The Intersection Magazine Podcast

Nicholas Kyanda
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We are the podcast version of Intersection Magazine. The magazine that talks about technology related solutions to everyday challenges. The magazine is split into three parts the first is what we call On The Scale where we compare two issue, personalities, products, the second is what we call Under The Scope where we take a deep dive into one subject and the third is called Search For Direction which has evolved into an interview with prominent personalities in the space. We love innovation and provide industry insights to enable decision making.

  1. 2d ago

    Episode 4: The Defensibility Mirage — Designing Moats on a Commoditized Foundation

    In the final episode of The Architectures of Intelligence, Alex and Devon dismantle one of the most persistent illusions in AI: the belief that the smartest model or the most powerful infrastructure creates lasting competitive advantage. As GPU computing becomes a pure commodity and upstream CapEx balloons into a $600 billion annual question, the real battle shifts to the application layer. The winners won’t be the teams that train slightly better models. They’ll be the ones building Service-as-Software — systems that don’t just assist humans, but execute the work and deliver finished business outcomes. Alex and Devon explore: Why seat-based SaaS pricing collapses when software does the laborConcrete examples of Service-as-Software in action (Sierra, Decagon, Harvey, Tennr, Flock Safety, and more)The true sources of defensibility: workflow ownership, proprietary context and outcome-labeled data, deep integrations, and trust architectureHow today’s AI agent frameworks (LangGraph, CrewAI, Microsoft Agent Framework, and others) are themselves becoming commodity infrastructureWhy the durable moat lies in owning the customer’s operational workflow rather than the underlying intelligenceClear-eyed, practical, and free of hype, this episode is essential listening for founders, product leaders, and operators building in the age of agentic AI. Hosts: Alex & DevonSeries: The Architectures of Intelligence

  2. Sep 27

    Episode 3: The Architecture of Speed — The Middleware Revolution

    Build-vs-buy heuristics and the rise of the hybrid application developer. A common pitfall for AI startups is "over-engineering"—spending millions of dollars and precious developer hours building custom infrastructure that does not actually differentiate their product. This episode tackles the build-versus-buy dilemma for AI product builders using a structured decision framework. We explore the critical role of "AI Application Infrastructure" (the middleware layer: agent orchestration, RAG, evaluation, and guardrails)and argue why a modular, hybrid architecture is the most defensible default for growth-stage startups. By buying commodity infrastructure, product teams can dramatically reduce time-to-value and focus their design talent entirely on user workflows.

  3. Sep 19

    Episode 2: The Death of the 'Seat' — Designing Systems of Execution

    SaaS 2.0, "Service-as-Software", and the collapse of traditional user-licensing. The SaaS 1.0 era was built on "systems of record" operated by humans, monetized through seat-based subscriptions. But as AI transitions applications from tools into "systems of prediction and execution," we enter the era of SaaS 2.0 (Service-as-Software)—where companies sell completed labor rather than software licenses. In this episode, we discuss how selling completed workflows dramatically expands the software market from a $1 trillion software market into a $4.6 trillion+ labor market. We also examine the massive design and economic shift this causes: when software does the job of an entire team, the "per-seat" business model collapses, forcing product designers to build transaction-based interfaces aligned with the value of completed work.

  4. Sep 12

    Episode 1: Shovels vs. Outcomes — Designing the New AI Stack

    Moving beyond raw capabilities to build invisible, outcome-oriented software. For decades, technology design was about building "shovels"—giving users tools to perform tasks manually. In this episode, we explore the foundational split in the modern AI stack: Infrastructure enables intelligence, while Applications package intelligence into outcomes. We unpack the structural differences between infrastructure companies (whose inputs are developer problems and outputs are raw capabilities) and application builders (whose inputs are user problems and outputs are completed outcomes). The core design challenge of the AI era is shifting away from exposing raw, intimidating model parameters (like open-ended chat boxes) toward crafting "invisible AI" that lives silently inside highly specialized, end-to-end user workflows.

  5. Sep 6

    August 2026 Magazine Audio Summary-AI and VC

    In this month’s issue, we have several ideas we are working on under the broad theme of investment.We start of with a piece from one of our favorite companies-NFX.The piece talks about the inevitability of superintelligence and what role this might play in the world of venture capital.The writer argues that the most important part is the creation of the ‘data highway’ that will underpin the union between superintelligence and the unavoidable changes that are coming. What we have seen is the focus on coding as the first wave, followed by the entrance into legal. The next challenge relates to biology and the ability of superintelligence to solve complex regulatory issues such as clinical trials. The suggestion of the article is then to define which problems are most worth solving.Our second section covers an area we have dealt with before although at the time we tackled it on its own. This time we look at Venture Capital and compare it to Corporate Venture Capital. We also loop in Angel Investing. We are interested in discovering what options exist for founders who are looking for capital.How do the interests of each group come into play? What kinds of timelines are in play? How much of the company might the founder have to give up or to offer?Next we have a Q and A with Daaki Joshua. Mr. Daaki has seen both sides of the table. A founder who has actually built something but also served on the investor side. Josh Daaki also sheds some light into his industry covering web design and graphic design as well as his work with StartHub Africa.In our Social Watch Section, we share an interesting conversation about how AI has invaded the wall street by open sourcing entire workflows.Our book recommendation is hashtag#Hooked by Nir Eyal. The book is written to help us understand what makes some products more habit forming than others. It is designed to help you build better but with a useful section within about the ethics of habits and addictions in product design too.

  6. Aug 28

    Audio Summary July 2026 Magazine-What comes after SaaS

    In the month of July we complete the section we started last month about the union between SaaS and #ArtificialIntelligence. As we mentioned before, there is nothing in the history of computing that has been as profitable as Software as a Service. We feature quite a lot of material from the NFX team and we think that this attempt to look into a future post #SaaS is worth reading. We are experiencing a major shift in how we work with major changes taking place use of AI in areas such as robotics. You could say we are at the same point in history where traditional implements were replaced by Agricultural Machinery and Industries rose to transform and create manufacturing. We also share a piece from ideas that were developed by the NFX team-Network Effects. We share the 16 #networkeffects (phenomenon where the value of a product, service, or platform increases as more people use it). This piece describes the various manifestations of these effects. How do these network effects work? How do they relate to one another? How do we make best use of these effects? In our Q and A, we have a conversation with Roland F Ganafa an important player in the AI space in Uganda. We are interested in his work and projects that he has undertaken. We are interested in finding out how he has navigated the business space as well as his interactions with government. Given the rapid changes that are taking place in the field of Artificial Intelligence we also have a conversation about how to position the country to be in a more competitive position.

  7. Aug 20

    Management and Organization Behaviour-Case Study

    On April 20, 2010, the Deepwater Horizon drilling rig in the Gulf of Mexico exploded, killing 11 crew members and triggering one of the worst environmental disasters in US history with 230 million gallons of spilled oil. While the physical blowout preventer failed, official investigations concluded this catastrophe was fundamentally a human tragedy of organisation and management. At the Macondo Well—known as the "well from hell"—operations were six weeks behind schedule and \$58 million over budget, costing BP \$1 million a day to run. Under this immense pressure, safety was deprioritized. Standard evaluations were skipped, pressure tests misinterpreted, and critical warning signs ignored. National commissions identified eight key organisational failures that aligned to cause the disaster. These included fragmented leadership structures, poor communication between BP and its subcontractors, inadequate training, and flawed risk assessment metrics that tracked time and cost instead of safety. In this episode, we use the Organizational Behaviour Field Map to dissect this tragedy. We look past the technical glitches to reveal how individual, group, structural, and leadership dynamics can combine with catastrophic consequences. Let’s dive in. 🎧

  8. Jul 22

    Ideas on the Adoption of AI across different Layers.

    Conversation about AI and the hardware layer with two industry experts. Taken from Andreesen Horowitz A16z podcast where Lisa Su CEO of AMD is interviewed by Bob Swan former CEO of Intel. High compute is the bedrock of generative AI. At the heart of this are high-performance Chips. Importance of Moore's Law. The balance between compute and form factor. The realities of R and D cycles in this industry. The need to stay away from traditional moates as means of staying ahead of your competition and instead focusing on integration within the ecosystem.  Conversation with Roland Ganafa AI Studio Uganda is an applied AI innovation studio. The key word is applied. A few examples of what we build: Ease Health - an offline AI clinical decision support tool for health workers, built on a fine-tuned medical language model. We serve as implementing partners of Crane AI Labs on this, running field research in Luweero District with co-investigators from Makerere University Hospital. It works without the internet, because that's the reality in most health facilities.  AskCrane - a civic AI assistant that runs on WhatsApp and USSD, designed to connect citizens to services across seven government agencies through NITA-UG's infrastructure. USSD matters because the majority of Ugandans are on feature phones. AI Studio Academy - our training arm, where we run training sessions with Adaption Labs, university tours, and weekly Build Nights with Africa's Talking.   Enterprises and institutions don't need another generic AI vendor. They need a partner who knows that the last mile in Uganda runs on USSD and mobile money, not broadband and credit cards."Africa cannot afford to be a passive consumer of AI built elsewhere. We need to own the infrastructure, the data, the models, and the institutions that shape how this technology lands here." Look at the current pattern. African data, our languages, our images, our behaviour, gets collected, shipped abroad, used to train models we had no say in, and then those models get sold back to us as products. We provide the raw material and buy back the finished goods. That's an extractive economic structure, and we've seen this movie before with other resources. So when I say we need to own those four things, I mean it practically: Infrastructure - where does the computation physically happen? If every AI query from Uganda is processed on servers abroad, we have no leverage, no data protection guarantees, and we export value with every request. Data - whoever holds the data decides what the models learn. If Ugandan data only exists inside foreign companies' training sets, our realities get represented on someone else's terms, or not at all. Models - we need the capability to fine-tune, evaluate, and eventually build models ourselves, not just consume APIs. Institutions - policy, standards, procurement rules, research bodies. Technology lands the way institutions allow it to land. Models - almost nothing is trained here yet, but Ugandans have proven we can shape global models. Institutions - this is actually a bright spot. The STI Secretariat under the Office of the President and Ministry of ICT and National Guidance are taking innovation seriously. Infrastructure - our biggest constraint. Compute is scarce and expensive, connectivity is uneven, and power reliability still shapes what's possible. Data - Uganda's languages and cultural knowledge are barely represented in any major model. The government is engaging earlier than most expected. The ecosystem has more talent than capital, and more energy than structure. On AI's role: I'd frame it as a leapfrog opportunity with a deadline. Uganda skipped landlines and went straight to mobile; skipped bank branches and went to mobile money. AI offers a similar jump.

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We are the podcast version of Intersection Magazine. The magazine that talks about technology related solutions to everyday challenges. The magazine is split into three parts the first is what we call On The Scale where we compare two issue, personalities, products, the second is what we call Under The Scope where we take a deep dive into one subject and the third is called Search For Direction which has evolved into an interview with prominent personalities in the space. We love innovation and provide industry insights to enable decision making.

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