Celebrating Women Driving the AI Revolution

Learn More About the Women’s Fund

Invest in women entrepreneurial leaders bringing their talents and perspective to promising markets — from femtech to software, pharma, biotech, CPGs, and more.

Max Accredited Investor Limit: 249

TAYLOR CHARTIER

CEO Modicus Prime, AV Portfolio Company

My advice is to join AI conversations in industry as early as possible to build a network that will facilitate your future contributions to the advancement of AI in scientific communities.

Q: Tell Us About Your Current Role.

I am the CEO of Modicus Prime and also a Venture Analyst for Valley Capital Partners backing AI and data-driven startups across the midwest and Silicon Valley. As a former data scientist and engineer from the pharmaceutical industry, I have supported FDA Biologics License Applications by advancing Quality by Design initiatives in Process Development, Manufacturing Sciences and Technology, and Commercial Manufacturing operations. I have also applied machine learning and AI for multivariate modeling to optimize pharmaceutical production and perform root cause analysis, most recently at Bayer Pharmaceuticals. After gaining deep expertise in product quality during my time in pharma, I started the AI software company Modicus Prime to provide pharma with compliant AI computational techniques to help safely and more efficiently produce drugs for patients.

The company was started at the onset of the pandemic 2020. Drug product quality and health were in the public eye. Especially as vaccine production kicked into full gear, cross-contaminations between Johnson & Johnson’s and Astra Zeneca’s vaccines became well known, with over 60 million doses discarded. Even though drug safety was a global focus at that time, drug product quality is a systemic issue, resulting in $50 billion lost annually by the pharmaceutical industry due to product quality failures and the legal and reputational damage that ensues. So this served as the impetus for starting the company. And as I had former career experience applying advanced ML and AI solutions to optimize pharmaceutical drug production, I was already familiar with what types of technologies could benefit Big Pharma and allow them to prevent the release of contaminated products.

Modicus Prime’s computer vision software automates biologics image analysis and is designed to solve for the cost, legal, and waste liabilities from product quality failures across R&D and manufacturing. We are a proud portfolio company of Alumni Ventures and are also a resident of Johnson&Johnson JLABS at the Texas Medical Center. The company’s proprietary GxP-compliant software, mpVision, enables scientists to independently train their own AI to achieve real-time classification of any imaging data — from biologic morphology analysis to commercial contamination detection — for comprehensive quality control across R&D and manufacturing. mpVision has been globally tested by scientists at Takeda Pharmaceuticals — our early public research partner— and its features are mapped to the unmet needs of the pharmaceutical industry, including real-time product quality assurance, full agency compliance, faster go-to-market, and reduced operating costs.

Q: Can you share your AI insights and experiences?

For women like me who are at the intersection of Science and AI, I can share a few strategic ways I’ve learned that can enable us to make the most impact on the AI community. The first strategy is to join professional AI and industry communities of interest as early as possible, even prior to joining the workforce. When in academia, for example, most individuals do not know whether they will pursue a path in venture capital, entrepreneurship, or a myriad of other career trajectories. But once that path becomes more clear, it is an uphill battle to introduce either your talent, service, funding, or product to communities without an already established reputation. Just as job referrals are crucial in the job market to landing positions, so are referrals in landing deals, selling products, and leading initiatives. Therefore, my advice is to join AI conversations in industry as early as possible to build a network that will facilitate your future contributions to the advancement of AI in scientific communities.

For female AI founders in particular, building a network becomes even more essential, as a mere 1.9% of venture capital is allocated to female founders. To highlight this point, my team attended a talk in Austin, TX, from female founder Fran Harris, who landed a deal on Shark Tank with her sports drink company. She conveyed what I think resonates with the wider female founder community. She called for investors to open their circles to female founders. This is precisely what our investor network has been doing for my company, Modicus Prime; they have been making connections with their associates and the partners of firms they prefer to invest alongside.

A second strategy to make a significant impact in the AI community is to be comfortable with learning and adopting AI solutions on a monthly timescale. Just as scientists conduct research in laboratories, individuals interested in AI should be conducting their own research by reading publications and leveraging open source communities. This strategy is mandatory in the rapidly changing field of AI. Otherwise, it is easy to lose a competitive advantage in the job market, in a product offering, and beyond. Adaptability is the most valuable asset in this evolving field.

Q: What is your AI Prediction for 2024?

As 2024 unfolds, significant advancements in AI are being applied to research and the automation of commercial manufacturing. The use of generative AI, for example, is becoming increasingly vital in the pharmaceutical industry, revolutionizing the way new molecules are designed and drug candidates are optimized. This advancement is enabling a more efficient approach to pharmaceutical development. In tandem, AI is being harnessed to enhance manufacturing processes, ensuring both improved efficiency and stringent adherence to quality control measures. The benefits derived from mature AI modalities on a commercial scale are finally hitting the COGs in compelling ways.

This year we are already observing substantial AI regulatory shifts, with a growing emphasis on transparency and traceability within the industry. These changes come as regulatory bodies increasingly look to solutions from industry leaders for guidance. This trend underscores the evolving relationship between technological innovation and regulatory frameworks, highlighting the importance of collaboration between the tech sector and regulatory authorities. Efforts are underway to develop Explainable AI policies, reflecting a broader industry push towards ensuring AI’s decisions and processes are understandable and accountable. The introduction of the EU’s AI Act further illustrates this shift towards greater oversight and ethical considerations in AI deployment. Establishing comprehensive legal frameworks that promote the use of trustworthy AI internationally will safeguard fundamental rights and ethical standards while addressing the potential risks posed by powerful AI models. We are all ultimately responsible for the safe development, deployment, and usage of AI. Together, these developments mark a pivotal moment in the journey towards a more transparent, accountable, and ethically guided AI landscape.

TRACY RUBIN

Partner Cooley LLP

 I expect that we are going to see an increasing volume of fine-tuned and purpose-built tools that are customized to accomplish specific tasks, providing the users with greater confidence in the outputs.

Q: Tell Us About Your Current Role.

I am a partner in Cooley LLP’s Technology Transactions Group. My practice focuses on counseling innovative technology companies on complex and transformative intellectual property transactions. I have a particular passion for complex asset deals, mergers and acquisitions, and diving deep on the latest and most transformative technologies. It is this latter interest that brought me to generative AI when it exploded roughly 15 months ago. Advising clients on generative AI technology involves a combination of leveraging my many years of experience advising on issues such as artificial intelligence and machine learning technologies, scraping, intellectual property infringement, and open source risks, together with extensive reading to understand the specifics of how generative AI technology works and industry trends. It is a fascinating area of practice.

Q: Can you share your AI insights and experiences?

A significant portion of my practice now involves advising companies on their use of third-party generative AI tools, as well as advising companies building these tools. Some common themes and risks to consider include:

• Confidentiality and Privacy: While enterprise versions of generative AI tools often offer heightened protections, many of the free versions expressly further train their models on any prompts entered by users. That use for further training could expose putatively confidential and private information voluntarily provided by the user to the generative AI provider and other users. Companies and other users of these tools should take care to consider the information they include in their prompts and the commitments made by the third-party providers.
• Ownership: The status of ownership of the outputs of generative AI tools is evolving and varies by country and the type of protection sought. For example, as of this blog post, the current guidance from the US Copyright Office suggests that there is presently no copyright protection for AI-generated works, while the US Patent and Trademark Office has allowed for patent ownership as a co-inventor of AI-assisted inventions. In using generative AI tools, companies should consider the importance of ownership of the outputs.
• Hallucinations and Bias: Generative AI tools function not as a search engine, but by predicting the likeliest next word or most correct sounding response based on the training data. However, that does not mean the responses are actually accurate. Similarly, biases or misinformation in the training data can be amplified in the outputs. Companies should subject any AI-generated outputs to human oversight and verification prior to implementation.
• Infringement Risk: Whether scraping of publicly available content and using that content to train generative AI models constitutes copyright infringement or is subject to a valid defense, such as fair use, is currently the subject of significant litigation and an open question that is likely to remain an open question at least into 2025. When developing or using generative AI tools, companies should keep this risk and the unknowns in mind.
• Coding Risks: Assisted coding tools are a popular use case that greatly enhance engineer efficiency but come with several of their own risks, including unidentified open source software, bugs, and security vulnerabilities in the outputs, in addition to the other risks discussed here. Companies should be sure to subject any AI-generated code to their standard code review processes, as well as scans and heightened scrutiny for open source software and security vulnerabilities.

Q: What is your AI Prediction for 2024?

Generative AI tools have incredible power and potential to change the way we work, interact, and innovate for the better. Much like the invention of the computer or the Internet, these tools can, among other benefits, provide powerful data insights, and accomplish rote or time-consuming tasks, allowing humans to focus on the more interesting and complex ones. On this theme, I expect that we are going to see an increasing volume of fine-tuned and purpose-built tools that are customized to accomplish specific tasks, providing the users with greater confidence in the outputs. For example, as a commercial lawyer, I would not trust a general purpose tool to create a first draft of a contract for me (it wasn’t trained to do that), but I might trust one that was built on my own prior precedent. I have been speaking with many clients and other companies focused on developing generative AI tools for these types of narrower use cases, and I am excited to see how those tools continue to evolve.

EMILY ZHAO

Principal Salesforce Ventures

Advice I would give, especially to female founders and builders out there, is that just because you are not from an AI background doesn’t mean you can’t build something great in this space.

Q: Tell Us About Your Current Role.

I’m a Principal on the Salesforce Ventures investment team, leading our AI investments. I joined the team two years ago and started my investment career almost six years ago in late-stage investing on the Private Equity team at Blackstone, which is as late as you can get!

My interest area aligned well with Salesforce Ventures’ mandate, which is backing the best B2B software companies across industries and stages. We are all enterprise software investors and we hope to provide value to our founders through our years of experience investing in this space and being focused on just B2B SaaS (Salesforce Ventures has been around for 15 years!!). The Salesforce connection also made our value-add unique in that we can truly harness the power and resources of Salesforce to help our portfolio companies.

Q: Can you share your AI insights and experiences?

A big challenge is being able to keep up with the space given how fast things are moving. There are probably 10 podcasts and five newsletters that I TRY to read or listen to. But it will use up all your free time, so be ready to fully live and breathe AI if you are truly interested in understanding this world and building something special! It won’t feel like work if that’s where your interest lies.

Advice I would give, especially to female founders and builders out there, is that just because you are not from an AI background doesn’t mean you can’t build something great in this space. There are so many examples of founders coming from another discipline of science or technology, or non-technical founders with experiences in owning the UI/UX and workflows of an industry, building something interesting and engaging.

Q: What is your AI Prediction for 2024?

New model architectures will become more popularized and recognized in 2024, given some of the limitations of transformers. We’ll see increased investment and development in non-text modalities (cross-pollination of image, audio, video, and everything in-between) which is already happening. There will also be more widespread application of AI in the physical world (e.g., robotics) and in highly specialized verticals like life sciences where dedicated models are being built to power robots and highly complex tasks involved in drug discovery.

CHARLENE LI

Author, Speaker, Coach

I became brutal about truly defining in all of my jobs what the most important things I had to do were and what was extraneous, then communicating and aligning with my managers and team.

Q: Tell Us About Your Current Role.

I catalyze transformation as an expert on disruptive transformation strategy and leadership. I’m an entrepreneur and NY Times bestselling author and have seen business, society, and the world undergo seismic changes. My work includes writing books, speaking, advising, and coaching companies and their leaders, and I’ve worked with 14 of the Dow Jones 30 companies.

I’m working on my upcoming book about creating a winning generative AI strategy.

Q: Can you share your AI insights and experiences?

“Mom, do you love your clients more than you love me?” asked my son as I said good night to him before taking an evening flight for work. That was a tough question, and I told him honestly: “I love you more than anything in the world. And I would not be the best mom possible if I also didn’t do my work and love my clients.”

As a working mom, and especially a woman of color with cultural and identity expectations, there’s a high bar of what it means to be “good.” At one point, a company I’ve worked at announced that everyone will get a grade — A, B, or C. I raised my hand immediately and said, “I’m going to be a happy B.”

I can certainly go for the A, but I know what was really important to me, what success looks like for me. I’m most creative when I hit about 45 hours a week, so while I certainly can do 60 or even 80 hours, I’d lose my edge. I became brutal about truly defining in all of my jobs what the most important things I had to do were and what was extraneous, then communicating and aligning with my managers and team. This advocacy helped me succeed in my role, and this approach became absolutely necessary when I became a parent.

I’d also advise us working moms not to work The Third Shift. The First Shift is our professional work. The Second Shift is the work we do at home and being a parent. The Third Shift is us reflecting on how well or poorly we were at our First and Second Shifts. Because what more can you do than the best that you can? So don’t work the Third Shift.

Q: What is your AI Prediction for 2024?

AI agents are entities designed to perceive their environment and take actions to achieve specific goals. AI assistants such as Alexa and Siri are examples of intelligent AI agents that  can anticipate a user’s request and in the future automatically collect data from the Internet without the user’s prompting.

The agents can only work if I have complete and total trust in them being a true co-pilot in my life and in my work. That level of trust with AI isn’t there today, and it definitely isn’t with organizations who are using AI while many don’t have a coherent strategy around it.

As such, there’s so much fear and uncertainty around this. Leaders are also reluctant to adopt AI because they are either unsure what they are doing right now and/or don’t know how their plans might change.

While in 2023, organizations could get away with a lack of clarity and strategy since generative AI was all new, developing, and changing, 2024 is when leaders need to know what they are going to do with AI and how it will impact its employees, driven from a human-centric perspective.

Learn More about the AI Fund

We are seeing strong interest in this fund as prior AI Fund vintages were oversubscribed, and we’ve had to establish a waitlist to accommodate interest.

If interested, we recommend securing a spot promptly.

Max Accredited Investor Limit: 249