AI Rebels

When does an AI tool actually make a business better?

Rob Wright, a founder of Waffl, joins Spencer and Jacob to explore the practical business case for AI. He traces Waffl's move from an education product to solving customers' specific problems, and describes how that work grew into custom development, business automations, and WafflOS.

The discussion starts with desired outcomes: what work can you remove, and what new value can you create? Rob shares a door manufacturer's workflow as an example, then walks through client discovery, planning the first sprint, weekly delivery, and the testing that helps a project improve.

The group also examines what custom software leaves you responsible for: maintenance, hosting, security, and the cost of getting things wrong. They consider why established SaaS products may remain useful as agents change how people interact with them, and why AI fluency and technical competence are different skills.

For anyone getting started, the closing advice is practical: try the tools, build something small, learn the foundations, and respect the limits of what you know.

CHAPTERS
00:00:00 Meet Rob Wright and Waffl
00:02:04 Finding a business problem worth solving
00:07:04 Why Waffl?
00:08:30 Mission planning and desired outcomes
00:11:16 Measure outcomes, not features
00:17:18 Fix the process before adding AI
00:24:22 Custom software still needs maintenance
00:27:34 SaaS survives; agents change the interface
00:30:09 From discovery to weekly working software
00:35:56 Three tiers of AI infrastructure
00:40:21 The founder grind
00:44:32 An automation side hustle
00:47:24 AI fluency and technical expertise
00:50:41 Fundamentals, access control and responsibility
00:54:05 Experience and relationships still matter
00:57:09 Start by using the tools
01:02:27 Find Waffl and the team

Follow AI Rebels for conversations with the people building and questioning the future of AI.

CONNECT WITH ROB AND WAFFL
Waffl / WafflOS: https://www.gowaffl.com/
Rob Wright on LinkedIn: https://www.linkedin.com/in/robertcharleswright/

AI can win championships, write convincing answers, and still get the facts wrong. What would make it worthy of our trust?

Spencer and Jacob sit down with Jonathan Schaeffer, the AI pioneer behind the checkers-playing system Chinook, for a candid conversation about privacy, bias, accountability, and the road to artificial general intelligence. Jonathan makes the case for independent AI audits, explains his approach to private local AI through KIND, and challenges the idea that language models alone will lead to AGI.

Jacob brings his experience as a CPA to the question of auditing systems that keep changing. Spencer explores whether language models could become one part of a larger architecture of intelligence. Along the way, Jonathan acknowledges that AI progress has repeatedly outpaced his predictions—even as he questions where the current approach can take us.

CHAPTERS
00:00 Meet Jonathan Schaeffer
01:02 A career built around chess, checkers, and games
04:48 Why trust becomes harder outside games
11:51 Privacy, local AI, and KIND
18:33 Can an AI model ever be unbiased?
25:30 The case for independent AI audits
29:42 How do you audit a system that keeps changing?
36:12 Grounding answers in your own data
38:30 Why language models make mistakes
43:26 Are LLMs a stepping stone toward AGI?
45:58 Will AI progress eventually level off?
49:50 Consciousness and reinforcement learning
54:56 Why education and accountability matter
57:33 Where to follow Jonathan

Guest links:
Jonathan / Synsira: https://synsira.com/
KIND: https://kind.synsira.com/
Jonathan's academic work: https://webdocs.cs.ualberta.ca/~jonathan/

Hosts: Spencer and Jacob
Guest: Jonathan Schaeffer

Sreedhar Peddineni has built companies through the internet, SaaS, cloud, and now AI, which makes his perspective especially valuable because he is not reacting to hype — he is comparing this shift to every major technology wave he has lived through. He argues that AI is moving far faster than most leaders and companies can absorb, and that the widening gap between what is possible and what teams are actually doing is becoming a serious competitive threat. His own experience growing GTM Buddy 3x without adding headcount shows what happens when AI moves out of demos and into the daily operating system of a company. The conversation gets practical fast, with real examples of how AI is changing strategy, financial planning, presentations, sales workflows, competitive research, and founder decision-making. For anyone in revenue, sales, or go-to-market, Sridhar breaks down why traditional sales enablement is failing and how AI can deliver the right insight at the exact moment a rep needs it. The takeaway is urgent: AI fluency is becoming a career requirement, and waiting for someone else to teach you may be the fastest way to fall behind.

Everyone's shipping agents. Almost nobody is asking what happens when a hundred of them go off the rails at 100% utilization while a customer is on the other end. Logan Kelly — CEO of Waxell and a guy who's been punched in the face by real-world sales operations — joins us to argue that a dashboard isn't governance, it's an autopsy, and that most teams are "cavemen playing with fire, hoping there's a bucket of water somewhere." We get into why governance actually *accelerates* agent development (brakes make F1 cars faster, not slower), why the real competitive fight isn't OpenAI vs. Anthropic but governing your entire agent fleet across providers, and why vibe-coded production apps are the next wave of preventable disasters. If you're building, deploying, or betting your company on agents, this is the conversation you can't afford to skip.

Barbara Wittman has spent 25 years cleaning up broken transformation projects, and the root cause is never the technology. In this episode, she breaks down how AI is exposing dysfunction that companies have been hiding for years: misalignment between business and IT, unchecked assumptions, and a total lack of shared understanding. She explains why boards pressure CEOs into AI adoption out of fear, why the people actually executing get sidelined, and why most “AI use cases” fall apart the moment you pick them apart. Barbara makes the case for “human infrastructure” as a real budget line item, not a buzzword, and shares frameworks so simple they fit on the back of a napkin. If you’re a founder, a transformation leader, or anyone trying to figure out why your AI investments aren’t paying off, this conversation will reframe how you think about the problem.

Bhaskar Sunkara built AppDynamics into a $3.7B company. Now he's back with Bicycle AI, an always-on agent that catches revenue leaks before your team even knows they exist. In this episode he breaks down why most AI products will never make it to production, how to actually build enterprise trust, and what "doing the boring stuff" really means. We get into real use cases across travel and payments where Bicycle is already saving companies millions. If you're building in AI or buying AI tools for your business, this one is required listening.

Most RAG implementations are fundamentally broken; and the company that coined "vector search" just told us why. In this episode, Roie from Pinecone breaks down the "Franken answer" problem plaguing AI systems, why naive retrieval falls apart at scale, and what most teams are getting wrong about evaluation. He reveals how the AutoGPT explosion nearly took down Pinecone's infrastructure overnight — and the radical architecture shift it forced them to build. We dig into why LLMs can't be trusted without grounding, what AI memory will actually look like in the age of agents and robots, and where the line between useful hallucination and dangerous fiction really sits. If you're building anything with RAG, vectors, or agents, this conversation will change how you think about it.

https://www.pinecone.io/

95% of enterprises are failing at AI. Not because the technology doesn't work, but because they're measuring the wrong things. In this episode, Nasuni CIO Dalan Winbush breaks down why adoption metrics are meaningless without real business impact, why his decentralized AI team failed and what he replaced it with, and how he's building an army of digital employees that will match his 800-person workforce. From his sales agent NORA to a hiring agent that cut time-to-hire by 80%, Dalan isn't theorizing. He's sharing the playbook he's running right now.

Insurance was built to profit off confusion, and Tuio is proving it doesn't have to be that way. Juan Garcia and his co-founders rebuilt insurance from the ground up as a fully digital, AI-native company in Spain, and they're running 3x the industry's average profit margins while charging customers less. The secret isn't just slapping AI onto old processes. It's rethinking every layer of the business, from data infrastructure to claims handling to marketing, so that AI actually compounds in value. If you want to understand what it really looks like when a company is built around AI instead of bolting it on, this is the episode.

A billion-dollar liquor company paid a rapper with ADHD low six figures to build them a custom video game, and he delivered it in two weeks. That rapper is Victor, and in this episode he shows two games side by side, built on the same timeline: one before AI, one after. He's also a New Yorker cartoonist, NSF grant recipient, patent holder, and TV producer who will tell you straight when AI is not the right tool for the job. He breaks down a business model where every customer becomes a permanent marketing engine, and walks through the writing coach he built specifically for neurodivergent creators like himself. If you've written off AI as a crutch for people who don't want to do real work, this is the conversation that complicates that.