The end of code as capital

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Digital transformation mindcandy 2 April 2025

Today’s topics are code as labour, accelerating digital transformation, Amazon AI, AI-resistant jobs, retail, BtB growth, and reasoning models.

The end of code as capital

Scarcity and Abundance in 2025

https://alexdanco.com/2025/03/27/scarcity-and-abundance-in-2025/

The eclipse of “Code as Capital” by a new metaphor that’s closer to “Code as Labour”

12 ways to accelerate digital transformation

https://www.cio.com/article/3846263/12-ways-to-accelerate-digital-transformation.html

Customer first, drop digital, be agile, create reusable tools and repeatable processes, add AI, be modular and focus on resilience.

Amazon’s AGI Lab Reveals Its First Work: Advanced AI Agents

https://www.wired.com/story/amazon-ai-agents-nova-web-browsing/

Amazon’s AI lab focuses on the decisionmaking capabilities of next-generation software agents—and borrows insights from physical robots.

The 10 most AI-resistant jobs

https://qz.com/most-ai-resistant-jobs-automation-human-workplace-1851771134

These occupations require human interaction and have the lowest risk of automation

As retail evolves, so too do physical stores and retail tech

https://martech.org/as-retail-evolves-so-too-do-physical-stores-and-retail-tech/

Agentic AI is one of the significant drivers of retail’s continued evolution, with 75% of retailers saying AI agents will become essential by 2026.

Unlocking profitable B2B growth through gen AI

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/unlocking-profitable-b2b-growth-through-gen-ai

Next best, meeting support, proposal responder, smart pricing, smart research and smart coaching. 

AI’s Dual Nature: Reasoning Models Emerge as Key Differentiator for Business

https://www.pymnts.com/artificial-intelligence-2/2025/ais-dual-nature-reasoning-models-emerge-as-key-differentiator-for-business/

Reasoning models are systems that not only generate outputs, but also think through answers, evaluate thinking paths and solve problems. While both generative and reasoning models fall under the umbrella of AI, their goals, architectures and applications differ.

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