A few days ago, I came across a post by [Jeffrey Bakker](https://medium.com/u/a1d50af3eb55?source=post_page---user_mention--b5f9d146ce38---------------------------------------) on Medium that hit closer to home than I expected. It is an eight-minute read and well worth your time. Bakker documents what so many people have been quietly experiencing: the process is broken, the tools are stacked in favour of employers, and most job seekers are navigating an increasingly automated system without any equivalent tools on their side.

His article confirmed what I have been watching happen, and it pushed me to write something of my own.

**Read Bekker's piece here:** [**Job Hunting in 2026: AI Is Everywhere, and Mostly Working Against You**](https://medium.com/prachub/job-hunting-in-2026-ai-is-everywhere-and-mostly-working-against-you-9773d3888b4f)

I have been building a tool called [JobsLobster](http://jobslobster.com/). Before I get to that, I want to spend some time on the problem itself, because I do not think it gets taken seriously enough.

## **What Job Hunting in 2026 Actually Feels Like**

The five stages of grief are not a metaphor when you are job hunting in 2026. It is a pretty accurate description of the emotional arc most people go through.

![Source: Reddit](https://miro.medium.com/v2/resize:fit:1400/1*Mw8RWjedj1ZOR4GqpO7P8w.png "Source: Reddit")

The numbers are brutal. Cold online applications now have a success rate as low as [0.1%](https://blog.hiringthing.com/2025-job-application-statistics-updated-data-you-need-to-know). The average job seeker is submitting over 150 applications to land a single offer. You send out 150 applications and get three rejections plus radio silence.

Meanwhile, the hiring process has quietly automated itself into something that feels almost designed to filter people out before any human ever gets involved. AI and mass applying mean most resumes never reach a human, and even interviews are increasingly handled by bots.

For job seekers, that has meant fewer responses, less transparency and growing uncertainty about how, or whether, to use AI themselves..

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/6878175b45af04b953695442c3f5b954048d8c28ab7acca83d9540bc6ed4f1a7.avif "Source: X")

And here is the cruellest part of the current dynamic: The majority of roles above $80K are filled through referrals, recruiters, and internal mobility rather than public job boards.

So the game most job seekers are playing, such as optimising their resume, applying through LinkedIn, checking Indeed every morning, is the wrong game entirely. The jobs that matter most are not even visible in the places most people are looking.

## **AI Was Supposed to Help. It Mostly Helped the Other Side.**

![Photo by Sebastian Herrmann on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*AhOWRARE8XAyVkky "Photo by Sebastian Herrmann on Unsplash")

This is the part that genuinely frustrates me.

AI has transformed the recruiter side of hiring. Applicant tracking systems have gotten dramatically better at filtering. AI screening tools can process thousands of applications in minutes. Automated ranking systems score candidates before a human eye ever touches the file.

The job seeker side? Mostly they got AI resume polishers that take their existing resume and make it shinier. Which sounds helpful until you realise the underlying problem is not resume polish. It is that a two-page document is a terrible representation of what a person actually brings.

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*9LzLz1Saz5wJIUjk "Photo by Resume Genius on Unsplash")

Think about what a resume is. It is a compressed, simplified, necessarily incomplete summary of a career. Most people write it under time pressure, undersell themselves significantly, and leave out entire categories of relevant experience because they are not sure how to frame it. Then they apply it to a job description that was written by a committee and filtered through an ATS that is looking for specific keyword matches.

The mismatch between what a person can actually do and what their resume conveys is enormous. And for years, nothing on the job seeker side has addressed that gap seriously.

The real problem is not that there are too many candidates. It is that too many capable people never get the chance to show what they can do.

That sentence is why we built [JobsLobster](http://jobslobster.com/).

## **What We Are Trying to Do**

![](https://miro.medium.com/v2/resize:fit:1400/0*xio6FvDmBS8rDfWs.png)

JobsLobster is a resume builder that starts from a different premise: **your resume is not the starting point. You are.**

Here is how it works.

You upload your current resume and paste in the job description for the role you are applying for. The AI agent analyses the **_gap_** between what your resume currently shows and what the specific job is actually looking for. Not just keyword matching. A real analysis of where your documented experience speaks to the role and where it falls short.

![Competence review by JobsLobster (Based on Author’s Resume)](https://miro.medium.com/v2/resize:fit:1400/0*mRWKy2u2VfeFDZZq.png "Competence review by JobsLobster (Based on Author’s Resume)")

Then it asks you **_questions_**.

This is the part that makes it different from every other tool I have seen. Because most of the time, the gap between your resume and a job description is not a gap in your actual experience. It is a gap in your documentation. You have done the relevant work. You just _never wrote it down properly_, or you wrote it down in a context that does not obviously translate to this particular role.

The agent surfaces those gaps specifically and asks you to fill them in with your own words. **_Tell me about a time you managed something like this. Walk me through how you approached that kind of problem. What did that project actually look like from your end?_**

Your answers get remembered. Every conversation, every detail you share about your career gets stored and used for future applications. So the second resume you build is better than the first. The fifth is better than the second. The agent is building a richer and richer picture of who you actually are, not just what fits on two pages.

![Job-tailored resume by JobsLobster](https://miro.medium.com/v2/resize:fit:1400/0*E0CpzrmlAstqCNoa.png "Job-tailored resume by JobsLobster")

Beyond the resume itself, JobsLobster generates a tailored cover letter for each application and runs a mock interview simulation based on the specific job description. So you can practise answering the questions this particular role is likely to generate, before you are in the room.

## **Why We Built It This Way**

Recruiters have serious tools. AI screening, automated ranking, and ATS systems that have been refined over the years. The asymmetry between what the hiring side has and what the job seeker side has is real and significant.

The playbook changed. Most job seekers are still running the old one.

[JobsLobster](http://jobslobster.com/) is an attempt to give job seekers something that actually matches the sophistication of what they are up against. Not a resume template. Not a keyword stuffing tool. A system that understands you specifically, builds your profile over time, and helps you present the full scope of what you bring to each individual role you apply for.

The people stuck in the application loop are not usually stuck because they lack capability. They are stuck because the system makes it brutally hard to communicate that capability clearly.

A generic resume going into an ATS is not a fair representation of a genuinely talented person. We are trying to build the tool that changes that.

## **Still Early, Still Listening**

![Photo by Jon Tyson on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*AJ8DP06nf6vTr3wP "Photo by Jon Tyson on Unsplash")

JobsLobster is still in active development. I want to be honest about that.

The core functionality is working: resume upload, job description analysis, gap identification, question-based profile building, tailored resume generation, cover letter, and mock interview. The agent memory is working. The job-tailored output is working.

There are things we are still improving. The UI is getting cleaner. The question quality is getting sharper. The output formatting is being refined based on what users find most useful.

This is exactly the stage where feedback matters most. If you try it and something does not work the way you expected, or something works well, and you want more of it, that feedback shapes what we build next.

A free trial is available at [jobslobster.com](http://jobslobster.com), with three applications per week. No credit card required to start.

If you are currently in the job-hunting trenches, I genuinely hope it helps. And if you have thoughts on what it should do better, I am listening.

The Quiet Desperation of Job Hunting in 2026

Why talented people keep getting filtered out

Why talented people keep getting filtered out The five stages of grief are not a metaphor when you are job hunting in 2026. It is a pretty accurate description 

Most people hand their AI coding agent a task and hope for the best.

Codex goes off, does a bunch of things, and you get back something that’s either close to right or quietly broken in three places you won’t notice until production.

There’s a better way. This is the same process I used to get [JobsLobster](https://jobslobster.com/) ready to add a blog with AI.

Here is what I actually did, along with the repeatable method I now use for every significant feature we add.

## **Start With a Skill Book, Not a Prompt**

At Elephant Stripes, the consulting company I run, we call it a phased implementation plan. You can also think of it as a _skill book_ for your agent.

The idea is simple. Before you write any code, you capture everything the agent needs to do good work.

That investigation document covers:

*   Investigation summary
    
*   Affected user flows, jobs, route, fixtures, traces, or artifacts ID,
    
*   Current observed behaviour
    
*   Desired behaviour
    
*   Relevant code entry points
    
*   Data shape and DB state
    
*   Production impacts
    
*   validation evidence, and
    
*   What’s explicitly out of scope
    

![](https://miro.medium.com/v2/resize:fit:1400/1*yBBkhrGjIjPtAgFOFOETMA.png)

You end up with two states clearly defined: where you are, and where you want to be. The gap between them is the work.

The skill book gives the agent examples of how you’ve done similar work before, your code shape preferences, and the product decisions already made. It eliminates a whole class of guessing.

## **The Per-Phase Operating Loop**

![Photo by Brecht Corbeel on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*zx5nDKKA4e79-Dd1 "Photo by Brecht Corbeel on Unsplash")

Once the skill book is ready, you build the implementation plan.

For this project, that meant seven phases covering SEO foundations, JSON-LD, robots, sitemaps, metadata, and, finally, a production push.

Each phase follows the same _loop_:

1.  Complete the phase work
    
2.  Update the plan document with what was done
    
3.  Document production impacts (migrations, environment variables, any changes needed in OpenClaw)
    
4.  Validate and smoke test
    
5.  Write a changelog entry into [changelog.md](http://changelog.md)
    
6.  Commit and push the branch
    
7.  Move to the next phase only if there are no blockers
    

That loop is important. It means every phase has its own record when it’s done. Production impacts get captured as you go, not reconstructed at the end. The changelog writes itself. You can push to production with confidence because every phase told you exactly what changed.

## **What JobsLobster Actually Looked Like Before the Fix**

I want to be honest about the state the app was in, because it’s probably not far from where a lot of fast-built apps end up.

The audit found:

*   No robots.txt and no sitemap.xml
    
*   Pages that should be noindex were being indexed
    
*   Only a default top-level JSON-LD blob, nothing route-specific
    
*   Every public page falls back to the same global SEO title and description
    

![Here’s a screenshot of what issues Codex found after the audit](https://miro.medium.com/v2/resize:fit:1400/1*LyRTGCyjCpcJPcn2DUsPPQ.png "Here’s a screenshot of what issues Codex found after the audit")

That’s a real app with real users. None of those issues was catastrophic. All of them were quietly holding back organic growth.

The benchmark I used was the Elephant Stripes site. It has route-specific JSON-LD, proper metadata, solid page speed scores, clean Search Console access, and automatic AVIF conversion on image upload. That’s the standard. The goal was to bring JobsLobster up to that level before building anything content-heavy on top of it.

## **Handing It to Codex**

Once the plan was ready, I opened a fresh Codex thread. New thread, clean context window.

The prompt told Codex to:

*   Work in the correct branch
    
*   Read the README, the implementation plan, and the phased deployment documents
    
*   Start with Phase 1 only
    
*   Follow the per-phase operating loop
    
*   Keep JSON-LD in sync automatically when admin fields change
    
*   Keep SEO controls and previews inline on the admin pages that own the content
    

![My prompt](https://miro.medium.com/v2/resize:fit:1400/1*wqLBl0m1j6-5bTtxtODNAQ.png "My prompt")

Then I let it run.

It worked through all seven phases. There are still a few things to tighten up. Image conversion to AVIF needs more attention. Some pricing data was pulled in from the wrong source. A couple of fields still need manual content.

But the foundation is solid. Robots.txt and sitemap exist. Metadata is no longer duplicated across every page. JSON-LD is route-specific. The Search Console and Page Speed APIs are wired up correctly.

When the blog goes in, it lands on a foundation that’s actually ready for it.

## **Why This Process Works**

The phased implementation plan does a few things that a single-shot prompt can’t.

It gives the agent a bounded scope for each phase. There’s no ambiguity about what Phase 1 is and isn’t. The acceptance checks either pass or they don’t. The agent updates the document as it goes, so you always have a record of what was done and what’s still pending.

It also keeps the context window clean. I started a new thread in Codex specifically because the context from planning and investigation would have muddied the build. Fresh thread, focused instructions, phased scope.

That’s the whole system. Investigate thoroughly, define current and desired state, wrap it in examples and a per-phase loop, hand it over with clean context.

It’s repeatable. It produces consistent results. And it gives you documentation as a byproduct rather than a separate task.

## **The Blog Comes Next**

The whole point of this exercise was to get to a place where a blog would actually do something useful. The plan is simple: as JobsLobster adds new features, those features get announced on the blog and emailed to signed-up users. Content that is useful because the SEO underneath it is properly structured from day one.

That’s the next step. And now the foundations are there to do it properly.

![JobsLobsters now has a blog page! Check it out!](https://miro.medium.com/v2/resize:fit:1400/1*yLKr_B-tW4ibRYE-L5qJMA.png "JobsLobsters now has a blog page! Check it out!")

If you want to watch the full process play out in real time, including where Codex hits snags and how the per-phase loop handles them, go and watch the YouTube video. It’s the uncut session, just over an hour. Worth seeing if you’re planning to use this approach yourself!

@[youtube](https://www.youtube.com/watch?v=f29P4iVBKxE)

* * *

_JobsLobster is an AI resume-builder that helps you tailor your resume to specific job descriptions. Try it for_ **_free_** _at_ [](http://jobslobster.com/)[_jobslobster.com_](http://jobslobster.com)_._

How I Used AI to Build A Blog Into My App

Revealing the secret sauce that makes Codex actually reliable

Revealing the secret sauce that makes Codex actually reliable Most people hand their AI coding agent a task and hope for the best. Codex goes off, does a bunch of things

You find a role that feels right. The salary works. The team looks decent. The job description reads like it was written for you.

So you open your resume.

And then comes that familiar sinking feeling. The resume you have was built for the last job you applied for. Or maybe the one before that. It’s close. But it’s not quite right for _this_ one. You’re going to have to rewrite it. Again.

This is where most job seekers lose hours they didn’t have to spare. And often, where they quietly lose the role too.

There’s no shortage of **AI resume builders** promising to solve this. But most of them do the same thing: take your existing resume and make it sound a bit shinier. They tighten the language, sprinkle in keywords from the job description, and call it tailored.

That’s useful, sure. But it’s not really solving the problem.

## **The Real Problem isn’t Your Resume. It’s Your Memory.**

![Photo by Vasilis Caravitis on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*bM8GgoRvSl0Kp97O "Photo by Vasilis Caravitis on Unsplash")

Here’s what’s actually going on.

Over the course of your career, you’ve done a lot. You’ve shipped projects, led teams, solved problems, and worn hats that were never in your original job description. You’ve built things, fixed things, and figured out things on the fly.

But virtually none of it is written down in a single place.

Your resume is a snapshot. Usually two pages. Usually written in a hurry. And almost always trailing well behind what you’ve actually done. It captures _some_ of your work. A handful of highlights. A selection of skills.

What it doesn’t capture is the full picture.

Think about it this way. Imagine you had a 40-page document tracking everything: every project, every role, every unexpected responsibility you took on, every skill you quietly built over the years. From those 40 pages, pulling out the perfect two-page resume for any given role would be straightforward.

You’d have so much to work with that the right version would practically write itself. It would take the best things you’ve done and cast you in the best possible light, every time.

That’s the idea: a new wave of AI job tools is finally starting to chase. Not just resume polishing, but something more like persistent career memory.

## **What “Persistent Memory” Actually Means for Job Seekers**

![](https://miro.medium.com/v2/resize:fit:1400/1*cOUk6FLnHaznZe6ZqM3_4g.png)

The concept is simpler than it sounds.

Instead of treating each job application as a one-off task, the smarter approach is to build an ongoing picture of who you are professionally. An AI agent you talk to over time. One that learns about your history, your projects, your strengths, and yes, your gaps too.

The more you tell it, the better it understands you. The better it understands you, the more precisely it can represent you to any employer, for any role.

This is the thinking behind [JobsLobster](https://jobslobster.com/), a new tool being built by [Elephant Stripes](https://elephantstripes.ai/). It’s still early, and we’re transparent about that. But the core idea is genuinely different from what most AI resume tools are doing today.

The goal isn’t just to help you write a tailored resume. It’s to create a living knowledge base about your professional life that gets richer every time you use it.

## **How It Works in Practice**

The process starts simply. You upload your existing resume and paste in a job description. No complicated integrations. Just two inputs and you’re underway.

![Click ‘Start resume preview’, upload your resume, and paste the job descriptions.](https://miro.medium.com/v2/resize:fit:1400/1*kZGsboxqrvJD7iI9VuyZNg.png "Click ‘Start resume preview’, upload your resume, and paste the job descriptions.")

From there, the app does something most resume tools skip entirely. It generates a **capability map**.

![Sample: Competence review by JobsLobster](https://miro.medium.com/v2/resize:fit:1400/1*AJYyfyAT04-Hq3BG3OAXFQ.png "Sample: Competence review by JobsLobster")

This is where it gets interesting.

The capability map compares what’s in your resume against what the job description is actually asking for. It doesn’t just flag keywords. It surfaces the specific gaps between how you’re currently presenting yourself and what this particular employer needs.

In practice, it looks like this: _“_**_Candidate shows AI product experience, but lacks detail on enterprise AI agentic lifecycle or Microsoft-stack delivery._**_”_ That’s a precise, actionable insight. Not generic feedback. Not a vague nudge to “add more detail.” A specific gap you can actually do something about.

Here’s what makes that useful. Say you’re applying for a role that asks for water utilities experience and you don’t have any. But you _do_ have public sector experience. The capability map will flag the gap and prompt you to fill it in by talking to the AI agent about what you _do_ have that’s relevant. The agent remembers. And next time you generate your resume, it has a much richer dataset to draw from.

You’re not inventing experience. You’re surfacing the real experience that simply never made it onto your two-page snapshot.

## **Everything the tool produces**

Once you’ve worked through the capability review, the outputs come together cleanly.

*   A **tailored resume** built from your full career picture, not just the version you uploaded
    
*   A **cover letter** shaped to the role
    
*   **Interview prep** with hints specific to the job description
    
*   A **mock interview** capability to practise before the real thing
    

Everything downloads as a PDF or DOCX, ready to send.

The persistent memory piece is the thread running through all of it. The more conversations you have with the AI agent about your work history, the better every output gets. It’s not a one-click fix. It’s closer to an ongoing conversation with something that’s genuinely trying to understand your professional life.

![Sample: Tailored resume generator by JobsLobster](https://miro.medium.com/v2/resize:fit:1400/1*KO7w8Zrp0FTB_zOtCh4jVg.png "Sample: Tailored resume generator by JobsLobster")

## **Why This Matters Beyond Just The Resume**

![Photo by Zulfugar Karimov on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*PUxF8iGjxAjoYWkA "Photo by Zulfugar Karimov on Unsplash")

There’s a framing here that doesn’t get said enough.

Employers already have sophisticated AI on their side. They’re using it to scan applications, rank candidates, and filter you out before a human ever reads your name. Getting noticed has become a technical challenge as much as a human one.

Job seekers haven’t had equivalent tools. Most people are still sending the same slightly-updated resume they’ve been using for years, hoping the right words line up with the right role.

That’s not a fair fight.

Persistent career memory matters precisely because it starts to close that gap. It’s not about gaming job applications. It’s about making sure the real depth of your experience, all the stuff that doesn’t fit neatly into two pages, actually gets a chance to show up when it’s relevant.

The capability map reflects this thinking clearly. It doesn’t tell you to overstate your experience. It tells you where you’re _underrepresenting_ yourself. There’s a meaningful difference between those two things.

## **Where It’s at Right Now**

![](https://miro.medium.com/v2/resize:fit:1400/1*DZT81R7_48pl6Nikgwlp-g.png)

JobsLobster is a work in progress. I’m still chipping away at the features and feel every day. However, our free trial gives you three job applications per week to experiment with, which is a solid way to explore it without any commitment.

If you’re in an active job search, that’s actually enough to feel the difference. Upload a real resume. Grab a real job description from Seek or LinkedIn. Run through the capability map and see what it surfaces. The exercise alone is worth doing, even if you end up writing the resume yourself.

If you need something a bit more robust, paid plans are available, but definitely give the free version a whirl first while we’re still building things out. We’d love to hear what you think in the comments. It seriously helps us make the tool better for you!

## **The Question Worth Sitting With**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*feHeg0UkANjvJJ1m "Photo by Resume Genius on Unsplash")

Most job search advice treats the resume as a static document. Update it, polish it, tailor it. That advice hasn’t changed much in twenty years.

But careers aren’t static. They’re full of context, nuance, and accumulated experience that a two-page document fundamentally cannot hold. The gap between what you’ve actually done and what fits onto a resume has only grown over time.

Tools that treat your career as a living, expanding picture rather than a fixed snapshot are pointing in a genuinely useful direction. Not because AI is magic, but because the problem of representing yourself accurately to employers is real, and the tools most people use aren’t solving it.

If you’re in a job search right now, or you sense one might be coming, it’s worth asking: what does your professional memory actually look like? Not just the resume you have open in a tab somewhere. The full picture of what you’ve built, what you know, and what you can do.

Getting that down somewhere, even imperfectly, is more valuable than another round of polishing the same two pages.

JobsLobster is one emerging answer to that question. It won’t be the last. But the question being asked is the right one.

> _What would your application look like if the AI helping you actually knew you?_

**Live demo: How to stop rewriting your resume for every job application**

@[youtube](https://www.youtube.com/watch?v=2cAOYHAkxKs)

* * *

_JobsLobster is available to try at_ [](http://jobslobster.com/)[_jobslobster.com_](http://jobslobster.com)_. Help us improve by leaving your feedback!_

How to Make AI Actually Know You for Job Applications

The persistent memory approach that changes things.

Jobslobster not only help you write a tailored resume. It’s to create a living knowledge base about your professional life that gets richer every time you use it..

Today is International Labour Day.

A day that started as a tribute to workers who fought for basic dignity, fair hours, and safe conditions.

A day that in 2026 carries a particular weight, because a lot of people are heading into the long weekend quietly worried about whether there will still be a job waiting for them when the world catches its next breath.

That worry deserves to be taken seriously, not dismissed with optimistic hand-waving about new opportunities and inevitable progress.

So let’s be honest about what is actually happening, because the full picture is more complicated, and ultimately more hopeful, than most of the current discourse suggests.

## **The Collective Action Problem Nobody Wants to Name**

El Salvador’s President [Nayib Bukele](https://x.com/nayibbukele/status/2043185432972722445) posted something on X last month that cut through a lot of noise.

![Source: X](/api/media/elephantstripes-prod/cms/originals/blog-inline/ab2d15836626eb840fd7e00ec900425f97115ccc4838ea582c3ed4569dca9dd1.avif "Source: X")

That is an unusually clear-eyed observation from a sitting head of government, and it names the structural risk that most corporate communication carefully avoids. Individual companies are making rational decisions. The aggregate result of those decisions is potentially irrational at the system level.

![Challenger Report Annual Job Cut Announcements 2015–2025 (Source: Challenger, Gray & Christmas, 2026).](https://miro.medium.com/v2/resize:fit:1400/0*iI-q68S-uEVSEEgC.png "Challenger Report Annual Job Cut Announcements 2015–2025 (Source: Challenger, Gray & Christmas, 2026).")

[Challenger, Gray & Christmas](https://www.challengergray.com/blog/2025-year-end-challenger-report-highest-q4-layoffs-since-2008-lowest-ytd-hiring-since-2010/) reported that AI was blamed for 50,000 layoffs in 2025. The tech sector is the most visible example, but the pattern is spreading into marketing, legal support, customer operations, and financial services. Real people are losing real jobs, and the downstream effects on spending, confidence, and community tax bases are real too.

Sam Altman has said publicly that some companies are using AI as a convenient scapegoat for workforce decisions that had more to do with pandemic over-hiring and poor financial management than genuine automation.

There is some truth to that. Corrections have been underway since 2022, and most have not been associated with AI adoption, which really began to affect coding in 2023 and 2024. In other words, the layoffs began for financial reasons.

But that framing is also a convenient deflection. **The jobs are still gone**. The scapegoat versus genuine cause distinction matters less to the person updating their resume at midnight than it does in a policy debate.

## **The “Tech Hiring Is Dead” Narrative**

@[youtube](https://www.youtube.com/watch?v=0j1HqEEDThc)

Dario Amodei at Anthropic has made statements about AI wiping out white-collar jobs and coding roles within the next few years. He said it plainly, and the comments rippled through the tech community in ways that reinforced a growing narrative: that a career in software development is now a bad bet.

I do not think that holds up against the actual data.

The [Indeed AI Tracker](https://www.hiringlab.org/2026/01/22/january-labor-market-update-jobs-mentioning-ai-are-growing-amid-broader-hiring-weakness/) reached a high of 4.2% at the end of 2025. Job postings mentioning AI have grown consistently since mid-2023. Employer job postings related to AI jumped 117% between 2024 and 2025.

LinkedIn data tells the same story. AI has already added more than [1.3 million new roles](https://www.weforum.org/stories/2026/01/ai-has-already-added-1-3-million-new-jobs-according-to-linkedin-data/?gad_source=1&gad_campaignid=22228224717&gbraid=0AAAAAoVy5F4JlvyK4rUqmaS-YvSZPOeID&gclid=CjwKCAjw-8vPBhBbEiwAoA39Woe6mQZX1_buFGUtI9YbtRr40-az4skZCiUwUA81om3E-BfNLAXS9RoCNTEQAvD_BwE), including AI Engineers, Forward-Deployed Engineers, and Data Annotators. More than 600,000 new AI-enabled data centre jobs have been created.

[The US Bureau of Labour Statistics](https://www.bls.gov/ooh/computer-and-information-technology/) projects computer and information technology occupations to grow at nearly three times the average rate for all jobs through 2034. Professionals with AI expertise earn 56% more on average than peers without it.

This is not a hiring market where tech is dead. It is a hiring market where the skills being rewarded have shifted dramatically, and where people who have not made that shift yet are experiencing real pain.

The distinction matters. “Tech hiring is dead” is a counsel of despair that leads people to exit a field where opportunity is actually growing. “Tech hiring has shifted and requires different skills” is uncomfortable but actionable.

For workers, developing and highlighting relevant AI skills may be the key to landing a job in 2026, particularly in occupations with otherwise muted hiring activity. That is not cheerful messaging. But it is accurate, and accurate is more useful than comfortable.

## **What the Broader Market Actually Looks Like**

![Photo by Srinivasan Venkataraman on Unsplash](https://miro.medium.com/v2/resize:fit:1400/0*Dm6u-bhVbOOpEsiB "Photo by Srinivasan Venkataraman on Unsplash")

The overall picture is one of genuine contradiction, which is why the conversation is so difficult.

Companies are laying off staff, insisting AI will “do more with less,” yet they haven’t found ways to deploy AI at scale. Recruiters say entry-level pathways are narrowing, but critical roles remain hard to fill.

Just over half of people say they’re job hunting in 2026, while nearly 80% feel unprepared to find a new job. That gap between the number of people searching and the number who feel equipped is where a lot of the anxiety is living.

Recent college graduates are bearing a disproportionate share of the disruption. Those aged 22 to 27 saw an unemployment rate of 5.6% at the end of last year, compared to the overall rate of 4.2% (The Federal Reserve Bank of New York, [2026](https://www.newyorkfed.org/research/college-labor-market#--:explore:unemployment)). Many of these graduates were headed into entry-level roles at law firms, research organisations, tech companies, and public sector agencies, precisely the environments now adopting AI to handle the tasks that basic training prepared those graduates to perform.

[The World Economic Forum](https://www.weforum.org/press/2025/01/future-of-jobs-report-2025-78-million-new-job-opportunities-by-2030-but-urgent-upskilling-needed-to-prepare-workforces/) estimates that AI could eliminate 92 million jobs by 2030 while creating 170 million new ones, a net gain of 78 million. That number is meaningless to someone who lost their job in accounting in 2025 and cannot afford to retrain for an AI engineering role by 2030. The aggregate optimism and the individual pain are both real at the same time.

## **Why This Prompted Us to Build Something**

At [Elephant Stripes](https://elephantstripes.ai/), my team and I have been watching this closely. Not from the outside, but as people who care about the practical reality of what workers are navigating right now.

The job seeker’s experience in 2026 is genuinely difficult in a specific way. The advice is abundant. The tools are everywhere. The templates are free and ubiquitous. And yet most people still have resumes that do not actually represent what they are capable of.

The standard AI resume approach makes this worse, not better. You paste your existing resume into a language model, it polishes the language and adds keywords, and you end up with a shinier version of the same document. Generic. Template-heavy. Optimised for an ATS screening tool that has seen ten thousand copies of the same format.

![](https://miro.medium.com/v2/resize:fit:1400/1*yqQR-REINmH1IeX8fFTUlQ.png)

That is why we built [**JobsLobster**](https://jobslobster.com/) differently.

JobsLobster does not just rearrange what you already have. It identifies what is missing. It asks you targeted questions about your actual skills, your specific contributions, and the outcomes you produced. And it builds something stronger from your answers, rather than from the limited version of yourself that ends up in a resume written in haste.

The differentiator is in the input. Most resume tools start with your resume. JobsLobster starts with you.

## **A Note on What Comes Next**

The transition happening right now is real and it is not finished. The disruption to entry-level work is real. The anxiety is legitimate. The companies using AI as convenient cover for decisions they made for other reasons are being less than honest about it.

And the future is not one in which technology simply eliminates work. Deloitte found that while 30% of organisations are exploring agentic AI options and 38% are piloting solutions, only 14% have solutions ready to deploy, and a mere 11% are actively using these systems in production. The wave is coming, but it is not a wall that hit overnight.

The workers who will navigate this best are the ones who are building AI fluency now, not as a panic response to headlines but as a steady, deliberate investment in the skills that the next decade of work will reward.

That is not naive optimism. It is what the data actually says.

Happy Labour Day. The work is changing. So are the people doing it.

* * *

_Are you interested in getting early access to JobsLobster? Drop us a message_ [_here_](https://elephantstripes.ai/contact) _to stay updated!_

What this moment means for workers everywhere Today is International Labour Day. A day that started as a tribute to workers who fought for basic dignity, fair...

Labour Day in the Age of AI Layoffs

_One million_ job applications. _Zero_ interviews.

That’s not a typo. A job seeker, armed with an AI agent that could auto-apply at scale, blasted his resume across one million open positions.

Every job board. Every portal. Every listing that matched his profile. Recruiters, overwhelmed by the flood of applications pouring in, couldn’t process even a fraction of what landed in their queues. The result? Total silence.

The story, shared by entrepreneur Nabeel Alamgir on X last year, reads like a dark joke. But for anyone who has spent serious time job hunting in 2025 or 2026, it feels more like a mirror. The job market isn’t just tough. It’s structurally broken.

And AI is one of the reasons why.

![Source: X](https://storage.googleapis.com/vtai-web-media-prod/cms/originals/blog-inline/1b483f165fa7be0854031270c352fb754acf351d6d0198ad0af1315801591a0a.avif "Source: X")

## **The Doom Loop Nobody Warned You About**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*eS3DWHLXf_ZBOGqd "Photo by Resume Genius on Unsplash")

The hiring process in 2025 and 2026 has become an AI-on-AI arms race, and ordinary job seekers are caught in the middle.

[AI adoption](https://www.herohunt.ai/blog/ai-adoption-in-recruiting-2025-year-in-review/) in recruiting surged from 26% of organisations in 2024 to 43% in 2025, with over half of companies now using AI somewhere in the hiring process. On the other side, 83% of companies said they would use AI to [review resumes](https://allwork.space/2025/10/haunted-by-algorithms-ai-recruiters-are-now-reviewing-ai-resumes-creating-a-spooky-undead-job-market/#:~:text=A%20Race%20to%20the%20Middle,the%20job%20description%20%E2%80%94%20in%20milliseconds.) in 2025, and 65% of job candidates have been using AI in the [application process](https://www.cnbc.com/2025/02/28/nearly-two-thirds-of-job-candidates-are-using-ai-in-their-applications-report-says.html).

The result is exactly what you would expect when two systems optimise against each other. When AI screening tools evaluate AI-written content, the screening step becomes pattern recognition against noise. Tools trained to find keywords find keywords, regardless of whether any real signal about a candidate’s capability sits behind them.

Reddit threads on this topic have become darkly funny in the way that only real despair can be. People describe the full pipeline:

> **AI-generated resume goes in, AI screener filters it, AI video interview runs it through facial expression and tone analysis, AI summarises the result for a recruiter who never actually reads it, and then total silence.**

One person put it plainly: _“AI resumes funnelling into AI interviews being sorted and summarised afterwards by AI.” Another responded: “I’ll take an AI interview over dead silence, but you still get ghosted after_.”

The irony is that both sides have automated themselves into a state of mutual noise. Candidates produce AI-polished applications that sound identical to each other. Screeners filter them by keyword patterns that reward sameness. The hiring manager never sees the interesting candidates because they all got filtered out for sounding too generic.

Used poorly, AI can actively hurt applications by making them seem generic or inauthentic. The people who figured this out early are already changing their approach.

## **What Actually Works (Based on Who is Getting Hired)**

![Photo by Vitaly Gariev on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*V8oUm32CRTAQNquv "Photo by Vitaly Gariev on Unsplash")

The good news is that people are still getting hired. The bad news is they’re mostly not getting hired through the channels everyone is flooding.

When Reddit’s r/recruitinghell asked in May 2025 what finally worked for people who received offers, the answers clustered around the same themes: strong portfolios, direct referrals, and human contact. Not mass applications. Not AI cover letter generators. **Connection**.

Here’s what’s actually moving the needle right now.

**Skip the black-hole portals when you can.** The major job boards are where applications go to die. Prioritise roles where a referral is possible, where a hiring manager’s name is listed, or where the company explicitly uses skills-based assessments instead of ATS filters. Some startups and smaller consultancies have ditched the automated screening process entirely in favour of short trial tasks or portfolio reviews done by an actual person. Those opportunities are worth ten applications on LinkedIn.

**If you’re in tech, build publicly.** Nothing signals competence like a visible body of work. Open source contributions, side projects, technical write-ups that explain your thinking — these create proof that a resume can’t replicate. A hiring manager who has already read your writing or used your project doesn’t need AI to tell them you’re qualified.

**Make AI part of your visible story, not your invisible crutch.** There’s a real distinction between using AI as a shortcut and using AI as a tool you’ve mastered. Portfolios that show how you’ve worked with AI, what you built with it, where you pushed back on its output, and what results you achieved; these stand out to hiring managers who are drowning in applications that all look the same. _“I used Claude to prototype X, validated it with real users, then iterated based on feedback_” says something fundamentally different than a polished but generic resume bullet.

**Get in the room before the job is posted.** The research consistently points to the same reality: 70 to 80 per cent of roles are filled through networks, referrals, and internal moves before they ever appear on a public board. Industry events, communities, niche forums, alumni networks; the pre-application phase is where the real job market lives.

**Follow up like a human.** A short, specific message to a hiring manager after applying, referencing something concrete about the role or the company, still cuts through. It signals you read the listing, you care about this specific job, and you’re a person, not an algorithm.

## **The Resume Problem Nobody is Solving Correctly**

![Photo by Resume Genius on Unsplash](https://miro.medium.com/v2/resize:fit:1260/0*2tEbatqXBykteBha "Photo by Resume Genius on Unsplash")

Here’s something uncomfortable: _most people’s resumes don’t fail because of poor formatting or wrong keywords. They fail because they don’t communicate what the person can actually do._

Generic descriptions of responsibilities, vague summaries of experience, bullet points that could apply to any professional in the field? These don’t help a hiring manager understand what’s specifically valuable about you. They just add noise to a pile that already has too much.

The common fix is to paste your resume into an LLM and ask it to improve the language. This produces a more polished-sounding document that still doesn’t reflect your specific skills.

It now also sounds like every other AI-improved resume in the pile. The ATS systems are getting better at spotting this. The hiring managers who read enough of them can spot it even faster.

Using AI to apply to more jobs doesn’t solve the problem. It amplifies it.

## **What AI Should Be Doing for Your Job Search**

The more useful direction is inward. Not using AI to help you apply everywhere, but using AI to help you understand and articulate what you actually bring to the roles you’re pursuing.

This is the gap my team at VideoTranslatorAI is working to fill. We’re close to launching a tool that uses AI to run a thorough Q&A process based on your current resume. It asks the questions a good recruiter would ask: about specific projects you worked on, decisions you made, problems you solved, results you can point to. It draws out the experience that’s already yours but rarely survives the translation into resume bullet points.

![JobsLobster - AI resume builder ](https://miro.medium.com/v2/resize:fit:1260/1*2hfZcYx_Z63tfo7fztfOhg.png "JobsLobster - AI resume builder")

The output is **specific** to you. It reflects your actual skills, your real work history, your genuine strengths. Not a generic template overlaid on your name. And because it’s built from real inputs, it holds up when a human interviewer starts asking follow-up questions.

It won’t apply to a million jobs for you. That’s by design.

## **The Honest Summary**

The job market is genuinely harder than it was a few years ago. The AI screening layer has made the standard application pipeline less reliable and more demoralising. The burnout people are experiencing is legitimate.

But the response that most people default to, applying to more jobs, polishing resumes with AI, adding more keywords, completing more AI interviews, is working within the broken part of the system rather than routing around it.

The people getting hired right now are largely doing a few things differently. They are building work product that can be seen. They are getting to hiring managers through people rather than portals. They are demonstrating genuine AI fluency rather than AI-assisted sameness. And they are writing resumes that reflect who they actually are, which is still the hardest and most important part.

None of this is easy. But it is more likely to work than sending your resume into another black hole and waiting for a ghost.

* * *

_Interested in our upcoming_ **_AI Resume Tool_**_? We’d love to hear from you._ _Drop us a message_ [_here_](https://videotranslator.ai/contact) _to stay updated!_

Applying to Jobs in 2026 Feels Like Screaming into a Void

The AI hiring loop is real. Here’s how to escape it. One million job applications. Zero interviews. That’s not a typo.

The AI hiring loop is real. Here’s how to escape it. One million job applications. Zero interviews. That’s not a typo. A job seeker, armed with an AI agent...

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Applying to Jobs in 2026 Feels Like Screaming into a Void