u/Dependent-Pick8591

The title still carries “sexiest job of the century” energy, but scan who’s actually posting US Data Scientist roles right now:
• New York: J&J, plus compliance-analytics roles
• Boston: Citizens Bank, Banklife
• Chicago: Federal Reserve Bank of Chicago, Capital One (Global Payments)
• Dallas: Federal Reserve Bank of Dallas, Capital One Financial, Raytheon, Hershey
The Fed shows up in two cities. Capital One shows up in two. The genuinely “tech” DS employers (Meta, Google, Roku) are mostly clustered in San Jose and NY — everywhere else it’s banks, insurers, government, and old-line corporates.
And with ~5,000 US openings, DS is the smallest of the big four analytical roles — well behind Product Manager (~14k), Software Engineer (~11k), and Financial Analyst (~7k). So the real picture: fewer openings than the hype suggests, and the ones that exist skew “every large company needs one or two” rather than “join a tech unicorn.”
Not saying it’s a bad career — but if you’re breaking in, targeting banks, insurers, and government is probably a more realistic path than chasing the handful of FAANG DS reqs everyone else is also fighting over.
Anyone doing DS at a bank or the Fed — is it as different from tech-company DS as it looks from the outside?
(Cross-role totals here are consistent with earlier in the week — the shift worth noting is in the composition, not the daily numbers.)

Sharing the numbers because the gap between “prestige” roles and “volume” roles is even wider than I expected.
The white-collar roles (US), by openings:
Product Manager — 14,436 (net −53, the only one shrinking)
Software Engineer — 11,342 (+36)
Financial Analyst — 6,819 (+17)
Data Scientist — 5,055 (+9)
PM still dwarfs SWE despite a decade of “just learn to code.” Data Scientist — supposedly the sexiest job of the century — has the fewest openings and the slowest growth, and a huge share now sit at banks and old-line firms (Capital One, Citizens Bank, the Fed in both Chicago and Dallas, J&J, Raytheon, Hershey) rather than tech unicorns. It quietly became an “every big company needs one or two” job.
Now the numbers that reframe everything — consumer/retail:
Walmart — 8,433
Ulta Beauty — 4,584
Dollar General — 3,861
Foot Locker — 1,946
Walmart alone has ~75% of the openings of every US Software Engineer role put together. And they’re not all shelf-stackers — Ulta’s hiring a Transportation Analyst, Dollar General is almost all store-manager roles. Low barrier, huge volume.
Big Tech is real but concentrated:
Google — 8,263 featured roles (+74), a lot of it offshore: Bengaluru, Ho Chi Minh City.
Apple 2,777, AMD 1,479 — but note AMD’s roles are research/verification, and Apple’s are heavily retail + international.
DISH is basically a wall of Field Technicians. “Tech company” doesn’t mean “software job.”
Where SWE actually clusters: San Jose (1,104) and NY (792) lead, but LA (441) is almost entirely defense — Northrop Grumman plus a stack of Anduril (robotics, sensor sim, active-clearance infra). Meanwhile the Concord/Bay cluster is all the buzzy AI names: Anthropic, Notion, Glean, Fivetran. Interesting that Anduril also shows up in the finance listings (defense-finance roles in LA).
Healthcare — real demand, messy data: the genuine players are Hendricks Regional Health, HCSC, NHC, and Medline — heavy on nurses (RN/LPN night shifts), CNAs, and clinical support. Amusingly the category also scooped up Junior Achievement (an education nonprofit) and Troon Golf (a golf-course operator), so take the label loosely. But the nursing demand underneath it is unmistakable.
TL;DR: The prestige roles (PM, SWE, DS) are real but capped, and PM is actually softening. The raw volume of open doors right now is in retail and healthcare. Data Scientist looks the most oversold relative to demand, and “tech company” hiring is often field techs and retail, not engineers.
Numbers are a one-day snapshot, so don’t read too much into the daily +/-. Happy to pull any specific role or city if people are curious.

There’s a genre of AI-coding takes that tries to crown one winner. After living in both camps for a while, I don’t think that’s the right frame. The tools split by where you want the AI to sit in your workflow, and the cheapest setup is just running one from each camp.
Execution agents (terminal-native): Claude Code, Codex CLI You hand over a goal and they plan, edit, run, and verify on their own. Big context windows (Claude Code goes up to ~1M tokens), so they can hold a whole codebase in their head. Where they earn their keep: 10+ file refactors, TDD loops, architectural changes — anything where you’d rather describe the outcome and review a diff than babysit each edit.
IDE assistants (augmented editors): Cursor, Aider They ride along as you type — fast, visual, and you approve every change. Smaller effective context, but that’s fine for the work they’re best at: bug fixes, UI tweaks, daily flow where you’re already in the file.
The one number worth knowing. In independent benchmarks, agents do measurably better on first-pass success — code that ships without human edits. Claude Code landed around 78% on a 100-task blind test, with the IDE tools a handful of points behind. But — and this is the catch nobody puts on the marketing page — agents burn roughly 4x the tokens to get there. You’re paying for that autonomy. Heavy agent use runs ~$200/mo; a heavy assistant habit is more like $50–80.
The honest caveat: the “agent vs assistant” line is blurrier in 2026 than it was a year ago. Cursor and Codex both shipped background/agent modes, so the camps are converging. And accuracy leadership flips depending on which benchmark you read (Terminal-Bench vs SWE-bench disagree on the top tool). So don’t over-index on any single percentage.
How I actually run it: live in the IDE assistant for routine work, switch to the terminal agent for big refactors and anything architectural. Two cheap subscriptions, and each does the thing it’s genuinely good at. The token bill stays sane because you’re not making the expensive tool do trivial edits.

Pulled a snapshot of the London market (4 July) for three of the classic “high-paying tech” roles. The totals are smaller than people assume, but the composition is the real story.
The counts:
Product Manager — 943
Software Engineer — 536 (the only one down on the day)
Data Scientist — 334
London is a payments-and-banking town wearing a tech costume.
Product Manager is almost wall-to-wall finance: Barclays posting three separate roles (including Barclaycard payments), Stripe, Wise, Modulr, euNetworks. If you PM here, payments domain knowledge is basically the local currency.
Data Scientist is the same picture — Capital One, Standard Chartered, and a run of “financial crime / portfolio hedging” roles. The outliers that stood out were Wayve (self-driving) and a defence listing.
Software Engineer is the most varied of the three: Meta and Google are present, but so is a clear defence cluster (Anduril, Shield AI) and, again, fintech — including a Wise fincrime/scam-prevention role posted at £87k–£111k.
Three patterns worth flagging:
SWE was the only role shrinking on the day. Small sample, but the “just learn to code” crowd might note that it’s the engineering count softening, not PM or DS.
“Financial crime / scam prevention” appears across all three roles. Feels like a genuinely growing London niche rather than a one-off.
Defence tech (Anduril, Shield AI) has clearly landed in London, not just the US.
Practical read if you’re hunting here: tailoring toward fintech/payments opens far more doors than chasing the handful of FAANG reqs everyone’s fighting over.
London fintech people — is the “everything is payments” impression accurate from the inside? And is the fincrime hiring as hot as these listings make it look？

Grabbed a snapshot of the London market today (3 July) across three of the usual “high-paying tech” roles. The totals themselves are modest, but who’s hiring is the interesting part.
The counts:
Product Manager — 943 (net +11)
Software Engineer — 536 (net −5)
Data Scientist — 334 (net +2)
What jumped out: this is a fintech/payments town, not a big-tech town.
Product Manager is dominated by finance and payments — Barclays (three separate roles, incl. Barclaycard), Stripe, Wise, Modulr. If you’re a PM in London, “payments domain knowledge” is basically the local currency.
Data Scientist is the same story wearing a different hat — Capital One, Standard Chartered, plus a bunch of “financial crime” and “portfolio hedging” roles. The non-finance ones that stood out were Wayve (self-driving) and a defence listing.
Software Engineer is the most varied — Meta and Google are here, but so is a clear defence cluster (Anduril, Shield AI) and, again, fintech (Wise, with a Sr SWE fincrime role posted at £87k–111k).
A few takeaways:
SWE was the only one of the three shrinking today (−5). Small sample, but worth watching.
“Financial crime / scam prevention” shows up across all three roles — feels like a genuinely growing niche in London hiring.
Defence tech (Anduril, Shield AI) has quietly landed in London too, not just the US.
If you’re job-hunting here, the practical read is: tailoring toward fintech/payments opens far more doors than chasing the handful of FAANG reqs everyone else is also applying to.
Anyone in London fintech — is the “everything is payments” impression accurate from the inside? And is the fincrime/scam-prevention hiring as hot as it looks?

Went through this cycle and watched people around me either coast or melt down, and the difference wasn’t intelligence or stats — it was timeline and organization. Sharing the rough phase-by-phase map that helped, in case it’s useful to anyone starting now.
~12 months out — explore & prep
Self-assessment. Clarify goals and research interests, and run 2–3 informational chats with people in programs/roles you want. This quietly shapes everything downstream.
Research & shortlist. Build a balanced list — reach, match, likely — and actually evaluate fit (advisors, funding, placement), not just rankings.
Standardized testing. Check GRE/GMAT/LSAT/MCAT requirements, register early, and give yourself 3–6 months to study.
~6 months out — build & submit 4. Recommendations. Ask early. Hand recommenders your resume, deadlines, and a few talking points — you’ll get stronger letters. 5. Essays & statements. Draft, get feedback, and tailor each statement of purpose to the specific program. The generic ones read as generic. 6. Submit & verify. Send transcripts, confirm receipt, and keep proof of every submission.
Final stretch — fund & decide 7. Financial planning. FAFSA early, chase assistantships/fellowships, set an application budget (fees add up fast). 8. Decision making. Compare offers side-by-side on funding, mentorship, and outcomes. Visit before you commit if you can.
The part that actually kept me sane — one master tracker. A single spreadsheet, one row per school, with columns for:
Deadlines (priority + final per school)
Testing (required / optional / waived)
Rec letters (number + who)
Essays (personal / purpose / diversity)
Fees (cost per app + waiver status)
Funding (fellowship & scholarship dates)
Status (not started / in progress / sent)
Sounds obvious, but having every deadline and fee in one place is the entire difference between “on top of it” and “wait, was that due yesterday.”
TL;DR: The calmest applicants didn’t work harder in the final month — they just started earliest and tracked everything. Would’ve saved myself a lot of panic knowing that going in.
Anyone further along want to add what they’d do differently? Especially curious how people handled recommenders who went quiet.

Had a snapshot of the US/global market today (July 3). Sharing the numbers because a few of them genuinely surprised me.
The white-collar roles (US), by openings:
Product Manager — 14,436 (net −53 today — only one that shrank)
Software Engineer — 11,342 (+36)
Financial Analyst — 6,819 (+17)
Data Scientist — 5,055 (+9)
First surprise: PM openings still dwarf SWE, despite a decade of “just learn to code.” Second: Data Scientist — supposedly the sexiest job of the century — has the fewest openings and the slowest growth. And a huge chunk of DS roles are at banks and old-line firms now (Capital One, Citizens Bank, the Fed in both Chicago and Dallas, J&J, Raytheon, Hershey) rather than tech unicorns. It quietly became a “every big company needs one or two” job.
Now the part that reframes everything — consumer/retail:
TJX (TK Maxx) — 10,284
Walmart — 8,433
Ulta Beauty — 4,584
JYSK — 2,938
H&M — 2,817
Mars — 1,461
A single retailer (TJX) has almost as many openings as every US Software Engineer role combined. And they’re not all cashiers — Ulta’s hiring a Transportation Analyst, Mars wants field sales reps. If you want volume and a low barrier to entry, this is where the doors are.
A few more patterns across the rest:
Where SWE clusters: San Jose (1,104) and NY (792) lead, but LA (441) is almost entirely defense — Northrop Grumman plus a wall of Anduril. Meanwhile Concord/Bay is all the buzzy AI infra names: Anthropic, Notion, Glean, Fivetran.
Finance is a NY story: New York has 1,000+ analyst openings, roughly 3x the next city. Interestingly Anduril shows up here too (defense finance roles in LA).
Big Tech, globally: Google alone had 8,263 featured roles today, and a lot of the hiring is offshore — Bengaluru, Ho Chi Minh City, Nanchang. Ericsson and Experian rounding it out.
Healthcare (with a caveat): the real players are AdventHealth (8,322), AbbVie (5,123), and Mass General. Amusingly the category also scooped up Sofitel and Hilton — so half the “healthcare” listings are pastry chefs and housekeeping. Data’s messy, but the nursing/pharma demand underneath it is real.
TL;DR: The prestige roles (PM, SWE, DS) are real but capped and, in PM’s case, softening. The actual volume of open doors right now is in retail and healthcare. Data Scientist looks the most oversold relative to demand.
Numbers are a one-day snapshot so take the daily +/- with a grain of salt. Happy to pull any specific role/city if people are curious.

Behavioral interviews used to wreck me because I tried to prep a unique answer for every possible question. Then it clicked: they’re all probing the same handful of traits. Learn one framework and know what’s actually being tested, and you can answer almost anything without spiraling.
The framework — STAR:
Situation – set the scene
Task – the challenge you faced
Action – what you did (spend most of your time here)
Result – the outcome, ideally with a number
Level up by adding a Reflection at the end — what you learned and later applied. Some people call it CARL or STARV; same idea, it just makes sure you land the business value.
What each classic question is really testing:
“Tell me about a failure.” → accountability. Own it, no excuses, then show the fix and what changed.
“Conflict with a coworker.” → emotional intelligence. Spend most of the answer on the resolution, not the clash.
“A time you went above and beyond.” → drive. Tie the extra effort to a measurable, positive outcome.
“Lead without authority.” → influence. Show buy-in you built through persuasion, tied to a concrete result.
“Your greatest weakness.” → self-management. A real, already-fixed weakness + the system you built to manage it.
“What does success look like here?” → alignment. Reference their metrics and concrete first-90-day goals.
Once you see the trait behind the question, you stop scrambling for the “perfect story” and just pick one that demonstrates it. Honestly took most of the pressure off.

Every “how to survive as an engineer in 2026” post is the same recycled skill list. The genuinely useful stuff — what to do when you’re actually let go — barely gets written down. So here’s the non-fluffy version:
If you get laid off:
• Know your rights first. In the US, the WARN Act generally requires 60 days’ notice for mass layoffs at employers with 100+ staff — but it has thresholds and exceptions, so read yours, don’t assume.
• Don’t sign the severance on the spot. You’re usually not obligated to sign same-day. Have an attorney skim it — non-competes, non-disparagement, and release clauses are where people get burned.
• Secure your own records now. Save your personnel file, performance reviews, and personal contacts before access is cut. Do not take company documents/IP — that’s a separate landmine.
• Handle health + cash before panic-applying. Compare COBRA vs. an ACA marketplace plan (ACA is often far cheaper), and budget for a search that’s longer than you’d like.
On the actual job side, the honest version:
• The differentiator in 2026 is architecture, judgment, and system-design trade-offs — not raw code volume, since a chunk of boilerplate is automated now.
• Interview loops increasingly probe AI/ML integration and scenario-based design, not just leetcode.
• If you’re eyeing a pivot, adjacent lands faster than a full reset: solutions architect, DevOps/cloud, security, dev advocate, fractional CTO.
The mindset shift that actually helps: a layoff is a logistics problem with a deadline, not a referendum on you. Work the logistics first, grieve later.
For folks who’ve been through it recently — what’s the one thing you wish you’d done in week one? And did pivoting adjacent (vs. grinding the same SWE loop) actually pay off?

Most of the posts I see here are about how the job market feels completely dead. I get it – ghosting, never‑ending “easy apply” funnels, and postings that stay up forever with no response.
I’ve been building a tool that reads hiring directly from employer career sites and ATS systems, and today’s snapshot of the US market looks a bit different from the doom narrative. These numbers are from live role counts, updated daily.
Here’s what the data is showing today in the US:
– Software Engineer: 11,197 featured roles. Still heavily concentrated in places like San Jose, New York, Seattle and LA, across big tech, AI, cloud, defense and video streaming companies.
– Data Scientist: 4,999 featured roles. New York, Boston, San Jose, Chicago and Dallas stand out, with both Big Tech and healthcare / finance players hiring for senior data and decision science roles.
– Product Manager: 14,421 featured roles. San Jose, New York, Atlanta, Chicago and Pleasanton have strong demand across tech, fintech, healthcare and enterprise software.
– Financial Analyst: 6,750 featured roles. New York, Chicago, Dallas, LA and San Jose are the usual hubs, but there are also corporate finance and FP&A roles at less “famous” companies that don’t always show up on generic boards.
Beyond individual job titles, big employers in healthcare and consumer are quietly hiring too:
– Tech companies like Google, Apple, Twilio, Airwallex and others show thousands of open roles across engineering, product, marketing and compliance.
– Healthcare groups like Hilton Worldwide brands, IHG, Trinity Health and Bupa are posting thousands of roles worldwide, from frontline care to IT and security.
– Consumer and retail brands (Coles, Walmart, TJX, Foodstuffs, Performance Food Group, Unilever and more) are adding everything from store management and warehouse roles to delivery management and marketing.
The point isn’t “everything is great” – hiring is clearly tighter than a few years ago. But when you look at live data, the picture is more “uneven” than “totally dead”: some metros and titles are still very active, and some big brands outside FAANG quietly have hundreds or thousands of openings.
If you’re stuck in a rut, a few ideas from the data:
– Consider adjacent titles (for example: product manager vs data / business analyst, or software engineer vs data / infra roles) in the same metros.
– Look at large non‑FAANG companies in healthcare, consumer, logistics and manufacturing, where tech, data and finance roles exist but don’t get as much attention.
– Don’t ignore midsize metros and “second tier” cities – they often have fresh postings and less competition than the usual coastal hubs.
If people here are interested, I can pull a snapshot for specific titles + cities (e.g. “data scientist in Chicago” or “product manager in Atlanta”) and share what the live role counts look like right now. What are you currently targeting?

I kept seeing “the ATS rejects 75% of resumes before a human sees them” and went looking for the source. There isn’t one. It traces back to a 2012 startup sales pitch — the company shut down in 2013. It’s been repeated so often it became “common knowledge.”
What’s actually happening, with real studies behind it:
The real filter isn’t a robot saying no — it’s volume. Application counts per role have roughly doubled since 2022 (AI auto-apply tools flooded the funnel). You’re not being rejected by an algorithm so much as buried under 200+ other applicants.
Modern ATS maps skills, not keywords. It translates “built a SQL database” → “SQL database architecture.” Keyword-stuffing is dead; demonstrated, specific experience is what surfaces.
The single best-supported move: use AI to edit, never to ghostwrite. An MIT/NBER experiment with ~480,000 jobseekers found algorithmic writing assistance (grammar, spelling, style) led to 7.8% more hires and 8.4% higher wages — biggest effect for non-native English speakers. Spelling errors were the most damaging thing they measured.
But fully AI-written applications backfire. ~49% of hiring managers say they toss resumes they suspect are AI-generated for “generic prose.” The winning combo: AI cleans up your writing, then you personalize hard.
So the practical version: fix spelling/clarity (ideally with a tool), mirror the posting’s real language, apply on the company’s own site, and accept that the brutal part is volume, not a mythical 75% robot wall.
For recruiters here — how much of the “ATS auto-rejects everyone” belief matches what your system actually does? And for job seekers, has cleaning up writing quality (vs. rewriting from scratch) actually changed your callback rate?

You apply, you wait, you hear nothing — half those listings were never real to begin with. So instead of another job-board dump, here's who's genuinely hiring right now, pulled straight from company career pages and updated today.
🏥 Healthcare is quietly booming — CVS Health net +895 in a single day. GSK +147.
🛍️ Retail is louder than the headlines — TJX net +6,803 today. GM +261, Walmart +120.
🛡️ Defense is staffing up hard — Anduril sitting on 4,820+ open roles, now pulling finance and data talent from banks.
💻 Software is basically all AI now — nearly every open role at Google, Meta, and Waymo today is AI/ML, Agents, or Robotics. San Jose alone: 1,000+ SWE roles.
📊 White-collar core still alive — 4,988 data scientist, 6,755 financial analyst, 14,398 product manager roles live across the US right now.
Real roles, no ghost jobs. Updated daily. Save this if your field's on here.
Which one surprised you most?

Spent way too long in the “grind 500 problems and pray” phase before it clicked that interviews mostly test a small set of patterns — and the real skill is recognizing which trigger maps to which pattern in the first 30 seconds.
Here’s the map I wish I’d had earlier:
Linear / subarray stuff (kills nested loops, O(n²) → O(n)):
• Two Pointers — sorted input, or converging/chasing indices. (Sorted Two-Sum is the classic.)
• Sliding Window — “longest/shortest contiguous subarray/substring that satisfies X.”
• Monotonic Stack — “next greater/smaller element” type questions.
Graphs / trees / search:
• Backtracking — generate all combinations/permutations, undo on dead end.
• Union-Find (DSU) — connectivity, cycle detection, “are these in the same group.”
• Topological Sort (Kahn’s) — dependencies / ordering in a DAG (course schedule is the tell).
The mental shift that mattered: spot the pattern first, then write the code. When I forced myself to name the pattern out loud before touching the keyboard, my solve rate jumped.
Obvious gap — this doesn’t cover DP, binary search, or heaps, which are their own beasts. What patterns would you add to a “first 6 you must know” list? Genuinely want to refine this.

From the latest snapshot of the US data, a few things stand out:
• Software Engineer roles are still concentrated in hubs like San Jose, New York, Seattle and LA, with thousands of open positions across big tech, defense, and AI companies.
• Data Scientist roles cluster in New York, Boston, San Jose, Chicago and Dallas, with both Big Tech and traditional corporates hiring.
• Product Manager roles are spread across San Jose, New York, Atlanta, Chicago and other metros, cutting across tech, healthcare, fintech and consumer.
• Financial Analyst roles are active in New York, Chicago, Dallas, LA, San Jose and more, from FAANG-adjacent companies to industrials and retail.
• Healthcare, tech, and consumer companies are all still posting new openings daily, not just recycling old job ads.
Every card in the screenshots comes from a live dataset that refreshes daily, so the numbers and companies move as hiring expands or contracts. It’s meant to be a “radar” for where the market is actually open right now, not last quarter.
If you want to explore this yourself:
• You can browse US jobs by title (Software Engineer, Data Scientist, Product Manager, Financial Analyst, etc.) and metro.
• You click straight through to the employer’s own application page — no sign‑ups, no “easy apply” middlemen, just official postings.
• You can filter by city, company, industry and more to see where demand is strongest and avoid wasting time on dead funnels.
The whole thing is free to use, and you don’t need an account to search. It’s just a different way of seeing the job market: more “live radar”, less guessing.
If you’re currently job hunting in the US (tech, data, PM, finance, healthcare, consumer, whatever), drop your target title + city in the comments. I’m happy to pull today’s numbers and top employers for your specific market and share more screenshots.

Went down a rabbit hole on interview formats. The headline finding from Schmidt & Hunter’s 85-year meta-analysis (still the gold standard in I/O psych):
• Structured interview — validity ~0.51, explains ~26% of job-performance variance
• Unstructured “let’s just chat” — explains as little as ~4%
“Structured” just means: same predetermined questions for every candidate + a scoring rubric. That’s it. It’s not a personality transplant — it’s a checklist and a rubric, and it roughly doubles how well your interview predicts who actually performs.
Two underrated reasons it wins beyond accuracy:
• You can actually compare candidates — same questions = a common baseline, instead of “I asked A about leadership and B about deadlines.”
• It’s far easier to defend legally — a documented, consistent process is much harder to challenge as biased.
The format you pick should still match the goal: behavioral (“tell me about a time…”, STAR) for past patterns, competency/work-sample for actual skills. One I’d push back on: stress interviews — the modern consensus is they mostly measure who tolerates being treated badly, and they wreck candidate experience.
For people who hire: has moving to a rubric actually changed your hit rate, or just slowed things down? And for candidates — can you usually tell within 5 minutes whether the interviewer has a structure or is winging it?

Snapshot from June 27 (US):
White-collar finally stopped bleeding — Software Engineer net +14, Data Scientist +8, PM and Finance roughly flat. After weeks of net-negative days, basically a flat line.
But the composition is the real story. Two patterns:
1. Defense tech is everywhere, and not just for engineers. Anduril and Northrop Grumman dominated the SWE board (robotics, cloud, embedded) — no surprise. The surprise: Anduril also took two of the top Financial Analyst roles in LA ("Government Rates Analyst," "Corporate Finance"). When a defense company is your top employer for finance roles in a metro, that's a sector absorbing displaced white-collar talent across the board, not just hardware/software.
2. Retail is out-hiring all of tech. Walmart added +372 featured roles in a single day. Performance Food Group +253. For comparison, the entire US Software Engineer category moved +14. The "blue-collar is AI-proof" thesis isn't abstract anymore — it's the daily numbers.
Meanwhile the AI-lab cluster keeps doing its thing (CoreWeave, Anthropic, OpenAI all hiring), but it's a narrow band of roles.
So the 2026 market in one snapshot: frontier AI + defense + frontline retail are moving; the generalist office middle is flat.
For people job-hunting right now — is anyone actually pivoting toward defense/govtech? Curious how the clearance barrier is playing out for folks without one.