Forsidebilde av showet The AI & Tech Society by Danar

The AI & Tech Society by Danar

Podkast av Danar Mustafa

engelsk

Teknologi og vitenskap

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AI, Technology & Leadership – Shaping the Future of SocietyStep into the future with a podcast that explores the shift from the industrial age to the digital era. We uncover how AI, robotics, data, and emerging technologies are transforming business strategy, leadership, and the role of humanity in a world driven by innovation.In every episode, you’ll discover:How artificial intelligence, robotics, and digitalization are redefining industries.The power of data-driven strategies in business, government, and public policy.The evolving role of leaders in navigating digital transformation.Who should listen:CEOs, CTOs, CIOs, AI product managers, startup founders, tech leaders, policymakers, and anyone passionate about innovation, leadership, and the future of work.From boardrooms to startups, we share the insights you need to lead in a data-first world. If you believe data is the new oil, this is your front-row seat to the trends shaping tomorrow.Host: Danar Mustafa, AI-leader & Founder based in Sweden.#digitalisering #digitaltransformation #industry4 #IoT #Analytics #AI #machinelearning #changemangement #strategy #businessmodel #digitalstrategy #agile #Genai #openai #google #meta #amazon #aws #microsoft #mistral #Sweden Hosted on Acast. See acast.com/privacy for more information.

Alle episoder

112 Episoder

episode Claude Code at the Organization Layer: What Actually Changes cover

Claude Code at the Organization Layer: What Actually Changes

WHAT ACTUALLY CHANGES WHEN CLAUDE CODE REACHES THE WHOLE ENGINEERING ORGANIZATION METRICS THAT ACTUALLY MATTER Stop measuring: * Lines of code per developer * Token consumption * Individual productivity Start measuring: * Cycle time (Claude-assisted vs non-assisted PRs) * Time to first PR for new hires * PR throughput with quality counterweight (defect rate, rollback frequency) * Incident resolution time * Maintenance burden trajectory NON-ENGINEERS BUILDING SOFTWARE Examples from one company: * Support team: Tool surfacing relevant past tickets and customer history * Finance team: Expense categorization assistant * HR team: Onboarding checklist app pulling from live systems What engineering built: * Architecture patterns for internal apps * Plugin marketplace with pre-approved skills/MCP connections * Managed permissions (read from X, write to Y, not Z) * Audit logs for AI-generated changes The shift: Engineering didn't build the apps. Engineering built the conditions under which apps could be built safely. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

22. mai 2026 - 19 min
episode Gemma 4: Google's Open-Source LLM Competing with Chinese Models cover

Gemma 4: Google's Open-Source LLM Competing with Chinese Models

WHY APACHE 2.0 MATTERS Previous Gemma licensing: * Custom "Gemma Terms of Use" * Usage-policy provisions * Constraints on commercial deployment Apache 2.0: * Fine-tune for commercial use ✓ * Redistribute fine-tuned variants ✓ * Embed in commercial products ✓ * No ongoing license obligations ✓ ON-DEVICE AI IMPLICATIONS What's new: * Full conversational AI on phones, offline * No data leaving device * No API costs * No connectivity requirements Use cases: * Healthcare apps (privacy) * Education (offline areas) * Finance (data sovereignty) * Any privacy-sensitive application DATA SOVEREIGNTY The shift: * European regulators increasingly uncomfortable with US-hosted APIs * GDPR requires either locked regions or self-hosted * Gemma 4 + Apache 2.0 = viable self-hosted option * Regulated industries now unblocked CHINESE MODEL GOVERNANCE QUESTIONS For Western organizations considering Chinese open models: 1. Training data provenance — Can you verify? 2. Embedded refusals/biases — Different content policies 3. Export-control compliance — Check with legal 4. Strategic precedent — Building on competitor infrastructure Not disqualifying, but requires conscious decision ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

14. mai 2026 - 18 min
episode Musk vs. Altman: The OpenAI Legal Battle Explained cover

Musk vs. Altman: The OpenAI Legal Battle Explained

FOR TECH LEADERS 1. Corporate structure creates 5-10 year litigation exposure 2. Nonprofit pivots require AG negotiation, not just board approval 3. Mission-aligned structures (PBC) gain credibility advantage 4. Document founder discussions formally 5. Co-founder departure terms matter more than ever FOR INVESTORS 1. Governance risk is now diligence requirement 2. Demand mission-protection documentation 3. Monitor AG agreements and state oversight 4. Understand partner-investor risk compounding WHAT TRIAL REVEALED > "The picture that emerged is not one of villains stealing a charity, nor one of crusaders defending a mission. It is one of co-founders making consequential decisions under significant uncertainty, with informal arrangements that proved inadequate to the scale of value the technology eventually created." KEY QUOTE > "Musk will likely lose the case but is succeeding at something his lawsuit may not have intended — establishing a public record of how AI labs are actually governed, and creating durable pressure for that governance to become more formal, more transparent, and more constrained." ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

10. mai 2026 - 19 min
episode AI cut 16,000 U.S. jobs a month — what the Goldman Sachs report actually says cover

AI cut 16,000 U.S. jobs a month — what the Goldman Sachs report actually says

Key insight: Premium is growing, not shrinking, as demand outpaces supply JEVONS PARADOX Definition: Increased efficiency often raises total consumption because lower per-unit costs expand demand faster than efficiency reduces use. Applied to AI: * AI makes workers 2x productive → firm needs fewer workers per task * But lower costs → more demand → potentially more workers in net Current data: * Augmentation roles: Jevons paradox is working (net +9,000 jobs/month) * Substitution roles: Not working (companies taking cost savings, not expanding service) THE APPRENTICESHIP CRISIS Problem: Junior roles serve two purposes: 1. Get work done 2. Train next generation of seniors If AI does #1, who gets #2? Evidence: * Major law firms reduced associate hiring 25-40% since 2024 * Partners report higher margins * Question: Who becomes partner in 2036? ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.

6. mai 2026 - 18 min
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