The Reasoning Show

SUMMARY: Exploring how to fully embrace AI-driven, agent-based software development, resulting in dramatically increased productivity and faster feature delivery. It highlights a broader shift in engineering—from writing code to orchestrating AI agents.
GUEST: Sam Ramji, CEO/Co-founder at Sailplane
SHOW: 1023
SHOW TRANSCRIPT: The Reasoning Show #1023 Transcript
SHOW VIDEO: https://youtu.be/q50s0oL37pQ
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
SHOW NOTES:
Halt and Retool (presentation) 
OpenAI Harness Engineering
Anthropic Harness Engineering

1. The “Halt and Retool” Moment
A single-day build and deployment of a production feature triggered a company-wide realization
Paused all development to reassess how AI fundamentally changes engineering workflows
Creating “shock moments” (like stopping work) is key to driving mindset shifts
2. From Coding to Agent Orchestration
Developers are shifting from writing code → managing AI agents
Work resembles “multi-boxing” or conducting an orchestra of parallel agents
Success depends on coordinating tasks, not executing them directly
3. The Rise of Harness Engineering
Defined as everything between raw AI prompts and production-ready output
Focus: eliminating friction across the software development lifecycle 
Key practices:Logging agent errors and friction points
Continuously refining workflows and tooling
Letting AI reflect on and improve its own mistakes

4. Spec-Driven Development Becomes Critical
Poor specifications lead to exponential inefficiencies
Teams now spend significantly more time on design and specs than coding
5. Measuring the Impact
~3x increase in code velocity
Near-zero “bit rot” 
Faster feature delivery—sometimes within 24 hours

6. Token Maxing & Developer Fitness
Higher token usage often signals better workflows and deeper integration with AI
Performance becomes about system design, not efficiency constraints

7. New Tools & Interfaces
Increased use of voice interfaces over typing
Terminal-first workflows replacing traditional IDE-centric approaches
AI-accessible knowledge bases becoming standard

8. The Future of Software Engineering
Within ~6 months: developers may stop writing code
Within ~12 months: developers may stop reading code
Focus shifts to:Intent, design, and orchestration. Domain expertise and problem modeling

FEEDBACK?
Email: show @ reasoning dot show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
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SUMMARY: How software development is rapidly evolving in the age of AI and automation. Matt Moore shares how his team is rethinking secure software supply chains, scaling infrastructure, and safely integrating AI agents into development workflows.
GUEST: Matt Moore, CTO at Chainguard 
SHOW: 1022
SHOW TRANSCRIPT: The Reasoning Show #1022 Transcript
SHOW VIDEO: https://youtu.be/9Q0kWkTYRs8
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo
SHOW NOTES:
Chainguard Factory 2.0
DriftlessAF

Scaling Challenges & “Factory” Evolution
Early automation relied on tools like GitHub Actions
At scale, simple systems broke due to:Massive event volumes
API rate limits (e.g., GitHub quotas)
Exponential fan-out effects

Key innovation: custom work queue + reconciliation model~90% event deduplication
Controlled throughput and backpressure
Improved reliability and system stability

Introduced Driftless 
Built on reconciliation principles (inspired by Kubernetes):Compare desired vs. actual state
Continuously reconcile differences

Benefits:Resilience to missed events
Automatic retries and recovery
Scales better than purely event-driven systems

AI Agents in Software Development
AI is dramatically accelerating development workflows
Chainguard uses agents to:Remediate vulnerabilities (CVEs)
Update dependencies
Fix failing tests and adapt to upstream changes

Key Design Philosophy
Least privilege → “least tool call”Avoid giving agents full system access
Provide narrowly scoped tools for specific tasks

Delegate execution to sandboxed systems (e.g., CI pipelines)
Focus on safe, controlled automation
Industry Shift: Velocity vs. Security
Explosion of AI-driven tools (e.g., autonomous PR generation)
Massive increase in development velocity
New risks:Poorly secured agent frameworks
Malicious or unsafe automation patterns

Key Takeaways
Scale changes everythingSimple systems break under massive workloads
Purpose-built infrastructure becomes necessary

Reconciliation > pure event-driven systems at scaleMore resilient, predictable, and controllable

AI is a force multiplier—but requires guardrailsUnrestricted agents introduce serious risk
Constrained, purpose-built agents are safer and more effective

Continuous learning is mandatoryAI tooling is evolving too fast for static skillsets
Teams must actively experiment and adapt

FEEDBACK?
Email: show @ reasoning dot show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
Instagram: @reasoningshow
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SUMMARY: How real-time power flow optimization at the edge is helping data centers and the electrical grid handle surging AI energy demands more efficiently. By unlocking hidden capacity and dynamically managing power systems, we explain how existing infrastructure can support significantly more compute without massive new buildouts.
GUEST: Marissa Hummon, CTO Utilidata
SHOW: 1021
SHOW TRANSCRIPT: The Reasoning Show #1021 Transcript
SHOW VIDEO: https://youtu.be/ItcpU8UjOFE
SHOW SPONSORS:
Nasuni - Activate your data for AI and request a demo
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
SHOW NOTES:
Utilidata (homepage)
AI Data Center to Receive 50% Capacity Boost with AI Power Orchestration
KEY TOPICS:
Differences between grid power dynamics vs. AI workloads
Edge AI for real-time power flow optimization
Unlocking stranded capacity in existing infrastructure
“4-to-make-3” vs. “4-to-make-4” data center design
AI training vs. inference power consumption patterns
Role of NVIDIA-powered edge compute modules
Grid modernization and coordination with utilities
Security and resilience in critical infrastructure
KEY MOMENTS:
From centralized AI models to edge-based decision-making
Defining efficiency: utilization vs. thermal performance
Why AI workloads aren’t as constant as they seem
NVIDIA partnership and edge compute in power systems
Using redundancy to increase usable capacity
Increasing density of AI compute and hidden capacity
Data center vs. utility responsibilities
Addressing data center bottlenecks and scaling challenges
Customer landscape: hyperscalers to enterprise
Security, resilience, and critical infrastructure
KEY INSIGHTS:
AI workloads are dynamic, not constant: Training and inference create fluctuating power demands that can be optimized.
Edge intelligence is critical: Real-time sensing and decision-making at the edge unlock efficiency gains not possible with centralized models.
Hidden capacity exists: Many data centers have up to 2x unused power capacity due to lack of visibility and control.
Software-defined power is the future: Faster control loops allow systems to safely exceed traditional design limits.
Efficiency = utilization: The biggest gains come from better use of existing infrastructure, not just improving hardware efficiency.
TAKEAWAYS:
AI infrastructure growth is as much an energy challenge as a compute challenge
Real-time, edge-based control systems are key to scaling sustainably
Existing grid and data center investments can go further with smarter orchestration
The future of AI scaling depends on aligning compute innovation with energy intelligence
FEEDBACK?
Email: show @ reasoning dot show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
Instagram: @reasoningshow
TikTok: @reasoningshow

SUMMARY: Shadow AI is growing much faster than known AI adoption across businesses. How can IT teams get Shadow AI under control?
GUEST: Uri Haramati, CEO at Torii
SHOW: 1020
SHOW TRANSCRIPT: The Reasoning Show #1020 Transcript
SHOW VIDEO: https://youtu.be/AUrh_xICPzM
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo
SHOW NOTES:
Torii (homepage)

Topic 1 - Welcome to the show. Tell us about your background and your focus at Torii. 
Topic 2 - Is Shadow AI really a security problem—or is it a product-market fit problem inside the enterprise?
Topic 3 - Why does Shadow AI spread faster—and become more dangerous—than traditional Shadow IT?
Topic 4 - What’s the first signal a company should look for to know Shadow AI is already happening?
Topic 5 - How do you balance visibility vs. control without killing the productivity gains that drove Shadow AI in the first place?
Topic 6 - How should organizations rethink ‘data loss prevention’ in a world where the leak is a prompt, not a file?
Topic 7 - What does a ‘well-governed’ AI environment actually look like in practice—day-to-day for an employee?
Topic 8 - “Do you think Shadow AI ever fully goes away—or does it become a permanent operating model that companies need to design around?”
FEEDBACK?
Email: show @ reasoning dot show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
Instagram: @reasoningshow
TikTok: @reasoningshow

SUMMARY: Have we reached a point where coding is a solved problem? And if so, what are the downstream effects on companies that need software to differentiate their business?
GUEST: Brandon Whichard, Co-Host of Software Defined Talk
SHOW: 1019
SHOW TRANSCRIPT: The Reasoning Show #1019 Transcript
SHOW VIDEO: https://youtu.be/q0mksIKcBzk
SHOW SPONSORS:
ShareGate - ShareGate Protect. Microsoft 365 Governance, we got this!
Nasuni - Activate your data for AI and request a demo
SHOW NOTES:
The New Kingmakers (Stephen O’Grady - 2014)
Developer Growth Rates
[Via ChatGPT]  A useful way to think about it:
Typing code → mostly commoditized
Designing systems → partially assisted
Owning outcomes → still very human
Topic 1 - How many years into Public Cloud did we assume that Cloud had solved the IT problem? 
Topic 2 - Developers - what are we solving for?
10% of time coding, mostly on the last 10-15% 
Lots of time in planning meetings (decoding requirements, resource planning, updates, etc.)
Decent amount of time fixing, troubleshooting, technical debt reduction
Topic 2a - Business people have unlimited ideas, and most ideas are money + tech
What would be their interface to problem solving without developers? (is this just a shift to consultants)
Is this a massive opportunity for a great PaaS 3.0 company (e.g. is Vercel an example?)
Topic 3 - [Hypothetical] Let’s assume a fairly normal company fired all their software developers tomorrow. How long before they could get a moderately complex new application of integration into production? 
Topic 4 - Nobody likes to work on legacy code - missing source, missing engineers, etc. What do we call any code written by AI that was abandoned within the last 6-12 months? 
FEEDBACK?
Email: show @ reasoning dot show
Bluesky: @reasoningshow.bsky.social
Twitter/X: @ReasoningShow
Instagram: @reasoningshow
TikTok: @reasoningshow