Meet Sakura Tanaka she runs a small AI startup in Tokyo. Her team built an image generation model that turns architectural sketches into photorealistic renders—useful for real estate developers who want to show clients what a building will look like before breaking the ground.

The product works. Clients love it. Revenue is growing.

But there's a problem: AWS is eating 43% of her gross revenue.

Every time a client generates a render, Sakura's startup pays Amazon for GPU compute. The markup is brutal. What should cost $0.80 in raw compute costs $2.50 after AWS takes its cut, last month her AWS bill was $47,000. Her team's salaries? $52,000 combined.

She's paying almost as much to Amazon as she's paying her entire engineering team.

This isn't sustainable. But what's the alternative? For years, there wasn't one. AWS, Google Cloud, and Microsoft Azure controlled the market. If you needed GPUs for AI workloads, you paid their prices or you don't build.

The Three Problems Crushing AI Startups

Sakura's problem isn't unique. Talk to any AI founder and you'll hear the same three complaints:

Cloud providers charge 70-80% markups on GPU compute.

Why? Because they control it

Amazon didn't build AWS to help startups, they built it to fund corporate profits. Every dollar you spend on compute includes:

• Infrastructure costs (reasonable)

• Corporate overhead (expensive)

• Shareholder returns (very expensive)

Example: A single NVIDIA H100 GPU costs about $30,000 to purchase. If you rent it from AWS, you pay roughly $2-3 per hour, that's $17,520-26,280 per year for a GPU that costs $30,000 to own outright.

Year 2? You've paid $60,000 for a $30,000 GPU.

Year 3? $90,000, the math doesn't work. But most startups don't have $300,000 in cash to buy 10 GPUs upfront. So they rent and Amazon profits.

In March 2024, AWS experienced a major outage in their US-East-1 region, for six hours half the internet went down.

• Streaming services stopped working

• E-commerce sites crashed

• SaaS platforms went offline

• AI models stopped responding

Founders watched helplessly as their products died. But the outage isn't even the worst part, the worst part is the control .

• Throttle your compute at any time

• Deplatform your account without warning

• Change pricing structures unilaterally

• Prioritize their own AI services over yours

Real example: In 2021, Parler (a social media app) was deplatformed by AWS overnight. Regardless of your political views on Parler, the message was clear: if you build on AWS, Amazon controls your fate.

AI startups face the same risk. Build a controversial AI model? Amazon can shut you down. Compete with an Amazon AI product? They can throttle your access, you don't own your infrastructure. Amazon does.

When US-East-1 goes down, it doesn't just affect one company. It affects THOUSANDS.

Because everyone builds on the same infrastructure

• Your app depends on AWS

• Your competitor's app depends on AWS

• Your users' other apps depend on AWS

When AWS fails, everything fails simultaneously, this isn't just inconvenient. It's existential.

Imagine you're Sakura. Your biggest client a real estate developer has a presentation to investors in 2 hours. They need 50 renders generated. You click "run batch job."

AWS US-East-1 is down.

The presentation fails. The client loses the deal. They blame you.

You didn't do anything wrong. But Amazon's infrastructure failed, and you paid the price.

Meet Decentralized GPU Networks

Sakura discovered Akash Network , a decentralized marketplace for GPU compute.

Instead of renting from Amazon's data centers, she now rents from:

• A gaming cafe in Seoul with spare GPUs overnight

• A mining operation in Iceland that pivoted to AI compute

• An independent data center in Singapore

Same GPUs. 85% cheaper. No single point of failure.

Here's how it works:

In a traditional marketplace:

• Seller lists GPU for rent

• Buyer bids on available compute

• Price settles at market equilibrium

No middleman taking 70-80% margins.

Example: That same NVIDIA H100 GPU that costs $2-3/hour on AWS?

On Akash it cost: $0.30-0.50/hour. that's 85% cheaper.

Sakura went from $47,000/month to $7,000/month for the same workloads, that's $40,000 back in her pocket every month. She used the savings to hire two more engineers.

On AWS, you need:

• A credit card

• Corporate verification

• KYC compliance

• Permission from Amazon

On Akash: You need a wallet. That's it, no one can deplatform you, no one can throttle your access, no one decides if your AI model is "acceptable."

You pay. You get compute. Simple.

When Sakura runs a batch job now, her compute is distributed across:

• 12 different providers

• 7 different countries

• 3 different continents

If one provider goes offline, the workload automatically shifts to another.

When US-East-1 went down, Akash users didn't even notice.

Because their compute wasn't centralized in Virginia. It was distributed globally across thousands of independent providers.

Infrastructure Returning to Its Owners

The compute wars aren't just about saving money. They're about who controls the future of AI.

Right now:

• Amazon controls compute → Amazon controls which AI models survive

• Google controls compute → Google controls AI development priorities

• Microsoft controls compute → Microsoft decides what's "acceptable on AI"

Whoever controls compute, controls AI.

Decentralized networks change that equation. With Akash:

• Developers control their destiny (no deplatforming risk)

• Competition drives prices down (no monopoly pricing)

• Innovation accelerates (lower costs = more experimentation)

• Resilience improves (distributed > centralized)

The Compute Wars Are Just Beginning

Sakura's story is playing out across thousands of AI startups right now.

Founders are realizing that:

• AWS margins are unsustainable

• Centralized gatekeepers are unacceptable

• Single points of failure are inexcusable

The solution already exists. It's just early.

Akash processes millions of dollars in compute monthly. But that's still a rounding error compared to AWS's $90 billion annual revenue, the question isn't if decentralized compute wins.

The question is how fast

Because every AI founder who does the math like Sakura did comes to the same conclusion:

Paying 70-80% markups to a company that can deplatform you at any moment doesn't make sense when there's an 85% cheaper alternative with no single point of failure.

Visualize Your Protocol's Narrative

The best tech in Web3 will fail if investors and retail users can't understand it. Akash is winning because their economic model is undeniable.

I'm Lino a Web3 Visual Strategist. I specialize in helping Blockchain Protocols translate their complex whitepapers into institutional-grade blueprints, pitch decks, and macro-narratives.

If this breakdown helped you see the signal through the noise, collect this entry or subscribe for the next narrative deep dive.

Follow me on Twitter: @linodefi1

Appreciate the support

Meet Sakura Tanaka she runs a small AI startup in Tokyo. Her team built an image generation model that turns architectural sketches into photorealistic renders—useful for real estate developers who want to show clients what a building will look like before breaking the ground.

The product works. Clients love it. Revenue is growing.

But there's a problem: AWS is eating 43% of her gross revenue.

Every time a client generates a render, Sakura's startup pays Amazon for GPU compute. The markup is brutal. What should cost $0.80 in raw compute costs $2.50 after AWS takes its cut, last month her AWS bill was $47,000. Her team's salaries? $52,000 combined.

She's paying almost as much to Amazon as she's paying her entire engineering team.

This isn't sustainable. But what's the alternative? For years, there wasn't one. AWS, Google Cloud, and Microsoft Azure controlled the market. If you needed GPUs for AI workloads, you paid their prices or you don't build.

The Three Problems Crushing AI Startups

Sakura's problem isn't unique. Talk to any AI founder and you'll hear the same three complaints:

Cloud providers charge 70-80% markups on GPU compute.

Why? Because they control it

Amazon didn't build AWS to help startups, they built it to fund corporate profits. Every dollar you spend on compute includes:

• Infrastructure costs (reasonable)

• Corporate overhead (expensive)

• Shareholder returns (very expensive)

Example: A single NVIDIA H100 GPU costs about $30,000 to purchase. If you rent it from AWS, you pay roughly $2-3 per hour, that's $17,520-26,280 per year for a GPU that costs $30,000 to own outright.

Year 2? You've paid $60,000 for a $30,000 GPU.

Year 3? $90,000, the math doesn't work. But most startups don't have $300,000 in cash to buy 10 GPUs upfront. So they rent and Amazon profits.

In March 2024, AWS experienced a major outage in their US-East-1 region, for six hours half the internet went down.

• Streaming services stopped working

• E-commerce sites crashed

• SaaS platforms went offline

• AI models stopped responding

Founders watched helplessly as their products died. But the outage isn't even the worst part, the worst part is the control .

• Throttle your compute at any time

• Deplatform your account without warning

• Change pricing structures unilaterally

• Prioritize their own AI services over yours

Real example: In 2021, Parler (a social media app) was deplatformed by AWS overnight. Regardless of your political views on Parler, the message was clear: if you build on AWS, Amazon controls your fate.

AI startups face the same risk. Build a controversial AI model? Amazon can shut you down. Compete with an Amazon AI product? They can throttle your access, you don't own your infrastructure. Amazon does.

When US-East-1 goes down, it doesn't just affect one company. It affects THOUSANDS.

Because everyone builds on the same infrastructure

• Your app depends on AWS

• Your competitor's app depends on AWS

• Your users' other apps depend on AWS

When AWS fails, everything fails simultaneously, this isn't just inconvenient. It's existential.

Imagine you're Sakura. Your biggest client a real estate developer has a presentation to investors in 2 hours. They need 50 renders generated. You click "run batch job."

AWS US-East-1 is down.

The presentation fails. The client loses the deal. They blame you.

You didn't do anything wrong. But Amazon's infrastructure failed, and you paid the price.

Meet Decentralized GPU Networks

Sakura discovered Akash Network , a decentralized marketplace for GPU compute.

Instead of renting from Amazon's data centers, she now rents from:

• A gaming cafe in Seoul with spare GPUs overnight

• A mining operation in Iceland that pivoted to AI compute

• An independent data center in Singapore

Same GPUs. 85% cheaper. No single point of failure.

Here's how it works:

In a traditional marketplace:

• Seller lists GPU for rent

• Buyer bids on available compute

• Price settles at market equilibrium

No middleman taking 70-80% margins.

Example: That same NVIDIA H100 GPU that costs $2-3/hour on AWS?

On Akash it cost: $0.30-0.50/hour. that's 85% cheaper.

Sakura went from $47,000/month to $7,000/month for the same workloads, that's $40,000 back in her pocket every month. She used the savings to hire two more engineers.

On AWS, you need:

• A credit card

• Corporate verification

• KYC compliance

• Permission from Amazon

On Akash: You need a wallet. That's it, no one can deplatform you, no one can throttle your access, no one decides if your AI model is "acceptable."

You pay. You get compute. Simple.

When Sakura runs a batch job now, her compute is distributed across:

• 12 different providers

• 7 different countries

• 3 different continents

If one provider goes offline, the workload automatically shifts to another.

When US-East-1 went down, Akash users didn't even notice.

Because their compute wasn't centralized in Virginia. It was distributed globally across thousands of independent providers.

Infrastructure Returning to Its Owners

The compute wars aren't just about saving money. They're about who controls the future of AI.

Right now:

• Amazon controls compute → Amazon controls which AI models survive

• Google controls compute → Google controls AI development priorities

• Microsoft controls compute → Microsoft decides what's "acceptable on AI"

Whoever controls compute, controls AI.

Decentralized networks change that equation. With Akash:

• Developers control their destiny (no deplatforming risk)

• Competition drives prices down (no monopoly pricing)

• Innovation accelerates (lower costs = more experimentation)

• Resilience improves (distributed > centralized)

The Compute Wars Are Just Beginning

Sakura's story is playing out across thousands of AI startups right now.

Founders are realizing that:

• AWS margins are unsustainable

• Centralized gatekeepers are unacceptable

• Single points of failure are inexcusable

The solution already exists. It's just early.

Akash processes millions of dollars in compute monthly. But that's still a rounding error compared to AWS's $90 billion annual revenue, the question isn't if decentralized compute wins.

The question is how fast

Because every AI founder who does the math like Sakura did comes to the same conclusion:

Paying 70-80% markups to a company that can deplatform you at any moment doesn't make sense when there's an 85% cheaper alternative with no single point of failure.

Visualize Your Protocol's Narrative

The best tech in Web3 will fail if investors and retail users can't understand it. Akash is winning because their economic model is undeniable.

I'm Lino a Web3 Visual Strategist. I specialize in helping Blockchain Protocols translate their complex whitepapers into institutional-grade blueprints, pitch decks, and macro-narratives.

If this breakdown helped you see the signal through the noise, collect this entry or subscribe for the next narrative deep dive.

Follow me on Twitter: @linodefi1

Appreciate the support