How to Get an AI Job in 2025: Targeting, Passion, and the New Rules of AI in recruiting

We’re living through a historic hiring moment. Venture dollars and headlines make it feel like one giant winner-take-all race toward a handful of model makers. But that perception hides two crucial realities for job seekers: valuations have surged, and the old startup risk-reward calculus no longer holds the way it used to. Understanding how this changes the fundamentals of AI in recruiting is the first step in building a smarter job search.

Most job advice focuses on résumés, interview prep, and letter-perfect LinkedIn profiles — all important, of course. But that guidance often misses the first, most strategic decision you must make: where to invest your limited and non-renewable resource — time. As I say repeatedly, you only get one shot of investing your time in a company. VCs can diversify with capital. You cannot diversify your attention the same way.

High valuations change the deal

OpenAI, Anthropic, Microsoft, and other major players already trade at sky-high multiples. That means two things for someone thinking about AI in recruiting:

• Employees at those companies still take startup-level risk, but the upside is compressed because much of the value is already priced in.
• If your equity only doubles or triples, it may not be worth the time, energy, and career risk you’re signing up for.

Put simply: you can work at a marquee model maker, and that’s great if you are the one-in-a-hundred-million talent getting a generational grant. For the rest of us, the math and the risk-reward profile suggest looking elsewhere.

Seed and pre-seed crowding: the feeding frenzy

On the other side, seed and pre-seed are crowded. There are thousands of early-stage AI startups, many of which raised money quickly because of the buzz. But early traction on paper doesn’t guarantee survival. In the near term — the next 12–18 months — many seed-stage companies will run out of runway or fail to scale into meaningful Series A traction.

That means if you have only one shot at putting your time into a company that could change your life financially and professionally, you may not want to bet on the very earliest stage. The choice isn’t binary — you can still join early and win — but the probabilistic tradeoffs have shifted. As someone hiring or hiring for yourself, think about where the expected value lies.

Where's the sweet spot? Series A and the margins of growth

So what’s left? I believe the sweet spot today sits around the Series A: late seed / immediately pre-A or right after A. These companies often have demonstrated some product-market fit or early customer traction and still have meaningful growth ahead. That’s where equity can compound substantially in ways that still reward the time you invest.

Series A businesses tend to have:

• Proof that the business model can scale
• Enough runway and investor commitment to grow aggressively
• Room for individual contributors to make high-leverage impact

In many cases you’ll find “seed-strapped” firms that function like A-stage companies but without all the VC drama. Those can be excellent opportunities — often with more autonomy and fewer distractions.

Start with targeting — before the résumé

Here’s a contrarian but practical point: start your job search by targeting companies, not by polishing your résumé. Take the time to evaluate potential employers the way you’d evaluate an investment. Consider:

• How much time you’ll need to invest to make the role succeed — is the expected upside worth that time?
• Does the company’s business model have runway to grow 5–10x (or more)?
• Is the company solving a problem you can be sustainably curious about?
• Is there a clear path for you to increase impact and therefore compensation?

Remember: VCs buy exposure to many startups; you’re buying exposure to one. Make that exposure deliberate.

Checklist for company targeting

• Stage fit: Look for companies in or around Series A, or high-quality late seed that looks A-ready.
• Capital quality: Who’s investing? Follow-me rounds that jack valuations can mean limited employee upside.
• Business signal: Early revenue or repeatable acquisition channels are better signals than buzz alone.
• Founder alignment: Do founders value employee equity and long-term team building?
• Problem passion: Can you imagine working on this problem for years without burning out?

Application strategies in the AI era

Once you’ve targeted where you want to spend your time, the typical job-hunt mechanics still matter: LinkedIn, a tailored résumé, polished LinkedIn profile, sharp cover letters, follow-ups, Loom videos, cold emails, and — when appropriate — networking. But the environment for cold applications has changed because of the dominant role of automation and AI in recruiting flows.

If you’re practicing AI in recruiting tactics, be aware of this: AI-generated bulk applications and systems that screen at scale have "poisoned the well." The cold-apply pipeline still produces hires, but it’s less effective and far more inefficient. Expect hundreds of patient applications for a single result, perhaps stretching out for months or even over a year.

Three practical paths to a role

When you boil it down, there are three realistic paths to landing meaningful roles today:

1. Cold persistence: Hundreds of targeted, high-quality applications over a long period. This can work, but it’s a grind and outcomes are uncertain.
2. Spearphishing (hyper-targeted outreach): Pick a single ideal company and treat them like the product you’re building for. Go deep, creative, and personal.
3. Network advantage: You know someone. This remains the most reliable route — referrals still cut through the noise.

If you aren’t already within a network, relocation to nodes like San Francisco or New York will help, but that’s not always necessary. Two non-network strategies — persistent cold applications or the spearphishing approach — can both work if you commit.

The spearphishing playbook (when to use it and how)

Spearphishing is not about deceit; it’s about laser-targeted outreach. When you choose one company and decide to invest tens of hours to demonstrate fit, you’re effectively creating a tailored product for them: your candidacy. This approach works particularly well for people who don’t fit standard job labels or whose backgrounds make them “square pegs” in a world of round-job descriptions.

What does a spearphishing campaign look like?

• Research the company thoroughly: product, go-to-market, investors, pain points, public statements, and engineering challenges.
• Map your skills to their concrete needs: tell a story about how your unique combination of experiences solves a real problem they have.
• Build original artifacts: a short video, a mini product prototype, a one-page strategic plan, or a microsite tailored to the team.
• Be creative: find unique ways to be remembered that are tasteful, relevant, and tied to the company’s mission.
• Follow up persistently but respectfully. Don’t spam. Show consistent value and curiosity.

I’ve seen people spend 50–60 hours on a single application and win. That level of effort tells the company two things: you’re obsessively curious about their problem, and you’re willing to do the work the role requires. Those signals are hard to fake and impossible for bulk-generated applicants to replicate.

Passion is the differentiator — AI can’t replicate it

This is the core thesis I come back to: AI can do many tasks — generate résumés, screen applicants, draft cover letters — but it does not have authentic passion for a problem space. The best companies were often built by teams who cared deeply about the problem, not the hype cycle. That energy is human, contagious, and durable.

“You cannot fake passion.”

Passion translates into problem-solving. Companies hire people to solve problems. Compensation is a crude signal of the scope of the problems you can solve. If you’re not sustainably curious about the work, you won’t stand out over time. If you are, you’ll find ways in — via network, persistent applications, or hyper-targeting.

How to show genuine passion

• Create artifacts that demonstrate your obsession: blog posts, small tooling, analyses of product weaknesses, or public prototypes.
• Talk concretely about tradeoffs and product decisions, not vague enthusiasm. Passion plus product literacy is potent.
• Show incremental learning: chronicle what you learned about the space and how your thinking changed.
• Be patient and consistent: passion shows up over months of engagement, not a single cover letter.

Network isn't luck — it’s a repeatable investment

Yes, network. It’s the third route, and it works because it bypasses the automated trenches of AI-driven recruiting. But this isn’t about nepotism; it’s about investing time to build industry relationships. Networking is systematic: meet people, add value, follow up, and cultivate genuine professional friendships.

If you’re not in the network today, figure out the node you want to be near and work your way toward it. Local meetups, product demos, hackathons, and consistent content distribution on channels like LinkedIn can build visibility. Live in one of the major nodes — or lean into the other two paths if you can’t relocate.

Practical steps you can start today

Below is a short, tactical roadmap you can follow this week to get momentum in AI in recruiting:

1. Define your targeting criteria: stage (A-ish), capital quality, problem area, learning upside.
2. Make a small target list: 10–25 companies that match your criteria.
3. Decide your strategy for each: cold apply, spearphish, or network.
4. If choosing spearphishing, allocate a 20–60 hour project plan for 1–3 top targets.
5. Prepare a multi-channel outreach play: thoughtful email, concise video, one-pager, and LinkedIn touchpoints.
6. Document what you learn publicly (blog, thread, or GitHub) to demonstrate sustainable curiosity.

How to weigh equity vs. salary in AI jobs

Weigh equity seriously. At early-stage companies with meaningful upside, equity can be life-changing. A high cash offer sometimes feels safer, but when you’re aiming to build wealth and influence through operating upside, equity matters more. That said, the calculus depends on your personal risk tolerance, runway, and life stage.

Ask questions about:

• Option pool mechanics and dilution assumptions
• Vesting schedule and acceleration clauses
• Post-money valuation and the company’s capital plan
• How likely the company is to reach a meaningful liquidity event

Equity doesn’t guarantee wealth. But when you join the right company at the right stage, it can be the lever that turns years of hard work into outsized outcomes.

How recruiters and hiring managers will view you

When recruiters and hiring managers look at applicants in the current AI recruiting environment, they’re sifting through three things:

1. Signal — evidence you can do the job (skills, past outcomes).
2. Fit — can you thrive in the company’s stage and culture?
3. Commitment — will you stay and push through the ambiguity?

Traditional résumés provide signal. Spearphishing and network touches provide fit and commitment. Use those levers intentionally to shape perception.

Common mistakes I see in AI in recruiting

• Applying blindly to every job and expecting volume alone to produce interviews.
• Chasing only marquee model makers without thinking about risk and expected return on your time.
• Undervaluing the importance of the problem space — passion is not optional.
• Neglecting depth for breadth: a thousand shallow applications rarely beat a handful of deep, tailored campaigns.

Final mindset: treat your job search like an investment

If you leave one idea with you, let it be this: treat your career capital as an investment portfolio where time is your scarce asset. Allocate it with the same discipline an investor uses with money. Choose companies where the expected upside compensates you for the time you’ll pour in, and where the problem space lights you up. That’s where you’ll beat automated filters and bulk applicants — because passion and deliberate investment can’t be rangified away.

“Your goal isn’t to land at OpenAI—it’s to discover the next OpenAI before everyone else does.”

There’s no single recipe. There’s no guaranteed pathway. But by combining thoughtful targeting (favoring Series A-ish stages), a willingness to get creative with hyper-targeted outreach, and a real, demonstrated passion for the problem you want to solve, you raise the odds of a meaningful outcome.

Key takeaways — tactical summary

• Start with targeting companies, not résumés. Decide where your time will be best spent.
• Avoid the extremes: big model makers often have compressed upside; seed stage is crowded and risky.
• Series A / pre-A is the current sweet spot for durable upside and meaningful impact.
• Show passion through artifacts and learning, not just words on a résumé.
• Pick a route: network, persistent cold apps, or spearphishing — execute relentlessly.
• Treat equity thoughtfully — it’s often where the real wealth is built in startups.

Closing thought

The ecosystems of AI hiring and AI in recruiting are shifting fast. Your best defense is a clear strategy: choose the right targets, show irrefutable passion and capability, and commit with the time that only you can spend. Recruiters will always value signal, and the market will always reward those who can build lasting solutions to real problems. If you align where you’re willing to invest your time with where the upside still exists, you’ll position yourself for both professional growth and, potentially, transformational outcomes.