Episode summary: Five Pacific Northwest climate tech investors — recorded live at Pacific Northwest Climate Week 2026 — break down where climate tech venture capital is actually flowing this year, why Seattle still trails Silicon Valley on VC dollars, and what they're really evaluating when a founder walks into the room. The conversation applies directly to anyone raising capital, building a company, or job searching in a market where AI has made it easy to look good on paper and harder to stand out.

Guest bios:

​Gabriel Scheer is Senior Director, Investments and Innovation, at Elemental Impact. Gabriel leads the transportation, energy, built environment and water portfolios - supporting 76 companies across those verticals. In addition to pipeline development and due diligence, he has directly overseen over 40 new and follow-on investments, deploying more than $30M in catalytic capital. He serves as a board observer for Artyc PBC, Mythos AI, Dimensional Energy, and Found Energy and has helped to co-design and manage more than 30 first-of-a-kind and early commercial projects in communities in Africa, Europe, and North America. Previously, Gabriel was on the founding team of Lime, where he led global data policy and transit partnerships and developed go-to-market strategies in North America. As the founder of two companies, he also contributed to the book "Smart Cities, Smart Mobility."

​Ben Shwab Eidelson is a co-founder and partner at Stepchange Ventures, an early-stage venture fund backing companies building software to accelerate energy abundance and upgrade critical infrastructure. Ben also co-hosts the Stepchange Show, a long-form podcast that tells the stories of human progress through the lens of transformative technologies, systems, and infrastructure. The Stepchange Show recently covered the history of data centers and the power grid, and has had over 250,000 downloads. Prior to Stepchange, Ben was a product leader and repeat founder, building two software companies—one acquired by Google and the second by Stripe. When not nerding out on infrastructure, Ben can be found with his wife chasing their 3 kids around local Seattle playgrounds.

​Susan Su is a climate tech investor and capital formation advisor to companies and funds across the energy transition. She built Toba Capital's climate investment practice from the ground up, spanning direct deals, fund-of-funds commitments, and co-investments, and currently serves as an advisor to the fund. She is a founding board member of the Carbon Business Council and serves on the Mission Alignment Committee at Prime Coalition, where she reviews catalytic capital investments for mission integrity. Susan is also the founder of Climate Money, a newsletter and podcast covering the business of decarbonization.

​Jonathan Azoff spent 20 years in silicon valley building rapid growth startups, including multiple exits to the likes of Zillow and Disney. He served as a fractional CTO to public companies (TNY.AX), and formerly led engineering teams at growth stage fintechs like Carta, Cardless and Pomelo. In his second act, he transitioned to the investor side of the table, joining the board of climate tech incubator Sweet Farm, and starting the deep tech venture firm SNØCAP with his two founding partners. He is one of the main individual investors behind The 9Zero Climate Innovation Hub, and is responsible for bringing the club to Seattle, where he lives now. He's an uncompromising advocate of great storytelling, having fun while doing good, and not taking himself seriously.

​Dr. Christine E. Boyle is General Partner at Burnt Island Ventures, a water-specialist venture capital fund, where she works with innovators to bring the next generation of water technologies to market. She was the CEO and founder of Valor Water, which sold to Xylem in 2018. At Xylem she served as VP of Digital Product Development following the acquisition. She serves on the boards of Aclarity Water, Subeca, Previsico, EPOCH Blue, and Waterly. Dr. Boyle is also a member of the Cal-Nevada American Water Works Association Board and a trustee of the American Water Works Association Management and Leadership Division. In Dr. Boyle's free time she plays league tennis, boats, and travels to post-socialist nations.

What you'll learn:

• How the 2026 climate tech VC numbers break down nationally — and why concentration in a handful of mega-deals is squeezing the middle of the funding stack
• Why Seattle ranks #6 nationally in VC dollars despite sitting on some of the country's deepest personal wealth
• What investors are actually screening for beyond the pitch deck, from response time to how a founder treats their own team
• How the hyperscaler-driven data center and energy boom is reshaping where capital flows, and what history (the 1970s WPPSS nuclear default) suggests about the risk
• Why human referral has become the deciding factor in both fundraising and hiring now that AI has made outreach nearly free
• What's missing from the Pacific Northwest climate tech ecosystem, according to the people funding it

Key moments:

• The panel debates whether nuclear, batteries, and data centers are "sucking up" regional capital or building a foundation the rest of the ecosystem can draw from
• Susan Su asks the other panelists to describe how their investment committees actually make decisions, not what they tell founders in a rejection email
• Christine Boyle and Ben Eidelson describe the informal signals — professionalism, stress response, pace of learning — that shape a funding decision
• The group discusses AI-generated pitch decks and why differentiation now matters more than polish

Resources mentioned:

• CTVC/Currence H1 2026 funding report
• Silicon Valley Bank, 2025 climate tech report
• PNW Battery Collaborative
• E8 Angels
• 9Zero (Seattle climate tech community hub) and its "Give, Get, Get" investor-list program
• Elemental Impact's Data Center Innovation Initiative (with Amazon, Google, Meta, and Microsoft)
• Climate Surge, an initiative from Climate Solutions
• "Climate Money" podcast (Susan Su)
• The Stepchange Show podcast (Ben Eidelson)

Archit Gupta went from never having written a line of code to building AI tools that grade hundreds of student cases and a Socratic AI tutor for a graduate class — in one year.

Archit is graduated from Foster's MSBA (Master of Science in Business Analytics) in June, and served on the committee that planned Foster's first AI Spark Day. Before Foster, he worked in M&A at KPMG, founded a consumer fintech startup focused on retail investors, and held product roles at a Singapore-based fintech. He entered the program with no software background and built AI-assisted grading and tutoring tools at UW with Professor Leonard Boussioux. He also founded a student club to pass institutional knowledge between Foster's one-year specialty master's cohorts.

• How to get past the AI "cold start" when you've never touched a command prompt
• How to use AI for grading at scale while staying FERPA-compliant (anonymization + open-source models + human in the loop)
• Why a Socratic AI tutor can protect learning where an unrestricted LLM undermines it
• How to design an A/B test that measures retention, not just scores
• Why "everything that can be automated need not be automated" — and how to tell the difference
• What rapid iteration looks like when you don't need an engineering team

Resources mentioned: n8n; Claude Code; open-source / frontier LLMs; FERPA; Ryan Holiday's newsletter; Foster's AI Spark Day.

First-year Foster MBA (Class of '27) Tejash Bagri explains how he turned a stalled application into an interview by building a go-to-market prototype before reaching out — and what that says about standing out when every candidate has the same AI tools. A practical case study for anyone in a competitive recruiting process.

Tejash was part of the core organizing team for Foster's inaugural AI Spark Day and leads the school's AI and Data Analytics Society. Before his MBA, he worked as chief of staff for a group of organizations in a startup environment, where he built AI-driven workflows for research, marketing, and hiring. He reached the final sixteen of Foster's Dempsey Startup Competition and is building a product focused on AI literacy in the classroom.

• How to use a prototype to get past a resume screen when everyone's resume looks optimized

• Why AI fluency only matters once it sits on top of real functional or industry expertise

• Why you should identify the two or three areas where you're genuinely above average — and build from there

• A staged model for AI maturity, and where most people stall

• Where to keep the human visibly in control during a live interview

• Luma and Meetup (for finding local industry events)

• Lovable, Replit, Databricks, Claude Code (build/prototyping tools)

• Company 10-K filings as interview-prep research

• Ethan Mollick's "jagged edge" framing of AI capability

Ahmad Ghabboun built a Demo Day–winning AI product during his MSIS program — after arriving with no plans to work in AI at all. He breaks down how his mindset shifted, how his design background made him a stronger prompter, and how to build AI fluency that actually holds up in interviews. Useful for students and early-career professionals trying to get AI-ready without faking it.

Ahmad Ghabboun is a Master of Science in Information Systems (MSIS) 2026 Graduate at the UW Foster School of Business. Before Foster, he spent roughly fifteen years in UX and product design, building web applications for startups. At Foster he built several generative-AI tools in his coursework, including Synapse, which won Best Business and Tech Product at the MSIS Demo Day. He is targeting product management and technical product roles.

• Why naming the specific AI model you use — and justifying it — matters more in interviews than saying "I use AI"
• How a design background translates into sharper, more technical prompts
• How to keep a human in the loop so AI assists your judgment instead of replacing it
• Why AI's tendency to agree with you makes human and second-model pushback essential
• How to stay current with fast-moving tools without trying to learn everything
• The difference between a productivity mindset and a learning mindset in school

• The third-quarter AI classes that moved AI from "not on my list" to his career focus
• The origin of Synapse: manually juggling answers across Gemini, Claude, and a third model
• How Synapse runs a dual-model validation and a judge step to flag gaps for technical PMs
• Why interview proctoring now detects AI use — and what a "perfect" AI answer signals to interviewers
• Ethan Mollick's "jagged edge" and why it shifts with every model release

Lovable; Replit; Gemini; Claude; ChatGPT; Jira; Azure DevOps; GitHub; Ethan Mollick's "jagged frontier" of AI capability.

Hannah Hoffmaster went from a self-described two-out-of-seven in technical skill to building multi-agent AI tools in a single year at Foster. This episode is for anyone — technical or not — trying to understand what genuine AI fluency looks like and how to build it.

Hannah Hoffmaster is a student completing the one-year MSIS program at the University of Washington Foster School of Business. She came to the program with some knowledge of statistics and R, but little coding experience. Through her coursework — including Prof. Leo Bousioux's AI and Generative AI in Business class — she developed the ability to design and build AI-powered tools, including a charity comparison platform and an ADHD-focused scheduling app. She describes experimenting with AI as something she now does for fun.

We covered alot of ground in this episode:

• How to think about AI as a build tool when you have no coding background
• Why "trust but verify" is the core discipline of working with AI, and how to operationalize it
• How to design a multi-agent workflow around the parts of a task you don't want to do
• What a deliberate, build-first job search looks like in a fast-moving field
• How to stay current as tools change — by building, researching versions, and talking to peers
• Why holding your career goals loosely can be an advantage in an uncertain market

Resources mentioned: GiveWise (Hannah's project); Offload and the "Nudge" chatbot (Hannah's project); Claude Code; Supabase; GitHub; Vercel; Lovable; ChatGPT; Gemini; Codex; Prof. Leo Bousioux's AI and Generative AI in Business course; Foster's AI club.

On this episode, I speak wtih Léonard Boussioux — Assistant Professor, Foster School of Business; Adjunct, Paul G. Allen School of Computer Science & Engineering, UW. PhD, MIT (machine learning & operations research). Co-founder of Universal AI. "Professor Leo," as his students call him, is a leader in AI education, research, experimentation, and adoption. He and I are on the Foster AI Taskforce, and sat down for this conversation in August of 2025.

Leo rejects the career advice you've heard your entire life: pick a lane, specialize, go deep. His counter-argument is that AI now lets you be — in his words — a specialist of everything. In this conversation, we dig into what that actually means for MBA students, career switchers, and anyone trying to figure out how to use AI without offloading their thinking to it.

We cover how Leo teaches non-coders to ship working products in a single class session, how he uses six different AI models to plan a vacation (and why), the new category of jobs emerging around human-AI collaboration, and why the people who panic about AI are usually the ones who haven't played with it yet.

1. Drive the AI. Don't delegate to it. The students who get worse at thinking are the ones who treat AI as a ghostwriter. The ones who get sharper treat it as a collaborator — pushing it in specific directions, rejecting outputs, iterating.

2. Build something this weekend. Reading about AI is not learning AI. Leo's students — most with zero coding background — ship working websites and games in a single class. Vibe coding tools like Lovable have collapsed the gap between idea and prototype to minutes. If you're an MBA recruiting into product, strategy, or consulting, the ability to prototype your own thinking is now a baseline skill, not a bonus.

3. The new jobs are at the human-AI seam. Automation creates a new category of work: deciding where humans belong in the loop, designing the workflows, catching the 5% of edge cases that have outsized consequences. Moderator, orchestrator, AI workflow consultant — these roles barely existed two years ago. Position yourself there. Learn more about Leo at https://leobix.com, or on LinkedIn.

Nathan Fitzgerald didn't come up through tech. He spent years as a lobbyist, moved into marketing, got laid off in 2024, and treated that moment as a forcing function: how do I build a skill set that doesn't become obsolete?

That question led him to Foster's MSIS program — and to a clear-eyed view of what AI can and can't do.

In this conversation, Nathan talks about what it actually looks like to learn AI tools from scratch when you're mid-career. We discuss the concept of cognitive offloading — the risk that you let AI do the thinking for you and end up unable to defend your own work. He talks about using PRDs as a prompting strategy, managing AI like a distributed workforce, and how he built a scrollytelling website for a job interview that he couldn't have made any other way.

Nathan's perspective is useful because he's not a tech native. He's someone who had to figure out where he brings value when the tools are doing more and more of the work — and he has concrete answers.

Cognitive offloading is a real risk. If AI writes the paper, you can't defend the paper. Nathan's rule: learn independently, then bring that knowledge to the tools.

Treat AI like a workforce, not a single tool. Break projects into tasks, write a PRD before you start prompting, and think of yourself as the manager. The pre-work is what keeps the output on track.

Portfolio over résumé. You can now show your thinking, not just describe it. Nathan built a full website to demonstrate his communications framework for a single job interview. That raises the bar for what "prepared" means.

AI ready means today, not ever. When asked if Foster made him AI ready, Nathan's answer: "I am — for today." Not a destination. A posture.

Nathan Fitzgerald is a graduate student in the UW Foster School of Business MSIS program. Before Foster, he worked in government affairs and marketing, most recently before a 2024 layoff that prompted his return to graduate school.

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Conversations on Careers and Professional Life is hosted by Gregory Heller and produced at the UW Foster School of Business.

Anshula Singh came into Foster's MBA program with five years of software engineering experience — building products at Salesforce and ServiceNow, working with machine learning, helping train early LLMs from the inside. She wasn't new to AI. She was already watching it closely.

In her second winter quarter, she took Software Entrepreneurship — a course where students pitch ideas on day two, form teams, and spend ten weeks building a company. Anshula's team built Authscript, an AI platform to automate prior authorization forms in healthcare. They got far enough to pitch in front of VCs.

Then new federal legislation made their product obsolete almost overnight.

In this conversation, Anshula talks about what it takes to build an AI startup under time pressure, what the experience taught her about when to use AI and when not to, and her advice for business students figuring out where they fit in a landscape that keeps shifting.

Know when to kill the idea. Find your idea killer early — before you're burning capital defending a thesis that no longer holds.

Customer discovery still beats AI research. The most valuable insights came from talking to people. AI supported the market sizing; the real signal came from humans in the room.

Make AI work with how you already work. Don't reinvent yourself for the tool. Figure out where AI makes you more productive in your existing workflow, and start there.

Keep your own voice. As models improve, your distinct perspective becomes more important, not less.

AI is ambient infrastructure. The question isn't whether it changes your work — it will. The question is how you position yourself within that change.

Anshula Singh is an MBA candidate at the UW Foster School of Business. Prior to Foster, she spent five years as a software engineer at Salesforce and ServiceNow, where she worked on machine learning applications and contributed to early large language model development.

Follow Conversations on Careers and Professional Life wherever you listen.

Conversations on Careers and Professional Life is hosted by Gregory Heller and produced at the UW Foster School of Business.

Two questions are on every student's mind right now: How will AI affect the job I'm trying to get? And how do I show up actually ready to use it?

AI Ready — a miniseries from Conversations on Careers and Professional Life -- will feature students from Foster's MBA and graduate programs talking honestly about how they're learning to work with AI. In the classroom. In recruiting. In the work they're already doing.

Foster launched an AI strategy this year built around a straightforward premise: graduates should leave ready to use AI as a real professional tool. That means a required AI bootcamp, a hackathon, and courses designed around practical AI applications in business.

I wanted to hear from the students themselves — what's actually landing, what surprised them, and what they think employers need to understand about this generation of graduates.

Conversations with Foster students and faculty on:

• Building real AI products with no prior technical background
• Using AI in startup development and pitch competitions
• What cognitive offloading actually costs you — and how to avoid it
• Keeping humans in the loop when the tools keep getting better
• What "AI ready" looks like in practice, not just on paper

Ted Jordan spent 24 years at Microsoft as a global program and account manager before becoming a consultant, professional speaker, and guest lecturer at UW Foster School of Business. His talk — From Outsider to Ally — reframes how we think about networking: less about tactics, more about making the other person feel seen.

In this conversation, Ted shares the specific approaches he used to build relationships inside one of the world's most complex organizations — and what he teaches MBA students about doing the same.

What we cover

• The subject line that gets a response every time
• Why curiosity is more valuable than a great question list
• How to turn a single conversation into an ongoing relationship
• The unsolicited message that makes you unforgettable to a hiring manager
• What new interns and first-year employees consistently get wrong
• Why your network becomes your sales force when you're in transition
• How Ted got his job at Microsoft — from a lunch invite and a three-word email

Key takeaways

1. Signal that you see someone as an expert — before you ask for anything
2. Follow the thread, not your script
3. Close the loop: act on advice, then report back
4. Your immediate colleagues are your most underused network
5. Depth beats breadth — stay long enough to become indispensable

Connect with Ted LinkedIn — Ted Jordan or visit TedJordanTalks.com to learn more about Ted.

Conversations on Careers and Professional Life is hosted by Gregory Heller, career coach and communication instructor at UW Foster School of Business.