Laura Jones explains that generative AI is raising the bar for creativity. When everyone can produce “pretty good” content, the real challenge is creating something that actually stands out. The risk is not poor output, but settling too quickly for what already works.

She argues that as products become more similar, brand becomes a signal of trust. Not in a visual sense, but in the experience behind it. At Instacart, that shows up in details like how a banana is selected. With over a billion bananas delivered and millions of orders including notes on ripeness, customers are expressing very specific preferences. That behavior led to both new product features and the creative idea behind their Super Bowl campaign.

The conversation also explores how teams should work with AI. While it can automate repetitive tasks and speed up iteration, it can also create a tendency to agree with what’s generated, especially when working alone. Laura emphasizes that the best ideas still come from people challenging each other, building on different perspectives, and pushing beyond the first acceptable answer.

Key takeaways:

• Mediocre is easier than ever, which raises the bar for originality
  When AI gets everyone to “pretty good,” the work that stands out has to go further. The bar is not lower. It is higher.
• Brand becomes trust when products converge
  As functionality becomes easier to replicate, the question becomes who you trust to get it right. Brand is the answer to that.
• Only do what only you can do
  Use AI to take on repetitive work, then spend your time on judgment, insight, and decisions that require a human point of view.
• Need-finding still requires real people
  Synthetic research can help, but it cannot replace observing real behavior. The banana insight came from what customers actually did.
• Human plus bot plus human
  Working only with AI makes it easy to agree and move on. The best ideas come from people challenging each other, with AI in the middle, not as the whole process.

Instacart: instacart.com
Super Bowl ad: Super Bowl (Instacart ad)
Laura LinkedIn: linkedin/laurajones
ro's post: ro.co/perspectives/super-bowl-economics

00:00 Intro: Originality vs AI Complacency
00:27 Meet Laura Jones
01:23 Brand as trust when products converge
03:50 Personalization and reducing mental load
06:24 What still matters in marketing
10:33 Why need-finding cannot be shortcut
14:09 Using AI without losing judgment
16:33 New channels and where customers actually are
21:35 Why “dopey ideas” matter
25:42 Human plus bot plus human
28:44 Inside the Super Bowl ad
31:47 From banana insight to product
34:49 Taking creative risks at scale
37:34 Fear, pressure, and team chemistry
46:24 AI and faster prototyping
53:26 The debrief

📜 Read the transcript for this episode:

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Dan Klein, professor at UC Berkeley and CTO at Scaled Cognition, explains that AI systems generate answers based on patterns in language rather than verified knowledge. This makes them highly capable across many tasks, but also means they can produce confident answers even when they are not fully accurate.

He introduces the “jagged frontier,” where AI performs very well in some areas and less reliably in others. Because responses are fluent and convincing, it is often hard to see where those limits are, which makes it important to stay engaged when using these systems.

The conversation also explores hallucinations as a natural part of generative systems. In some cases, this is what makes them valuable, especially for creative or open-ended tasks, while in other cases reliability becomes more important.

Finally, Dan highlights that working effectively with AI is a skill. As more people start using these systems in their daily work, knowing how to guide them, evaluate outputs, and apply them in the right contexts becomes increasingly important. He also shares how his team at Scaled Cognition is tackling this challenge by building AI systems with fundamentally different architectures, focused on determinism and reliability — aiming to ensure systems follow rules, reflect underlying data accurately, and behave predictably in high-stakes, policy-driven use cases.

Key Takeaways:

• AI is designed to sound right, not to know it’s right
  Models generate fluent answers without knowing whether they are correct, which means users need to actively evaluate outputs
• You have to learn where AI works and where it doesn’t
  Capabilities are uneven, and understanding those limits is key to using AI effectively
• Working with AI shifts your role from creator to editor
  Instead of starting from scratch, you are reviewing, refining, and validating what the model produces
• Most people are using AI without knowing how to manage it
  Skills like delegation, verification, and judgment are becoming essential, but are not widely taught

Dan's LinkedIn: linkedin/dan-klein/
Scaled Cognition Website: scaledcognition.com
Scaled Cognition LinkedIn: linkedin/company/scaledcognition/
Scaled Cognition X: x.com/ScaledCognition

00:00 Intro: Fluency vs Truth
00:34 Meet Dan Klein
02:53 Why Fluency Misleads
05:11 How LLMs Guess
07:30 What Is Hallucination
08:54 Deception and Alignment
11:22 Why Agents Break
12:48 Chaining and Determinism
16:01 When Hallucination Helps
22:33 Beyond Scale for Reliability
30:40 Synthetic Data Training
31:10 Enterprise Agent Use Cases
33:44 Healthcare Risks
39:13 Enterprise Literacy Gap
41:27 Delegation and AI Management
54:37 The Debrief

📜 Read the transcript for this episode: nobody-is-getting-new-manager-training-for-their-ai-team-with-dan-klein-uc-berkeley/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.

Melissa Cheals leads Smartly, a payroll and people management platform serving 24,000 small and medium businesses in New Zealand. In this conversation, she shares how AI is reshaping product development, leadership, and how organizations operate.

A key moment comes when her team estimates new features will take 12 months and $1M to build. Instead of accepting it, Melissa pushes back, using AI to better understand her team’s perspective and communicate the need for change more effectively. This becomes a broader shift in how she approaches leadership, using AI to think more clearly and navigate conversations with less friction.

The discussion expands into strategy. Companies now face a fundamental choice: become AI-native or continue building on existing systems. As AI adoption increases, it also exposes silos and bottlenecks. Melissa shares why cross-functional collaboration—and leaders actively engaging with AI themselves—is critical to navigating this shift.

Key Takeaways:

• Becoming AI-native is a defining decision
  It’s not just a technology shift. Leaders need to decide whether to rebuild around AI or continue layering it onto existing systems, and that choice shapes how the company operates.
• AI shifts us from scarcity to abundance
  Many organizations still think in terms of limited time and resources, but AI changes what’s possible and forces leaders to rethink how big they can think and what they can achieve.
• AI is a leadership amplifier
  Beyond productivity, AI helps leaders think more clearly, reframe conversations, and communicate change in a way that is both effective and respectful.
• Leaders can’t delegate AI
  Without hands-on experience, it becomes difficult to challenge assumptions, guide teams, or make informed decisions about what’s possible.

Smartly: smartly.co.nz
LinkedIn Melissa: linkedin.com/melissa-cheals
LinkedIn Smartly: linkedin.com/company/smartlynz/

00:00 Intro: Challenging AI Assumptions
00:28 Meet Melissa Cheals
01:17 The Spark For Change
02:36 Vision And Early Signals
03:48 Hiring For Transformation
06:12 Unlocking Data With AI
08:27 Breaking Silos Across Teams
10:39 Why Leaders Must Learn AI
13:42 Leading With AI And Clarity
17:05 The AI-Native Decision
21:45 Thinking Bigger With AI
25:23 Less Meetings More Writing
26:33 The Self-Disruption Imperative
29:11 Breaking Silos With Value Streams
31:28 Managing Fear And Change
32:50 Learning And Shipping Faster
34:58 Debrief

📜 Read the transcript for this episode: ai-native-or-not-the-defining-choice-for-companies-right-now-with-melissa-cheals-ceo-of-smartly/transcript

For more prompts, tips, and AI tools. Check out our website: https://www.beyondtheprompt.ai/ or follow Jeremy or Henrik on Linkedin:

Henrik: https://www.linkedin.com/in/werdelin
Jeremy: https://www.linkedin.com/in/jeremyutley

Show edited by Emma Cecilie Jensen.