Ashley_Nicholson

Six Figure Tech Founder Helping You Become More Tech Savvy and Keep Up with New Technology

[Ashley_Nicholson @AshleyNich63890]

@freehand_ai FREEHAND

[Freehand.ai @freehand_ai]

We saved $260M in cash for cos. like Meta, Unilever, and J&J by finding baseless charges across 19M+ invoices.

Now, we've raised $75M to extend our guarantee: if we can't find $500K in overpaid invoices, we'll pay you $10K.

Book a demo: https://t.co/FbUCc391D5

------------------------

How it works ⬇️

PROBLEM:

A Fortune 500 gets a million invoices a year. Most of them are for a couple hundred dollars.

Checking one properly means opening the contract, pulling the rate card, matching the PO, and hunting down the bill of lading that proves the shipment moved the way the carrier says it did.

All of that work to defend $200. So nobody does it.

The pile goes to a human. The human audits only a sample (12%) and pays the rest (88%) blindly. That's where the money goes.

At a pharma co, we found an uncontracted surcharge billed across global carriers for two years. $26M nobody questioned, because every individual line looked ordinary.

Each surcharge is small enough that escalating it costs more than paying it. And there are a million of them.

------------------------

SOLUTION:

Freehand’s AI reads everything: every line of every invoice, against every contract, rate card, transaction data, bill of lading, warehouse record, time sheet, and email exchange between you and the supplier. It also analyses every past invoice, and transaction data with that supplier,
ever.

It holds all of this knowledge in a Context Graph. When an invoice arrives, it knows what you should pay, what actually shipped, if the service was delivered or not, what the SLA was, and what this supplier billed you for the same service last quarter.

Then it proactively:

> writes up the dispute with the evidence attached
> emails the supplier
> calls her when the email goes quiet
> Slacks your purchases team for more information
> Follows up until the invoice comes back corrected.
> Approves the correct invoice
> Pays it across different currencies and tax structures
> And accrues the right amount to your general ledger

Freehand’s will do all of that to recover $20, because it is doing it across all millions of invoices at the same time, saving 5-10% of company spend with 100% SOX compliance and auditability.

-------------------------

🚨 RT + reply "FREEHAND" and we'll send you the AI upskilling guide that's already helped 750+ displaced workers move into more secure, higher-paying AI-native roles through Freehand's Transition Bootcamp.

[Ashley_Nicholson @AshleyNich63890]

@freehand_ai The appeal for me isn't just the recovery. It's not having to staff a team to chase $50.

[Ashley_Nicholson @AshleyNich63890]

How I went from tech outsider to being featured on the NASDAQ:

If you had asked me a year ago, if I would be featured in Times Square,
I would have never believed you.

But then it happened.

When I started out in tech, I felt:

↳ Out of place
↳ Determined
↳ Excited
↳ And overwhelmed with information.

In the beginning, I didn't even know if I belonged in tech.
↳ Despite always being gifted in math since childhood.

How could I possibly compete with people who had
↳ been coding since they were teenagers?

After mastering calculus, I wanted to study computer science.

I liked math and puzzles,
↳ And connecting data,
↳ to make sense of the world.

I took multiple math and computer science courses in college.

But I ultimately switched tracks:
↳ I felt like too much of a tech outsider at 19.

I studied international relations:
↳ Lived in 7 countries.
↳ Learned to speak 5 languages.
↳ Spoke at the United Nations.

But I couldn't forget my first passion.

I started out in a small technology firm.

From there I grew:
↳ Managed a project team and 8-figure technology sales proposals.
↳ Director at a company acquired by Accenture for 1.3 billion.
↳ Leader at a Caltech start-up that raised $65 million in VC funding.
↳ Founded my own AI and analytics company, Avenir Technology.

During this period, I:
↳ Read every book on technology I could get my hands on.
↳ Taught myself basic coding languages.
↳ Studied for certifications and took Georgia Tech courses.

I started learning to drown out the doubts:
↳ And focus on understanding how technology
↳ could solve problems.

Plus, a mentor reminded me that my unique background was an asset.
↳ He told me:
↳ "The best innovators are those who can
↳ bridge different worlds and see what others miss."

I started owning my journey:

↳ I stopped apologizing for my non-traditional background.
↳ I realized how much I mastered.
↳ I began winning more projects.
↳ And focused on our mission.

Now, I'm proud of our distinctive perspective.

How has your unique trajectory impacted your career?

What has defined you?

Share below.

♻️ Share with someone in your network who needs inspiration.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

@oelma__ 68...

[Ashley_Nicholson @AshleyNich63890]

The $200,000 Stanford AI degree just became worth a lot less.
Not because the education isn't world-class:

Because Stanford just released all their flagship AI and Machine Learning courses for free on YouTube.

This changes everything about how we learn AI.

1/ The legendary courses are now accessible to everyone for free:

These aren't watered down versions. These are the exact same courses Stanford charges tens of thousands for:

↳ CS221 – Artificial Intelligence: Principles & Techniques (Core AI foundations)
See: https://t.co/1TWKekLKhz.

↳ CS224U – Natural Language Understanding (How machines interpret meaning)
See: https://t.co/7rCR1WXO6c.

↳ CS224N – NLP with Deep Learning (transformers, embeddings, modern NLP)
See: https://t.co/RPdpv14INi.

↳ CS229 – Machine Learning by Andrew Ng (the legendary ML course)
See: https://t.co/4dHXT4UOU0.

↳ CS229M – Machine Learning Theory (Mathematical backbone)
See: https://t.co/OuvwnfKcqY.

↳ CS329H – ML from Human Preferences (reinforcement learning meets alignment)
See: https://t.co/pinzVwxn00.

↳ CS230 – Deep Learning by Andrew Ng (Neural networks, CNNs, RNNs)
See: https://t.co/dQDAjFKikY.

↳ CS234 – Reinforcement Learning (Agents, environments, reward-driven learning)
See: https://t.co/vF6t8NlR48.

↳ CS330 – Deep Multi-Task & Meta Learning (How models learn to learn)
See: https://t.co/JRGq4mKqdc.

2/ Why this matters more than you think:

The AI skills gap isn't closing because of cost barriers.
↳ Traditional education takes 4+ years and costs a fortune.
↳ Most professionals can't afford to go back to school.
↳ By the time you graduate, the field has already moved on.

Stanford just eliminated the biggest barrier to AI education.

3/ The real opportunity here:

You don't need a Stanford degree to work in AI anymore.
You need Stanford level knowledge.

And that knowledge is now free.

The question isn't whether you can afford AI education.
It's whether you can afford not to take advantage of it.

What's stopping you from diving into AI learning now that these barriers are gone?

Which course are you most excited to explore?

Share below.

♻️ Share this with someone who needs to see this.
➕ Follow me, @AshleyNich63890 , for more tech insights.

Credit: Dr. Kion Ahadi for surfacing these incredible resources. Give him a follow!

[Ashley_Nicholson @AshleyNich63890]

AI is a bubble.
It will pop and the market will crash:

I've heard this exact line daily for the last two weeks.

From investors, technology leaders, clients, and even my team.

Here's my take:

AI isn't a bubble like the dot-com crash or 1929.

Yes, there are warning signs:

1/ Many companies are overvalued:
↳ Both start-ups and larger tech companies.
↳ A correction is likely coming.
↳ But this is visible in basic P/E ratios.

2/ The labor market is splitting:
↳ It's a K-curve between income brackets and skillsets.
↳ Unemployment is high, but AI talent is expensive.

3/ The circular nature of AI investments is concerning:
↳ Microsoft owns 27% of OpenAI.
↳ Microsoft is the second-largest cloud provider.
↳ NVIDIA supplies the chips that power everything.
↳ AWS just partnered with OpenAI to rent data centers.

We are seeing these headlines over and over.
Is anyone else confused by this?

But it's not a complete bubble.

Here's why:

1/ The AI transformation is real:
↳ LLMs have proven applications across industries.
↳ Small models are growing rapidly and context engineering is gaining steam.
↳ No-code platforms are democratizing AI and changing application development.
↳ Coding tools like GitHub Copilot and Claude are already transforming programming.

This isn't going away.

2/ AI research and investment is expanding beyond GenAI:
↳ Robotics, world models, and computer vision are all growing.
↳ While GenAI gets most of the funding, other areas are advancing.
↳ There have been AI winters before, but it's feels unlikely now.
↳ Investors will likely diversify into these areas, if the bubble begins to pop.

3/ Data centers and energy infrastructure will continue to explode:
↳ This is the foundation for future growth.
↳ Beyond the Big Tech players, small modular nuclear power and distributed data centers are growing.

4/ The full ROI of GenAI hasn't been realized yet:
↳ Most corporate AI projects aren't being implemented well.
↳ Poor change management, people management, and use case selection are holding back results.
↳ As companies learn to deploy AI effectively, the value will become clearer.

The bottom line:

Even if some companies fail, the technology is transformative.

Many companies, especially smaller ones, are thriving.

This doesn't feel like the dot-com bubble.
It feels different.

What do you think?

Is AI a bubble that will pop soon? Why or why not?

Share below.

♻️ Share with someone in your network who needs to see this.
➕ Follow me, @AshleyNich63890 for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

@elonmusk That's why I have hated to see the mass migration from finance to tech. Engineers are usually not assholes. Wallstreet types are. Ugh....

[Ashley_Nicholson @AshleyNich63890]

Everyone's celebrating OpenAI's $38 billion Amazon Web Services (AWS) deal.
But here's what nobody's saying:

OpenAI just became Amazon's tenant.

Not their partner.

And that changes everything.

Here's what this really means:

1/ OpenAI Can't Go It Alone:
↳ They need hundreds of thousands of NVIDIA GPUs.
↳ Tens of millions of CPUs.
↳ They can't build this infrastructure themselves.
↳ Every single ChatGPT conversation? It runs on Amazon's servers now.

2/ Amazon Just Locked Them In:
↳ ChatGPT inference through 2027? Amazon.
↳ Next-gen model training? Amazon.
↳ Agentic AI workloads? Amazon.
↳ OpenAI gets the headlines. Amazon gets the control.

3/ The Real Winner Here:
↳ $38B in guaranteed revenue for Amazon.
↳ Zero infrastructure risk.
↳ They own the compute layer.
↳ While OpenAI chases the next breakthrough.

4/ What Everyone's Missing:
↳ This isn't about algorithms anymore.
↳ It's about who controls the infrastructure.
↳ Amazon just locked in the foundation of AI.
↳ OpenAI gets the glory. Amazon gets the power.

The most important battles in tech aren't fought in code.

They're fought in data centers.

And Amazon just won.

What does this mean for the future of AI independence?

Share below.

♻️ Share with someone in your network who needs to see this.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

Sam Altman dropped $6.5B on a designer:

Not a programmer.
Not an AI engineer.
Not a data scientist.

A designer.

Jony Ive.

↳ Apple's Chief Design Officer.
↳ Led design on iMac, iPod, iPhone.
↳ Only designer with a private office.

Everyone's asking:
"Why would a tech company pay THAT much for design?"

Wrong question.

The right question:
"Why is everyone else NOT paying for design?"

Look closer:
↳ Designers making $80k while engineers pull $180k.
↳ First to get laid off when budgets tighten.
↳ Treated like "pixel pushers."

Meanwhile...

OpenAI just proved great design is worth more than ever.

Someone needs to turn AI from this hot mess into something your grandma can use.

That someone?

Designers.

Because Jony Ive fundamentally changed how humans interact with technology.

Sam Altman gets this.

The real challenge:
↳ Not making AI smarter.
↳ Making AI invisible.
↳ Making AI feel... human.

This isn't about:
↳ Hardware vs software.
↳ OpenAI vs Apple.
↳ Or the money.

This is about who owns the future of human-computer interaction.

To designers:

Stop:
↳ Blaming others for not understanding design.
↳ Complaining about not getting a seat at the table.
↳ Waiting for change.

Start:
↳ Drawing your line in the sand.
↳ Obsessing over tiny details.
↳ Designing for impact.
↳ Embracing the new.
↳ Building for the future.

My prediction:
OpenAI isn't just developing another device/screen.
It's an always-on wearable.

It's time for the next chapter of computing.

Are you ready?

Share your thoughts on Ive's first AI device below.

♻️ Share with someone who needs to see this.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

A 47% error rate for dark-skinned women.
And only a 1% for light-skinned men:

Dr. Joy Buolamwini discovered this shocking disparity when she tested facial recognition systems.

And it sparked a revolution in AI ethics.

Picture this:
Joy was a computer science student at MIT, working on an art project.

The facial recognition system couldn't see her face.
It only worked when she put on a white mask.

That moment changed everything.

Most people would have just moved on.
But Joy asked a different question:

"If this system can't see me, who else is it missing?"

So she did what any good researcher would do.
She tested it systematically.

The results were devastating:

↳ Commercial facial recognition systems had massive racial and gender bias.
↳ Error rates for dark-skinned women were up to 34% higher.
↳ The technology worked best for light-skinned men.

But here's what makes Joy extraordinary:

She didn't just publish a paper and walk away.

She founded the Algorithmic Justice League.
A movement to fight bias in AI systems.

And it worked.

Her research forced tech giants to face reality:

↳ Microsoft improved their facial recognition accuracy by up to 9x for women.
↳ IBM completely withdrew from facial recognition.
↳ Amazon put a moratorium on police use of their Rekognition system.

Joy proved something powerful:
One person with data and determination can change entire industries.

Her work shows us that responsible AI isn't just possible.
It's essential.

Because the algorithms shaping our world should serve everyone.
Not just the people who build them.

What role do you think researchers should play in ensuring AI is implemented responsibly?

Share below.

♻️ Share with someone who needs to see this story.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

99% of enterprise AI projects are failing in the middle.
Not at the top. Not at the bottom:

In the middle layer of your organization.

I call it the "AI Implementation Paradox" after working with many companies at Avenir Technology:

Your executives demand transformation.
Your frontline teams want AI tools.
Your middle managers are crushed between them.

McKinsey reports that 75% of companies use Gen AI in at least one function.

But Deloitte found 43% of managerial tasks will be impacted without proper guidance.

Here's what's happening in your organization right now:

1/ The Executive Layer: Mandates Without Methods:
↳ C-suite sets ambitious AI targets (which are often unrealistic...).
↳ Allocates insufficient resources and training.
↳ Expects rapid transformation and ROI.
↳ Underestimates complexity of integration.
↳ Can't see ground-level implementation barriers.

2/ The Middle Manager Layer: Responsibility Without Authority:
↳ Accountable for delivering executive vision.
↳ Only 35% receive adequate AI training.
↳ 24% of their tasks face automation threat.
↳ Lack technical expertise to evaluate solutions.
↳ Fear being replaced or made irrelevant.

3/ The Frontline Layer: Tools Without Strategy:
↳ 71% of teams already using AI daily.
↳ Many are deploying shadow AI without governance.
↳ This creates departmental data silos.
↳ And they bypass security and compliance protocols.
↳ They generate inconsistent, unmeasurable results.

After implementing AI strategies across industries, we've found middle managers are the key to success or failure.

Here's a framework that can transform middle managers from barriers to champions:

1/ Realign Incentives & Metrics:
↳ Shift from "managing headcount" to "driving outcomes."
↳ Create specific KPIs for AI-enabled productivity.
↳ Reward process redesign and automation.
↳ Measure value creation, not just cost reduction.

2/ Build Technical Confidence (Not Coding Skills):
↳ Develop function-specific AI literacy programs.
↳ Create cross-functional AI capabilities.
↳ Establish tech evaluation frameworks for non-technical leaders.
↳ Connect them with internal champions.

3/ Implement Clear Decision Frameworks:
↳ Document decision makers for AI tool selection.
↳ Create governance models with guardrails.
↳ Build escalation paths for implementation challenges.
↳ Enable manager autonomy.

4/ Deploy Structured Change Management:
↳ Only 35% of companies have this for AI adoption (!).
↳ Map before and desired state workflow transformations.
↳ Establish feedback loops.
↳ Create safe spaces for experimentation.

The gap between AI aspiration and implementation isn't about technology.

It's about organizational dynamics.

Your middle managers aren't resisting change.
They're waiting for you to equip them to lead it.

What's one thing you're doing to empower your middle managers to champion AI adoption?

Share below.

♻️ Share with someone who needs an AI strategy.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

@Matt_Pinner 35 for me. Now, guess which ones. 😎 Ha!

[Ashley_Nicholson @AshleyNich63890]

74% of new web content is AI-generated.
And that's what's killing AI:

It's not your imagination.
The quality of LLMs is getting worse.

Over the last few weeks,
I've been talking to colleagues about this.

With AI models, including LLMs, there are always these problems:

↳ Model drift or data drift.
↳ Contamination.
↳ Data "poisoning" of the model.
↳ Data quality issues.

Sometimes I also wonder if we are not slowly reaching a strange vicious cycle.

Large language models need to be trained on enormous amounts of data.
↳ Meaning more than a petabyte of data.
↳ Many have trillions of parameters.
↳ Most of this is public data on the internet.

Here's where things get interesting:
↳ 74% of new website content are created using AI.
↳ 72% of social media and marketing content online is created using AI.
↳ By the end of 2025, experts predict up to 90% of all content online,
↳ Will be created by AI.

Here's the problem:

↳ LLMs train on internet data.
↳ People create internet content using LLMs.
↳ The LLMs then train on that data.

It's a vicious cycle.

And it will make LLMs bland.
And less useful.

Also, it could lead to an increase in bias.
↳ It more difficult for the models to generalize.
↳ And create an echo chamber.

It could even lead to model collapse.

So, how can we break the cycle?

↳ Create and use hybrid data sets.
↳ Use cross-validation methods.
↳ Minimize bias using bias detection for training.
↳ Conduct regular audits of AI systems.

It's up the foundational model providers to do this.
Otherwise, the models will probably decay.

This will have an enormous impact on the value we all get from the models.

And the future we are building on top of them.
It's time to talk about this.

What do you think should be done to keep foundation models and LLMs useful?

Share your thoughts below.

♻️ Share this for someone who needs to learn about AI.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

@oelma__ Capri, Italy.

[Ashley_Nicholson @AshleyNich63890]

@StevenMusielski Yes, I would agree. Also, it has learned to "make us happy" and sometimes LLMs sound borderline obsequious. The irony about counterinitiatives is that generative adversarial networks (GANs) use a process of finding an idea and then a counter.

[Ashley_Nicholson @AshleyNich63890]

AI isn't unbiased.
But here's how much it is (according to Harvard...):

Harvard has found a WEIRD bias in generative AI.

When it comes to AI, we compare AI to "human" performance.
AI learns bias from biased human data.

But Harvard researchers asked another key question:

"Which humans exactly?"

Humans aren't all the same.

We have different ways of thinking, different philosophical frameworks, different traits, different values, and different problem-solving abilities.

Harvard decided to test 94k+ people across 65 countries.

They compared their answers to LLM responses.

Harvard researchers found our “global” AI thinks a lot like a 25-year-old American software engineer.

They actually came up with a name for it:  WEIRD bias:
↳ Western
↳ Educated
↳ Industrialized
↳ Rich
↳ Democratic

Every time you ask AI about “people,” you’re really getting:

↳ Suburban mindsets in rural Turkey
↳ Western values in Egypt
↳ German spending habits in China

And this isn’t just an intellectual issue. It influences:

↳ Business strategy
↳ Customer behavior
↳ Government missions

Think about it.

Only 67% of the world has access to internet.

Internet content disproportionately comes from developed countries.

Bias today can compound into much bigger decision-making errors tomorrow.

For AI to be effective, data diversity isn't a nice to have.

It’s not about more data.
It's about the right data.
It's about different data.

What perspectives do you think could be missing from data and therefore AI tools?

Share below.

♻️ Repost to help your network learn about AI bias.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

Coding is no longer the bottleneck. Product management is.
One of the godfathers of AI just dropped a bomb:

Andrew Ng correctly noted that coding is no longer the challenge.
It's product management.

And he's right.

In technology, we become so obsessed with coding.
↳ With building.
↳ With writing specs.
↳ With pushing releases.
↳ And shipping.

With AI, there's been a shift.

Building isn't the problem.
An amazing team can build anything.
The real work is deciding what is worth building.

And what is not worth building.

Too many tech leaders get this wrong.
They chase shiny features.
And the latest emerging technology.

But without a real problem to solve,
Or building to solve the problem, there is no impact.

As a leader, it isn't your job to accelerate the processes,
Sometimes it's to slow the processes down and lead the project in the right direction.

Before moving forward with any project, you should ask yourself:
↳ What problem are we actually solving?
↳ Why does this problem matter?
↳ How will we measure the results?
↳ Who does this problem matter to?

Trust isn't built on more shiny features.
It's built on better decisions.

How has AI changed your approach to coding and creating products?

How has it changed your strategic direction?

Share below.

♻️ Repost to help your network
➕ Follow, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

Behind every confident decision,
is an invisible power that you can't ignore:

It's data governance.

Data governance often gets seen as unnecessary bureaucracy or compliance.

In reality, governance is powerful.

It’s the root of trust, agility, confidence, and better decisions.

What people think it is:
↳ Restricting data access unnecessarily.
↳ Complex policies that sit on a shelf.
↳ Jargon that no one understands.
↳ Killing projects with complex rules.

What it actually is:
↳ Making data understandable, accessible, and usable for everyone.
↳ Creating a shared understanding and definitions.
↳ Protecting data quality to increase trust.
↳ Balancing compliance with operations and results.
↳ Protecting sensitive information.
↳ Enabling quicker, more confident decisions that are reliable.

At its core, governance is about certainty.

It's about allowing you to use data responsibly, consistently, and with confidence.

What do you think is the most important aspect of data governance?

What is often forgotten?

Share below.

♻️ Repost to help others understand the power of data governance.
➕ Follow me, @AshleyNich63890 , for more tech insights.

[Ashley_Nicholson @AshleyNich63890]

Breaking: AWS just dropped 10 guides for building effective AI agents:
Here's how to avoid being left behind:

1/ Agentic AI patterns and workflows on AWS:
🔗 https://t.co/82Ek0emBSB

2/ Foundations of agentic AI on AWS
🔗 https://t.co/uJ87nxyYzL

3/ Writing best practices for RAG:
🔗 https://t.co/FY6mTp4Os3

4/ Agentic AI frameworks, protocols, and tools on AWS:
🔗 https://t.co/wXM915aN3x

5/ Building architectures for agentic AI on AWS:
🔗 https://t.co/T9dKBbsls3

6/ Creating RAG solutions on AWS for healthcare:
🔗 https://t.co/46Nkr8W8uJ

7/ Choosing an AWS vector database for RAG use cases:
🔗 https://t.co/Tqa6BpPnl2

8/ Building serverless architectures for agentic AI using AWS:
🔗 https://t.co/bnvhblX6km

Which topic are you interesting learning first?

Share below.

♻️ Repost to help your network.
➕ Follow me, @AshleyNich63890 , for more tech insights.