My Take on Why AI’s Getting Cheap and How Crypto Could Fix Its Biggest Headaches
Hey everyone, I am Nihar Shah (or NehharShah on GitHub/X), a fullstack engineer with over 5 years in AI, blockchain, and distributed systems. I have been hands-on with zk proofs, agent platforms, and ML integrations, and lately, I have been exploring how AI is commoditizing while crypto tackles trust gaps. No hype, just practical insights. Lets break it down simply.
Picture This: AI Everywhere, But What’s the Catch?
Imagine a world where spinning up a powerful AI model costs next to nothing under $100K for something decent, down from millions a few years ago. That is not far off. Hardware is getting way more efficient (up 40% yearly) and costs are dropping (30% annually for key parts). Open source models are closing the gap on big players we know.
But here is the twist. While everyday AI gets affordable (think $294K for models like DeepSeek R1 in 2025), the super advanced frontier ones are costing more maybe $1B by 2027. Still, this means AI is no longer rare. It is like a tool anyone can grab. Debates rage on full costs reported final runs are low (e.g., $5.58M for DeepSeek-V3), but including R&D, servers, and ops, estimates hit $500M-$1.3B for comparable efforts.
Here is a quick look at training costs from Stanford, Epoch AI, and recent disclosures:
Model: GPT-3 (OpenAI),
Year: 2020,
Estimated Training Cost: $4.6 million
What It Means: The old standard, set the bar.Model: PaLM (Google)
Year: 2022
Estimated Training Cost: $3–12 million
What It Means: Efficiency starting to show.Model: GPT-4 (OpenAI)
Year: 2023
Estimated Training Cost: ~$78 million
What It Means: Big jumps for top-tier stuff.Model: DeepSeek-V3
Year: 2024
Estimated Training Cost: $5.58 million (reported final run; full estimate $92–123M)
What It Means: Base model; debates on total costs including infrastructure.Model: DeepSeek R1
Year: 2025
Estimated Training Cost: $294,000 (reasoning-focused; full ecosystem ~100× higher)
What It Means: Low-cost efficiency win, but full ecosystem adds massive overhead.Model: Latest Frontier Peaks
Year: 2025
Estimated Training Cost: Up to $500M+ (100K H100 clusters)
What It Means: Costs escalating for frontier models; $1B+ expected by 2027.Jobs? From IMF studies, AI won’t wipe them out. It will boost productivity. But it could widen global gaps without policies, though it might reduce wage inequality by disrupting high income roles. Polls show half of us are worried about bigger divides. The real value shifts to things AI can’t touch easily, trust, proving stuff’s real, human gut calls, and physical resources like energy or GPUs.
What if your code gets hacked or spits out fakes? That is where things get tricky.
How Crypto Could Step In: Insights from My Builds
I have hit walls like deepfakes and scams in my work with AI APIs and blockchain. Crypto’s ideas, like blockchains and proofs feel like a natural fit to verify AI without middlemen. In 2025, DeAI trends show most projects (70+ analyzed) centralize compute offchain (only 10% truly decentralized), but zkML advancements like ZKLoRA (1-2s verification for Llama-scale models) are pushing real stakeholder control.
Trust: The Big One, and Crypto’s Fix
With AI cheap, fakes are everywhere. How do you know something is legit? Crypto builds trust hubs via blockchains proving stuff without peeking at secrets.
ZKML (Zero-Knowledge Machine Learning): Basically, it verifies AI did the math right without showing private data. Like checking a locked box. Recent wins include open sourced ZKLoRA for fast, privacy preserving proofs.
Some projects I am watching, with 2025 traction:
GaiaNet (GAIA): Raised $20M in Series A this summer. They are doing edge nodes with Samsung for private AI. Buzz has been quiet lately on X, but Solana ZK proofs look promising.
Polyhedra (zkML leader): Strong 2025 roadmap with zkPyTorch, EXPchain for AI, and zkGPT integrations. Pioneered zkML since 2020; recent GPU accel pushes 5M+ proof layers/sec, 90% less energy.
Nous Research: Solid on open models with ZK; no huge news lately, but worth following for semantic analysis.
These let you add fingerprints to media like This came from this model, using real data while keeping privacy.
For quick traction snapshot:
Project: Bittensor
2025 Key Metrics/Traction: $1B+ market cap; 100K+ nodes in its ML network
Notes: Decentralized ML economy; turns AI into a programmable market.Project: Numerai
2025 Key Metrics/Traction: $500M+ in predictions; 10,000+ data scientists participating
Notes: Crowdsourced AI for finance; rewards accurate models with tokens.Project: Inference Labs
2025 Key Metrics/Traction: $6.3M raised; building Bittensor-based zkAI security
Notes: Verifies AI agents and off-chain compute; reduces black-box risks.Project: Polyhedra
2025 Key Metrics/Traction: zkML in production; partnerships with Berkeley RDI
Notes: zkGPT and EXPchain; zk expected to capture 10–20% of the $2.7T AI market by 2032.Tracking and Verifying AI Outputs
Crypto can trace an AI creation’s history to catch fakes. Tools like Polyhedra’s zkML (tons of GitHub stars) verify things onchain, like trades or predictions.
This builds confidence when AI is flooding everything.
Handling Hacks and Messy Stuff
AI can get jailbroken or leak data. Crypto’s staking (bet tokens on good behavior) and slashing (lose ‘em if you screw up) could keep systems safe.
Human in the Mix: Reward people with tokens for overseeing key decisions. Spot odd behavior? Vote to fix it.
Feels like a community safety net.
Sorting Out Hardware and Power Issues
AI needs heavy GPUs, but they’re scarce. Crypto incentivizes sharing idle ones with tokens.
io.net: Pulled in $20M+ earnings by October 2025, with over 100K workers and 49M tokens dished out. It’s cool for turning spare tech into shared power (up to 90% cheaper than AWS).
Others like Grass (for data) and Ritual (verified runs) make AI work on mixed setups.
Breaks down barriers without relying on giants, though most DeAI still centralizes compute.
The Downsides: Let’s Be Real
I am excited, but crypto x AI isnt perfect. Energy use is huge, and volatility kills projects. Chainalysis full 2025 report shows $45B in illicit volume (down 24% YoY, just 0.4% of total crypto), with major hacks like $1.5B incidents and AI boosted scams surging. AI incidents up 56.4% (233 cases per Stanford). Biases in data could make things worse, and slow decentralized stuff might lag plus chip bans widen global gaps.
IMF warns uneven rollout hits emerging markets hard. Fixes? Better audits, fair data, inclusive designs.
Wrapping Up: Why This Matters to Me
Blending AI and crypto isn’t hype. It is about fixing real pains for better, trustworthy systems. Stanford says AI investments topped $252.3B in 2024; add crypto, and we are talking big innovations like the Machine Economy. As an engineer and builder, I am stoked to build in this new scarcity space trust and resources as the edge.
If you are tinkering with DeAI, share your thoughts! Check my site (niharshah.xyz) or LinkedIn for more, or hit up my GitHub for code. What’s your take?