The Daily AI Show: Issue #62

Welcome to Issue #62

Coming Up:

The Death of the Fixed Price: How AI Changes Buying

AI Broke Hiring: Why Finding a Job Feels Impossible

Mapping AI: The Five Clusters That Define the Field

Plus, we discuss the latest stats on who uses AI, how too much personalization could ruin or improve your next dinner out, what wilderness stewardship means in an age of AI, and all the news we found interesting this week.

It’s Sunday morning.

Europe’s deepfake-label law kicks in today, which means that video of your dog giving a TED Talk now needs a tiny “Generated by AI” sticker.

Let’s just go ahead and put this in the “good idea” column.

The DAS Crew - Andy, Beth, Brian, Eran, Jyunmi, and Karl

The Death of the Fixed Price: How AI Changes Buying

The era of fixed prices is fading. AI-driven dynamic pricing is already common in airlines, ride-sharing, and hotels, and it is now spreading into everyday purchases like retail, e-commerce, and even fast food. This shift means prices are no longer just reacting to broad market factors. They are increasingly shaped by algorithms that analyze complex data sets in real time to adjust pricing for each situation.

Dynamic pricing has three stages. The first is traditional adjustments based on time, demand, and inventory. The second, already in use, is AI-enhanced dynamic pricing where algorithms handle vast amounts of data to set prices faster and more precisely, but the price remains mostly uniform for everyone. The third stage is personalized pricing, where the price shown could vary for each person based on their data and calculated willingness to pay. Regulators like the FTC have warned this could cross into “surveillance pricing,” raising fairness and transparency concerns.

Airlines like Delta are testing AI-driven pricing at scale, aiming to expand its use significantly. Amazon changes prices millions of times a day. Fast food chains have faced backlash for experimenting with AI-based price adjustments. As this spreads, questions emerge: Will AI -tracked loyalty to a brand mean paying more because the algorithm knows you value the convenience of a trusted brand? This is the obverse of aggressive discounting to convert a new customer. How will consumers respond if two people buying the same product see different prices?

Agents and counter-algorithms could give consumers leverage, negotiating better deals on their behalf. But the power imbalance between companies using massive AI systems and individuals shopping for deals is only getting larger.

WHY IT MATTERS

Pricing Gets Personal: Algorithms may set different prices for each person, reshaping how we think about fairness in markets.

Loyalty May Cost More: AI can learn who is willing to pay more, turning brand loyalty into a pricing disadvantage.

Consumers Need AI Too: Smart agents could become essential to avoid overpaying in algorithm-driven marketplaces.

Regulation Will Struggle: Laws built for fixed or simple dynamic pricing will need updates to handle personalized AI pricing.

The Market Could Fragment: As personalized deals grow, traditional price tags may disappear, leading to negotiation-driven commerce.

AI Broke Hiring: Why Finding a Job Feels Impossible

AI was supposed to make hiring smarter and faster. Instead, it has created a paradox that hurts both employers and job seekers. On one side, applicants can now apply to hundreds of jobs in hours with AI-generated resumes and cover letters. On the other side, HR teams face a tidal wave of applications, many of which are low-quality applicants with tuned or even faked presentations. The result is a hiring system overwhelmed by automation and losing the human connection that makes employment relationships meaningful.

The flood of applications forces HR departments to use automated filters like Applicant Tracking Systems and AI screeners. These tools often misjudge candidates, rejecting qualified people while passing through spam or deepfake applications, with companies now facing another challenge: by 2028, up to 25% of job applicants could be fake, aided by AI impersonation tools during phone and video interviews.

The problem is not only technical.

There is a power imbalance between applicants and employers. Candidates resort to automation and AI hacks to stand out because traditional hiring processes favor those already close to power networks. Innovation is needed, but change will take time. Some experts believe hiring will get worse before it gets better, forcing a complete redesign of how companies recruit and validate talent.

Building a visible, authentic presence online is becoming more important than resumes. Candidates with strong LinkedIn profiles, public portfolios, and evidence of AI skills are more likely to bypass broken filters and get noticed. For businesses, AI alone cannot solve this hiring crisis. They must rethink their systems, focus on equity, and develop processes that combine technology with real human evaluation.

WHY IT MATTERS

Automation Overload: AI makes it easy to apply for jobs, overwhelming HR teams and degrading the hiring process.

Fake Applicants Are Rising: Deepfakes and AI impersonation are making it harder to verify candidates.

Traditional Resumes Are Fading: Public digital footprints and portfolios now play a bigger role in hiring decisions.

Human Connection Still Wins: Networking and community involvement remain the strongest path to meaningful jobs.

Hiring Systems Need Reinvention: Without redesigning the process, both employers and applicants will continue to lose.

Mapping AI: The Five Clusters That Define the Field

AI often feels like an endless web of models, chips, and apps. A recent analysis helps untangle this complexity by showing how 15 distinct AI domains naturally cluster into five interconnected groups. Understanding these clusters gives a clear, top-level view of how AI evolves and where innovation is heading.

1. Core Technical Foundation: This is the science of AI, the base layer where algorithms, neural networks, natural language processing, computer vision, robotics, reasoning systems, and planning algorithms live. These are the building blocks for every modern AI system.

2. Data-Centric AI: Data is what powers AI’s intelligence. This cluster includes data collection, curation, labeling, synthetic data generation, privacy, governance, and the big data technologies that feed models. Without high-quality, well-managed data, even the best algorithms fail.

3. Implementation and Applications: This is where AI meets the real world. It includes industry applications across healthcare, finance, manufacturing, supply chain, and transportation, as well as consumer-facing uses like assistants, chatbots, agents and image generation. It also covers machine learning frameworks, hardware, GPU and other processing chips, data centers, cloud services, and MLOps tools that support deployment.

4. Knowledge and Innovation: AI advances because of research, education, and collaboration. This cluster includes academic research, open source contributions, historical AI development, and interdisciplinary work connecting AI with fields like cognitive science, physics, biology, and the arts.

5. Societal Context and Market Dynamics: AI does not exist in isolation. This cluster spans ethics, governance, regulation, and the economic forces shaping AI’s future. It captures how investment, public policy, and societal debate influence what gets built and deployed.

The connections between these clusters matter. Technical advances rely on clean, labeled data. Applications cannot scale without hardware and infrastructure. Research drives breakthroughs that shift markets and policy. And societal pressures feed back into research priorities and deployment strategies.

Understanding AI through these clusters helps leaders and builders see where their efforts fit into the bigger picture and where new opportunities or risks may emerge.

Just Jokes

Did you know?

A July 2025 AP-NORC nationwide poll found that 60% of US adults report having used artificial intelligence (AI) to search for information, making this the most common use case for AI. Usage is highest among younger adults, with 74% of those under 30 saying they use AI to find information at least occasionally.

Other key findings:

• About 40% of Americans report using AI for work tasks or generating ideas.

• Roughly one-third use AI to assist with writing emails, creating or editing images, or entertainment.

• Generational divide: Around 60% of adults under 30 use AI for brainstorming ideas, versus about 20% of those age 60+; young adults are also much more likely to report daily use for these tasks.

• Companionship: Use of AI for companionship is low overall (16%), but higher among younger adults.

• The frequency of use is also higher among young adults: about 28% of under-30s search for information with AI several times a day, with an additional 11% doing so once a day.

Confidence and skepticism:

• A 2024 AP-NORC/USAFacts poll found that two-thirds of Americans lack confidence in the reliability of information from AI chatbots and search results; only a small minority are highly confident in AI's factual reliability.

• 43% believe that AI will make it more difficult to find factual and accurate information about the upcoming presidential election, while only 16% think it will make it easier.

Demographic variations:

• Older adults are much less likely to use AI at all with almost two-thirds of those 60+ report no personal use.

• Women and those with lower levels of education are significantly more likely to be non-users.

• Daily use remains low overall: only 14% of Americans report using AI daily for personal activities, and 15% report daily use at work.

Source

This Week’s Conundrum
A difficult problem or question that doesn't have a clear or easy solution.

The AI Wildness Conundrum

Conservationists now deploy AI drones and autonomous sensors that can track animal populations, detect poachers, predict wildfires, and even recommend reshaping ecosystems to prevent collapse. These systems protect habitats at scales humans never could. Entire regions could soon thrive only because an unseen layer of algorithms manages balance.

But wilderness has always meant a place beyond human control, a space where life adapts on its own, even when it is brutal or uneven. If AI silently engineers the outcome, protecting species and restoring lost habitats, is that wilderness thriving, or a managed garden we only pretend is wild?

The conundrum
When nature survives only because algorithms orchestrate its rhythms, do we celebrate a new era of environmental stewardship, or face the reality that wildness itself has been redesigned into something human-made?

Want to go deeper on this conundrum?
Listen/watch our AI hosted episode

Anthropic’s Valuation Skyrockets Despite Trailing OpenAI
Anthropic is raising new funds at three times its previous valuation from four months ago, signaling rapid growth and strong investor confidence despite having far fewer users than OpenAI.

Deeper Insight:
Anthropic’s trajectory shows that differentiation in AI safety and model quality can attract significant capital and user growth even without the scale of OpenAI. This could lead to intensified competition among the top labs.

The Great AI Infantilization: Resistance in Business Adoption
A Substack article by Carlo Iacono highlights "learned helplessness" in AI adoption, where professionals act helpless in the face of the complexity of the AI wave, instead of embracing competence with AI tools.

Deeper Insight:
Scaling AI in businesses is slowed not just by technical hurdles but by cultural behavior. Overcoming this mindset may be as crucial as improving AI technology itself.

Google Launches Opal: Text-to-Automation Tool
Google’s Opal allows users to describe a process in text and have it automatically built into an automation flow, complete with a usable drag-and-drop front-end workflow app.

Deeper Insight:
Opal could simplify complex workflows, positioning Google as a competitor to tools like N8n and Zapier. As it matures, expect tighter integration with Google Workspace and Gemini.

Notebook LM Adds Video Overviews
Notebook LM now supports generating video summaries with slides and visuals, expanding its audio-overview feature.

Deeper Insight:
AI-driven video summaries enhance how information is consumed and shared. This could make Notebook LM a stronger choice for educators, marketers, and content creators seeking multi-format outputs.

ChatGPT’s New Study Mode Introduced
Study Mode turns ChatGPT into a Socratic tutor, asking leading or probing questions to deepen understanding instead of providing direct answers.

Deeper Insight:
By fostering critical thinking rather than spoon-feeding answers, Study Mode addresses concerns that AI makes students intellectually passive. It may also reshape tutoring and personalized education.

Spotify’s AI DJ Plans Conversational Features
Spotify is developing a conversational AI DJ that can discuss music history, context, and lyrics during playback.

Deeper Insight:
This could redefine music streaming, making it interactive and educational. Partnerships with lyric platforms like Genius could further enrich the experience.

First AI Musician Signed to Record Label
An AI musician named “I am Oliver,” known for using Suno to produce music, has been signed by Hallwood Media, marking a new milestone for AI-created music.

Deeper Insight:
This move legitimizes AI music creation in the recording industry. It will likely spark debates about creativity, authenticity, and how royalties should be distributed for AI-generated content.

Debate Over AI’s Role in Higher Education Intensifies
An Inside Higher Ed article highlights professors resisting AI adoption, arguing it threatens grading integrity and traditional learning models.

Deeper Insight:
As universities struggle with AI policy, students risk graduating without modern skills. Institutions that fail to adapt may face declining enrollment and relevance.

Google Search Adds Persistent Canvas to AI Mode
Google’s AI-enhanced search now includes creation of a “canvas” for collecting, organizing, and saving search findings across sessions.

Deeper Insight:
Search is evolving into a workspace where AI not only finds answers but builds structured, reusable research boards. This could reduce reliance on separate tools for managing research.

Harvard Develops Quantum Metasurfaces for Computing
Harvard researchers created a wafer-thin quantum metasurface that can bend light and replace bulky waveguides in quantum computers.

Deeper Insight:
This breakthrough could make quantum computing hardware smaller, less energy-intensive, and more practical, potentially accelerating commercial quantum computing adoption.