Armand Ruiz

Director Data Science & AI Elite

Leading the Product Management org for the AI Portfolio of products focused on Generative AI and Traditional ML. Products include: watsonx.ai and watsonx.gov

Vice President of AI Platform

I am building the world's largest team of 𝐀𝐈 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 by

- Recruiting hundreds of new engineers around the globe
- Reskilling our existing talent
- Offering them top-quality training in new-generation AI

Responsible for Pre-Sales Generative AI Pilots with the new IBM watsonx.ai Platform.

Director watsonx team - Client Engineering

Director of the group IBM Data Science Elite team. We prove the value of IBM Watson solutions by co-creating pilots with our customers. I managed a team of 90+ data scientists that worked on 100s of complex AI implementations.

Director Data Science

I manage a rockstar team of Product Managers leading the product portfolio of IBM Watson AI products.

Product Director - Product Management, Watson Studio and Machine Learning

Leading new Data Science products for IBM

Lead Product Manager - IBM Data Science and Watson Machine Learning

Bring Open Source data science tech to IBM stack

Product Manager for Data Science Open Source Extensibility 

In charge of Data Science projects in EMEA

Data Science Advocate - IBM Data Science & Machine Learning

Support sales in Data Science opportunities

Technical Seller Data Science & Smarter Cities

Internship to bring new innovations to IBM

Intern - Technical Software Engineer, Extreme Blue

Vodafone

Optimize 3G network coverage using Data Science

Data scientist - Radio Optimization Engineering

Federació Catalana de Tennis

Tennis Coach

VP of Product - AI Platform @IBM

As the Director of AI Engineering at IBM, I lead the watsonx team, a global group of over 1,000 AI Engineers who work on cutting-edge Generative AI solutions for various industries. With more than 12 years of experience in AI, I have a passion for democratizing practical AI skills and empowering professionals to leverage AI to transform their business.

Previously, I managed IBM's Data Science and AI Elite, delivering complex AI implementations for hundreds of clients. 

I also led the product management of IBM Watson Studio and Watson Machine Learning, an AI platform used by hundreds of customers and SaaS apps powered by AI. I hold a Master of Science in Telecommunications from Universitat Politècnica de Catalunya and speak three languages: Spanish, Catalan, French, and English.

Connect with me!

✉️ JOIN MY NEWSLETTER: Every Saturday morning, you'll get 1 actionable tip to launch and grow your AI strategies in business in less than 4 minutes. → GO HERE: https://www.nocode.ai/

📧 Email: armand@nocode.ai
X: @armand_ruiz


"It is kind of fun to do the impossible"
All opinions are my own and don't represent my employer.

Called it. 🎯 The Model Context Protocol (MCP) is officially moving to the Linux Foundation under the new Agentic AI Foundation.

I’ve been saying for a while that open governance is the only viable path forward for AI infrastructure. Today’s news proves it.

This brings Anthropic, OpenAI, Microsoft, Google, and others under one neutral banner. 

10,000+ active servers. 
97M+ SDK downloads. 
Zero vendor lock-in.

If you want an ecosystem to flourish, you have to open it up. The era of Agentic AI just got a serious upgrade.

Read the news here: https://lnkd.in/g7vy5-x2

At NeurIPS2025, Google presented Titans, a new architecture that combines the speed of RNNs with the performance of Transformers. 

The trade-off between infinite context and computational cost is the biggest hurdle in AI right now.

While Transformers are accurate but expensive to scale, and linear models are fast but lose detail, Titans delivers the best of both worlds by introducing Test-Time Memorization.

The Core Innovation: Instead of a static state, Titans uses a deep neural memory module that actively updates while running, utilizing a "Surprise Metric":
- Low Surprise: Routine data is processed efficiently without clogging memory.
- High Surprise: Anomalies and key details are instantly integrated into long-term memory.

The Results:
- Scales effectively to 2M+ token context windows.
- Outperforms larger models (including GPT-4) on extreme long-context reasoning benchmarks.
- Generalizes beyond text to genomics and time-series.

This is a move from passive processing to active, efficient learning.

Jensen just dropped a masterclass on the future of AI. 

It was great to hear from him in a long-format podcast conversation. 

Three key insights he shared:

1/ Human language is the new syntax: The "tech divide" is closing. You won't need C++ to code the future; you just need to speak.

2/ The "30-Day" Mindset: Nvidia operates like they are always 30 days from bankruptcy. This paranoia fuels their innovation and resilience.

3/ Safety is an engineering problem: Forget the sci-fi movie plots. AI safety is just like cybersecurity—as capabilities rise, so do the guardrails.

Pic taken during my epic commute

No alternative text description for this image

Current mood: "Joining a new company is akin to an organ transplant - and you're the new organ. If you're not thoughtful in adapting to the new situation, you could end up being attacked by the organizational immune system and rejected."

Learning fast, exploring the culture, and pushing myself to bring out the best work I’ve ever done. 

Loving the challenge. 🤩

As a leader, your job is to run a tight ship.

That doesn’t mean control. It means precision.

Clear goals. 
Clear roles. 
Clear standards.

In fast-moving teams, especially in AI, ambiguity is expensive and confusion kills momentum.  Alignment compounds it.

Gemini 3 is here and it isn’t just another model release. It’s Google firing on every AI cylinder: infra, research, product, and distribution.

A few things stand out:

1.	Full-stack advantage, finally realized.
Gemini 3 is the first model shipped into Google Search on day one. That’s not just marketing ; it’s how you put frontier AI in front of billions.

2.	The agent era is no longer theoretical.
With Deep Think mode, long context, multimodal understanding, and real-time planning, Gemini 3 moves beyond chat. It’s not a chatbot. It’s a problem-solver.

3.	AI is getting better at “reading the room.”
It reflects a core shift: models are starting to grasp intent and nuance and not just data.

Benchmarks back it up: topping LMArena, solving “Humanity’s Last Exam”, setting a new bar in math and science tasks. But that’s the sideshow.

More info here: https://lnkd.in/gWeXGwhC

I cannot wait to try it out

JSESSIONID

li_at

Talks about #ai, #nocode, #deeplearning, #machinelearning, and #artificialintelligence

building AI systems

𝗔𝗜 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 𝟭𝟬𝟭

Current AI is often described as a "statistical correlation machine." It predicts the next word or pixel based on patterns it has seen before. World Models represent a shift toward causal understanding—giving machines a way to simulate the future.

What is a World Model? Think of it as an internal flight simulator. Instead of just recognizing an environment, the AI builds a mental model of how that environment works. It uses this model to test hypotheses ("If I drop this glass, it will break") without needing to actually perform the action.

The "𝗟𝗮𝘂𝗻𝗱𝗿𝘆" Example Imagine training a robot to fold clothes.

- 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot must physically interact with the clothes millions of times, learning through endless trial and error.

- 𝗪𝗶𝘁𝗵 𝗮 𝗪𝗼𝗿𝗹𝗱 𝗠𝗼𝗱𝗲𝗹: The robot simulates the physics of the fabric in its head. It plans the fold internally, predicts the outcome, and only moves its arm when it is confident. This results in safer, faster, and more generalizable learning.

𝗛𝗼𝘄 𝘁𝗵𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝘀 𝗳𝗿𝗼𝗺 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗟𝗟𝗠𝘀: While Large Language Models (LLMs) are masters of syntax and semantics, they often struggle with physical causality. World Models differ because:

𝟭. 𝗧𝗵𝗲𝘆 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗲𝗰𝗮𝗹𝗹: They don't just retrieve information; they run mental experiments.

𝟮. 𝗧𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻 𝗹𝗮𝘄𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗹𝗲𝘀: They infer the physics of their environment from data rather than being hand-coded.

𝟯. 𝗧𝗵𝗲𝘆 𝗮𝗿𝗲 𝗽𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲: They predict "what happens next" in a state-space, not just a token-space.

𝗧𝗵𝗲 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: The next frontier is the convergence of these technologies. By combining the reasoning of LLMs with the grounding of World Models, we move closer to Embodied Intelligence—software that can read, watch, plan, and act in the physical world.

Scaling laws in AI are nowhere near finished. The latest Gemini 3 results show it clearly.

Pre-training: Many believed progress had plateaued. Yet the jump from 2.5 to 3.0 is one of the biggest leaps we have seen. When you improve data, architecture, and compute together, you keep breaking ceilings. No walls in sight.

Post-training: This might be the real frontier. We are still early in how we align, steer, and specialize models. The innovation curve here is just beginning, and the gains on 3.0 prove it.

Good insights from Deepmind engineers this week.

Better models are not coming slowly. They are coming faster.

Plan for continuous capability jumps, not incremental upgrades. The disruption curve is steepening.

it is very cool to work everyday with people in person. i think  product innovation and magic really happens with real human interaction

AI is making 10X engineers 1000X, and 1X engineers 0.1X 

stealing this quote from Shadi Copty 🙃

Fortusight, Founder | AI advisor | Ex-Dir of Llama @Meta & Ex-VP of Watson @IBM

AI Evals Explainer

Generated with Nano Banana Pro

It’s insane how many hours I’ve seen senior technical leaders burn on architecture charts. 

All that time spent polishing pixels... now you just describe it in plain English and boom and nano banana AI handles the rest.

By the way, this is a solid, modern and agile AI stack. To build at x10 speed you need to have the right building blocks.

Fortusight's AI stack

A lot of people ask me this question, I'll start with the absolute core:
1. I use Agno for multi-agent framework, they're fast, elegant and robust.
2. Exa for web grounding, I consider Exa a bigger booster than LLM choice for my use case.
3. FMP for premium, clean, reliable financial data turned into tailored tools.

For LLMs, I use O3 for multi-agent orchestration and planning, GPT 4.1 for digesting web content and supporting my A1Facts flow; Flash2.5 for structured extraction and Gemini 3 pro for synthesizing investment thesis.

I constantly progress the choices of LLMs prioritizing quality over price.

For tools, I use Cursor with Opus 4.5 (recently replacing Gemini 2.5 pro); and Figma make - which I absolutely love.

For internal ops agents, (1) managing coverage requests (2) digesting user feedback (3) analyzing product analytics (4) writing my weekly newsletter -- all built with Agno and Gemini 3 pro.

Less exciting but just as critical :-) Streamlit for front end [being replaced with react], and Firebase for database/auth/analytics -- both choices have been really critical for prototyping rapidly with my users.

The "secret sauce" is secret (lol), but I will say solving factuality in evolving market dynamics is a significant effort, and using expert-system techniques and simulations augments LLMs where they are critically weak.

Happy to share more if you have any questions -- and obviously here's the link if you want to try it out (https://lnkd.in/g7eR86dr)

Introducing SAM 3D: Powerful 3D Reconstruction for Physical World Images

and it’s not your typical 3D reconstruction tool.

It does what previous models couldn’t:
- Reconstruct real-world objects and scenes from a single image.
- Handle occlusion, indirect views, and cluttered backgrounds.
- Estimate human pose and shape with surprising accuracy.

Why does this matter?
Because for the first time, we’re seeing 3D perception at the scale, quality, and accessibility of today’s 2D models.

The architecture behind SAM 3D borrows from LLMs: pre-training on synthetic data, followed by post-training on real-world images using a human-in-the-loop ranking engine. The result is a feedback loop that continuously improves both the data and the model.

The implications stretch far beyond creative media. Robotics. E-commerce. Sports medicine. Interactive avatars. You name it.

And the best part? 

It's fast. 
Real-time fast.

Meta’s already using SAM 3D to power a “View in Room” feature in Facebook Marketplace; turning static listings into immersive experiences.

The gap between virtual and physical is shrinking. SAM 3D is a serious leap forward.

Learn more:
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗯𝗹𝗼𝗴: https://lnkd.in/g8w6dvAB
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gd_HQE9c
- 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gp3p9jaf
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀: https://lnkd.in/gfwmcKGK
- 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆: https://lnkd.in/g9CPHEXJ
- 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: https://lnkd.in/guSDTnU3

A sharp, modern AI tech stack for building a serious startup.

Fortusight from Shadi Copty lays out a blueprint that actually reflects where the industry is going:

multi-agent architectures, real-time grounding, premium data, and a diversified LLM strategy tuned to specific jobs rather than brand loyalty.

1/ Agno for agent frameworks.
2/ Exa for grounding.
3/  FMP for clean financial data.
4/ A portfolio of models (O3, GPT-4.1, Flash2.5, Gemini 3) each picked for its superpower.

Cursor + Opus 4.5, Figma Make, Firebase, and Streamlit/React for fast product loops.

And a layer of expert systems + simulation to fix factuality where LLMs keep failing.

This is the kind of stack that lets a small team punch far above its weight.

🚀

Open article: Financial Analysis App by fortusight.streamlit.app

Exciting project for you all to check! I tried an earlier prototype and it was very impressive

Startup life, here I come :-)

Thrilled to share with you my new adventure, Fortusight - a project I've labored on nights and weekends for the last 2 years.

It's a tool for the serious investor that wants to deeply understand a company before buying and holding for 5-10 years.

It uses AI Agents to read through filings, earning calls, analyst reports, news, press-releases - forms an opinion on a company's competitive position, management's friendliness to shareholders, and creates bull/base/bear scenarios for valuation.

To quote a friendly hedge fund manager "I used to have 5 people doing all of this" - each company is analyzed for 192 different perspectives, with A1Facts to ensure factuality for high stakes decisions.

Here's how you try it: https://lnkd.in/g7eR86dr

Let's go 🚀 🚀 🚀

🚨 Stop scrolling. The math on this is barely real.

Get a $799/mo institutional AI research platform for just $50/mo.

The catch: Only 10 spots open. 

RIAs & Fund Managers—this is a complete no-brainer. 👇

https://fortusight.ai/

I need your help 🙏

I'm looking for design partners for my baby startup that just got pre-seed funded.

If you know a wealth fund manager, a small RIA, a family office that wants to reduce their equity research costs from ~$75K to less than what they pay for starbucks coffee - share this link with them: https://fortusight.ai  

For 10 design partners, I'm giving away Fortusight for $50/month instead of $800/month for a year in exchange for feedback to help guide the product's development.

The beta with our users has been incredibly successful, with recurring usage of our equity deep dives.

This will propel the company forward, and one day I will find ways to repay your kindness - many thanks ❤️

Gradient Labs

- Building an AI consultancy that turns 6-hour tasks into 15-minute wins (without the usual tech headaches)

- Lead the "keep it surprisingly simple" approach that helps Fortune 500 teams fall in love with AI automation

- Created a jargon-free zone where teams cut manual work by 95% and actually enjoy the process

- Transform complex AI projects into "why didn't we do this sooner?" success stories

Founder

Recorded Future

- Generative AI Enablement: Initiated the adoption of GenAI tools within the team, expanding from 5 to 60 seats in 1.5 months. Demonstrated value through practical use cases and secured leadership approval for broader deployment.

- Automation of Data Processing: Led an automation project to process unstructured data with LLMs, reducing processing time by 47% and saving 99% in labor costs, equivalent to freeing up ~6 workdays per month.

- Advisory to Client CISOs: Provided strategic insights on GenAI risks and defenses, advising CISOs on practical uses of LLMs for both operational effectiveness and security.

- Production Application Support: Supported development teams in enhancing LLM applications by advising on optimal prompting and RAG techniques, increasing their utility and accuracy in production.

Senior Manager - Strategic Intelligence 

- Enable our macro trend and executive research teams to deliver high-quality content to Recorded Future’s most strategic clients

- Research, create and deliver CISO-level presentations on a variety of security-related topics (e.g. cloud security, crypto money laundering, evolving actors, etc.)

- Developing a high-output research team that’s providing world-class threat intelligence

Manager - Strategic Intelligence 

- All of the below, but tailored toward Recorded Future’s most strategic clients (e.g. large, complex, and advanced security programs)

Senior Threat Intelligence Consultant

- Continuously conveyed timely and relevant threat intelligence to executives for improved risk management and decision-making

- Worked alongside architecture and engineering teams to ensure quality API implementation by troubleshooting, documenting, and reviewing architecture designs before and after integrations

- Trained clients on various fundamental and emerging security topics, such as maturing threat-hunting processes, operationalizing MITRE, implementing Sigma detection analytics, etc.

- Worked with fortune 500 security teams, maturing their threat-hunting processes with data-driven and objective identification of threat actors via the MITRE ATT&CK Framework

Threat Intelligence Consultant 

Mastercard

Designed and implemented strategies in emerging technology, digital competition, regulation, and consumer trends in the digital payment ecosystem. 

- Led nine separate initiatives resulting in three company-wide projects. Initiatives spanned across machine learning, open data/API infrastructure, blockchain, homomorphic encryption, Digital I.D., and climate change.

- Designed the overall go-to-market strategy for a key Mastercard digital product globally. This strategy consisted of prioritizing 35+ markets based on product and ecosystem readiness. 

- Ensured strong team collaboration and communication by managing cross-functional teams from every region.

Manager -  Digital Strategy & Market Solution Deployment

Developed key executive relationships with digital stakeholders across HSBC via a weekly innovation newsletter and educational discussions. This weekly newsletter organically grew from 2 HSBC employees to 80+ HSBC employees, within a 4-month timeframe, opening up new partnership opportunities for both organizations.

- Advised HSBC on how Mastercard could assist with a re-platforming initiative moving from a monolith architecture to a cloud-based microservice architecture.

- Led five regional account teams with prioritizing different digital initiatives by analyzing competition, technology, and consumer trends, ending in four global pilot projects.

Innovation & Strategy Lead

Performed as a  generalist across 4 separate backend payment systems 

- Trained 25 employees via 30+ video tutorials.
- Self- educated on how to use automated testing software.
- Saved an estimated 200+ hours of employee time.  
- Created 20+ training videos empowering employees to automate their own testing processes.
- Co-created two intern summer programs, mentored 30+ interns. 
- Presented at three separate universities to recruit on behalf of Mastercard.

Technical Configuration & Testing Specialist

MasterCard

- Acted as the leader and project manager leveraging the AGILE methodology for an intern group developing an automated customer questionnaire, this project successfully resulted in saving 100's of hours worth of customer implementation time.

Summer Intern - Customer Configuration Management

Consumers Energy

- Assisted in producing informative films for gas customers. 

- Mapped out, budgeted, developed, and marketed new gas piping projects for customers throughout Michigan.

- Updated customer information using Microsoft Access and SAP software.

Cooperative Assistant

•	Validated mailing addresses within SAP
•	Increased the data quality verification process through developing an automated application for a fortune 500 company
•	Attended weekly design meetings for IT security, which broadened my view in this area.
•	Built logic statements within Excel to automate a matching process between two separate address databases

Customer Data Quality Intern

“We need AI!” said every company ever. But most end up with expensive tools nobody uses. I fix that. Here's my journey:

2015-2020: At Mastercard, I learned something important. Most companies get excited about new tech but struggle to make it work. I led 9 big tech projects. Three became company-wide successes.

2020-2024: At Recorded Future, I helped big companies use AI the right way. 

The results were clear:
- Cut work time by 47% with AI
- Cut proposal creation from 5 days to 30 minutes
- Saved teams 6 extra days each month

Today: I run Gradient Labs. We make AI simple and useful. Our clients cut their manual work by up to 95%. Best part? They keep full control of their systems.

Want to learn about AI without the confusion?

- Weekly tips on LinkedIn → linkedin.com/in/dylantdavis
- Step-by-step guides on YouTube → youtube.com/@d-squared70
- Free AI insights in my newsletter → https://embeds.beehiiv.com/025f95aa-709c-46bc-88f7-1550d56f0714

Ready to see how AI can transform your business? Book a quick 15-minute chat at gradientlabs.co

P.S. I believe AI should be easy to use. No complex jargon. No fancy terms. Just tools that work.

Dylan Davis

You have a $3,000/month database bill.
You want to add AI search to your product.
Your new bill: $30,000/month.

This is why most companies never ship AI features.

Here's what nobody tells you:

1KB of text becomes 16KB of vectors.

You're not just storing your data anymore.
You're storing a bloated version that's 16x bigger.

And it gets worse:

Traditional databases store everything in RAM or on 3 expensive SSDs.

At $5 per gigabyte for RAM.
At 60 cents per gigabyte for SSDs.

Your cute little feature just became a budget killer.

So most teams do this:

They limit which users get AI features.
They restrict how much data gets indexed.
They make the search slower to save money.

They ship a worse product because they can't afford better infrastructure.

But here's what changed:

Object storage costs 2 cents per gigabyte.
That's 30x cheaper than SSDs.
250x cheaper than RAM.

The trick is knowing which data needs to be fast.

Your active users? Cache it. Lightning fast.
Your inactive data? Keep it cold. Dirt cheap.

Think about it:

Not every codebase is being edited right now.
Not every document is being searched this second.
Not every customer is active simultaneously.

So why pay to keep everything hot?

Cursor indexes millions of codebases this way.
Notion handles 10 billion vectors.
Linear doesn't think about infrastructure anymore.

They all cut costs by 90%+.

Not by using less data.
By only paying for speed when they need it.

This is how you ship AI features profitably:

Stop storing everything in expensive RAM.
Put cold data in cheap object storage.
Cache only what's actively used.

Your $30K bill becomes $3K.
Your margins survive.
Your AI features ship.

The future of AI products isn't better models.

It's better infrastructure economics.

—

(Credit: Simon Hørup Eskildsen, CEO of turbopuffer)

—

P.S. If you want my free 30-day AI insights series, comment "Purple Unicorn" below. 🦄

I help mid-size teams with AI automation | Save time, cut costs, boost revenue | No-fluff tips that work

Stop asking what AI can do.
Start asking what your team hates doing.

Vercel's CTO discovered the sweet spot for AI agents by accident.

They went around the company asking one simple question:
"What do you hate most about your job?"

The answers revealed something powerful.

These tasks share 3 traits:

They're boring because they're tedious and repetitive.

They require mini-judgments that prevented full automation.

They eat up substantial time in people's actual jobs.

This is the perfect zone for current AI agents.

Vercel deployed 3 internal agents using this principle:

Lead qualification for sales inquiries.
Pre-work for abuse reports.
Data warehouse query analysis.

All three handle work people dreaded doing manually.

The pattern works because these problems are:

Easy enough for today's agents to solve.
High impact because they consume real work hours.
Already proven painful by the team who does them.

Most companies overthink AI deployment.

They chase the biggest problems or the flashiest use cases.

But the best opportunities hide in the work your team complains about.

The stuff that makes them say "I hate doing this" every single day.

That's where agents deliver immediate value.

(Malte Ubl, CTO of Vercel)

—

P.S. If you want my free 30-day AI insights series, comment "Purple Unicorn" below. 🦄

Most AI companies are stuck optimizing the wrong thing.

They're still playing the cherry-picking game.

Show the best output. Hide the rest. Hope no one notices.

But the Google DeepMind team behind Imagen 3 realized something:

The worst image matters more than the best.

Here's why:

When every model can generate one perfect image, that's not a competitive advantage.

The real key is consistency.

What's the worst output someone gets when they need your model to work?

That's the metric that determines if professionals can actually use it.

Think about it:

A designer can't use a model that works 80% of the time.
An advertiser can't risk a broken output on deadline.
A teacher can't show students results that occasionally fail.

The teams building Gemini 2.5 Flash (aka "Nano Banana") shifted their entire evaluation strategy.

They stopped celebrating their best outputs.
They started fixing their worst ones.

This is what they call the "lemon picking phase."

And it had a huge impact.

Because when you raise the floor instead of the ceiling, you unlock entirely new use cases.

Professional workflows that require reliability.
Educational tools that need factual accuracy.
Business applications where consistency matters more than occasional brilliance.

The insight applies beyond AI image models:

Your product's weakest moment defines its utility.
Your team's worst performer sets your baseline.
Your most frustrated customer reveals your real quality.

Excellence isn't about your highlight reel.

It's about eliminating your lowlights.

––

(Credit: Oliver Wang and Nicole Brichtova from Google DeepMind's)

—

P.S. If you want my free 30-day AI insights series, comment "Purple Unicorn" below. 🦄

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