AI Didn't Kill Engineering Jobs: What the Latest Data Means for IT Talent Strategy at Japanese Firms in the Philippines

AI Didn't Kill Engineering Jobs: What the Latest Data Says About "Growing Demand" and the IT Talent Strategy of Japanese Firms in the Philippines

Is it really true that "AI lets you cut development headcount"? Drawing on the latest hiring data, this article walks through the practical steps Japanese firms in the Philippines can take to raise engineer productivity and prepare for rising demand.

---

Part 1: Why This Matters

Step 1: The Philippine Business Context (3 min)

The claim that "AI will take engineers' jobs" has been a constant talking point for the past few years. Yet new data released in June 2026 suggests reality may be moving in the opposite direction from what people expected. When a venture capital firm analyzed a large volume of hiring data, the result was that engineering roles were, in fact, the most resilient category of all.

This is a very immediate concern for Japanese companies doing business in the Philippines. The Philippines is a treasure trove of English-speaking IT talent, and many Japanese firms base their software development and maintenance operations here. If you shrink your hiring plans on the assumption that "we can cut engineers because we have AI," you risk missing out on talent just as demand is rising, and falling behind your competitors.

Put the other way around, the value of engineers who can wield AI well is only going to go up. If you invest in teaching the Philippines' young IT talent how to use AI tools and in raising their productivity, you can do more development with a limited budget. What's needed now is to reframe AI not as a "reason to cut" but as an "opportunity to strengthen."

Monday morning, your office in Manila. You've just opened an email from headquarters in Japan that reads, "Consider reviewing development headcount in light of AI adoption." Showing the screen to the Filipino engineering manager at the next desk, you begin: "Take a look at this news article. According to the latest data, the more a company adopts AI, the more engineers it hires. Could we work out together how to explain this to headquarters so we can protect the excellent people we already have?"

Step 2: Key Points From the Original Article (5 min)

Here are the facts the original article reports, organized around the figures and proper nouns.

TechCrunch — "AI was supposed to kill engineering jobs, but new data suggests they're the most resilient" (June 24, 2026)

This table was created for learning purposes based on facts from publicly available information. For details, please check the original article at the link above.

Related: see How AI Helps Philippine Business Leaders Stay Competitive in 2026.

Step 3: Comprehension Check (5 min)

Q1. By what percentage did overall hiring at major tech companies fall compared with 2019? And was the decline in engineering roles larger or smaller than that? Hint: Overall was down 25%. The figure for engineering roles is 11%.

Q2. At the 12 major firms, what percentage of new hires in 2025 were engineers? Hint: It's risen somewhat from the 46% of 2019.

Q3. Compared with 2019, by what percentage did early-stage startups hire more engineers in 2025? Hint: It's a single-digit increase.

Q4. As of March, what did Anthropic's head of economics say about AI's impact on employment? Hint: The gist was that "no major impact has been observed yet."

Q5. What is the "Jevons paradox" that the article cites? Hint: Think about whether, when efficiency rises, demand for that resource decreases or increases.

---

Related: see How AI Agent Development Helps Philippine Businesses Automate Beyond Prompt Engineering.

Part 2: Putting It Into Practice

Step 4: Implementation Steps in the Philippines (10 min)

Here are the steps for translating this theme into practice—not as "use AI to cut engineers" but as "use AI to raise engineer productivity and prepare for growing demand."

In the first step, it's important to write out honestly who spends how much time on what. In Philippine workplaces, much of the work runs on the tacit understanding of "this is roughly how we do it," and simply putting that into writing reveals where improvements can be made.

When selecting tools, rather than rolling them out company-wide all at once, it's best to trial with two or three motivated engineers. Because the usage fees are often for overseas services billed in dollars, build your budget with currency fluctuation in mind.

Step 5: Common Mistakes and How to Avoid Them (5 min)

Mistake 1: Jumping to the conclusion that "we have AI, so we can cut people"

If you decide, the moment you bring in AI tools, that "now we only need half the engineers," you'll lose talent just as demand is rising. The data in the original article shows the opposite reality: the more a company uses AI, the more engineers it hires.

Bad example: Immediately after adopting development-assistant tools, you report to headquarters that "we can halve the hiring quota next term."

Good example: First, spend three months measuring how much productivity rises. Only after seeing the result do you consult headquarters about which new development the freed-up capacity should be directed toward.

Mistake 2: Handing data to tools and leaving it unmanaged

If you input customer information into an AI tool just because it's convenient, you can end up with incidents such as data leaks. The Philippines has a Data Privacy Act, and violations carry penalties.

Bad example: You keep pasting data that includes customers' names and contact details into an AI tool without checking the settings.

Good example: First confirm whether the input data is set not to be used for training. Decide as a team a rule of anonymizing personal data before handing it over, and put that rule in writing.

Mistake 3: Skipping the explanation to local staff

If you push AI adoption forward solely on the basis of headquarters' convenience and don't convey the purpose to local engineers, it will be received as "a tool to take away our jobs" and provoke pushback.

Bad example: At a company-wide morning meeting, you simply say "start using this tool tomorrow" without explaining the reasons.

Good example: Set up an occasion to carefully explain why you're adopting it and how it affects employment. Share the purpose—"to raise productivity and take on more interesting development"—and always make time to take questions.

---

Part 3: Going Deeper

Step 6: Related Technical Terms (5 min)

The Jevons paradox is the idea that when the efficiency of using a given resource rises, the amount of that resource used actually increases. For example, as more fuel-efficient cars come into use, people end up driving longer distances. At a development base in the Philippines, too, as AI raises the speed of writing code, you become able to take on that many more new development projects—so engineers' work, if anything, increases.

Agentic AI refers to AI that, without a person directing it step by step, makes its own judgments and carries out work toward a goal. Think of asking a cook to "make curry" and having them handle everything from prepping the ingredients to seasoning. If a development team in Manila uses agentic AI, they can hand off routine code-writing, freeing engineers to focus on harder design work.

AI-powered coding tools are software that helps with the work of writing programs. They suggest how to continue code you've started writing, or spot mistakes. When IT companies in the Philippines have new engineers use such tools, the range of what those engineers can learn without a senior colleague's help widens, shortening the time it takes for them to become productive.

Entry-level white-collar jobs refers to clerical and professional work taken on by people with little experience. It's easy to picture as the first work assigned to young hires straight out of school. For Japanese firms in the Philippines, whether AI takes over this kind of entry-level work, or whether young hires use AI to grow faster, is an important point in deciding hiring policy.

Headcount is the number of people working in a given department or company—a simple figure of how many are on a team. When you apply to headquarters for a Philippine base's budget, how you increase or decrease engineer headcount is the item that draws the most attention, and it's also used as material for explaining the effects of AI adoption.

Step 7: Thinking About Application to Your Own Company (10 min)

Divide your engineers' work into "parts to hand to AI" and "parts where people grow"

Thinking hint: Try dividing your current development work into routine tasks that are easy to hand to AI and tasks that require human ideas. Hand the former to AI, and you can concentrate people on the latter.

How to raise the value of local engineers in the age of AI

Thinking hint: Think not in terms of reducing hiring, but of developing the people you already have so they can wield AI. Just a little investment in training changes how much each person produces.

Back up your explanations to headquarters with data

Thinking hint: Against headquarters' expectation that "AI lets us cut people," think about how to construct an explanation that "we should actually be strengthening"—while showing productivity figures.

Next action: First, pick two or three engineers and have them trial an AI coding-assistant tool for one month. If you record the time it takes to complete a single feature before and after adoption, you'll have your first piece of evidence to demonstrate the effect in numbers.

---

Part 4: FAQ

Q1. If we bring AI coding tools into our Philippine base, can we really cut engineers?

Judging from the data in the original article, the reality is that the more a company uses AI, the more engineers it hires. Rather than cutting people in the short term, it's more realistic to raise the productivity of the people you already have and redirect the freed-up capacity to new development. Hold off on any decision to cut until you've seen the results of a few months of measurement.

Q2. Is it legally problematic to put customer data into an AI tool?

The Philippines has a Data Privacy Act, and the handling of personal data is overseen by the NPC (National Privacy Commission). Avoid inputting customers' names and contact details as-is; configure the tool so the data isn't used for training, and anonymize it before use where possible. Because violations carry penalties, be sure to set internal rules before adoption.

Q3. May we change local staff's employment terms on the grounds of AI adoption?

Changes to employment terms must follow the rules of DOLE (the Department of Labor and Employment) and require careful procedure. In the Philippines there's a culture that takes even verbal promises seriously, so always put the changes in writing and proceed only after obtaining the person's consent. Unilateral changes to someone's disadvantage are a source of trouble.

Q4. Headquarters in Japan is demanding "headcount reduction through AI." How should we respond?

It's effective to engage through numbers rather than emotion. Record how development speed changed before and after adoption, and demonstrate the value not as "reduction" but as "the same number of people can do more development." Attaching third-party data like that in the original article makes for a persuasive case.

Q5. Should we hold off on hiring new engineers?

If anything, the opposite. With AI tools, the range of what new hires can learn without a senior colleague's help widens, shortening the time it takes for them to become productive. If you build a development system on the premise of AI, hiring young people pays off as an investment in the future. The Philippines is rich in young IT talent, so make the most of this strength.

---

Tips for Making the Most of This (3 Tips)

First, record productivity in numbers for three months

The effects of AI adoption are far more persuasive when described in numbers rather than impressions. If you record the time it takes to complete a single feature before and after adoption, you can use it both for reports to headquarters and for winning over the local team. Make this record your first step.

Decide your data-handling rules before you adopt anything

If you prioritize convenience and put off data handling, it leads to incidents such as data leaks. Decide as a team, before adoption, what may be input and what must be anonymized, and put it in writing. This becomes the foundation for legal compliance in the Philippines.

Share the "purpose" with local engineers before you start

It's important to convey that AI is not a tool to take away jobs but a tool for taking on more interesting development. If you explain the purpose of adoption and set up an occasion to take questions before starting, you'll draw out cooperation rather than pushback.

---

Bonus: How to Make Use of PH AI Works

PH AI Works supports the practical use of AI and technology for Japanese companies expanding into the Philippines and for Japanese business professionals based here. On this article's theme—"raising engineer productivity with AI" and "developing local IT talent"—you can consult with us with the local circumstances in mind.

As a next step, you can consult with us on matters such as the following:

• Selecting AI coding-assistant tools and how to proceed with a small-scale trial
• Building rules for handling data when using AI tools, in light of the Philippines' Data Privacy Act
• Designing AI training programs for local engineers, and building the numbers to explain productivity to headquarters

Feel free to get in touch first. Consultations are free.

---

References & Sources

• TechCrunch — "AI was supposed to kill engineering jobs, but new data suggests they're the most resilient" (June 24, 2026)
• National Privacy Commission (NPC)
• Department of Labor and Employment (DOLE)