Female founders leading the way: Q&A with Isha Patel, Co-Founder of Kale

As Women’s History Month continues to unfold, we’re delighted to highlight some of the remarkable female founders at Pear. We’re dedicated to supporting diverse entrepreneurs and are proud that 41% (and growing!) of our investments are in companies with at least one female founder. This is a truly remarkable statistic in our industry, and we take immense pride in it.

Throughout March, we’ll be featuring Q&As with some of these inspiring entrepreneurs. In this series, you’ll hear from them about their experiences in founding burgeoning startups and how they’re collaborating with Pear to turn their visions into reality.

This week, we’re excited to present this Q&A between Vivien and Kale’s Co-founder Isha Patel on the journey growing Kale from the ground up. We met Kale’s Co-founders, Isha and Luis, when they were still building a startup called Palette, focused on a completely different idea. We knew they were an outstanding team from our first conversation with them, so they joined our PearX program where we explored many different ideas together – from social video apps to travel apps to superfan communities – and they ultimately landed on the idea of Kale. I’m excited to share more about Kale and their journey in this week’s installment of our Women’s History Month series!

Tell us a little bit about Kale and what problem you’re tackling.

Kale is on a mission to empower creators to translate their social value into economic value, no matter their size. They are flipping traditional influencer marketing on its head by rewarding everyday, trustworthy creators for talking about brands they actually shop at. 

By tapping into authentic voices, Kale provides brands with a more cost-effective marketing channel, than FB advertising and influencer marketing. Instead of paying one influencer with one million followers, Kale makes it easy for a brand to recognize thousands of their longtail superfan customers.

What inspired you to start your own company, and what were some of the initial challenges you faced? What keeps you motivated?

Luis, my cofounder, and I sat next to each other at LinkedIn for 5 years, building and scaling video to 700M users and 30M brands.  Living and breathing the small creator ecosystem for 5+ years at LinkedIn, we were blown away by the level of engagement, intimacy and clout smaller creators have among their audience. 

We started talking to a bunch of creators on TikTok and Instagram, who would tell us that they had a personal relationship with each of their 3K-5K followers. We were stunned! Small creators have accrued valuable social currency over the years, but no one has figured out how to tap into them. There isn’t an efficient way for brands (who are hungry for user-generated content) to work with them at scale, while maintaining authenticity and relatability. So we started tinkering with the idea of Kale.

To me, building an impactful product means you are saving someone time, money or energy. What motivates me most is hearing that Kale does exactly that for our users: brands and creators. Plus, our team never ceases to inspire me with their drive, curiosity and creativity to tackle problems.

To me, building an impactful product means you are saving someone time, money or energy.

How did you go about securing funding for your startup and how did you evaluate potential VC partners? What advice would you give to other entrepreneurs (especially other womxn) looking to raise capital? 

We went through the Pear’s Summer PearX batch in 2021. As first-time founders, we leaned on Mar to advise and guide us through the fundraising process. 

Kale is something brand new. It’s a new category that sits at the intersection of social, finance and marketing. When pitching investors, we weren’t able to cleanly map ourselves as “the Uber for X” or the “Yelp for Y”. So for us, it was important to find partners who shared our vision of what a world looks like when everyday people can capitalize on their social influencers, instead of the 1% of big social media celebrities.

We found that in Kirsten Green at Forerunner, who has spent her career understanding the needs, wants and desires of really cool brands. She has an intuitive understanding of what CMOs at the modern generation of brands need at any given moment.

In Mar, we found an extension of our founding team. For example, she is in our Excel sheets with us, helping us understand the biggest business models. Plus, as a former founder, she just gets it.

At the end of the day, my opinion is that investors are not just people who cut your company a check. The impactful investors are the individuals who are the weeds with you, thinking through the business model, customer journey and market shifts. 

The impactful investors are the individuals who are the weeds with you, thinking through the business model, customer journey and market shifts. 

Now that you are building your team, what qualities are you looking for in potential hires?

Hiring is like deciding the invitation list to a dinner party. Each new attendee brings something to the conversation: a life lesson, previous work experience or a really warm smile. To keep the conversation dynamic, it’s crucial to have a team of diverse individuals (backgrounds, experiences, skills). 

Looking back on your journey so far, what lessons have you learned that you wish someone had told you when starting out?

As a founder, get comfortable with selling as soon as you can. Figure out your own style when it comes to sales, hiring and raising capital.

Lean on other founders. To navigate murky waters, whether technical or emotional, I’ve found that other founders have an intrinsic sense of empathy.

Rewire your brain when it hears a no. You’ll hear no’s all day long: potential investors, customers and candidates will top the list. What I’ve learnt is there is no such thing as a no. Every door is left open if you are able to process “the why” behind the “no”. Each no teaches you how you can clarify your pitch. Two years into our journey, no’s are becoming even more motivating than yes’s.

Two years into our journey, no’s are becoming even more motivating than yes’s.

What advice would you give to aspiring entrepreneurs (especially womxn!) who are just starting out on their own journeys?

Go out in the world and talk to your customers. You’ll never waste time if you’re talking to potential buyers. Find the right environment to meet your customers, whether that’s at a conference, hosting an event yourself or texting a group chat of friends.

Figure out a sustainable business model. If you are B2B, try not to give away your product for free, otherwise you don’t know what your customer’s willingness to pay is. Without someone paying for your product, you don’t know if you have product-market fit. You want to hear that your price is too high or low because that informs your pricing strategy. 

Finally, what’s next for Kale and why are you excited about your space and your team? 

With our enthusiastic creator community and innovative brand partners, like Free People, OLIPOP and Notion, Kale is redefining how creators and brands work together on social media. We’re reinventing marketing strategies for brands who have been overly dependent on Facebook ads and influencer marketing. 

We’re really excited for where we are taking our creator ecosystem – it’s going to be something really special that allows anyone who has influence to start monetizing, not just influencers! 

Thank you so much, Isha. We are thrilled to be partners and cannot wait to see where Kale goes. As Women’s History Month continues, we look forward to sharing more stories from our incredible female founders and celebrating their achievements in entrepreneurship.

Perspectives in AI: from search to robotics with Hussein Mehanna, SVP Cruise and Pear’s Aparna Sinha

On March 1st, Pear’s Aparna Sinha hosted a fireside chat with Hussein Mehanna, SVP of Engineering for AI/ML at Cruise for a discussion on next generation AI/ML technologies. Hussein has a long and deep history of innovation in machine learning engineering spanning speech recognition, language models, search, ads, and ML platforms at companies including Google, Facebook and Microsoft. He is currently focused on ML-driven robotics, especially autonomous vehicles at Cruise.

This is the first in a series of AI/ML events Pear is hosting. To hear about future events, please sign up for our newsletter and keep an eye on our events page.

The exciting conversation lasted for over an hour, but below is a summary with some highlights from the talk between Aparna and Hussein:

Q: You’ve been building products at the forefront of AI throughout your career, from search, to speech to ML platforms and now robotics and autonomous vehicles. Tell us a little bit about your journey, and the evolution of your work through these products?

A: My journey began with a scholarship for neural networks research in 2003, followed by a role at Microsoft. I eventually joined Facebook and worked on Ads that pushed the limits of ML and from there moved to a more broadened role of working with ML platforms across the company. I then joined Google Cloud’s AI team to explore disruption of enterprise through ML. I learned over the years that robotics is the biggest field facing disruption with machine learning, and autonomous vehicles is the biggest application of that. So I joined Cruise both out of interest in robotics and a pure interest in cars. 

Q. Ads, in fact, also at Google was the birthplace of a lot of the advanced AI. And now AI is absolutely into everything. 

A: Absolutely. There was a system in Google Ads called Smart ass. It was actually one of the first known large scale machine learning systems. And the person who developed them, Andrew Moore, eventually became my manager at Google Cloud AI. You’d be surprised how many lessons to be learned from building machine learning for ads that you could implement in something as advanced as autonomous vehicles.

You’d be surprised how many lessons to be learned from building machine learning for ads that you could implement in something as advanced as autonomous vehicles.

Q: We are seeing the emergence of many AI-assistive products, co-pilot for x, or auto-pilot for y. But you’ve spoken about AI-native products. Are AI-assistive products and AI-native products fundamentally different?

A: Yes, they are. An AI-native product is one that cannot exist, even in MVP form, without machine learning. Examples include autonomous vehicles or speech recognition software like Alexa. On the other hand, AI-assistive products can help humans in various ways without necessarily using machine learning. In fact, Google search, people may not know that, but Google Search started with more of a data mining approach than machine learning. 

Q: What is the gap between building an AI-assistive product versus an AI-native product?

A: The gap is huge. Building an AI-native product assumes full autonomy, while building an AI-enhanced product assumes a human being will still be involved. For example, the technology used for driver-assist features (level 1-3 autonomy) versus fully autonomous driving (level 4-5 autonomy) require vastly different approaches and parameters.  Autopilot is actually classified as Driver Assist. But then once you remove the driver completely, from behind the wheel, you get into level 4, level 5, autonomy. Level 5 is maybe less dependent on a predefined map, you could just throw the robot anywhere, and they’ll figure its way.  It’s very important for founders, entrepreneurs, product managers to understand, are they building something that assists human beings, and therefore assumes a human being, or something that completely replaces them.

Q: Where do generative AI and GPT technologies fall on the spectrum?

A: Generative AI and GPT – so far – are human-assisted technologies that require a human being to function properly. Today, they are not designed to replace humans like technologies used for level 4-5 autonomy.

Q: At a high level, what are the components and characteristics of a fully autonomous system? I’ve heard you call it an AI brain.

A: So let me project the problem first, from a very high level on driving, because I suspect most of us have driven before. For a full autonomous system the first component is perception, you need to understand the environment,  and essentially describe the environment as the here and now. This is a vehicle, it’s heading this direction, with this velocity; here’s a pedestrian, he or she is x distance away from you, and they’re heading that way, and that’s their velocity. Here’s a pile of dirt. And here’s a flying plastic bag. And here’s something that we don’t know what it is, right? So perception is extremely important. Because if you don’t understand the environment around you, you don’t know how to navigate it. 

Now, what’s very, very important about perception is that you can’t build a perception system that is 100% perfect, especially a rich system that describes all sorts of things around you. And so one of the lessons we’ve learned is, you can build multiple levels of perception. You can build a level of perception that is less fine grained. A machine learning system that just understands these two categories can generalize better. And it’s very important for your perception system to have some self awareness so that it tells you the rich system is confused about this thing here. So let’s go to the less  sophisticated system and understand whether it’s something that is safe to go through or go around. Now the reason why you need the rich system is because it gives you rich information. So you can zip through the environment faster, you can finish your task faster. And if your rich system is accurate, let’s say x percent of the time with a little bit of unsureness, then it’s okay to drive a little bit slower using the less rich, less refined system. So that’s number one about perception. 

The second component of autonomous driving is prediction, which involves understanding how agents in the environment will interact with each other. For example, predicting whether a car will cut you off or slow down based on its behavior. However, predicting the future behavior of other agents is dependent on how your car will behave, leading to an interactive loop. We’ve all been in this situation, you’re trying to cross the road, there seems to be a car coming up. If you’re assertive, very likely, in crossing the road, the car will stop. Or if they’re more assertive, you’ll probably back off. At Cruise, we no longer separate the prediction system from the maneuver planning system. We have combined them  to decide jointly on what is the future behavior of other agents and our future, to solve extremely complicated interactive scenarios, including intersections with what they call a “chicken dance” where cars inch up against each other. We now call this the “behaviors” component.

The third component is motion planning and controls, where the car starts actually executing on its planned trajectory with smoothness. This component plays a huge role in delivering a comfortable ride because it can accurately calculate the optimal braking speed that reduces jerk (or discomfort). Most of our riders feel the difference immediately compared to human driving where a human driver could pump the breakers harder than necessary. Simulation is also a critical component of autonomous driving, which is often considered only as a testing tool but is, in fact, a reverse autonomous vehicle problem. Simulation involves building other agents that behave intelligently, such as human drivers, pedestrians, and cyclists. At Cruise, we have seen massive improvements in simulation since we have taken a big chunk of our AI and Autonomous Vehicle talent and put them in simulation. The technology we are working on is generalizable and broadly applicable to any robotics problem, such as drones and robots inside warehouses. 

I like to tell people that by joining Cruise, people are building their ML-driven robotics career, which can be applied to many other places. The stack of perception, prediction, maneuvering, and simulation can be scaled to other robotics problems. Robotics is pushing AI technology to its limits as it requires reasoning, self-awareness, and better generative AI technologies.

Robotics is pushing AI technology to its limits as it requires reasoning, self-awareness, and better generative AI technologies.

Q: The concepts you described here of predicting and simulating, giving your AI system a reasoning model, and self awareness, in terms of how confident it should be. These are lacking in today’s generative AI technologies. Is this a future direction that could produce better results? 

A: I do believe robotics is going to push AI technology to its limits, because it is not acceptable that you build a robot that will do the operation 99% of the time, correct, the 1% of the time can introduce massive friction.

Generative AI is very impressive, because it sort of samples a distribution of outputs, for a task that is not extremely well defined. There’s so many degrees of freedom, it’s like, give me a painting about something. And then it produces a weird looking painting, which in reality is an error. But you’re like, Wow, this is so creative. That’s why I say generative AI and particularly chatGPT do not replace human beings, they actually require a human operator to refine it.  Now it may reduce the number of human beings needed to do a task. But it’s L3 at best.

Now, in order to build an L4 and above technology, especially if it has a sort of a massive safety component. Number one, you need various components of this technology to have some self awareness of how sure they are. And us as humans, we actually operate that way with a self awareness of uncertainty. L4 technologies are not going to be able to be certain about everything. So they have to be self aware about the uncertainty of whatever situation they’re in. And then they have to develop sort of policies to handle this uncertainty versus chance it up to tell you whatever, statistically, it’s learned without self awareness of its accuracy. 

Q: What do you think about the combination of generative AI and a human operator in various fields such as education and healthcare?

A: Using generative AI alongside a human operator can result in an incredible system. However, it’s important to be mindful of the system’s limitations and determine whether you’re creating an L3 system with more degrees of freedom or an L4 system with no human oversight. In the field of education, generative AI can be a valuable tool, but it’s crucial to acknowledge that education is a highly sensitive area. On the other hand, in healthcare, as long as a physician reviews the outcomes, there are considerable degrees of freedom.

Q: I’ve heard great reviews from riders using Cruise’s service in San Francisco. What was your experience like in a driverless ride?

A: My first driverless ride was in a Chevy Bolt vehicle with a decent sensor package on top. At first, I felt a little anxious, but quickly realized that the vehicle was an extremely cautious driver that obeyed stop signs and braked very well. The vehicle optimized the braking and turning speeds, which made me feel safe and comfortable. I have seen the same reaction from family and friends who have ridden in the vehicles.

I think that the new Origin car is amazing and looks like the future. It’s a purposely built car for autonomy with no steering wheel and has two rows of seating facing each other. I believe that it’s going to be a very different experience from the current driverless rides, as it becomes real that there’s no driving and the car is really moving itself. The feedback from multiple people who have experienced it is that it’s as big as their first driverless ride. I also think that people will love the Origin car because it’s more comfortable and cautious than any vehicle with a driver, and it looks like the future. The first version of the Origin car should be deployed this year, and I hope that many people will have the opportunity to experience it and enjoy it within the next year or two.

Q: What are some open questions and unsolved problems as we move forward in building autonomous vehicles?

A: One open question is how to move towards end-to-end learning for autonomous vehicles, which would involve creating a single, large machine learning model that takes in sensor inputs and produces control signals, rather than the current system, which is heavily componentized. Another question is how to create an equivalent to the convolutional operator, a key component in computer vision, for autonomous vehicles. This is still an early stage field that requires significant investment to develop.

Q: At Facebook, you pioneered a new approach to AI platforms that then also later permeated our work at Google Cloud. And I think it was a very meaningful contribution. Can you explain why platforms are important for machine learning productivity?

A: I pioneered a new approach to AI platforms at Facebook that focused on productivity and delivering machine learning models quickly. I believe that productivity is key for successful machine learning because it allows for quick iteration and a faster feedback loop. In my opinion, platforms are the best mechanism to deliver machine learning models quickly and make machine learning a reality.  I believe what is much more powerful than building one model that is centralized, that serves everybody is to empower everybody to build the models they want, and to tweak them, and to tune them the way they like. And that’s where a machine learning platform comes in. And I do believe that is very much true in our organization. And I’ve seen that happen at Facebook, where at one point, around 2017, we had 20% of the company, either interacting or building machine learning models one way or another.

Q: In summary, are we at an inflection point in machine learning? Can autonomous systems approaches influence responsible AI more broadly?

A: I believe that we are at an inflection point where machine learning is expected to have a massive impact on multiple fields, including autonomous vehicles, robotics, and generative AI. Robotics is pioneering this concept of reasoning and understanding the environment and incorporating it, simulating it, and building your machine learning system to be accurate enough and understand the externalities. All of it is on this foundational bedrock of having great platforms which will enable quick iteration and a faster feedback loop. 

I also believe that the advanced work happening in robotics and autonomous vehicles will influence the future of AI, potentially leading to a more holistic and safe system that is oriented towards reasoning. In my opinion, one potential impact of autonomous vehicle technology on machine learning is around responsible AI. We should have one strategy for product safety, rather than separate strategies for product safety and ML safety. As an autonomous vehicle engineer, I spend more time evaluating the effectiveness of the system than building and tuning the ML model. The ability to evaluate the system effectively will become increasingly important, and I hope that there will be a generation of ML engineers that are used to doing so.

I believe that we are at an inflection point where machine learning is expected to have a massive impact on multiple fields, including autonomous vehicles, robotics, and generative AI.

We’d like to extend our sincerest thanks to Hussein Mehanna for joining us for this insightful chat. His expertise and experience in the field provided valuable insights into the current and future states of AI/ML. We look forward to hosting more conversations on AI, so please keep an eye on our events page!

Female founders leading the way: Q&A with Conduit Tech’s Co-Founders

Happy International Women’s Day! As Women’s History Month unfolds, we’re delighted to highlight some of the remarkable female founders at Pear. We’re dedicated to supporting diverse entrepreneurs and are proud that 41% (and growing!) of our investments are in companies with at least one female founder. This is a truly remarkable statistic in our industry, and we take immense pride in it.

Throughout March, we’ll be featuring Q&As with some of these inspiring entrepreneurs. In this series, you’ll hear from them about their experiences in founding burgeoning startups and how they’re collaborating with Pear to turn their visions into reality.

First up, we’re thrilled to present this Q&A between Danielle and Conduit Tech Co-founders Marisa Reddy and Shelby Breger on their journey to date.

We’ve known Shelby since 2018 when she joined our Pear Fellows program. When Shelby and Marisa reached out to us to share what they were working on last year, we were blown away by their hustle. From meeting HVAC contractors at local hardware stores to joining technician trainings, it was a no-brainer for us to partner with the team. I’m excited to share more about them in this first installment of our Women’s History Month series!

Tell us a little bit about Conduit and what problem you’re tackling!

Conduit Tech is focused on enabling the critical trades that form the backbone of the built world. We are starting by developing innovative system sizing and sales enablement tools to support HVAC Professionals in designing, selling, and installing high-efficiency HVAC systems. Conduit Tech’s tooling will be integrated into the workflows of residential HVAC Pros, enabling them to do what they do best – provide comfort, health, and energy savings to their clients. 

What inspired you to start your own company, and what were some of the initial challenges you faced? 

We are incredibly motivated by the potential to make the lives of the contractors we work with easier. The HVAC industry is facing over a 100,000 person labor shortage, affecting every single role in the industry. We know that anything that can streamline workflows, decrease time spent on manual tasks, and enhance sales conversion can be incredibly powerful for the day-to-day of an owner or team member.

How did you go about fundraising for Conduit and how did you evaluate potential VC partners? What advice would you give to other entrepreneurs (especially other womxn) looking to raise capital? 

We were fortunate to meet Pear early in our days in grad school, and we have known the Mar & Pejman since 2018. When we were offered a spot in PearX, it was a no brainer – an opportunity to pursue our dream of building tools for contractors, while knowing we would be supported and pushed throughout the journey. 

We’ve looked for mentors and coaches as our investors – people with whom we can be our full selves around, and are willing to ask us tough questions. 

When it comes to advice to founders, it’d be to invest in building relationships with VCs long before you need capital. Ultimately your investor is a partner you’ll have on board for years – and you’ll want to be able to evaluate whether they will offer the support that matches with your needs.

What role has mentorship and community played in your personal and professional development, and how have you sought out mentorship throughout your journey?

Our mentors have been vital to our company journey. Not only do we each have personal mentors, who have continuously been resources throughout our career, but in more recent years our community at Stanford has been incredible. We took a phenomenal course at Stanford, Stanford Climate Ventures (SCV). Through SCV, we met not only some of the most incredible human beings working in climate, but mentors who have provided the support that has so significantly altered the course of our company’s journey. 

We’ve also been fortunate to surround ourselves in a few communities of entrepreneurs (Breakthrough Energy Fellows, PearX) – who are incredible resources on everything from managing difficult conversations, to thinking about how to recruit the best talent. 

Now that you are building your team, what qualities are you looking for in potential hires?

We have an incredible team (actively seeking to add folks) of low-ego, mission & growth-oriented and adaptable team members. We actively seek diversity of thought and backgrounds.

Looking back on your journey so far, what lessons have you learned that you wish someone had told you when starting out?

This is true in all of life, but is certainly very true when it comes to working hours as a founder: your most valuable resource is time. Working hard is critical, but it comes down to whether you spent your time in the most effective way possible. This is so hard to figure out, but mentors and incredible partners can help you strategize on where to spend your limited effort.

What advice would you give to aspiring entrepreneurs (especially womxn!) who are just starting out on their own journeys?

Don’t let self doubt get in your way!

Finally, what’s next for Conduit and why are you excited about your space and your team? 

We are so proud of our team – our team is kind, low-ego and brilliant, and very committed to building the best product for our industry. We are humbled to be working with contractors across the country as we build the right solution for them, and are grateful for their support. Conduit Tech is growing 🙂 (we’re looking to bring on a Senior Full Stack Engineer and Senior Product Designer). And finally, we’re launching our product in market this summer!

Thank you, Shelby and Marisa. We’re so thrilled to be on this journey with you. As Women’s History Month continues, we look forward to sharing more stories from our incredible female founders and celebrating their achievements in entrepreneurship.

Announcing Pear’s newest Partners

We’re proud to announce our newest partners: Keith Bender, Vivien Ho, and Addison Leong!

Keith started at Pear almost three years ago and has already made a considerable impact in his time as a Principal. He focuses on vertical software, marketplace, and platform businesses, as well as leading all of our LatAm investments and our data-based sourcing team. Keith’s investments include Sudozi, Chainpass, Miter, Menta, and more. A Harvard Business School and Harvard College grad, Keith launched our Harvard Garage engineering fellowship and developed an HBS case study on Pear that is taught in the popular VC/PE course. He was named this year to Silicon Valley Bank and Terra Nova’s EVC List of 50 emerging investors charting the industry’s future. His people-focused, data-driven approach to investing is unparalleled, and we couldn’t be more grateful to have him on our side as a Partner.

Vivien embodies all that we are at Pear: not just investors, but operators. She invests in health — human health, planet health, and financial health including partnering closely with Budgie Health, Osmind, Valar Labs, Fairstreet, Stellation Care, Supercharge Finance, Syro and more. She helps founders hire key clinical and engineering leaders, introduces them to their first customers and surrounds founders with Pear’s braintrust of knowledge. Further, she spearheaded Pear’s Healthcare Playbook Podcast, interviewing leading healthcare founders, leaders and operators on building digital health businesses from 0 to 1. In her first year, Vivien started Pear VC’s Female Founders Circle, a biannual 3 month program with 3 cohorts and 105+ female technical founders in the community. We’re so grateful for Vivien’s leadership and her superpower in bringing communities together at Pear, and we’re very excited for her much deserved promotion to Partner. 

Addison has been a long-standing member of the Pear team: first as a Pear Garage member during his time at Stanford, then as a Pear Fellow, then an Engineer in Residence, Director of Engineering, and now Engineering Partner. In true Pear spirit, he wears many hats, from  spearheading our next-generation intelligent software and data layer that enables Pear to find and pick the best founders, to conducting technical interviews for our portfolio companies, to vetting highly technical investments. Addison draws on his experience as a software engineer, product manager, and designer to be our tech force multiplier, enabling us to hit well above our weight class: he has been instrumental in getting many initial engineering teams off the ground, single-handedly built the Demo Day software that has driven more than $60M in seed investments for our companies, and performed data analyses that inform our overall investing strategy.

Congrats Keith, Vivien, and Addison! We’re excited for the road ahead. 

The only talent offering in venture built for founders

In the months immediately following a fundraise, even the best founders struggle to hire. The difference between making the right hire and doing it quickly is often the difference between building a successful company and failure. As part of Pear’s seed platform, we’ve built a founder-first talent service to ensure that all of our companies have the highest chance of success.

These days, having a talent partner in venture capital is almost a given. Despite the prevalence of talent teams in venture, there remains a massive disconnect between what founders need and the services talent teams provide. The majority of teams are either too small to make a meaningful impact or unable to support the diverse talent needs across early and late stage companies.

This puts talent teams in a difficult position, forcing them to decide which founders receive support and limiting the type of support they offer. There’s a chasm between what founders truly need and what talent teams are able to provide, greatly reducing the impact of the support they offer.

At Pear, we’re determined to bridge this gap. Over the last 12 months, Nate (Meta, Uber) and I have been quietly piloting an in-house talent offering that directly addresses the shortcomings of the current venture talent model. With the recent additions of Maryna (Plaid, Neuralink, SpaceX) and Laura (Brex, Afterpay, Uber) to our team, we’re excited to officially launch our one-of-a-kind talent service, designed to directly address the most critical needs of our founders.

1. We’re early-stage specialists and early-stage specialists only. 

We focus solely on early-stage startups. We don’t pick and choose who we support. All of our founders receive the support they need from a talent team with deep expertise in the space.

2. We’re committed to making the first hires for our seed companies. 

We believe that simply making candidate introductions and giving advice is not enough. The only metric that really matters to founders is hires. That’s why we are committed to making the first hires for our seed companies.

3. We’re hands-on. We’re in the trenches with our founders.

We’re a hands-on talent team that works closely with our founders. We won’t just stop by to offer advice— we’re involved in the hiring process from start to finish, acting as the internal recruiting partner until our founders are ready to hire on their own.

4. We teach our founders how to hire for themselves. 

We aim to help our founders build a strong hiring foundation ensuring they have the necessary tools and skills required to maintain success in the long-term. Helping founders hire is important, but it means nothing if they can’t do it on their own later on. 

5. We do it for free.

We believe helping founders hire is a core offering that all VCs should provide. Our talent services are built into our DNA and are always free for our portfolio companies.

What’s next?

Beginning this year, all seed companies we invest in will have access to Pear’s talent support. The best companies are built by the best teams. By directly helping our founders make their first hires, we increase the chance of their success. 

Our goal at Pear has always been to help founders build incredible companies, and we’re proud to be doing just that.

Welcoming Maryna Sivaieva to Pear’s Talent team

Even the best founders struggle to find the best possible team. As part of Pear’s Seed program, we’ve built a one-of-a-kind talent team that will make your first hires for you. On the heels of yesterday’s announcement welcoming Laura to the talent team, we’re thrilled to share another exciting hire: Maryna Sivaieva! 

Maryna comes to Pear with almost 10 years of recruiting experience under her belt. In 2009, she came to the US from Ukraine for her Masters, but life happened and she stuck around. Her first job in recruiting was hiring drivers and ops for a trucking company, but by 2014, Maryna was recruiting at SpaceX. In 3 years, she worked her way up from Recruiting Coordinator to Senior Technical Recruiter, supporting most of the tech orgs in the company. “SpaceX taught me almost anything can be done with the right team and attitude.”

Maryna’s interest was piqued in Brain Machine Interfaces right around the time Neuralink was founded. In 2017, Maryna became Neuralink’s first recruiter. She built the talent function from scratch and hired dozens of highly specialized employees— all while the company was in stealth. Maryna built Neuralink’s recruiting infrastructure and end-to-end hiring lifecycle: headcount allocation, requisition creation, referral programs, interview processes, leveling, compensation, and approval process. Neuralink grew from 20 to 120 in her time there, and in 2020, Maryna moved on to Plaid to help scale the company from 250 to 1200. Her experience is extensive and unmatched, and we couldn’t be more grateful to have her on our team. 

At Pear, we’re more than investors— we’re ex-founders and operators, and we intimately understand that the secret to early-stage company building is finding the right people. We invest deep into talent, and that’s what brought Maryna on board. “After talking to many VCs, I found Pear’s commitment to founders and trust in their internal team very attractive. The deciding factor was Pear’s vision and deep appreciation for Talent function.”

Our Talent team’s approach to partnering will leave the founders with a concrete recruiting skill set, and by extension, will set their companies up for long term success.”

Want to connect with Maryna? Shoot her an email at maryna@pear.vc

Welcoming Laura Wright, the newest addition to our Talent team

We rang in 2022 by welcoming Matt, Pear’s first ever Talent Partner. In the last year, the Pear talent team has grown from 0 to now 4 members. We couldn’t be more thrilled to welcome the newest addition: Laura Wright. 

Laura is a Seattle native and graduated from Santa Clara University. She has 8 years of technical recruiting experience across a variety of industries including fintech, logistics, and real estate tech. Laura joined Uber in 2017 and supported Leadership hiring for Uber’s Eats, Maps, Marketplace, and Rider/Driver teams. At Uber, she worked alongside Nate Hirsch— who would eventually bring her to Pear…

In 2020, Laura began overseeing technical recruiting at Afterpay, helping scale the business into new markets across Asia and Europe. Most recently at Brex, Laura was brought on to lead hiring for Brex’s specialized technical pipelines like data, security, infrastructure, and machine learning, as well as international teams.

Laura has extensive experience recruiting across the board from early-stage startups to hyper-growth companies. She believes that the center of her career has always been relationships, and the next step was VC. “I see this opportunity at Pear to be much larger than just joining a company, but rather a community of people with the shared goal of helping take organizations from 0 to 1.” 

At Pear, we’re all about relationships. Our approach is founder-first, and that’s the resounding ethos behind our talent services. “I’m excited to partner with founders, support in making their first hires, and ultimately help shape the beginning chapters of their company. I can’t wait for the day we can look back together and say ‘remember when…’”

Want to connect with Laura? Shoot her an email at laura@pear.vc or find her on LinkedIn.

Using AI to spot investment trends: how ChatGPT surprised me and why I’m on the hunt for the next big AI startup

I recently joined Pear as a Visiting Partner focused on early-stage investments in Machine Learning / Artificial Intelligence and Developer Tools. As I kicked off this new chapter, I sought advice on how to find the next billion dollar startup. The natural place to start: ChatGPT. 

Every time I asked ChatGPT which areas in technology are likely to cause the greatest disruption in the next 5 years?”, it gave me different lists, everything from quantum computing to 5G to Biotech and medicine. But AI/ML was always on top of its list. So to get some detail on the potential of AI/ML, I asked “which problems should AI-based startups solve to maximize growth and ROI?”

Results from ChatGPT question: “which problems should AI-based startups solve to maximize growth and ROI?”

I noticed that ChatGPT picked up acronyms like ROI and expanded them correctly, and correctly interpreted queries with misspelled words (e.g., ‘distributed’ was corrected to ‘disruptive’ based on the context of the chat). That made me sit up in my chair!

ChatGPT’s responses are high level. So I tried to pin it down by asking what specific products our startup should build. It provided a specific list, but I could not get it to stack rank or put numerical values against any of these.

Results from ChatGPT question around what specific products our startup should build.

To go deeper, I picked the ‘Personal Assistants’ product idea, and asked 

1. “How would we monetize our personal assistant?” and got this reply:

Results from ChatGPT question: “How would we monetize our personal assistant?”

2. And, “how would we differentiate it relative to competition?”

Results from ChatGPT question: “How would we differentiate it relative to competition?”

These were surprisingly good answers: multimodal chatbots that are personalized and integrated with enterprise systems could be quite useful. Finally I asked ChatGPT to write the business plan and it did!  I shared this business plan with Pear’s healthcare investors and it even passed a few checks. Now if only ChatGPT could generate founders and funding, we’d be all set!

But let’s fast forward to the future, which is what Pear’s portfolio companies are building. First of all they are using AI to solve real problems, such as automating an existing workflow. For example, Osmos.io uses a form of generative AI to create data transformations in a no-code data ingestion platform that’s replacing fragile, hard to maintain hand-coded ETLs. Sellscale uses generative AI to create better marketing emails. Orby.ai discovers and automates repetitive tasks in Finance, HR, customer service, sales, and marketing. And CausalLabs.io is creating the infrastructure layer for UI that is increasingly generated, optimized and personalized by AI. 

Kirat Pandya, Co-Founder and CEO of Osmos, a Pear portfolio company that helps companies scale their customer data ingestion and drive growth with self-serve uploaders and no-code ETL

These companies are building on foundational models like GPT and expanding them to get users accurate, effective outcomes that are orders of magnitude more efficient. When a technology creates orders of magnitude better outcomes, it is a game changer.

We believe AI in 2023 is what PC’s were in 1983 or the internet was in 1995 – it will power enterprises and consumers to do things they couldn’t before and generate enormous value in the next five years. Much of it will come from startups that are in the very early stages today.

This brings me back to why I joined the fast-moving startup ecosystem at PearVC in the first place: the time is now, and the opportunity to build the future with our next billion dollar startup is here.

Welcoming Aparna Sinha to Pear as our newest Visiting Partner

We’re excited to announce that Aparna Sinha has joined Pear as a Visiting Partner! With a career spanning Google, Stanford, McKinsey, NetApp, and Open Source contributions, Aparna brings a wealth of insight in enterprise, developer and AI/ML to Pear companies. 

Since Pear began, we’ve always been proud to not just be investors, but experienced operators. Our team’s most recent addition is no different— Aparna joins us after almost 10 years at Google, most recently as a senior executive leading Kubernetes and Developer Product groups. 

Aparna first got hooked on entrepreneurship while pursuing her PhD in Electrical Engineering working on high speed data communication as an Intel graduate fellow at Stanford. She took a class with Mar Hershenson and followed Mar’s startup, writing a case study on it. Aparna also helped expand the undergraduate business plan competition at Stanford to engineering PhD students and post-docs.

Aparna’s list of accomplishments over the last 10 years at Google can go on and on. She launched an ML-enhanced CRM system for Google’s consumer products, and earned a patent on Android IoT device schema generation. She was part of the early team that created Kubernetes, and scaled the business to a top revenue generating service. Aparna and her team won two ‘Feats of Engineering’ awards at Google for innovations in Kubernetes. Aparna’s product portfolio grew to include Serverless computing as well as Developer Experience tooling for Google Cloud. 

Her work has been covered by press including TechCrunch, Forbes and SiliconAngle. She was highlighted in 2018 as a Power Woman in Cloud. She reconnected with us after leaving Google, and we’re thrilled to have her on board. 

“I believe that enterprise software is being disrupted by cloud and machine learning, creating an opportunity for massive value generation by startups. Best of all, this opportunity favors experienced founders who move fast and rethink the systems that run our world. 

I enjoy working with 10X thinkers, open source maintainers and deep technical founders. I’m good at helping convert technical innovation to customer value generating business.”

Aparna wants to help founders with identifying new market opportunities, productizing technical innovation, building the MVP, scaling revenue, utilizing cloud, and more. “At Pear, I see a unique opportunity for me to help founders with my knowledge, experience and network, successfully bringing innovations to market.”

Interested in connecting with Aparna? Reach out at aparna@pear.vc or on Twitter @apbhatnagar

Welcoming Katie Li to Pear as our Data Analyst

We’re excited to announce that Katie Li joined our team a few months ago as a Data Analyst! She brings a data-driven approach to sourcing investments and tracking talent at Pear.

Katie first learned about Pear in high school, back when it was still Pejman Mar Ventures. Years later, she would hit it off with Arash and Keith at a venture event, and we couldn’t be more grateful to have Katie officially onboard as our newest Data Analyst.  

Katie graduated from Cornell, where she studied Operations Research & Information Engineering and Computer Science. During college, Katie heard about Pear again through hackathons, pitch competitions, and entrepreneurship groups. She spent time as an autopilot engineer working on autonomous search & rescue drones, a hackathon organizer, and a campus scout for Sequoia Capital. Katie was an investor at Zetta Venture Partners in New York, where she focused on early-stage investments in enterprise AI companies and led data-driven sourcing for the firm.

Katie bonded with Arash and Keith over new developments in AI/ML and data-driven sourcing. She was drawn to Pear’s hands-on approach to early-stage investing, whether it was partnering with young founders or creating high-touch cohorts with PearX. “I was impressed by the amount of hands-on support the team gives to early-stage companies, and love that the team has pursued a diverse set of interests through starting communities, programs, speaker series, podcasts, and more. Everyone I’ve met at Pear is also hardworking, not afraid to take initiative, and truly cares about the firm’s mission and our portfolio companies – this all makes it incredibly exciting to be at Pear.”

Katie is spearheading data projects to source investments as well as support Pear’s investment team, platform team, and portfolio companies. She’s passionate about increasing accessibility to venture capital and diversity in investments, and is a big advocate of incorporating data-driven strategies to help accomplish that.

“I’m excited about working with a team that provides early-stage companies with a nurturing environment and gives its all in championing the next generation of category-defining companies, and Pear is that team.”

Interested in connecting with Katie? Reach out at katie@pear.vc