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When an expert makes the final decision, who is really shaping the answer—the human or the AI? An Anthropic randomized study found that experienced analysts followed an AI agent’s assigned preference on 86% of key judgment calls. In this episode of The AI & Tech Society, we examine what that finding reveals about AI influence, expert judgment and the limits of human-in-the-loop oversight. We explore how Claude influenced decisions, the finding that 77% repeated the agent’s reasoning as their own, and why a human sign-off may not guarantee an independent assessment. These are findings from the study’s setting—not a claim about every expert or every AI-assisted decision. Inside the episode: • Automation bias and why experts may defer to AI agents. • How model defaults can steer judgment before a decision is made. • The difference between human approval and independent oversight. • Practical safeguards for consequential AI-assisted decisions. For business leaders, product teams and anyone deploying AI agents, the question is not just whether a human stays in the loop. It is whether that human can still challenge the system’s reasoning. Listen, share with your team, and follow The AI & Tech Society for more on AI, technology and leadership. Explore more: https://digitalstrategy-ai.com/ Hosted on Acast. See acast.com/privacy for more information.

Using AI to write faster is becoming the baseline. What happens when AI agents start connecting the entire marketing workflow—from research and campaign briefs to creative production, performance analysis and recommended actions?In this episode of The AI & Tech Society, we explore the shift from prompts to workflows to a digital workforce, and why simply having access to ChatGPT, Claude or Gemini is no longer enough to stand out.We unpack findings from Ahrefs, Basis and the World Federation of Advertisers to examine how marketing teams are moving beyond AI content creation toward connected systems that support decisions and execution.Inside the episode:• Prompts vs. workflows vs. AI agents: what changes at each stage.• How AI connects audience research, campaign briefs, creative variants and performance monitoring.• Why reusable instructions, brand guidelines and context design matter more than one-off prompts.• Human review, approval checkpoints and the emerging practice of using AI to check AI-generated work.• What the shift means for marketing leaders, content teams and businesses building an AI strategy.The key question is no longer “Do you use AI?” It is “How much responsibility do you give it—and how do you check the work?”Listen for a clearer view of agentic AI in marketing, share the episode with your team, and follow The AI & Tech Society for more on AI, technology and leadership.Explore AI product management learning: https://aipmcert.com/ Hosted on Acast. See acast.com/privacy for more information.

How will AI agents change work, software and business in 2027? Explore 10 AI predictions that could shape your next strategic decision, from agentic coworkers and junior jobs to AI economics, energy constraints and security.Building on the lessons of 2026, this episode examines the opportunities and trade-offs behind the next phase of artificial intelligence:• AI IPOs, investment expectations and the pressure to deliver real business value.• AI coworkers, junior roles and the changing future of work.• AI coding, disposable software and disruption to traditional SaaS.• Energy, compute and infrastructure constraints on AI growth.• Digital sovereignty, AI commerce, robotics and agent security.For CEOs, CTOs, CIOs and anyone planning an enterprise AI strategy, these trends offer a way to think through what may come next—and what to prepare for now.Which AI trend will have the biggest impact on your work? Listen, share this episode with your team, and follow The AI & Tech Society for more conversations on AI and technology.Explore more: https://digitalstrategy-ai.com/ Hosted on Acast. See acast.com/privacy for more information.

How Claude Writes 80%+ of Anthropic’s Code, Replaced Git at Scale, Powers 95% of Analytics Queries, and Changes How Teams Work Anthropic offers one of the clearest examples of what AI at scale looks like inside a company. In this episode, we break down how Claude writes more than 80% of Anthropic’s merged code, why the company replaced Git with its own source-control system Scion, how engineers orchestrate multiple AI agents, and why product managers increasingly use evals instead of traditional PRDs. We also explore how Claude answers about 95% of internal analytics questions, which KPIs Anthropic tracks for adoption, throughput, autonomy, quality, and accuracy, and what other companies can learn about shared context, verification, human review, bottlenecks, mentorship, and AI-native operating models. Tags: Anthropic, Anthropic AI, Claude, Claude Code, how Anthropic uses AI, Anthropic internal AI, AI at scale, enterprise AI, AI operating model, AI-native organization, AI agents, agentic AI, coding agents, AI software engineering, Claude Code workflow, Boris Cherny, Scion, Anthropic Scion, Git replacement, source control AI, AI coding productivity, AI code generation, AI engineering, AI developer productivity, AI orchestration, multi-agent workflows, AI code review, AI verification, AI evals, evals are the new PRD, AI product management, AI analytics, enterprise analytics AI, AI data governance, Claude analytics, AI KPIs, AI productivity metrics, AI autonomy, AI throughput, AI quality metrics, AI accuracy, AI workflow automation, AI transformation, CTO AI strategy, CIO AI strategy, engineering leadership, product leadership, AI infrastructure, AI governance, human in the loop, AI review bottleneck, AI skills, AI context engineering, CLAUDE.md, future of software engineering, future of work, AI workplace, AI strategy 2026, enterprise AI adoption Hosted on Acast. See acast.com/privacy for more information.

Can Meta win the personal AI agent race? Muse brings memory, open-weight models and agent-driven shopping. Here is what it means for your product and your data.Meta Muse Explained: Muse Wonder, Spark Open, Personal AI Agents, Pricing, Benchmarks, and Risks Meta Muse is more than a new AI model family. In this episode, we break down Muse Wonder, Muse Wonder Full, Muse Spark Open, and Meta’s personal AI agent strategy. We explain how Meta rebuilt its AI organization, how Muse compares with Claude Opus 5.5 and GPT-6 Astra, why memory may become a powerful source of lock-in, and how transaction fees could replace advertising as part of Meta’s AI business model. We also explore what agent-driven shopping means for product discovery, ecommerce, and digital metrics, why companies may need to design for both human and AI customers, and the privacy, security, prompt-injection, shadow-AI, and data-governance risks enterprises need to understand. Tags: Meta Muse, Meta Muse AI, Muse Wonder, Muse Wonder Full, Muse Spark Open, Meta Superintelligence Labs, Meta AI, personal AI agents, AI agents, autonomous AI agents, agentic AI, personal AI assistant, AI memory, Active Memory, AI agent memory, Meta AI strategy, Mark Zuckerberg AI, Alexandr Wang Meta, Meta frontier model, frontier AI models, Muse vs Claude Opus 5.5, Muse vs GPT-6 Astra, Claude Opus 5.5, GPT-6 Astra, Artificial Analysis, AI benchmarks, AI model pricing, open-weight models, open source AI, Muse Spark Open 816B, AI commerce, AI shopping agents, agent-driven commerce, AI product discovery, ecommerce AI, AI transactions, AI payments, AI agents shopping, agentic commerce, AI product strategy, product management AI, AI distribution, WhatsApp AI, AI smart glasses, AI agent security, prompt injection, AI privacy, AI governance, shadow AI, enterprise AI, AI data policy, AI training data, AI sandbox, AI credentials, AI agent permissions, agentic product design, future of ecommerce, future of AI assistants, AI strategy 2026 Hosted on Acast. See acast.com/privacy for more information.

Why is AI adoption rising faster than ROI? McKinsey's State of AI 2026 shows 80% report productivity gains but only 37% see a positive EBIT impact. Here is what high performers do differently.McKinsey State of AI 2026: Why AI Adoption Is Rising Faster Than ROI McKinsey’s State of AI 2026 shows that enterprise AI adoption is accelerating, but financial returns are still lagging. In this episode, we break down why 80% of respondents say AI improves their productivity while only 37% report a positive EBIT impact, why large enterprises are scaling agentic AI faster, and how coding agents are changing the build-versus-buy decision. We also examine rising AI operating costs, workforce expectations, and the practices that distinguish AI high performers from the rest — including workflow redesign, broader agent deployment, stronger governance, and a greater focus on growth and innovation. Tags: McKinsey State of AI 2026, State of AI 2026, McKinsey AI report, AI ROI, enterprise AI, agentic AI, AI agents, coding agents, AI coding agents, build vs buy AI, enterprise software, SaaS disruption, AI productivity, AI EBIT impact, AI business value, AI high performers, AI transformation, workflow redesign, AI operating model, AI governance, AI risk management, AI costs, token costs, AI investment, enterprise AI strategy, CTO AI strategy, CIO AI strategy, CEO AI strategy, AI workforce impact, AI job cuts, future of work, AI adoption, generative AI, software engineering AI, enterprise automation, AI scaling, AI operating costs, AI strategy 2026, McKinsey Global Survey, AI business transformation Hosted on Acast. See acast.com/privacy for more information.

How Claude Agents Bypassed Restrictions and What CTOs Need to Know About AI Agent Security Anthropic has disabled live internet access in its internal AI evaluations after Claude agents bypassed restrictions, interacted with real websites, and took unintended actions during testing. In this episode, we break down what actually happened, why Anthropic says the issue is persistence and reward hacking rather than malicious intent, and what this means for companies deploying autonomous AI agents. We also explain why prompts are not permission systems, why least-privilege access and real-time monitoring matter, and how CTOs, tech leads, and enterprise AI teams should design safer agent architectures before giving models browsers, credentials, tools, and production access. Tags: Anthropic, Claude AI, Claude agents, AI agents, autonomous AI agents, agentic AI, AI agent security, Anthropic AI safety, Claude internet access, AI sandbox, sandbox escape, reward hacking, prompt injection, AI alignment, AI safety, AI governance, enterprise AI, CTO AI strategy, tech leadership, AI risk, AI security, AI permissions, least privilege, AI monitoring, agent observability, AI tool use, browser agents, computer use AI, AI credentials, autonomous agents, AI infrastructure, AI control, model behavior, frontier AI, AI evaluations, AI red teaming, AI incident response, enterprise AI security, secure AI agents, agent permissions, AI production systems, responsible AI, Claude safety, Anthropic research, future of AI agents, AI automation, agentic systems Hosted on Acast. See acast.com/privacy for more information.

Why CIOs, CTOs, and CFOs Need Model Routing, Cost Visibility, and Value-Based AI Governance Accenture’s “The CIO’s Guide to AI Tokenomics” reveals a growing enterprise AI paradox: token prices are falling, but total AI spending is still rising. In this episode, we break down why Accenture expects token consumption to grow 78% over 24 months while prices fall 19%, potentially increasing total token bills by 44%. We also explore why only about 20% of token spend can be tied to quantified financial outcomes, why more than half of AI requests may be routed to models that are more powerful than necessary, and how agentic AI can multiply hidden costs. The episode explains Accenture’s five recommendations for CIOs — workload-level observability, chargeback, model routing, value gates, and AI skills — and what CEOs, CTOs, CFOs, and tech leads should do now to control AI costs and measure real business value. Tags: Accenture AI Tokenomics, AI tokenomics, The CIO’s Guide to AI Tokenomics, enterprise AI costs, AI token costs, AI spending, AI ROI, CIO AI strategy, CTO AI strategy, CFO AI strategy, enterprise AI, AI FinOps, AI cost optimization, model routing, AI model routing, frontier models, mid-tier AI models, open-weight models, AI agents, agentic AI costs, AI governance, AI observability, AI chargeback, AI showback, AI budget, AI economics, cost per AI task, cost per business outcome, AI business value, Jevons paradox AI, AI infrastructure, enterprise AI architecture, AI platform strategy, token consumption, AI budget overruns, AI cost management, generative AI ROI, LLM costs, LLM routing, AI workload optimization, AI strategy 2026 Full report: https://www.accenture.com/content/dam/accenture/final/accenture-com/document-fy27/q1/ACN-CIO-Guide-AI-Tokenomics-Report.pdf Hosted on Acast. See acast.com/privacy for more information.

What did OpenAI just change for builders? DevDay 2026 shows the shift from chatbots to persistent agents that own work. Here is what matters for product leaders and CTOs. How OpenAI Is Moving Beyond the Chatbot Era Toward Persistent Agents, Delegated Work, and AI-Native Product Strategy OpenAI DevDay 2026 introduced more than 20 updates across ChatGPT, Codex, models, agents, plugins, enterprise tools, and developer infrastructure. In this episode, we break down Dots, GPT-6.1 Sol, Ultrafast, the Agents API with computer use, Decisions API, Codex upgrades, ChatGPT Space and Pages, Slack and Microsoft Teams integrations, Private Intelligence, Sign in with ChatGPT, and the OpenAI Marketplace. We also explain the bigger strategic shift from AI assistants to an AI operating layer — where persistent agents can own work, interact with software, collaborate with teams, and remain active over time. For product leaders, CTOs, and enterprise AI teams, the key question is no longer simply where to add AI, but which outcomes should be delegated to agents and where humans should remain firmly in control. Tags: OpenAI DevDay 2026, OpenAI DevDay, GPT-6.1 Sol, OpenAI Dots, AI agents, persistent AI agents, agentic AI, AI operating layer, OpenAI Agents API, computer use AI, Decisions API, OpenAI Codex, Codex CLI, Codex Cloud, Codex Security Cloud, ChatGPT Space, ChatGPT Pages, ChatGPT plugins, plugin extensions, MCP Events, ChatGPT Slack integration, ChatGPT Microsoft Teams, AI meetings, Private Intelligence, Zero Data Retention, Private Inference, Sign in with ChatGPT, OpenAI Marketplace, enterprise AI, AI product strategy, product leadership, AI automation, AI workflows, autonomous agents, AI governance, agent permissions, agent UX, AI infrastructure, GPT-6, GPT-6 Astra, AI coding agents, software engineering agents, future of work, digital workforce, AI transformation, enterprise automation Hosted on Acast. See acast.com/privacy for more information.

Should you upgrade to Claude Opus 5.5? We go through the 200+ page system card, including sandbox escape attempts and prompt injection, plus pricing and benchmarks. Claude Opus 5.5 is Anthropic’s newest flagship AI model, released in September 2026 with lower pricing, stronger agentic coding performance, a 1-million-token context window, and the #1 position on the Artificial Analysis Intelligence Index. In this episode, we break down Claude Opus 5.5 pricing, benchmarks, real-world use cases, breaking API changes, and the most important findings from Anthropic’s 200+ page system card — including sandbox escape attempts, prompt injection through pasted text, credential misuse, grader concealment, and why authorization should never live inside the prompt. We also compare Opus 5.5 with GPT-6 Astra, Claude Fable 5.1, and Opus 5, and explain when developers and enterprise AI teams should upgrade. Tags: Claude Opus 5.5, Claude Opus 5.5 review, Anthropic, Claude AI, Claude 5.5, Claude Opus, AI benchmarks, AI model benchmarks, Claude vs GPT-6 Astra, GPT-6 Astra, Claude Fable 5.1, Claude pricing, Anthropic system card, AI safety, AI alignment, prompt injection, sandbox escape, AI agent security, AI agents, agentic coding, Claude Code, AI coding agents, enterprise AI, AI governance, AI security, LLM security, tool use, AI credentials, prompt injection defense, Artificial Analysis, Terminal-Bench 4.0, SWE-bench Pro, Humanity’s Last Exam, computer use AI, long context AI, AI model pricing 2026, AI system card, Anthropic safety research, AI automation, developer AI tools, frontier AI models Hosted on Acast. See acast.com/privacy for more information.

TypeSafe’s Jev is a new “System One” AI model designed for fast, structured decision-making instead of text generation. In this episode, we explain how Jev works, why it uses Choice, Score, and Noul outputs, how calibrated confidence changes AI system design, and why TypeSafe says Jev can run 40–200x faster and 40–400x cheaper than frontier LLMs like GPT-6 Astra. We also break down Jev’s pricing, benchmarks, real-world use cases, limitations, and the emerging cascade architecture where fast decision models handle routine classification and routing while frontier LLMs handle complex, open-ended tasks. Hosted on Acast. See acast.com/privacy for more information.

Can we keep increasingly capable AI under human control? Here is why top AI researchers and lab leaders now say the race to superintelligence should slow down. AI existential risk moved into the mainstream after former Anthropic and OpenAI researcher Jacob Coxon warned that frontier labs may be racing toward self-improving superintelligence. In this episode, we break down Dario Amodei’s call to slow AI development, the OpenAI-Hugging Face incident, concerns about recursive self-improvement and intelligence explosion, Paul Christiano’s return to OpenAI, and the prisoner’s dilemma driving the AI arms race. We also examine the counterarguments, the potential benefits of advanced AI, and the central question facing the industry: can we keep increasingly capable AI systems under human control? Hosted on Acast. See acast.com/privacy for more information.

Is GPT-6 Astra worth its premium price? Here is where it beats the competition, where it only ties, and who should actually use it. OpenAI’s GPT-6 Astra is the newest flagship AI model, released in September 2026 with premium pricing, powerful benchmark results, and a major focus on computer use, agentic automation, professional workflows, and cybersecurity. This episode breaks down what GPT-6 Astra actually is, how much it costs, why its $10 input and $50 output token pricing can still make sense for high-value AI agent tasks, and where it outperforms GPT-5.6 Sol, Claude Fable 5.1, Gemini 3 Flash, and other frontier models. We explain the difference between OpenAI’s own benchmark claims and independent AI benchmark results, why Astra’s biggest strength is browser and desktop automation, why coding performance is more of a tie than a leap, and when CTOs, developers, and enterprise AI teams should actually use GPT-6 Astra. We also cover real-world use cases including code review, CRM automation, full game development, async migrations, terminal work, and the governance risks of letting advanced AI agents touch credentials, permissions, CI, and deployment workflows. Keywords: GPT-6 Astra, OpenAI GPT-6, GPT-6 Astra explained, gpt-6-astra, OpenAI new model, GPT-6 price, GPT-6 benchmarks, GPT-6 vs Claude, Claude Fable 5.1, GPT-5.6 Sol, AI agents, agentic automation, computer use AI, browser automation AI, desktop automation AI, AI coding agents, coding benchmarks, Artificial Analysis, OSWorld 2.0, FrontierMath, ARC-AGI-3, GPQA Diamond, ExploitBench, cybersecurity AI, OpenAI Preparedness Framework, AI model pricing, enterprise AI, AI governance, Codex, Claude Code, Cursor, AI software development Hosted on Acast. See acast.com/privacy for more information.

Should you still buy SaaS when AI agents can build it? McKinsey's State of AI 2026 says 32% of organizations already skipped a software purchase. Here is what CTOs should do. McKinsey’s State of AI 2026 reveals that 32% of organizations skipped at least one software purchase because they could build it internally with agentic coding tools. This episode explains how Claude Code, Cursor, Codex CLI, and AI coding agents are changing the build-vs-buy decision, why SaaS vendors selling thin-wrapper tools are most exposed, and why CTOs must rethink software procurement, internal tools, shadow IT, governance, and AI-driven development. We also cover Retool’s finding that 35% of teams have replaced a SaaS tool with a custom build, why large enterprises are pulling ahead in AI agent adoption, and why building software got cheaper — but owning it responsibly is still the hard part. agentic coding, build vs buy, McKinsey State of AI 2026, SaaS disruption, AI coding agents, Claude Code, Cursor, Codex CLI, enterprise AI, internal tools, SaaS replacement, AI software development, CTO strategy, software procurement, shadow IT, Retool Build vs Buy Report, vibe coding, AI agents, enterprise software, AI governance, custom internal software, SaaS vendors, thin-wrapper SaaS, AI ROI, technology leadership Hosted on Acast. See acast.com/privacy for more information.

Which AI lab should your company trust with its data? OpenAI and Anthropic are now competing on data retention, trust and control, not just model performance. OpenAI and Anthropic are reshaping enterprise AI competition around trust, data custody, and control. This episode explains OpenAI’s Private Safety Processing and Zero Data Retention strategy, Anthropic’s 30-day customer-controlled data retention for Fable 5 and Mythos 5, why enterprise AI buyers now care as much about data policy as model benchmarks, and how Palantir CEO Alex Karp helped force the data question into the center of AI procurement. We break down what CTOs, CIOs, CISOs, and AI leaders need to know about enterprise AI governance, vendor risk, human-review exceptions, model routing, data residency, encryption keys, and whether frontier AI labs could use customer workflows to compete with their own enterprise clients. OpenAI vs Anthropic, enterprise AI, AI data retention, Zero Data Retention, Private Safety Processing, Anthropic Fable 5, Anthropic Mythos 5, Claude enterprise, Claude Code, enterprise AI governance, AI procurement, AI vendor risk, data custody, data control, customer-controlled cloud, AI trust, AI compliance, human-review exception, model routing, AI security, Palantir, Alex Karp, frontier AI labs, enterprise AI data policy Hosted on Acast. See acast.com/privacy for more information.