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Choosing the Right AI: Agents, LLMs, or Algorithms?
Meta pushes back on OpenAI claims, U.S. plans AI power expansions, German watchdog warns Apple and Google about DeepSeek and more!
Everyday AI
June 27, 2025
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Today in Everyday AI
6 minute read
š Daily Podcast Episode: Choosing the right AI isn't simple with algorithms, LLMs, and agents vying for attention. Discover the nuances as we break down agentic AI, data's role, and risk management in AI projects. Give it a listen.
šµļøāāļø Fresh Finds: OpenAI announces next DevDay, Google improves Ask Photos and ChatGPT is now connected to Canva. Read on for Fresh Finds.
š Byte Sized Daily AI News: Meta pushes back on OpenAI claims, U.S. plans AI power expansions and German watchdog warns Apple and Google about DeepSeek. For that and more, read on for Byte Sized News.
š§ Learn & Leveraging AI: Why spend hours trying to figure out the right AI workflow? Hereās how you can easily decide when to use what AI and how. Keep reading for that!
ā©ļø Donāt miss out: Did you miss our last newsletter? We talked about Meta scores legal win in copyright battle, OpenAI CEO calls out NYT publicly, Meta snatches 3 researchers from OpenAI and more. Check it here!
Choosing the Right AI:āÆ Agents, LLMs, or Algorithms? š¤
Everyone wants the latest and greatest AI buzzword.
But at what cost?
And what the heck is the difference between algos, LLMs, and agents anyway?
Also on the pod today:
ā¢ Importance of Data in AI Training š
ā¢ Risk Factors in Agentic AI Projects šØ
ā¢ Innovation through AI Experimentationš”
Itāll be worth your 33 minutes:
Listen on our site:
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Subscribe and listen on your favorite podcast platform
Listen on:
Hereās our favorite AI finds from across the web:
New AI Tool Spotlight ā Aha is an AI-powered influencer marketing team, Twos is an AI-powered list and note-taking app, and the Shorty app helps you create viral videos with AI.
OpenAI ā OpenAIās next DevDay will be October 6, 2025.
OpenAI DevDay
Oct 6, 2025 in San FranciscoOur biggest one yet:
- 1500+ developers
- Livestreamed opening keynote
- Hands-on building with our latest models & tools
- More stages & more demosdevday.openai.com
ā OpenAI (@OpenAI)
4:46 PM ā¢ Jun 26, 2025
OpenAI is hiring the team behind AI recommendation startup Change Minds.
Google ā Google is rereleasing its Ask Photos AI tool with improvements like faster speed.
Google Gemini has added a new AI function in Sheets.
Content Creation ā You can now connect Canva to ChatGPT
AI Audio ā Suno AI has acquired a browser-based editing tool called WavTool.
AI in Government - Denmark is tackling deepfakes by giving people copyright to their own features.
1. Meta Pushes Back on OpenAIās $100M Signing Bonus Claims ā
At Metaās recent all-hands meeting, CTO Andrew Bosworth dismissed OpenAI CEO Sam Altmanās claim that Meta was offering $100 million signing bonuses to poach AI talent, calling it an exaggeration focused on a very small group of senior leaders.
Bosworth acknowledged Meta is actively recruiting from OpenAI and has several hires in the pipeline but emphasized the broader AI talent market isnāt as overheated as Altman suggests. This exchange highlights the fierce competition for top AI experts amid rapid advancements in superintelligence projects.
2. U.S. Accelerates Energy Access to Power AI Expansion ā”ļø
The Trump administration is gearing up to roll out executive actions aimed at easing power grid connections and freeing federal land to fuel Americaās AI data centers, according to Reuters. This move comes as AIās explosive growth demands a surge in electricityāpower needs could grow more than thirtyfold by 2035, straining existing infrastructure.
By cutting red tape on energy projects and streamlining permits, the administration hopes to jumpstart AI expansion and maintain a competitive edge against China.
3. German Data Watchdog Targets Chinese AI App DeepSeek šØ
A German data protection official has urged Apple and Google to remove the Chinese AI app DeepSeek from their app stores, citing illegal data transfers to China and inadequate compliance with EU privacy laws. This move follows Italyās earlier ban, as concerns grow over Chinese companiesā access to personal data under national influence.
The German commissioner emphasized that DeepSeek failed to provide convincing proof of data protection measures, pushing for stricter scrutiny on apps operating cross-border.
4. Googleās Gemini AI Gains More Phone Privacy Control š±
Starting July 7th, Googleās Gemini AI will let Android users control phone features like calls and messages even if they opt out of data sharing for AI training, according to Android Police. This update means Gemini can act more like a personal assistant without feeding user conversations into Googleās AI models, addressing privacy concerns that initially caused confusion.
While Gemini replaces Google Assistant later this year, users retain the power to disable app connections anytime, keeping control over their data.
5. Meta Eyes Voice Cloning Startup Play AI šļø
Meta is reportedly in talks to acquire Play AI, a startup specializing in voice cloning technology, aiming to boost its consumer-facing AI capabilities, Bloomberg reports. Play AIās platform allows users to clone voices for applications like customer service, which could soon integrate into Metaās creative tools alongside its existing chatbot and video features.
This move signals Metaās intent to deepen its AI tech stack by adding audio innovations, potentially reshaping how creators and businesses use voice AI.
6. Google Unveils Doppl: AI-Powered Virtual Outfit Try-On App š
Google just launched Doppl, an experimental app available now in the U.S. that uses AI to create a digital version of yourself for virtually trying on outfits from photos or social media screenshots. Unlike previous virtual fitting tools that showed clothes on generic models, Doppl personalizes the experience by animating your own body, even generating videos to preview how clothes move in real life.
This move builds on Google Shoppingās virtual try-on tech but aims to make style exploration more accessible and interactive in a standalone app.
š¦¾How You Can Leverage:
Michael Abramov runs a data labeling company with 480 employees.
Every single day, his calendar shows a blocked 30-minute slot.
Not for strategy meetings. Not for investor calls.
For playing with AI tools like a curious kid testing new toys.
This isnāt productivity theater. Michaelās company literally prepares datasets that train AI models for other businesses.
He watches AI startups get āblade cutā by Big Tech weekly. His survival depends on knowing which tools actually work versus which ones just look impressive on LinkedIn demos.
The uncomfortable truth about choosing AI? Most companies are making terrible decisions because theyāre chasing buzzwords instead of solving real problems.
We walked through the messy reality of picking between algorithms, large language models, and agents.
Michael shared his framework for avoiding the ācorporate bladeā thatās slicing through AI companies. Plus why being ādata-drivenā might be destroying your decision-making process.
Here are three insights that will change how you think about AI implementation.
1 ā Personal AI Experimentation Beats Team Workshops š§āš¬
Michael discovered something counterintuitive about AI adoption.
When CEOs experiment personally before delegating to teams, success rates skyrocket.
His approach eliminates what he calls the āmost people in the world problem.ā Employees make sweeping statements like āmost people are afraid of losing jobsā without any actual data. They extrapolate personal fears into universal truths.
Personal experimentation works differently.
You naturally gravitate toward your biggest daily pain points. Email chaos. Calendar nightmares. Slack overwhelm.
These become your organic testing ground.
Michael told us exactly how this plays out. You stop asking philosophical questions like āShould we use agents or LLMs?ā Instead, you ask tactical ones: āWhat fixes my email problem right now?ā
The magic happens when leaders share specific results, not general AI enthusiasm. Michaelās team sees concrete examples of time saved, not vague promises about transformation.
His company even runs weekly technical education sessions where employees show each other what actually worked. No formal presentations. Just real tools solving real problems.
Try This:
Block 30 minutes daily for two weeks. Pick your most frustrating daily task. Test three different approaches against it - a simple algorithm, an LLM tool, and an agent-based solution. Document actual time saved, not how impressed you felt.
Share specific results with your team using exact numbers and concrete outcomes.
2 ā The Corporate Blade Cuts Faster Than You Think š”ļø
The biggest threat to AI startups isnāt competition from other startups.
Itās getting sliced by giants who can replicate entire business models in weeks.
His example? Perplexity.
Multibillion-dollar valuation. Impressive growth metrics. But their entire value proposition is wrapping ChatGPT with better search capabilities.
OpenAI could recreate this in two weeks if they wanted.
This isnāt theoretical. Michael watches calendar assistants, AI therapists, and specialized agents get absorbed into major platforms monthly.
The pattern reveals something brutal. Most AI businesses are actually feature requests for existing platforms.
Companies building on foundation models without defensible moats? Theyāre essentially product managers for Big Tech.
When your entire value proposition becomes one menu option in ChatGPT, you donāt have a business.
Smart enterprises recognize this dynamic works both ways. Instead of betting on specialized AI vendors, theyāre developing internal capabilities that platforms canāt simply absorb.
Try This:
Audit your current AI vendor relationships this week. For each tool, ask one question: āCould this become a native ChatGPT feature within six months?ā If yes, start building internal alternatives immediately.
Focus your AI budget on tools that integrate with proprietary data or processes competitors cannot replicate.
3 ā Pseudo Data-Driven Thinking Kills AI Projects šµ
Michaelās team taught him something unexpected about being ādata-driven.ā
It often creates worse outcomes than making decisions with zero data.
After pushing his team to be more analytical, employees started presenting āproofā that proved nothing. Theyād correlate random metrics and claim causation.
Itās more dangerous than gut instinct because it appears scientific.
People relate things that are completely unrelated. They say things like āmost people in the world are afraidā without defining what āmostā means, which people, or where that data came from.
True data quality requires understanding what metrics actually predict AI success.
Most companies track the wrong things. Adoption rates instead of business impact. Chatbot engagement instead of resolution efficiency. Automation hours instead of customer satisfaction changes.
The companies winning with AI focus obsessively on outcome metrics that existed before AI arrived. Customer response times. Error rates. Revenue per employee.
If you canāt draw a clear line from your AI tool to improvement in pre-existing business metrics, youāre probably fooling yourself.
Try This:
Identify three AI tools your company uses right now. Stop measuring adoption metrics completely. Instead, track business outcomes that existed before AI - response times, error rates, or revenue per employee.
Set a 90-day deadline. If you cannot draw a direct line from the AI tool to measurable improvement in these pre-existing metrics, eliminate the project.
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