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How AI leaders answer a big question

answers_to
Read-onlyIdempotent

How the people building AI answer one of the big questions people ask about it, from the moments Real Alpha cut: grouped by which way they answer, the biggest group first, each with what the speaker says, who they are, the show and date. The questions: is-ai-a-bubble (Is AI a bubble?); which-jobs-will-ai-replace (Which jobs will AI replace, and which are safe?); will-ai-replace-software-engineers (Will AI replace software engineers?); will-ai-replace-doctors (Will AI replace doctors?); will-ai-replace-lawyers (Will AI replace lawyers?); is-saas-dead (Is SaaS dead?); when-will-we-get-agi (When will we get AGI?); is-openai-profitable (How does OpenAI make money, and is it profitable?); ai-predictions (What will AI look like in the next few years?); are-ai-agents-ready (Are AI agents ready for real work?); is-ai-paying-off (Is AI paying off for companies?); is-ai-dangerous (Is AI dangerous?); will-ai-make-cyberattacks-worse (Will AI make cyberattacks worse?); can-anyone-compete-with-nvidia (Can anyone compete with Nvidia?); is-there-a-gpu-shortage (Is there still a GPU shortage?); is-there-enough-power-for-ai (Is there enough power for AI?); is-moores-law-dead (Is Moore’s law dead?); is-ai-progress-slowing (Is AI progress slowing down?); is-open-source-ai-catching-up (Is open-source AI catching up?); is-china-catching-up-in-ai (Is China catching up in AI?); is-ai-overhyped (Is AI overhyped?); will-ai-replace-google-search (Will AI replace Google search?); is-vibe-coding-the-future (Is vibe coding the future?); is-a-computer-science-degree-worth-it (Is a computer science degree still worth it?); are-ai-wrappers-a-real-business (Are AI wrappers a real business?); how-do-ai-companies-make-money (How do AI companies make money?); should-ai-be-regulated (Should AI be regulated?); will-ai-cure-diseases (Will AI cure diseases?); when-will-humanoid-robots-be-useful (When will humanoid robots be useful?); are-self-driving-cars-safe (Are self-driving cars safe?); will-ai-boost-economic-growth (Will AI boost economic growth?); will-ai-replace-accountants (Will AI replace accountants?); will-ai-replace-teachers (Will AI replace teachers?); will-ai-replace-customer-service (Will AI replace customer service jobs?).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesThe question's id, like "is-ai-a-bubble"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the description does not need to re-litigate safety. It adds genuine behavioral context beyond that: results are ordered by group size, grouping is by stance on the question, and each entry carries speaker/show/date provenance. It does not say whether all answers are returned or a sample, which caps it below 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence and result-shape clause are well front-loaded and earn their place. However, the bulk of the description is a near-verbatim repetition of all 34 enum values, which is largely redundant with the schema and makes the definition long and unwieldy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so describing the return structure is necessary and the description does it well (grouping, ordering, per-item fields). Minor gaps remain: no statement about coverage/completeness of answers or whether an unknown/unmatched question yields an empty result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the enum already constrains the parameter, so the baseline is 3. The description earns more by glossing each slug in natural language (e.g. 'is-openai-profitable (How does OpenAI make money, and is it profitable?)'), which genuinely helps the agent map a user's phrasing onto the right id.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('how the people building AI answer one of the big questions') and goes further, describing the exact shape of the result: answers grouped by stance, largest group first, each with quote, speaker, show and date. An agent can tell this is a curated answer-roundup tool, distinct from raw moment retrieval siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: the agent infers it must pick one of the listed question ids. There is no explicit 'use this when...' and no mention of the alternatives in the sibling set (what_did_they_say, search_moments, transcript_excerpt) or how to choose among them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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