AI-enabled jobs in the Philippines: an in-depth report

A report analyzing 3,489 AI-related job listings in the Philippines found that workers who wire AI into company workflows earn a median of ₱120,000 per month, more than double the ₱56,000 median for those who merely use AI tools. Only 15% of roles are entry-level, while 42% require senior or lead experience, creating a market where juniors without AI experience struggle to enter and companies cannot find experienced staff.

15% to 20% of the global digital workforce is experiencing meaningful job-related anxiety due to fears of being replaced by AI, and 65% to 71% have broad concerns. We believe this anxiety is a symptom of a noisy and constantly shifting landscape, rather than a fundamentally existential one unless you're a junior or have zero AI-related experience, in which case: read on . So we decided to ask the following questions: - Which AI-related jobs are currently in demand in the Philippines part 1 - What does retraining for those jobs look like part 2 Methodology We scanned major Filipino job sites for AI-related roles, 3,489 listings in all, and analysed them to pick the signal from the noise. See the full methodology in the annexes annexes . Part 1: the demand - picking the signal from the noise rule of thumb: ignore job titles when searching for an AI-enabled role, and look at the listed skills instead Out of 1,107 job listings we found 983 unique job titles . Diving deeper we found that every listing has a fingerprint: canonical skill set, role category, centrality, industry. We were able to use those fingerprints to unearth 24 job title archetypes . Go ahead and slide the demand and pay sliders to highlight the weighting of your choice. e.g. demand = 1 and pay = 1 means you're looking for a job in high demand that pays well. Ignore the skill slider for now, we'll return to it later. Pay medians cover the ~30% of listings that post a salary; the dashed row had too few to compute one. Two distinct buckets: using vs. building rule of thumb: using AI at work does not guarantee high pay, however wiring AI into the company pays more at every level of experience Using AI: median pay ₱56,000/mo In this scenario, the worker might be copy and pasting things in and out of, say, ChatGPT. As such, despite ChatGPT speeding up their workflow, their pace is still the limiting factor. The operator is still the slow cog in the machine, and the PH market prices this around ₱56k median. Wiring AI into the company: median pay ₱120,000/mo In this scenario, the worker wires AI deeply into their or the company's workflow, striving for efficiency and automation. So the automatable tasks can truly happen at software speed, whilst the worker still handles the portions of the work requiring taste, judgement, ownership etc. The market prices this around ₱120k median. Now one may ask whether the gap can simply be attributed to seniority, and the answer is yes, partly : the wiring bucket is more prone to wanting seniors or leads 61% of its salaried listings, against 32% in the using bucket . However despite this, and at every level of experience, wiring still pays more. At mid level: ₱75k vs. ₱58k , at the senior level: ₱135k vs. ₱106k . Experience explains roughly half of the pay gap, and wiring AI in explains the rest. Read the relevant annex annex-pay-gap if you're curious about the statistics used to uncover this fact. Experience required rule of thumb: if you have no AI experience, train by any means necessary: YouTube, side projects, portfolio pieces, open source, bootcamps This is existential: the data shows that, without climbing the first rung yourself, the doors remain shut. Only 15% of the roles we encountered were entry-level, 39% were mid-level, while 42% wanted seniors or leads. Employers are paying the ₱120k because they are struggling to find the experienced staff they need. That's a genuinely strange jobs market, for both applicants and companies. Juniors / applicants with little to no AI experience have no way in, and companies are craving experienced staff and are unable to find them. The majority of those jobs are remote rule of thumb: don't let your search default to on-site: remote and hybrid are 63% of these jobs, and both pay more - 63% of the roles are remote or hybrid; only 37% are on-site. - Remote pays more than on-site: a median of ₱67,500 against ₱50,000 for on-site roles, and hybrid tops both at ₱97,500. - The majority of the demand is international, via staffing and VA agencies. Part 2: the retraining map In the graph below, click or hover on a job in the right column to see what skills it requires or click or hover on a skill to see what skills & jobs it unlocks . If you remember, the table in Part 1 has a "skill" slider. Here is what it does: we've estimated how many hours it takes to acquire each skill, and a job's total "skill" value is the sum of every skill prerequisite. Slide the "skill" slider left to favour jobs you can train into quickly; slide it right to favour builder jobs that might take weeks, months, or years to learn. rule of thumb: if you have great soft skills, and don't want to compete with the rest of Asia on hard skills: become a Product Manager In session 2 /sessions/2 and session 3 /sessions/3 , we put you through PM roles: defining what the software does, without building it yourself. The sessions required strong leadership skills under time pressure, product taste, judgement, and the ability to spec out the app explicitly. And now we have the numbers: PM is the most-listed archetype in the building tier and pays a ₱155,000 median. In fact, this is our core thesis in our still unpublished, and unproven whitepaper for this group: Filipinos are in a strong position to take on PM roles due to their above average English literacy skills, combined with excellent soft skills, on average we are happy for the sessions to either prove or disprove this thesis . And think about it for a second: we are all migrating to PM roles. Telling AI precisely what to build is very much a PM role itself. The five best-value skills Let's finish with the obvious question: which skills give you the greatest bang for your buck, the buck being your training hours? For every skill in the map, we divided demand how many of the 1,107 listings ask for it by the hours it takes to learn that skill alone our estimates, assuming you already have its prerequisites from the map . The top five: | skill | helps unlock | listings asking | est. hours | |---|---|---|---| | workflow automation | 7 archetypes, up to ₱155,000 product manager | 233 | 60 | | prompt engineering | 3 archetypes, up to ₱125,000 llm application engineer | 146 | 40 | | ai tool usage | 1 archetype: operations coordinator | 44 | 20 | | api integration | 2 archetypes, up to ₱68,750 enterprise systems integrator | 61 | 40 | | generative ai | 3 archetypes, up to ₱130,000 machine learning engineer | 71 | 60 | Workflow automation is the biggest bang for your buck: it has no prerequisites, and roughly 60 hours with tools like n8n, Make or Zapier gets you there. It also helps unlock seven jobs on the map, including the Product Manager role. And it keeps paying after that: prompt engineering, ai tool usage and api integration all build on top of it in the map, so their hours are add-ons, not fresh starts. rule of thumb: if you're starting off on your journey, learn workflow automation, prompt engineering, and generative ai: they open up the most paths towards jobs for the fewest hours of training Recap Let's quickly recap our rules of thumb: - ignore job titles when searching for an AI-enabled role, and look at the listed skills instead - using AI at work does not guarantee high pay, however wiring AI into the company pays more at every level of experience - if you have no AI experience, train by any means necessary - don't let your search default to on-site: remote and hybrid are 63% of these jobs, and both pay more - if you have great soft skills, and don't want to compete with the rest of Asia on hard skills: become a Product Manager - if you're starting off on your journey, learn workflow automation, prompt engineering, and generative ai: they open up the most paths towards jobs for the fewest hours of training Wrap up This data now informs what we cover in our free Claude Code Manila sessions. If you found this deep dive into PH AI job data useful, want more analyses like this one, have questions, comments, objections, or simply want to say hi, feel free to connect on LinkedIn https://www.linkedin.com/in/shyal-beardsley/ . And if you're hiring: I'm an Infrastructure Developer with ample fullstack experience, currently open for work in the Philippines. Annexes Fair word of warning: annexes are heavily claude-written . Annex A: the anxiety numbers The 15-20% and 65-71% ranges in the intro don't come from a single survey: we put them together from several, each one measuring workers exposed to AI. The sources: Adaptavist late 2025, n = 4,000 knowledge workers across the US, UK, Germany and Canada: 20% report stress or anxiety from fear of AI replacement, 35% report hoarding their skills, and it rises to 40% among Gen Z respondents ; the Stack Overflow Developer Survey 2025: roughly 15% of developers view AI as a threat to their job, with a further 21% unsure ; Reuters/Ipsos 2025, US adults: 71% worried AI will put too many people out of work permanently ; and EY 65% of surveyed workers anxious about AI replacing their own job . Three things to bear in mind before you use those ranges. First, the surveys measure different things: "stress or anxiety" Adaptavist , "perceived threat" Stack Overflow and "worry about displacement in general" Reuters/Ipsos, EY are not the same thing, which is why we give you a range rather than a single number. Second, the people surveyed differ, and none of them is the Philippine workforce: the ranges describe similar digital-economy workers elsewhere. Third, self-reported anxiety moves with question wording and news cycles. Nobody has measured this for the Philippines specifically, and that gap is part of why this report exists. Annex B: how the numbers were produced Collection. We collected on 2026-07-27, from two sources: JobStreet Philippines public search API, with the full listing text retrieved per listing and Kalibrr public job-board API, filtered to Philippine listings . We ran ten query terms: artificial intelligence, machine learning, generative AI, AI engineer, AI automation, AI agent, LLM, prompt engineering, chatgpt, AI. We deduplicated across queries by listing ID. The yield: 3,489 unique listings 3,023 JobStreet, 466 Kalibrr . Every listing carries first-seen and last-seen timestamps, so repeat scans can measure persistence and reposting. Structuring. A language model Claude Haiku 4.5, batched read each listing's full text and converted it to a fixed schema: does the role genuinely involve AI the noise gate , how central AI is core: the job is building AI systems; applied: a job done with AI tools daily; peripheral: AI as a nice-to-have , one of eleven role categories, seniority entry, mid, senior, lead, or unspecified , employer industry, named tools, skill phrases, and salary. Of the 3,489 listings, 3,447 made it through the rest had descriptions too short to classify, or failed extraction . The noise gate passed 1,107 listings 32.1% : 721 applied, 334 core, 52 peripheral. Vocabulary normalization. Extraction produced 5,365 distinct skill phrases and 1,564 distinct tool names. We merged synonyms and spelling variants into canonical terms with a language-model pass over the full vocabulary "AI prompting", "prompting" and "prompt engineering" all become prompt engineering . Every demand count in this report is a count of canonical terms. Salary normalization. Where a listing posts a salary, we take the midpoint of the posted range, converted to monthly PHP when the posting states another period or currency. Roughly 30% of AI-role listings post salaries. One known error class per-year figures mislabelled as per-month, which convert to absurd monthly values is excluded by capping at ₱600,000/month. Every pay figure in this report is a median over these midpoints: annex D explains why. The 24 archetypes. The 1,107 AI roles carry 983 unique titles, so titles can't tell us what the job really is. So we represented each listing by its canonical skill set instead, and clustered within role category a hard rule: an engineer and a marketer never merge , using bisecting 2-means on unit-normalized skill vectors with cosine similarity and deterministic seeding. Cluster budgets are proportional to category size, 25 in total; clusters smaller than 8 listings fold into 'other'. The result: 24 clusters covering 1,106 listings one listing had no usable skills . Why roughly 25: each archetype needs enough members posting salaries for an honest median. A language model proposed the archetype names from each cluster's skill profile and sample titles, and we reviewed them; the hover descriptions are put together from up to 25 member listings per archetype. The taxonomy is frozen as version 1: future scans classify new listings into it by nearest centroid rather than re-clustering, so the trend lines can be compared from scan to scan. If 'other' ever exceeds roughly 10% of new listings, we re-cluster to version 2. The 1,106 skill fingerprints projected from 343 skill dimensions down to two PCA, mean-centered unit vectors, power iteration . Each dot is one listing, coloured by centrality. The projection is computed rather than drawn: the grouping you can see content top-left, automation upper-right, building-with-AI lower-right is what the clustering formalizes. The overlap between the colours is real too, which is why we cluster within role category rather than globally. The map's edges. Three edge types, three derivations. Stated edges skill to job : the top skills in an archetype's fingerprint with a share of at least 0.12, capped at 3 per archetype; the edge weight is the share of the archetype's listings naming the skill. Implied edges baseline to job : discipline tokens in the archetype's member titles developer, engineer, programmer , emitted when at least 35% of titles carry the token and no stated edge already covers the pair this captures the prerequisites job ads assume rather than state . Prerequisite edges skill to skill : for every skill pair with at least 8 co-occurring listings, we compare conditional probabilities, and draw an edge from A to B when P A present | B present is at least 0.30 and beats the reverse conditional by a factor of at least 1.4. The asymmetry gives us the direction: specialist listings ask for their foundations, however not the other way around. We insert edges strongest-first with a cycle guard, then transitively reduce, so a job connects to the skills closest to it, and the rest of the path comes through the chain. The hours. The learning hours behind the skill slider are estimates, not measurements: the extra hours each skill takes to reach an employable level, assuming the skill's prerequisites in the map are already met, for a digitally literate learner with no coding background. A job's total is the sum over its full upstream path. These are the only numbers in the report that aren't measured, and they're labelled as such wherever they appear. Annex C: limitations Coverage. Two job boards. We don't yet scan company career pages, LinkedIn, or specialist boards, so enterprise employers that hire only through their own systems are under-represented. The keyword net misses things too: a role that is AI-heavy in practice but never says so in its ad is invisible to us. Selection bias in pay figures. Only about 30% of AI-role listings post salaries, and the employers who post may differ from the ones who don't. Every pay comparison in this report carries this bias, including the bucket gap in annex D. Extraction error. Every structured field is a language model's reading of free text. We haven't yet run a human-validated error-rate sample, so until we do: treat individual listings as approximately right, and aggregate counts as reliable to within a few percent, not down to the last listing. Stated-skill bias. Job ads name whatever filters candidates, and leave out what they take for granted. We measured this directly: developer, engineer or programmer appear in 62-93% of member titles across the building tier's engineering archetypes, whilst software engineering rarely appears as a stated skill; spreadsheet skills appear in over half of all AI-role listing bodies whilst almost never showing up as a stated requirement. The implied-edge layer in the map exists to correct for this, however the correction is partial. Clustering impurity. Hard clustering misfiles some listings. A known example: a group of full-stack developer listings sits inside the QA automation archetype, because both name test automation. We estimate archetype impurity at roughly 10-15%, and archetype medians pick up small distortions from it. A snapshot, not a trend. Every figure describes listings live during the July 2026 collection window. Nothing here measures growth, decline or turnover: that requires the repeat scans the pipeline is built for. Demand floor. Job postings measure external hiring demand only. Companies that upskill existing staff never post, so every corporate-demand reading here is a floor, not a total. Annex D: the using vs building pay gap, tested The buckets section claims that jobs building with AI pay more than jobs using it. Here is the test behind that claim. The samples. Of the 1,107 AI roles, salaries are posted on 232 using-AI listings and 84 building-AI listings. Each listing's salary is the midpoint of its posted range, in monthly PHP, with the same mislabelled per-year outliers capped out as everywhere else in this report. The medians: ₱56,000 using vs. ₱120,000 building . Every salaried AI-role listing is one dot, jittered within its lane; the vertical bars mark the lane medians. Look at the shape as well as the medians: the applied lane is dense at the low end with a long right tail the highest-paid applied listings are the AI-assisted developers , whilst the core lane sits shifted right with a wide spread. The two overlap substantially, which is why our claim is about medians and likelihoods rather than a wall between the buckets. Why medians and not averages. Salary data is skewed: a few very high postings drag an average upward, and one mislabelled listing can move it by thousands. The median the middle listing barely moves when an outlier lands. That's why every pay figure in this report is a median. The test. For skewed data like this, the standard tool is the Mann-Whitney U test. Instead of comparing averages, it asks: if you repeatedly drew one listing from each bucket at random, how often would the building one pay more? If the two buckets really paid the same, that would hover near 50/50. The result. U = 14,329, z = 6.39 normal approximation with tie correction , two-sided p ≈ 1.7 × 10⁻¹⁰, i.e if using-AI and building-AI jobs truly paid the same, a gap this consistent would show up by chance far less than one time in a billion. The effect size rank-biserial r = 0.47 is the practical way to read it: draw one listing from each bucket at random, and the building one pays more roughly 3 times out of 4. The seniority check. The 'Building with AI' bucket is more prone to wanting seniors: 61% of the salaried ones want seniors or leads, vs. 32% of the 'using AI' ones, and experience commands pay on its own. So we re-ran the same test within each seniority level: - Mid-level: ₱58,250 using, 100 listings vs. ₱75,264 building, 19 , U = 1,386, z = 3.16, p ≈ 0.002, rank-biserial r = 0.46. - Senior: ₱105,600 65 vs. ₱135,000 41 , U = 1,736, z = 2.62, p ≈ 0.009, r = 0.30. - Entry and lead point the same way, however each holds only 10 salaried building listings, too few to test honestly. A reweighting check gives the split: if the 'Building with AI' bucket had the 'using AI' bucket's experience mix, its median would sit near ₱91,000 rather than ₱120,000. So comparing like with like, the building premium shrinks from more than double to roughly 30% extra, and the rest of the pay gap is experience mix. Both parts are real, and the body's rule of thumb claims only the first. What this does and does not establish. It establishes that the pay difference between the buckets is real, and that it survives holding experience level fixed. It does not pin the medians down to the peso they move by a few thousand with the sample , and it carries the one bias both buckets share: only listings that post salaries are counted, and the employers who post salaries may differ from the ones who don't. Neither problem changes the direction or the rough size of the gap. Annex E: the data and reproducibility Dataset. One SQLite database holding every stage: raw listings full text, source, timestamps: 3,489 rows , structured extractions 3,447 rows , the canonical vocabulary mapping 6,929 term rows , the frozen taxonomy 24 archetypes and 1,106 listing assignments , and per-run scan records. Collection window: 2026-07-27. Models used. Structuring and vocabulary normalization: Claude Haiku 4.5. Archetype naming and description writing: Claude Sonnet 5. Clustering, edge derivation and all statistics are deterministic code, not model output, and re-run identically from the database. Pipeline. Four stages, each re-runnable and incremental: scan pull and deduplicate listings, refresh last-seen timestamps , extract structure new listings , canonicalize merge new vocabulary , classify assign new listings to the frozen taxonomy . A single export step regenerates the graph data all three edge types, with the thresholds from annex B from the database. Every figure in this article is derived from these tables, with no hand-entered numbers except the learning-hour estimates, which live in one reviewed file. Cadence. The July 2026 run is the baseline. We plan to re-scan weekly: because the taxonomy is frozen and classification is deterministic, week-over-week changes in demand, pay and archetype mix can be compared directly. If we ever need corrections or a re-cluster, we'll publish them as versioned updates, so nothing in this report changes silently. Access. The dataset contains only public job-listing content. If you want to verify a figure or run your own analysis, contact us through the site and we'll share the relevant extract.