Pioneer Square Labs has launched more than 40 tech startups and vetted 500-plus ideas since creating its studio a decade ago in Seattle.

Now it’s testing whether its company-building expertise and data on successful startup formulas can be codified into software — with help from the latest AI models.

PSL just unveiled Lev, a new project that aims to be an “AI co-founder” for early stage entrepreneurs.

Developed inside PSL and now rolling out publicly, Lev can evaluate ideas, score their potential, and help founders develop them into companies.

Lev grew out of an internal PSL tool that used PSL’s proprietary rubric to score startup ideas. The studio decided to turn it into a product after outside founders who tested early versions wanted access for themselves.

Here’s how it works:

• Users start by entering an idea (along with any associated information/background) and selecting “venture” or “bootstrap.”
• Lev walks founders through milestones from solution to customer discovery, go-to-market, and product build.
• It can generate “assets” like interview scripts, outreach templates, competitive maps, pricing models, brand palettes, customer personas, landing pages, potential leads, and even product specs.

“We’re mapping a lot of the PSL process into it,” said T.A. McCann, managing director at PSL.

Lev’s structured workflow sets it apart from generic chatbots, said Shilpa Kannan, principal at PSL.

“The sequencing of these components as you go through the process is one of the biggest value-adds,” she said.

Lev joins a growing number of startups leveraging AI to act as an idea validation tool for early-stage founders, though its precise approach makes it stand out.

Upcoming features will add team-building and fundraising modules and let users trigger actions — such as sending emails or buying domains — directly from within the platform.

McCann envisions Lev eventually connecting to tools like Notion and HubSpot to serve as a “command center” for running a company — integrating tools, drafting investor updates, tracking competitors, and suggesting priorities. There are several competitors in this space offering different versions of “AI chief of staff” products.

On a broader level, Lev raises an existential question for PSL: what happens when a startup studio teaches an AI to do the things that make a startup studio valuable?

“In some ways, this is ‘Innovators Dilemma,’ and you have to cannibalize yourself before someone else does it,” McCann said, referencing Clayton Christensen’s concept of technology disruption.

PSL also sees Lev as a potential funnel for entrepreneurs it could work with in the future. And it’s a way to expand the studio’s reach beyond its focus on the Pacific Northwest.

“It’s scaling our knowledge in a way that we wouldn’t be able to do otherwise,” McCann said.

Kannan and Kevin Leneway, principal at PSL, wrote a blog post describing how PSL designed the backbone of Lev and how the firm used it to generate its own high quality startup ideas at higher volumes with lower cost.

“As we see more and more individuals become founders with the support of AI, we are incredibly excited for the potential increase in velocity and successful outcomes from methodologies like ours that focus on upfront ideation and validation,” they wrote.

Kannan told GeekWire that PSL is prioritizing founders’ privacy and intellectual property. “We are making intentional product and technical decisions to ensure Lev is designed from the ground up to safeguard ideas and founder data, including guardrails on data we collect and our team can access,” she said.

For now, PSL is targeting venture-scale founders — people in tech companies or accelerators with ambitions to build fast-growing startups. But McCann believes Lev could eventually empower solo operators running multiple micro-businesses.

Lev is currently free for one idea, $20 per month for up to five ideas, and $100 per month for 10 ideas and advanced features. It’s available on a waitlist basis.

Lev also offers a couple fun tools to help boost its own marketing, including a founder “personality test” and an “idea matcher” that produces startup concepts based on your interests and experience.

Every Friday at 5 p.m., Diego Oppenheimer gets an email that remembers his week better than he does. It pulls from his calendar, meeting transcripts, and inbox to figure out what really mattered: decisions made, promises to keep, and priorities for the week ahead.

“It gives me a superpower,” said Oppenheimer, a machine-learning entrepreneur best known as the co-founder of Algorithmia, who’s now working with startups as an investor in Seattle. 

What’s notable is that Oppenheimer didn’t buy this tool off the shelf — he built it. What started as a personal experiment turned into a challenge: could he still code after years away from writing production software?

With the rise of AI-powered coding assistants, he realized he could pick up where he left off. His personal project, with the unglamorous name “Actionary,” has grown to somewhere around 40,000 lines of what he jokingly calls vibe-coded “spaghetti.” It’s messy but functional.

Oppenheimer’s do-it-yourself AI assistant is more than a novelty. It’s a window into a broader shift. Individuals and companies are starting to hand off pieces of judgment and workflow to autonomous systems — software that analyzes data, makes recommendations, and acts independently.

Exploring the agentic frontier

This emerging frontier is the subject of Agents of Transformation, a new GeekWire editorial series exploring the people, companies, and ideas behind the rise of AI agents. A related event is planned for Seattle in early 2026. This independent project is underwritten by Accenture.

For this first installment, we spoke with startup founders and DIY builders working to replicate different aspects of the work of great executive assistants — coordinating calendars, managing travel, and anticipating needs — to see how close AI agents are getting to the human standard.

The consensus: today’s agents excel at narrow, well-defined tasks — but struggle with broader human judgment. Attempts to create all-purpose digital assistants often run up against the limits of current AI models. 

“I might have my travel agent and my finance agent and my stock trading agent and my personal health coach agent and my home chef agent, etc.,” said T.A. McCann, a Seattle-based serial entrepreneur and managing director at Pioneer Square Labs, on a recent GeekWire Podcast episode. 

McCann foresees these narrow agents handling discrete tasks, potentially coordinated by higher-level AI acting like a personal chief operating officer.

But even the term “AI agent” is up for debate. Oppenheimer defines a true agent as one with both autonomy and independent decision-making. By that standard, his system doesn’t quite qualify. It’s more a network of models completing tasks on command than a self-directed entity.

“If you asked a marketing department, they would say, absolutely, this is fully agentic,” he said. “But if I stick to my AI nerd cred, is there autonomous decision-making? Not really.”

It’s part of a much larger trend. The market for AI workplace assistants is projected to grow from $3.3 billion this year to more than $21 billion by 2030. according to MarketsandMarkets. Growth is being driven both by enterprise giants such as Microsoft and Salesforce embedding agents into workplace software, and by startups building specialized agents. 

A report by the newsletter “CCing My EA,” citing an ASAP survey, notes that 26% of EAs now use AI tools. Some fear job loss due to AI, but most top EAs see AI as an augmentation tool that frees time for strategic work. 

From summaries to scheduling

One company exploring this emerging frontier is Read AI, a Seattle-based startup known for its cross-platform AI meeting summarization and analysis technologies, which has raised more than $80 million in funding.

Co-founder and CEO David Shim revealed that Read AI has been internally developing and piloting an AI executive assistant called “Ada” for tasks including scheduling meetings and responding to emails.

Ada replies so quickly that Read AI has been working on building in a delay into the email response time so that it seems more natural to the recipients.

Shim has been personally testing the limits of the technology — giving Ada access to a range of workplace data (from Outlook, Teams, Slack, JIRA, and other cloud services) and letting the assistant autonomously answer questions about Read AI’s business that come in from the company’s investors in response to his periodic updates.

“It answers questions that I would not have the answer to right off the bat, because it’s not just pulling from my data set, but it’s pulling in from my team’s data set,” Shim said during a fireside chat with GeekWire co-founder John Cook at a recent Accenture reception.

Shim laughed, “I’m willing to take that risk. We’re doing well, so I don’t mind giving out the data.” 

However, there are limitations. Ada can struggle with complex multi-person scheduling or tasks requiring data it can’t access, and can still occasionally hallucinate. To manage this, ReadAI incorporates human oversight mechanisms like “sidebars” where Ada asks for confirmation before sending replies to messages deemed more sensitive or difficult. 

Shim argues against the idea of building a single, all-encompassing agent. 

“The approach of agents doing everything is not the right approach,” he said. “If you try to do everything, you’re not going to do anything well.”

Instead, he believes successful AI assistants will focus on solving very specific problems, much like Google Maps gives driving directions without trying to be a general travel agent. 

The “book-me-a-hotel” challenge

Travel is a use case that’s close to the heart of Brad Gerstner, founder and CEO of Altimeter Capital. Gerstner is known for backing some of the biggest names in tech — from Snowflake to Expedia — and for distilling big tech shifts into simple tests, such as his hotel booking challenge.

The specific example he gave at the 2024 Madrona IA Summit in Seattle was telling an AI agent to book the Mercer Hotel in New York on a specific day at the lowest price — a common challenge for business travelers.  

“Until we can do that, we have not built a personal assistant,” he said. 

That’s part of the larger problem Michael Gulmann, a former Expedia product executive, set out to solve with the startup Otto, which is developing an AI agent specifically for business travelers.

As shown publicly for the first time at this year’s Madrona conference, Otto tackled Gerstner’s specific challenge. After receiving the request to book the Mercer Hotel on a specific day, it found the cheapest available room, confirmed the price and details, and completed the booking, with minimal prompting, within about two minutes.

“Who would have thought that Brad Gerstner wanted the cheapest room?” Gullman joked.

Otto handles various aspects of travel. It understands and learns detailed user preferences — from specific amenities like rooftop bars to preferred airline seats, hotel room types, and loyalty programs — using this knowledge to refine searches and make personalized recommendations. 

As Gulmann explained in an interview, Otto doesn’t use a single monolithic model. It coordinates a bunch of narrow agents: one to interpret messages, another to manage loyalty programs, another to handle payments. Together they simulate a small operations team working behind the scenes.

Otto confirms details with the user before completing purchases, even though it could do that autonomously. Gulmann described that precaution as psychological, not technical — knowing that most people aren’t yet comfortable with AI buying things without their involvement.

After learning about Otto’s capabilities, Gerstner was impressed and wanted to see how it performs as it moves into public beta, said Mike Fridgen, a venture partner at Madrona, which incubated the company.

The grand challenge of scheduling

If hotel booking is the acid test for autonomous assistants, scheduling meetings is the everyday nightmare.

That’s the problem Howie is trying to solve. The Seattle startup’s AI assistant lives in the email inbox. CC Howie on a thread, and it proposes times, confirms with all parties, creates invites, and adds meeting links.

Howie works from a detailed “preferences document,” inspired by how experienced executives train their human EAs — which cafés are acceptable for meetings, how late is too late on Fridays, etc.

The company recently launched publicly with $6 million in funding and a growing number of paying customers. It uses a hybrid model: AI supported by human reviewers. That helps avoid the tiny errors that destroy trust — mixing up time zones, dropping a name from a thread, or misreading social cues.

The system simulates decisions internally, flags potential errors for review, and escalates anything ambiguous to a human before hitting send. 

“If you think about the things that a great human EA does, software is not replacing that anytime soon,” said Howie co-founder Austin Petersmith.

In fact, Petersmith said, many of Howie’s users are human EAs themselves, using it to offload logistics. “Nobody wants to do scheduling,” he said. “Everybody wants the machines to take this particular task on.”

As models improve, Petersmith hopes Howie can expand into other “meta-work” — the administrative overhead that keeps knowledge workers from the higher-value activities that are still the realm of humans.

More time in the day

For Diego Oppenheimer, this isn’t a hypothetical issue. “I’m extremely calendar dyslexic,” he explained. “I’ll triple-book myself. I’ll agree to go to places I shouldn’t be. I’ll travel to the wrong city. Really bad.”

Over the years, he relied on human EAs and a chief of staff to keep him on track. But when he stepped back from running a company full-time, hiring someone just to manage his complex, multi-role calendar no longer made sense. So he built Actionary to help. It sends the Friday recap to catch him up on the week, flagging issues right before his weekend “reboot.”

Oppenheimer’s project won the People’s Choice Award at an AI Tinkerers event in New York last month. But he is very clear: Actionary is a personal project, not a product in the making. He developed it for himself, and can’t imagine taking on the headache of feature requests and technical support from others.

He’s bullish on the larger trend, and a user and investor in tools like Howie. But he also recognizes that AI agents can’t match the comprehensive skills and judgment of a human EA, let alone a chief of staff in a higher-level strategic role.

Oppenheimer’s ultimate goal is more straightforward, but still ambitious. “I’m trying to make time in the day,” he said. “That’s what I’m trying to do.” 

GeekWire’s Todd Bishop reported and wrote this article with editing assistance from AI tools including Gemini and a custom OpenAI GPT trained in GeekWire’s editorial approach. All facts, quotes, and conclusions were reviewed and verified prior to publication.