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

JSESSIONID

li_at

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

building AI systems

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. 🤩

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

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.

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.

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.

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

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

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)

AI Evals Explainer

Generated with Nano Banana Pro

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

🚨 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 ❤️

AVEVA

• Spearheaded overhaul of Customer Success operations across APAC (12 markets; ~150 staff)
• Reducing escalations by 35% and improving SLA compliance to 98%
• Drove margin expansion; commercialized support offerings to lift gross margins by 4 percentage points within six months
• Piloted GTM launch for global Solutions Support, generating $3 M net-new ARR in the first quarter
• Scaled governance, documentation & process frameworks, cutting onboarding time by 50% and ensuring 100% audit readiness
• SAP ERP transformation, unifying 8 business units, reduced incidents by 42% through improved scheduling and request workflows

Senior Program Manager | Change Manager | Transformation

Cisco

• Spearheaded data-driven workflow adoption across Sales, CS, Engineering, Marketing & CX; aligning 5 functions and empowering 400+ users to make real-time decisions
• Drove change management initiatives, influencing VP/C-level stakeholders to embed AI practices, boosting team productivity by 25%
• Scaled a $16 M data platform to deliver a 360° view of the customer journey, reducing churn by 15% and accelerating cross-sell by 20%
• Piloted cross-functional automation in Finance, Sales, Marketing & Support, cutting manual handoffs by 60%

Senior Program Manager | Change Manager  | Data & AI

ServiceRocket

• Scaled automation across Marketing, Legal, Procurement, Finance, Project & Helpdesk teams
• Implementing the Atlassian stack to automate 120+ workflows and slash manual tasks by 40% 
• Drove “digital by default” shift, lifting team throughput by 50% and reducing average ticket resolution time from 24 hrs to 8 hrs 
• Spearheaded creation of a centralized workflow knowledge base, cutting onboarding ramp-up by 60%

Senior Program Manager | Change Manager | Business Automation

Acronis

• Increased APAC partner revenue by 50% through onboarding, enablement, and operational performance reviews across 25+ active MSPs.
• Developed a new quarterly business review (QBR) framework for partners, aligning metrics to sales ops KPIs and increasing forecast accuracy by 35%.
• Introduced lead-to-revenue operational tracking that improved sales pipeline visibility and partner accountability.
• Reduced onboarding time by 45% with an optimized playbook that included training, documentation, and co-selling blueprints.
• Launched an automated renewal and license management process, improving retention and reducing manual processing errors by 70%.

Program Manager | Change Manager | Channel Partner

Huawei Technologies

• Spearheaded development of a modern infrastructure networking platform for MSPs, extending refresh cycles to 3 years and cutting capex by 30% 
• Drove solution design & delivery; executed 25+ POCs on AWS/Azure, generating $1.5 M in new engagement pipeline 
• Scaled post-deployment support processes, improving platform uptime to 99.9% and reducing incident response times by 45%

Program Manager | Change Manager | Solution Architect

KEMP Technologies

• Led 30+ end-to-end deployments of KEMP load-balancing solutions across hybrid cloud environments, ensuring 99.99% uptime for mission-critical applications.
• Reduced enterprise client onboarding time by 40% by standardizing implementation workflows and introducing reusable technical architecture templates.
• Designed scalable network topologies for clients in finance, government, and healthcare, improving application delivery speed by up to 60%.
• Created pre-sales automation tools and diagnostic scripts, streamlining the solution assessment process and cutting response time to RFPs by 50%.
• Collaborated with product and operations teams to influence roadmap decisions based on customer usage data and real-world deployment challenges.

Solutions Architect

• Designed and deployed enterprise IoT architecture for asset tracking across utility and energy sectors, improving asset visibility by 80% and reducing downtime by 30%.
• Collaborated with cross-functional teams (sales, engineering, PMO) to deliver tailored smart metering solutions that aligned with client digital transformation goals.
• Led solution scoping and PoC design for large public sector clients, accelerating project sign-offs by 3 months on average.
• Integrated energy and utility data feeds into operational dashboards, enabling real-time monitoring and predictive maintenance.
• Facilitated C-level strategy sessions with clients to align IoT deployment with business continuity, asset lifecycle, and cost-optimization objectives.

Principal Consultant

• Delivered 80+ Cisco ecosystem audits, identifying $3M+ in service optimization opportunities.
• Designed and deployed IoT-enabled asset management solutions, aligned to customer infrastructure priorities.

Senior IT Engineer

PicNet Pty Ltd

IT Engineer

5G Networks Pty Ltd

IT Support Engineer

Senior Program Manager | Change Manager | AI GTM

Senior Program Manager & Change Manager with 15+ years driving $100M+ revenue growth, building data platforms, and scaling global teams across APAC and EMEA. 

Proven track record in P&L optimization, digital transformation, and stakeholder alignment; from VP to field teams.

I am passionate about:

-> P&L Management & Margin Expansion
-> Digital Transformation & Platform Scaling
-> GTM Strategy & Revenue Acceleration
-> Cross-Functional Leadership & Change Management
-> AI & Data-Driven Decision Making
-> Digital Marketing and growth Loops


Results:

-> Driving margin % growth commercializing support offerings and services

-> Built a $16M data platform at Cisco to give full 360 view of customer journey -- unified 5+ business functions

-> Change management and influencing VP / C-level stakeholders in making data driven decisions

-> Cross function leadership, building and automating workflows within Finance, Sales, Marketing, Support, Project and Product Management teams

-> Led APAC-wide customer success ops overhaul -- improving NPS by 10 pts

Houman Asefi

Australia doesn’t have a “work ethic problem.”

Lyra CEO shamed Australians in public.

I just read a post claiming Australia needs a “serious wake-up call” because people in San Francisco are still grinding at 11:30pm, taking calls after 5pm, and “having the time of their lives” doing it.

Cool.

But if you want SF hustle…

Then match SF salaries, SF equity, SF upside, SF tech density, and SF opportunity flow.

Because the culture isn’t the secret.

The incentives are.

In San Francisco:

A 24-year-old engineer can make $350K+ total comp.
Startup equity has real liquidity paths.
Ambition is rewarded, not tall-poppied into oblivion.
The ecosystem is so dense that late nights actually compound into something meaningful.

People grind because the reward curve is insane.

In Australia?

We’re asking people to stay until 11:30pm for:

AUD $120–160K salaries,
0.01% “equity,”
A cost of living crisis,
A talent market where most “startups” are actually consulting firms with hoodies.

You can import SF expectations.

But you can’t import SF culture without importing SF compensation.

The equation is very simple:

SF pace = SF pay.
AUS pay = AUS pace.

Want Australians to work like they’re in Silicon Valley?

Then pay them like they’re in Silicon Valley.

Otherwise, you’re building burnout.

And let’s be honest; the real issue is that too many Australian founders want SF output at Woolies pricing.

If you want the ecosystem to rise, amazing.

But don’t shame Australians for choosing balance when the economics don’t justify the grind.

Raise the incentives → the culture will follow.

Not the other way around.

Operations & Strategy | I write about the messy middle of tech, capital, and creativity.

Yesterday, my post about an Australian startup leader telling Australians to “work until 11:30pm to match Silicon Valley” went viral.

100,000+ impressions in a day, hundreds of comments, and now mainstream media coverage in the The Daily Telegraph.

He responded on his Linkedin page.

Let me be very clear about something that was not clear in his “explanation post”:

There was no apology. 

Not a single line acknowledging that shaming everyday Australians was wrong.

Instead, he doubled down.

He reframed. 
He clarified. 
He rationalised.
 
But he did not take responsibility.

And here’s why that matters:

1. Great leaders communicate with clarity, not excuses.

If you say something that publicly insults people; especially the country you operate in; you apologise.

You don’t dance around it.

You don’t hide behind founder-hustle clichés.

2. No founder needed this lecture.

Founders already work late nights.

CEOs already grind long hours; and they’re compensated extremely well for it.

This isn’t a revelation.

It’s reality in every country, not just the US.

3. Everyday Australians are doing it even tougher.

People are working two or three jobs just to survive the cost-of-living crisis.

People with families, mortgages, commutes, shift work, second jobs.

And they’re being told they’re “not working hard enough”?

That’s not leadership.
That’s not culture-building.
That’s not “inspiration”.

That’s disrespect.

4. And still; no apology.

Not to the tech community.
Not to the startup ecosystem.
Not to the workers he publicly dismissed.
Not to the country whose talent he expects to build his company.

Just more deflection and self-justification.

So let me say this directly:

If you publicly shame Australians, you should publicly apologise to Australians.

If you want Silicon Valley culture, match Silicon Valley salaries.

If you want people to follow you, start by owning your words.

Until then, this isn’t about hustle.

This isn’t about ambition.

This isn’t about founders.

This is about a leader who talked down to the very people he depends on; and still doesn’t see why Australians are rightfully angry.

Leadership starts with clarity.

Clarity starts with accountability.

Accountability starts with an apology.

We’re still waiting.

I once had a boss who was threatened by my LinkedIn posts.

I wasn’t posting company secrets.

I wasn’t bad-mouthing the brand.

I was sharing ideas on AI, productivity, business... stuff I was genuinely learning and exploring.

But one day, he pulled me aside and said:

"Only post about our company. Only share our approved marketing materials. That’s what’s valuable to our customers."

Translation?

“Don’t think for yourself. Don’t build a voice. Don’t grow.”

That was the moment I realized:

Some bosses don’t want you to grow. They want you to shrink to their level.

They confuse control with leadership.
They confuse silence with loyalty.
They confuse visibility with ego.

Here’s what I believe instead:

Sharing is caring ; even in business.

Creativity should be encouraged, not contained.

If your manager sees your curiosity as a threat, you’re in the wrong room.

Today, I surround myself with people who lift me up; not limit me.

If you're dealing with a boss who clips your wings, hear this:

🚫 Don’t shrink to fit.

✅ Find someone who lets you fly.

Toxic boss story time!

A few years ago, a boss pulled me aside and gave me the wildest “leadership advice” I’ve ever heard.

She said:

“When someone presents, find something in their deck they can’t answer.

 Ask questions that embarrass them.

That’s how you show strength.”

Not “ask to learn.”

Not “ask to solve the problem.”

Not “ask to help the team.”

Just ask to dominate.

Her logic:

If they stumble, you look superior.
If they freeze, you look powerful.
If they fail publicly, you win.

Crazy.

This mindset is how companies rot from the inside.

Culture isn’t built by who gets the last word... it’s built by who brings out the best in others.

And here’s the ironic part:

She didn’t last.

Two years later, she was gone.

But the lesson stayed with me:

👉 Not everyone asking a question is curious.

 Sometimes they’re performing.

👉 Not every critic wants you to win.

 Sometimes they’re protecting their ego.

👉 Not every “tough leader” is actually helping the company.

 Sometimes they’re just loud.

So how do you build a high-performing culture when you’re surrounded by this energy?

A few things helped me:

1. Model the behaviour you wish existed.

 Not the behaviour you inherited.

2. Ask questions to clarify, not to corner.

3. Celebrate good work publicly. Give feedback privately.

4. Protect psychological safety... it’s the oxygen of performance.

5. Don’t mirror toxic people. Outlast them.

Culture isn’t built by titles.

It’s built by habits.

And sometimes the most powerful thing you can do is simply refuse to play someone else's broken game.

Question for you:

Have you ever had a manager give you advice that made you think, “There is no way this is how leadership is supposed to work”? 

What did you learn from it?

People quit on day 47. 

I didn't. 

Here's what happened.

Every spike you see on this chart?

I wrote that day.

Every flat line you see?

I wrote those days too.

LinkedIn’s algorithm is brutal when you’re an up-and-coming creator.

You can pour your whole heart into a post… and the platform rewards you with silence.

Most people stop there.

Because inconsistency is easier than invisibility.

What changed everything for me was this simple decision:

I will show up every single day.

Regardless of results.

Every. Single. Day.

And something interesting happens when you do that for long enough:

The algorithm can’t ignore you.

Your audience can’t ignore you.

Your work rate becomes your advantage.

The graph you see is not 1.5M impressions.

It’s 1.5M proof points that consistency always beats talent, luck, and algorithms.

If you’re in the early stages of building:

 Don’t chase virality.
 Chase volume.
 Chase discipline.
 Chase the version of you who keeps showing up when no one is watching.

Because one day...

the platform, the audience, and the opportunity will all catch up.

Keep going. 

You’re closer than you think.

Last week I got roasted on LinkedIn for saying Australia’s startup ecosystem is weak.

Not the founders ... the ecosystem.

People replied with the same tired line:

“Aussie founders are too chill. Too comfy. Not hungry enough.”

Give me a break.

Every founder I know; in Sydney, Perth, Hobart, whatever ... works like a psycho when they’re building from zero to one.

Hunger is not the issue.

BUT:

🇦🇺 Australia has 9 unicorns.
🇺🇸 The U.S. has almost 500.

Not because we’re lazy.

Because our ecosystem is designed to produce… nothing.

Let’s talk numbers:

California + New York VC investment (2024):
 👉 $140B+ USD

Entire Australia (2024):
 👉 $1.5B USD

That’s not underfunding.

That’s negligence.

Meanwhile, Australian capital is busy doing… what?

Buying the 17th investment property and high-fiving each other over a 9% yield.

Risk appetite here is basically zero.

Everyone wants a guaranteed-to-double house, not a moonshot company.

And then we wonder why we’re not producing Atlassian every year.

Australia could be a top-3 innovation nation behind the U.S. and China.

We have the brains.
We have the talent.
We have the ambition.

But we keep acting like a mining economy pretending to be a tech nation.

If we’re serious about fixing this:

1️⃣ Government needs to stop worshipping iron ore and start co-investing in deep tech
2️⃣ Angel incentives need a reboot (stop punishing early-stage risk)
3️⃣ R&D refunds shouldn’t feel like a Centrelink appointment
4️⃣ Build a sovereign AI fund and actually bet on something
5️⃣ Stop celebrating 20-year-old property investors like they’re visionaries

Australia isn’t lacking founders.

It’s lacking courage.

Until we fix that, 9 unicorns is not a stat ... it’s a national embarrassment.

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The Battle

Armand Ruiz

Houman Asefi

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taplio

The Battle

Armand Ruiz

Houman Asefi

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Armand Ruiz's LinkedIn Performance

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Performance ScoreArmand Ruiz's LinkedIn Performance