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I Might Be Replaced by an Agent, So I Ran the Numbers

I Might Be Replaced by an Agent, So I Ran the Numbers

Section titled “I Might Be Replaced by an Agent, So I Ran the Numbers”

Quantifying AI replacement risk with data: a deep dive into how the HagiCode team uses six core formulas to redefine how knowledge workers evaluate their competitiveness.

Background

Section titled “Background”

With AI technology advancing at breakneck speed, every knowledge worker is facing an urgent question: In the AI era, will I be replaced?

It sounds a little alarmist, but plenty of people are quietly uneasy about it. You just finish learning a new framework, and AI is already telling you your role might be automated away; you finally master a language, and then discover that someone using AI is producing three times as much as you. If you are reading this, you have probably felt at least some of that anxiety.

And honestly, that anxiety is not irrational. No one wants to admit that the skills they spent years building could be outperformed by a single ChatGPT session. Still, anxiety is one thing; life goes on.

Traditional discussions usually start from the question of “what AI can do,” but that framing misses two critical dimensions:

  1. The business perspective: whether a company is willing to equip an employee with AI tools depends on whether AI costs make economic sense relative to labor costs. It is not enough for AI to be capable of replacing a role; the company also has to run the numbers. Capital is not a charity, and every dollar has to count.
  2. The efficiency perspective: AI-driven productivity gains need to be quantified instead of being reduced to the vague claim that “using AI makes you stronger.” Maybe your efficiency doubles with AI, but someone else gets a 5x improvement. That gap matters. It is like school: everyone sits in the same class, but some score 90 while others barely pass.

So the real question is: how do we turn this fuzzy anxiety into measurable indicators?

It is always better to know where you stand than to fumble around in the dark. That is what we are talking about today: the design logic behind the AI productivity calculator built by the HagiCode team.

So I made a site: https://cost.hagicode.com.

About HagiCode

Section titled “About HagiCode”

HagiCode is an open-source AI coding assistant project built to help developers code more efficiently.

What is interesting is that while building their own product, the HagiCode team accumulated a lot of hands-on experience around AI productivity. They realized that the value of an AI tool cannot be assessed in isolation from a company’s employment costs. Based on that insight, the team decided to build a productivity calculator to help knowledge workers evaluate their competitiveness in the AI era more scientifically.

Plenty of people could build something like this. The difference is that very few are willing to do it seriously. The HagiCode team spent time on it as a way of giving something back to the developer community.

The design shared in this article is a summary of HagiCode’s experience applying AI in real engineering work. If you find this evaluation framework valuable, it suggests that HagiCode really does have something to offer in engineering practice. In that case, the HagiCode project itself is also worth paying attention to.

The Core Formulas: 6 Key Metrics

Section titled “The Core Formulas: 6 Key Metrics”

1. Total Annual Employment Cost

Section titled “1. Total Annual Employment Cost”

A company’s real cost for an employee is far more than salary alone. A lot of people only realize this when changing jobs: you negotiate a monthly salary of 20,000 CNY, but take home only 14,000. On the company side, the spend is not just 20,000 either. Social insurance, housing fund contributions, training, and recruiting costs all have to be included.

According to the implementation in calculate-ai-risk.ts:

Total annual employment cost = Annual salary x (1 + city coefficient) + Annual salary / 12

The city coefficient reflects differences in hiring and retention costs across cities:

City tierRepresentative citiesCoefficient
Tier 1Beijing / Shanghai / Shenzhen / Guangzhou0.4
New Tier 1Hangzhou / Chengdu / Suzhou / Nanjing0.3
Tier 2Wuhan / Xi’an / Tianjin / Zhengzhou0.2
OtherYichang / Luoyang and others0.1

A Tier 1 city coefficient of 0.4 means the company needs to pay roughly 40% extra in recruiting, training, insurance, and similar overhead. The all-in cost of hiring someone in Beijing really is much higher than in a Tier 2 city.

The cost of living in major cities is high too. You could think of it as another version of a “drifter tax.”

2. Blended Token Unit Price

Section titled “2. Blended Token Unit Price”

Different AI models have separate input and output pricing, and the gap can be huge. In coding scenarios, the input/output ratio is roughly 3:1. You might give the AI a block of code to review, while its analysis is usually much shorter than the input.

The blended unit price formula is:

Blended unit price = (input-output ratio x input price + output price) / (input-output ratio + 1)

Take GPT-5 as an example:

  • Input: $2.5/1M tokens
  • Output: $15/1M tokens
  • Blended = (3 x 2.5 + 15) / 4 = $5.625/1M tokens

For models priced in USD, you also need to convert using an exchange rate. The HagiCode team currently sets that rate to 7.25 and updates it as the market changes.

Exchange rates are like the stock market: no one can predict them exactly. You just follow the trend.

3. Annual AI Cost

Section titled “3. Annual AI Cost”
Average daily AI cost = Average daily token demand (M) x blended unit price (CNY/1M)
Annual AI cost = Average daily AI cost x 264 working days

264 = 22 days/month x 12 months, which is the number of working days in a standard year. Why not use 365? Because you have to account for weekends, holidays, sick leave, and so on.

We are not robots, after all. AI may not need rest, but people still need room to breathe.

4. The Core Innovation: Equivalent Headcount

Section titled “4. The Core Innovation: Equivalent Headcount”

This is the heart of the whole evaluation system, and also where the HagiCode team’s insight shows most clearly.

Affordable workflow count = Total annual employment cost / Annual AI cost
Affordability ratio = min(affordable workflow count, 1)
Equivalent headcount = 1 + (productivity multiplier - 1) x affordability ratio

That formula looks a little abstract, so let me unpack it.

The traditional view would simply say, “your efficiency improved by 2x.” But this formula introduces a crucial constraint: is the company’s AI budget sustainable?

For example, Xiao Ming improves his efficiency by 3x, but his annual AI usage costs 300,000 CNY while the company is only paying him a salary of 200,000 CNY. In that case, his personal productivity may be impressive, but it is not sustainable. No company is going to lose money just to keep him operating at peak efficiency.

That is what the affordability ratio means. If the company can only afford 0.5 of an AI workflow, then Xiao Ming’s equivalent headcount is 1 + (3 - 1) x 0.5 = 2 people, not 3.

The key insight: what matters is not just how large your productivity multiplier is, but whether the company can afford the AI investment required to sustain that multiplier.

The logic is simple once you see it. Most people just do not think from that angle. We are used to looking at the world from our own side, not from the boss’s side, where money does not come out of thin air either.

5. Cost-Benefit Ratio

Section titled “5. Cost-Benefit Ratio”
AI cost ratio = Annual AI cost / Total annual employment cost
Productivity gain = Productivity multiplier - 1
Cost-benefit ratio = Productivity gain / AI cost ratio
  • Cost-benefit ratio < 1: the AI investment is not worth it; the productivity gain does not justify the cost
  • Cost-benefit ratio 1-2: barely worth it
  • Cost-benefit ratio > 2: high return, strongly recommended

This metric is especially useful for managers because it helps them quickly judge whether a given role is worth equipping with AI tools.

At the end of the day, ROI is what matters. You can talk about higher efficiency all you want, but if the cost explodes, no one is going to buy the argument.

6. Risk Level

Section titled “6. Risk Level”

Risk is categorized according to equivalent headcount:

Equivalent headcountRisk levelConclusion
>= 2.0High riskIf your coworkers gain the same conditions, they become a serious threat to you
1.5 - 2.0WarningCoworkers have begun to build a clear productivity advantage
< 1.5SafeFor now, you can still maintain a gap

After seeing that table, you probably have a rough sense of where you stand. Still, there is no point in panicking. Anxiety does not solve problems. It is better to think about how to raise your own productivity multiplier.

Gamified Design: 7 Special Titles

Section titled “Gamified Design: 7 Special Titles”

To make the results more fun, the calculator introduces a system of seven special titles. These titles are persisted through localStorage, allowing users to unlock and display their own “achievements.”

Title IDNameUnlock condition
craftsman-spiritCraftsman SpiritAverage daily token usage = 0
prompt-alchemistPrompt AlchemistDaily tokens <= 20M and productivity multiplier >= 6
all-in-operatorAll-In OperatorDaily tokens >= 150M and productivity multiplier >= 3
minimalist-runnerMinimalist RunnerDaily tokens <= 5M and productivity multiplier >= 2
cost-tamerCost TamerCost-benefit ratio >= 2.5 and AI cost ratio <= 15%
danger-oracleDanger OracleEquivalent headcount >= 2.5 or entering the high-risk zone
budget-coordinatorBudget CoordinatorAffordable workflow count >= 8

Each title also carries a hidden meaning:

TitleHidden meaning
Craftsman SpiritYou can still do fine without AI, but you need unique competitive strengths
Prompt AlchemistYou achieve high output with very few tokens; a classic power-user profile
All-In OperatorHigh input, high output; suitable for high-frequency scenarios
Minimalist RunnerLightweight AI usage; suitable for light-assistance scenarios
Cost TamerExtremely high ROI; the kind of employee companies love
Danger OracleYou are already, or soon will be, in a high-risk group
Budget CoordinatorYou can operate multiple AI workflows at the same time

Gamification is really just a way to make dry data a little more entertaining. After all, who does not like collecting achievements? Like badges in a game, they may not have much practical value, but they still feel good to earn.

Data Sources: An Authoritative Pricing System

Section titled “Data Sources: An Authoritative Pricing System”

The calculator’s pricing data comes from multiple official API pricing pages to keep the results authoritative and up to date:

This data is updated regularly, with the latest refresh on 2026-03-19.

Data only matters when it is current. Once it is outdated, it stops being useful. On that front, the HagiCode team has been quite responsible about keeping things updated.

Practical Example

Section titled “Practical Example”

Suppose you are a developer in Beijing with an annual salary of 400,000 CNY, using Claude Sonnet 4.6, consuming 50M tokens per day on average, and estimating that AI gives you a 3x productivity boost. The simulated input looks like this:

const input = {
  annualIncomeCny: 400000,
  cityTier: "tier1",           // Beijing
  modelId: "claude-sonnet-4-6",
  performanceMultiplier: 3.0,
  dailyTokenUsageM: 50,
}


// Calculation process
// Total annual employment cost = 400k x (1 + 0.4) + 400k/12 ~= 603.3k
// Annual AI cost ~= 50 x 7.125 x 264 ~= 94k
// Affordable workflow count ~= 603.3 / 94 ~= 6.4 workflows
// Equivalent headcount = 1 + (3 - 1) x 1 = 3 people

Conclusion: if one of your coworkers has the same conditions, their output would be equivalent to three people. You are already in the high-risk zone.

If you discover that your current AI usage is “not worth it” (cost-benefit ratio < 1), you can consider:

  1. Reducing token usage: use more efficient prompts and cut down ineffective requests
  2. Choosing a more cost-effective model: for example, DeepSeek-V3 (priced in CNY and cheaper)
  3. Increasing your productivity multiplier: learn advanced Agent usage techniques and truly turn AI into productivity

In the end, all of this comes down to the art of balance. Use too much and you waste money; use too little and nothing changes. The key is finding the sweet spot.

Technical Architecture Highlights

Section titled “Technical Architecture Highlights”

When designing this calculator, the HagiCode team made several engineering decisions worth learning from:

  1. Pure frontend computation: all calculations run in the browser, with no backend API dependency, which protects user privacy
  2. Configuration-driven: all formulas, pricing, and role data are centralized in configuration files, so future updates do not require changing core code logic
  3. Multilingual support: supports both Chinese and English
  4. Instant feedback: results update in real time as soon as the user changes inputs
  5. Detailed formula display: every result includes the full calculation formula to help users understand it

This design makes the calculator easy to maintain and extend, while also serving as a reference template for similar data-driven applications.

Good architecture, like good code, takes time to build up. The HagiCode team put real thought into it.

Conclusion

Section titled “Conclusion”

The core value of the AI productivity calculator is that it turns the vague anxiety of an “AI replacement threat” into metrics that can be quantified and compared.

The equivalent headcount formula, 1 + (productivity multiplier - 1) x affordability ratio, is the core innovation of the entire framework. It considers not only productivity gains, but also whether a company can afford the AI cost, making the evaluation much closer to reality.

This framework tells us one thing clearly: in the AI era, not knowing where you stand is the most dangerous position of all.

Instead of worrying, let the data speak.

A lot of fear comes from the unknown. Once you quantify everything, the situation no longer feels quite so terrifying. At worst, you improve yourself or change tracks. Life is long, and there is no need to hang everything on a single tree.

If This Helped You

Section titled “If This Helped You”

Visit cost.hagicode.com now and complete your AI productivity assessment.

References

Section titled “References”

Data source: cost.hagicode.com | Powered by HagiCode

In the end, a line of poetry came to mind: “This feeling might have become a thing to remember, yet even then one was already lost.” The AI era is much the same. Instead of waiting until you are replaced and filled with regret, it is better to start taking action now…

Section titled “Copyright Notice”

Thank you for reading. If you found this article useful, likes, bookmarks, and shares are all welcome. This content was created with AI-assisted collaboration, and the final version was reviewed and confirmed by the author.