framework for governing AI spend, and why CFOs need income-statement language, not token dashboards.

- Circuit Breaker: The open-source runtime governance tool Joakim shipped to validate the thesis before Unitflow's full launch.

- The Uber Cost Shock: How cost-cutting headlines from big companies changed the tone of AI adoption in a matter of months.

- The Usage-Based Pricing Trap: Why real-time pricing for non-deterministic AI runtime is one of the hardest unsolved problems in SaaS.

- Understand the Customer's Story: Joakim's biggest founder lesson, that the shortcut isn't shipping fast, it's understanding what the buyer actually needs.

🎧 Full episode on all podcast platforms

💬 Should CFOs get their own AI dashboards, or is token spend just an engineering-team problem? Let us know in the comments!

🔔 Please like and subscribe! Every subscriber helps our channel grow.

#DN #RuntimeEconomics #AIGovernance #JoakimHauge #Unitflow #AICostManagement #Startups #AIAgents #EnterpriseAI #FinTech

Timestamps:

0:00 Intro

0:40 From quant finance and risk management to AI governance

2:19 Why he's building in public

5:32 The engineer vs CFO stakeholder gap in AI adoption

7:56 Runtime economics defined in one sentence

8:20 The Uber cost shock: why April changed everything

14:06 Volatility in AI cost and how to price it

19:34 What Unitflow actually does: the three pillars

23:54 Why CFOs are locked out of the AI dashboards

25:11 Frontier vs cheaper models: when to use each

27:09 Why expensive models can actually be cheaper

27:44 The GitHub Circuit Breaker tool

30:43 Founder lesson: understand the customer's story

34:04 The unsolved problem of real-time usage-based pricing