GOTO - The Brightest Minds in Tech

This presentation was recorded at GOTO Copenhagen 2025.
https://gotocph.com

Kent Beck - Software Engineer & Creator of Extreme Programming

RESOURCES
https://bsky.app/profile/kentbeck.bsky.social
https://www.kentbeck.com
https://github.com/KentBeck
https://twitter.com/KentBeck
https://www.linkedin.com/in/kentbeck
https://tidyfirst.substack.com/about

ABSTRACT
So close and yet so far. We see similar behaviors in The Forest & The Desert, but with opposite meanings. Similar words but opposite meanings. Superficially similar goals but working out at completely different scales.

What is The Forest? What is The Desert? Why are they so different, despite the similarities? And how can we get from crumbs to cake and stay there? [...]

Download slides and read the full abstract here:
https://gotocph.com/2025/sessions/3659

RECOMMENDED BOOKS
Kent Beck • Tidy First? • https://amzn.to/4gscjjK
Kent Beck & Cynthia Andres • Extreme Programming Explained • https://amzn.to/3sBASDG
Kent Beck • Test Driven Development • https://amzn.to/3U4AXLs
Kent Beck, Fowler, John, William, Don & Gamma • Refactoring • https://amzn.to/3SFBYbN
Kent Beck • Implementation Patterns • https://amzn.to/3sBlCGL
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This Q&A session was recorded at GOTO Copenhagen 2025.
https://gotocph.com

Joanna Bryson - Professor of Ethics and Technology, Responsible Data Science for Human Development

RESOURCES
https://bsky.app/profile/j2bryson.bsky.social
https://mastodon.social/@j2bryson
https://twitter.com/j2bryson
https://www.linkedin.com/in/bryson
https://joanna-bryson.blogspot.com

Links
https://digital-markets-act.ec.europa.eu/index_en

DESCRIPTION
Joanna Bryson challenges many popular assumptions about AI and AGI. She argues that current generative AI systems are not autonomous intelligences but powerful tools that compress and reproduce human cultural knowledge at scale. While these systems can appear intelligent, they remain dependent on human-created data, corporate incentives, and carefully engineered guardrails. Bryson cautions against anthropomorphizing AI and emphasizes that many concerns about AI stem from misunderstandings about how these systems actually work.

The discussion ultimately centers on responsibility and governance. Bryson argues that transparency, accountability, and democratic oversight matter far more than debates about machine consciousness. Whether discussing AI companions, recommendation algorithms, open-source models, regulation, or AI-assisted software development, her conclusion remains consistent: technology is not destiny. Engineers, companies, policymakers, and citizens all have agency in shaping how AI is deployed, regulated, and integrated into society. [...]

Read the full abstract here:
https://gotocph.com/2025/sessions/3783

RECOMMENDED BOOKS
Chris Miller • Chip War • https://amzn.to/4aBCUKT
Henry Farrell & Abraham Newman • Underground Empire • https://amzn.to/3YdXdGX
Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ
Alex Castrounis • AI for People and Business • https://amzn.to/3NYKKTo
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This interview was recorded for the GOTO Book Club.
http://gotopia.tech/bookclub

Check out more here:
https://gotopia.tech/episodes/451

Ajay Chankramath - Founder & CEO at Platformetrics & Co-Author of "The Platform Engineer’s Handbook"
Kaspar von Grünberg - Founder & CEO at Stealth & Author of "Thinking in Platforms"

RESOURCES
Ajay
https://www.linkedin.com/in/chankramath
https://github.com/achankra
https://x.com/ajchantw
https://chankramath.com
https://platformetrics.com

Kaspar
https://www.linkedin.com/in/kvgruenberg
https://github.com/Kasparvongruenberg
https://kasparvongruenberg.com

Links
https://peh-packt.platformetrics.com

DESCRIPTION
Ajay Chankramath — author of The Platform Engineer’s Handbook — joins Kaspar von Grünberg to unpack why he wrote a 14-chapter, code-first practitioner's guide instead of another theory-heavy platform book. The conversation's core thesis: the reason developers don't adopt platforms isn't a technology gap, it's a product discipline gap — a failure to treat developer experience as a first-class outcome with a real feedback loop. Ajay walks through the book's arc, from Kubernetes and service-mesh foundations through self-service portals to enterprise-grade concerns like policy-as-code and FinOps, and makes a pointed case for building on 100% open-source, vendor-agnostic tooling as a hedge against geopolitical and licensing whiplash.

The most relevant thread for engineers building with AI today: citing a McKinsey finding that only 6% of AI initiatives show real productivity gains, Ajay argues that agentic AI doesn't reduce the need for platform engineering — it raises the stakes. As coding agents become genuine actors in the SDLC rather than tools, IDPs need a new layer for agent context, memory, tool registries, and guardrails, and that layer "must be built, owned, and operated by you," not bought off the shelf.
His conclusion: the differentiator in the AI era isn't which frontier model you use — it's whether your platform foundations are solid enough to make agents safe and productive at all.

RECOMMENDED BOOKS
Ajay Chankramath • The Platform Engineer's Handbook • https://amzn.to/4eCSibM
Ajay Chankramath & Eamonn Ryan • Domain-Driven Platform Engineering • https://amzn.to/3TdiC3J
Chankramath, Cheneweth, Oliver & Alvarez • Effective Platform Engineering • https://amzn.to/3OnxN8i
Kaspar von Grünberg & Luca Galante • Thinking in Plat­forms • https://weaveintelligence.io/thinking-in-platforms-book
Gregor Hohpe • Platform Strategy • https://amzn.to/4cxfYdb
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This interview was recorded for GOTO State of the Art in April 2026.
https://gotopia.tech

Read the full transcription of this interview here:
https://gotopia.tech/articles/450

Abby Bangser - Principal Engineer at Syntasso & Team Topologies Advocate
Charles Humble - Freelance Techie, Podcaster, Editor, Author & Consultant

RESOURCES
Abby
https://bsky.app/profile/abangser.bsky.social
https://twitter.com/a_bangser
https://github.com/abangser
https://www.linkedin.com/in/abbybangser
https://www.syntasso.io/members-area/abby/profile

Charles
https://bsky.app/profile/charleshumble.bsky.social
https://linkedin.com/in/charleshumble
https://mastodon.social/@charleshumble
https://conissaunce.com

Links
https://blog.container-solutions.com/paula-kennedy-on-platform-team-responsibilities-patterns-and-anti-patterns
http://sites.libsyn.com/406853/syntasso-coo-paula-kennedy-on-platform-team-responsibilities-patterns-and-anti-patterns
https://www.oreilly.com/library/view/platform-as-a/0642572243777
https://github.com/Cloud-Native-Platform-Engineering/cnpe-community/issues/79
https://www.kratix.io
https://leaddev.com/ai/nobody-knows-what-programming-will-look-like-in-two-years
https://leaddev.com/ai/shipping-faster-thinking-less-the-ai-code-verification-trap
https://www.cncf.io/blog/2023/11/20/announcing-the-platform-engineering-maturity-model
https://platformengineering.com/features/portals-and-pipelines-arent-enough-avoiding-the-platform-facade

DESCRIPTION
Abby Bangser opens with a clear-eyed status report on platform engineering: the concept of centralizing shared capabilities with self-service delivery is well understood, but the execution keeps going wrong in the same way. Organizations move from DevOps to platform engineering, but their platform teams end up becoming the new bottleneck — a centralized group drowning under the weight of the entire organization's requests, which is exactly what DevOps was supposed to fix. Abby traces this to an architectural problem: too many platforms are still built as centralized Terraform machines rather than as a marketplace of composable offerings. Her "platform as a product" test is blunt and useful: has the team ever said "no" to a feature request, or deprecated something?
If not, they don't have a product — they have a request queue.

The AI dimension is where the conversation gets most urgent. Abby's position is direct: AI agents are the new forcing function for platform maturity. The biggest misconception she wants to dismantle is the persistent equation of platform engineering with infrastructure-as-code: renaming your Terraform team doesn't count. Platform engineering is about building an experience — for human developers and increasingly for AI agents — that is self-service, compliant, and coherent at organizational scale. The Team Topologies model of interaction modes (from high-collaboration to fully automated on-demand APIs) gives a useful health check for where a platform actually sits on that maturity curve.

RECOMMENDED BOOKS
Chankramath, Cheneweth, Oliver & Alvarez • Effective Platform Engineering • https://amzn.to/3OnxN8i
Gregor Hohpe • Platform Strategy • https://amzn.to/4cxfYdb
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This interview was recorded for the GOTO Book Club.
http://gotopia.tech/bookclub

Check out more here:
https://gotopia.tech/episodes/449

Simon Brown - Author & Creator of "The C4 Model"
Susanne Kaiser - Independent Tech Consultant & Author of "Architecture for Flow"

RESOURCES
Simon
https://simonbrown.je
https://bsky.app/profile/simonbrown.je
https://twitter.com/simonbrown
https://linkedin.com/in/simonbrownjersey
https://c4model.com

Susanne
https://bsky.app/profile/suksr.bsky.social
https://mastodon.social/@suksr
https://www.linkedin.com/in/susannekaiser1
https://susannekaiser.net

Links
https://structurizr.com

DESCRIPTION
Simon Brown explains that the C4 Model started not as a grand design theory, but as a practical answer to an embarrassing problem: running a workshop on architecture diagramming, he realized he couldn't understand any of the diagrams being produced. The model formalized his own consulting practice of hierarchical handover documentation — context diagram at the top, containers below, components and code at the bottom — giving it a name and making it teachable. His core advice is to start with just the top two levels: context and container diagrams. These change infrequently, are quick to draw, and deliver immediate value. Levels three and four — components and code — are included for completeness but age rapidly with every commit, and in most cases aren't worth the maintenance overhead.

Two especially practical insights emerge from the conversation. First, how to handle microservices: if your team owns all the services inside a system boundary, each service is a collection of C4 containers; if you depend on another team's service that you can't see inside, model it as an opaque software system. This maps naturally to how domain boundaries and team ownership actually work in practice. Second, the surprisingly important distinction between modeling and diagramming: C4's real value is the shared vocabulary — systems, containers, components, code — not the visual notation itself. Teams using entirely different tooling can communicate clearly because they've agreed on what the words mean. Simon is firm that there will be no C4 v2; the model is intentionally lightweight, and adding more would risk turning it into the next UML — comprehensive, heavy, and largely abandoned.

RECOMMENDED BOOKS
Simon Brown • The C4 Model • https://amzn.to/4xB9V33
Susanne Kaiser • Adaptive Systems With Domain-Driven Design, Wardley Mapping & Team Topologies • https://amzn.to/3XTmNCc
Nick Rozanski &, Eóin Woods • Software Systems Architecture • https://amzn.to/4cOLtTv
van Kelle, Verschatse &Baas-Schwegler • Collaborative Software Design • https://amzn.to/4iv0N8I
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