Here’s a new episode of The Reddy Rundown, crafted so you don’t have to frantically follow everything in the AI news space wondering what you are missing as a an exec in 2025 trying to keep up.

I am Shawn Reddy. CEO and founder at AI Advantage Agency. I build marketing systems and I care about creative that works. Below is what actually matters for operators this week and how it should change what you ship.

The feud that actually touches your funnel

Musk and Altman traded shots in public again. Fun to watch. Easy to dismiss. Here is the real lesson for marketing leaders

1. Distribution risk is not academic. Store rankings and feed reach can tilt on relationships and policy shifts. Treat any single channel advantage as rented

2. Build alternate paths for discovery and retention now. Search. Social. Direct. Partner. Email. Own at least two and test the third every week

3. Do not anchor brand safety to personalities. Anchor it to logs. If the platform moves you have receipts and a plan to throttle spend fast

   
   

My move

Create a distribution scoreboard. For every campaign track reach cost conversion and time to first qualified call by channel. When a platform shakes the tree you will move spend by numbers not vibes

OpenAI circles brain computer tech. Ignore the hype. Take the signal

OpenAI is circling brain interface ideas. That does not change next quarter. It does signal an appetite to control the stack from chips to assistants to the interface itself. For marketing that means

1. Assume faster model loops and more native workspace features. Fewer tool switches if you play inside their garden

2. Do not let that collapse your data independence. Keep your clean room your event taxonomy and your evaluation sets in your control

   
   

My move

Keep creation and analysis inside a unified workspace for speed but store truth in your own data layer. That preserves freedom to swap tools without a replatform

Local models are now a real option for parts of the stack

Running competent models on your own machine is not a party trick anymore. For creative operations that means

1. Privacy friendly copy experiments for regulated clients

2. Low cost batch work such as variant generation and rough cuts

3. Faster iteration loops when the network is slow or the cloud is capped

   
   

My move

Split tasks. Use local for ideation drafts and style transfer. Use hosted models for reasoning heavy work and live production

Data cleanup beats a new toy every time

Lockheed and IBM talked publicly about cutting tool sprawl and unifying on a single data layer. That is the right order of operations for any team that wants reliable AI in production

1. Decide the system of record for contacts content and outcomes

2. Reduce duplicate tools by half

3. Add evaluation before you add another assistant

My move

Give every campaign a single source table with inputs outputs and the decision trail. When creative and ops argue the table wins

Perplexity throws a headline at Chrome. Read the moment not the headline

Whether or not a bid is real the direction of travel is obvious. Answer engines and new browsers are pulling questions away from legacy search. That changes content and spend

1. Write to win the follow up question not just the first answer

2. Ship summaries that cite your own primary research so engines prefer you

3. Buy tests in answer surfaces and measure assisted conversions not only last click

My move

Rebuild your FAQ content as a question graph. One page per question with citations media and a clear next question path

What I am watching

1. How much of our creation and QA can move into a single workspace without losing audit trails

2. Which answer engines actually drive pipeline not just traffic

3. Where local models lower cost while keeping quality steady for human review

A quick playbook to run this week

1. Create a distribution scoreboard for your top three channels

2. Define your evaluation set for creative and copy. Use it to gate launches

3. Map your data truth. One table to rule campaign inputs decisions and outcomes

4. Spin a local model for safe copy drafts and style studies. Keep sensitive data local

5. Publish one question graph page and track the follow up click

Tools I am using or testing and how to use them

1. Ollama Use case. Run a competent model on a laptop for headline generation variant copy and quick data cleaning. Keep drafts private and cheap. (Ollama)

2. LM Studio Use case. A friendly desktop app to chat with local models. Great for on plane copy edits and fast A B line tests without cloud calls. (LM Studio)

3. Perplexity Use case. Research assistant for competitive scans and quick source backed briefs. Save threads as citations in content. (Perplexity AI)

4. IBM watsonx data Use case. Build a single data layer for marketing analytics so models and dashboards read the same truth. (IBM)

5. Vertex AI evaluation Use case. Stand up basic evaluation for prompts agents and creatives. Use it to re certify campaigns monthly and before big spend. (Google Cloud)

6. Lindy meeting agent Use case. Auto capture client calls and push action items into your task system. Feed summaries into your CRM so follow ups are not missed. (Lindy)

7. Vanta AI security assessment Use case. If you sell into security aware buyers run this assessment and publish the results. It turns compliance into a trust feature for your marketing site. (Vanta)

8. ChatGPT Team workspace Use case. Centralize creation research and approvals with admin controls. Use Projects for briefs and keep a tone guide pinned so writers stay on brand. (OpenAI, OpenAI Help Center, The Verge)

Looking for a community of like-minded individuals who are interested in AI and Entrepreneurship? Join our free community here to get started: The AI Advantage Community Thank you for reading! -Shawn