# Ian Welsh Argues Western AI Investors Back Losing Bets

> Source: <https://letsdatascience.com/news/ian-welsh-argues-western-ai-investors-back-losing-bets-45581445>
> Published: 2026-05-28 05:30:50.466807+00:00

Photo: 
ianwelsh.net
 
· rights & takedowns
According to Ian Welsh's blog post on IanWelsh.net, the author compares token costs and performance between US-hosted models and the Chinese service DeepSeek V4. The post reports pricing figures: 
Claude
 at 
$5
 input and 
$25
 output per million tokens, 
DeepSeek
 at 
$0.28
 input and under 
$1
 output per million tokens (with discounts), and a reported cached cost of 
$0.0002
 per million tokens. Ian Welsh reports personal spending of 
$3,000$5,000
 per month on Claude Code before switching to DeepSeek and says his work now costs about 
$5/week
 versus an asserted 
$1,000/week
 for the same workload on Claude. The post argues that Chinese AI offerings, combined with open-source models and efficiency, create a competitive advantage over Western models.
What happened
According to Ian Welsh's blog post on IanWelsh.net, the author describes switching from 
Claude
 to 
DeepSeek V4
 after observing large cost differences. The post gives pricing comparisons: 
Claude
 at 
$5
 input and 
$25
 output per million tokens, 
DeepSeek
 at 
$0.28
 input and under 
$1
 output per million tokens (with current discounts), and a reported cached cost of 
$0.0002
 per million tokens. The author reports previously spending 
$3,000$5,000
 per month on Claude Code and now paying about 
$5/week
 on DeepSeek, contrasted with a claimed 
$1,000/week
 for the same workload on Claude.
Editorial analysis
Industry-pattern observations: cost-per-token and caching efficiency materially change economics for high-volume, token-intense workloads. Organizations evaluating cloud vs self-hosting often find that aggressive caching and lower inference costs reduce marginal price pressure and can enable different trade-offs between latency, control, and total cost of ownership.
Industry context
Industry-pattern observations: public commentary emphasizing low-cost, open-source models reflects a broader debate on whether proprietary frontier models retain value when near-parity performance is available at far lower running cost. Analysts and practitioners have repeatedly noted that cheaper inference shifts the levers for product design, deployment scale, and operational budgets.
What to watch
For practitioners: monitor independent benchmarks for quality parity, reproducible latency and cost measurements for comparable workloads, and availability of permissive licenses or self-hosting tooling that materially reduce vendor lock-in. For investors and builders: track whether cost reductions are sustained at scale and whether open-source projects maintain active maintenance and security practices.
Scoring Rationale
The post highlights a material cost argument that is relevant to practitioners evaluating self-hosting and vendor lock-in. The piece is an opinionated synthesis with no independent benchmarking beyond the author's claims, so relevance is moderate rather than industry-shaking.
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