This episode explores the evolving intersection of artificial intelligence (AI) and intellectual property (IP), featuring legal experts Brooke Quist and Michael Wiggins. The discussion covers the current and future impact of AI on legal practices, especially in patent, copyright, and data use. Key takeaways include the challenges of integrating AI into law, the importance of proprietary datasets, legal and ethical considerations for AI-driven IP, and guidance for organizations in managing AI risks and opportunities.

Jeff Dance: In this episode of The Future Of, we’re joined by Brooke Quist, partner at Seed IP, and Michael Wiggins, chief legal officer here at Fresh Consulting, to explore the future of AI and IP—meaning intellectual property. By way of introduction, I want to give some quick intros and then ask our guests about their experience getting into the IP and law space. Grateful to have you both.

Michael: Thanks for having us.

Brooke Quist: Happy to be here.

Jeff Dance: Brooke has been an attorney for almost 30 years. He’s currently a partner at Seed IP and counsels clients on intellectual property matters and portfolio development, including patent, trademark, and copyright law. He lives in the Seattle area with his family, has a Juris Doctor from the University of Texas School of Law, and an engineering degree from the University of Southern California. He’s also done coursework at UC Berkeley on generative AI, which is relevant to today’s topic.

Michael Wiggins, as I mentioned, is currently the chief legal officer here at Fresh and a great one at that. He’s worked with us for over 10 years full time and even longer part time, advising and counseling us. Before that, he ran his own firm with a couple of partners for 10 years, seeing a lot as a legal professional, including intellectual property. He’s a graduate of Seattle University’s law program and also lives in the Seattle area with his family. Here at Fresh, he’s done a lot of M&A work and looked at technology investment areas involving intellectual property. He’s built great frameworks that have supported our growth, balancing risks and opportunities, and has been great to have on our team. Excited to have you both here for this important topic, given how fast AI is changing things.

Brooke, before we get into the topic, I’m interested—what drew you to intellectual property and portfolio development specifically?

Brooke Quist: Thanks, Jeff. It’s kind of an interesting story. A lot of patent attorneys stumble into it—it’s a bit of an obscure area of law. But for me, there was some serendipity. My father had a PhD in metallurgical engineering and worked for Boeing for 35 years. He actually had a couple dozen patents on the wings of Boeing’s planes—the 707, 737, 747, 757, and 767. So I was meeting patent attorneys at 11 or 12 years old as my dad went to testify in different hearings. There was even a big case Boeing had against the US government based on one of my dad’s patents, so I had an early introduction to the field.

I decided to follow in the family footsteps and go into engineering. I worked for Northrop—now Northrop Grumman—on the F-18, the B-2 bomber, and a few other interesting projects. But during the aerospace downturn of the early ’90s, the profession outlook wasn’t great, so I pivoted back to patent law. I had kept in contact with the field and done some summer internships with patent law firms. It was an interesting background, and when I came out in the late ’90s, patent law was really taking off—so it was fortuitous timing.

Jeff Dance: Great background. It’s a powerful combination to have that depth of engineering and law experience. Michael, you had a similar journey, right? You had an undergrad in molecular biology and biochemistry from UW, but ended up in law. Tell us more about your journey.

Michael: Sure. I started my college career thinking I would go into medical science, biotechnology, or pharmaceuticals. Toward the end of college, I interned at a small biopharmaceutical company in Seattle. I was there long enough to realize I didn’t want to do that from a strict science and technology approach. So I pivoted to law school, sat for the bar after graduating, and took the patent bar and passed it. Since then, I’ve been at the confluence of policy, law, and IP, but not in the strict practice as Brooke does.

“AI because it lets us do more with less”

Jeff Dance: Thanks for that background. Let’s dive into the future of AI and intellectual property. First, let’s talk about the current landscape, then shift toward the future with its opportunities and challenges. The International Bar Association and the Center for AI and Digital Policy recently found that 100% of larger firms—500+ lawyers surveyed—reported using AI in their operations. If that’s true, and 500 is a decent sample size, how are you both using AI in your legal work, or how do you plan to use it? Brooke, start with you.

Brooke Quist: That’s a question that can be interpreted several ways because AI can be used in so many ways. I’d be really curious to know, for those answering, are they using it to sort resumes, build their HR portfolio, or something else? For example, I have a friend at a small tech company who uploaded all their HR materials into a local AI platform so people can ask questions about HR issues and get quick answers.

The bigger question is whether they’re actually using AI in their legal practice, like creating or editing documents or drafting new applications. That’s a more interesting and potentially concerning area. As attorneys, we tend to be more conservative and worry about confidentiality. There’s always concern about whether information you put into an AI platform is being used to further train the platform.

A lot of AI companies now have toggles so you can decide if your information is used for training. Still, many law firms are not comfortable with that. But I’ll say, when email first came out, a lot of firms were uncomfortable sending legal documents by email too. That may change over time as comfort grows.

I can say we’re actively looking for opportunities to use AI to produce better products for our clients or do it more efficiently, but we haven’t yet seen a real breakthrough that takes it to the next level. For truly creative work, I don’t think AI is quite there yet. For summarizing unstructured data, AI is great. If you play to its strengths, there are definitely places for it. But a lot of advanced legal work requires a creative, fine touch that AI hasn’t reached yet.

Jeff Dance: Okay, Michael?

Michael: I appreciate Brooke’s perspective. Being in-house, I have a budget to contend with—I’m not an hourly biller, I have one client. In-house counsel functions always have to do a lot with a little, just from a resourcing perspective. I have the benefit of a legal background, so I can use AI fairly broadly, knowing I have the background to catch when something isn’t right or is a hallucination.

People in my position are excited about AI because it lets us do more with less. That’s the first part. The second part is it allows me to fine-tune my relationship with outside counsel, like Brooke. As in-house counsel, I manage our external legal function. If I can be more educated internally, I can ask sharper, better questions of our outside counsel. That helps manage the budget and keeps me conscious of what our external partners are working on. AI helps me do that better in a number of ways.

How are law firms using AI?

Jeff Dance: What are you seeing as strengths and weaknesses in the current state—where is it most useful, Michael? And I’d like Brooke’s opinion as well.

Michael: In areas of my role that are more legal-focused—less business, more legal compliance—Brooke hit it on the head: the biggest lift is being able to process and digest large unstructured data sets. That’s always been a huge lift, and AI makes it much easier. That’s the biggest strength in the legal function, though I don’t use it in a creative sense. As my role shifts more into business, M&A, and investment—looking at trends rather than just compliance—it’s helpful for content creation and moving important matters forward.

Jeff Dance: Got it. Brooke, what about you?

Brooke Quist: I’ll answer both hypothetically and practically. Practically, it’s probably more useful for people who are more senior, because as Michael said, you can quickly recognize when AI makes a mistake, which it does frequently. But you can benefit from its brainstorming. For example, if you have a contract with 15 provisions and want to know if you should add any, an AI platform can give you 10 more suggestions—eight might be terrible, but two could be really good.

If you’re a younger tech worker or attorney without the skillset to distinguish good from bad suggestions, it can be problematic. Relying on it without that background can get you into trouble.

Looking at potential applications, for a company like yours, you could build a database of past contracts with different vendors. If a new vendor sends you a contract, you could ask AI to compare it to your database and quickly find significant differences. For a 40-page contract, you might spot four or five key changes from the norm. That’s powerful when you have the data to take advantage of it.

Michael: If I could add, that’s a key point—if you already have a robust dataset, it’s quite valuable. My understanding is a lot of smaller or medium-sized law firms want to mine data outside their proprietary information, but it’s not yielding the benefits. They treat AI as an enhanced Google search, putting contracts in and asking, “Is this okay?” But “okay” is contextual and relative. Those with the data are leveraging this better, which is one of the strengths. So your question is good, but the answer is relative to what data you have and how it’s structured.

AI & IP: patents & copyrights

Jeff Dance: Right. If you have all the contracts your company has written, that’s a nice dataset to pull from. You can compress that knowledge and use it. So, it’s important to consider what you have and how it’s structured when thinking about the strengths and weaknesses of AI.

If we get into intellectual property, Brooke, you’re at the forefront of seeing this unfold. How is AI impacting intellectual property? And for the audience, could you define intellectual property from your perspective and help us understand how generative AI relates to IP today?

Brooke Quist: Sure. When people think of intellectual property, they most commonly think of patent, copyright, trademark, and trade secret law, plus a few related things like name, image, and likeness. With AI, the main areas are patents and copyrights.

Let’s start with copyrights, because that’s where it made the news first. Many large data models were trained on internet data—third-party data. Using third-party data has advantages because there’s more of it, but the problem is you don’t know if it’s good or bad data. There’s also the issue of training on copyrighted material.

That’s the first area where AI has hit the courts. Many conservative voices worried there would be copyright infringement issues. We’re just starting to see the first cases. The latest involved Meta, and so far, decisions have favored the AI companies. Courts have said AI use is transformative and fair use. In copyright, the main exception to infringement is fair use.

In one district court case in California, the judge said feeding copyrighted material to train AI was like training schoolchildren to write well. If that trend continues, it’s good for AI platforms and tech companies, but it’s not clear if it will hold. In a recent case, the judge hinted that if the argument had been market dilution instead of infringement—arguing that AI dilutes the market by reproducing works quickly—the outcome might have been different.

So far, in the copyright space, it may be leaning toward the AI companies, and that’s what they’re banking on. On the patent side, it’s more long-term. We’re not talking about court cases yet, but about applications working their way through the patent office.

One issue is whether AI can be considered an inventor. The short answer is no, from the patent office so far, but there are many applications of that. Another issue is patent eligibility in software. In general, the Patent Office suggests AI is patentable, and I’ve had several successful cases. But there was a recent case where the court found the AI was not patent eligible. If you dig into the holding, it seems the judges wanted to say AI patents will occur, just not this patent. If you just take a generic field and limit it to AI, that’s not enough. You need to explain what’s different and what the advantages are.

If you’re just automating something humans did before, that’s not enough for patentability. Every patent attorney is analyzing this opinion for strategies, and we think there are good strategies going forward. Happy to talk about those in more detail if you’d like.

Jeff Dance: Fascinating. As you understand AI and the “black box” nature—how some of this comes out, the brain-like aspects, hallucinations, and creativity we see in imagery—there are novel aspects that are hard to decipher. Michael, what are your thoughts on how AI is impacting intellectual property?

The patentability issue

Michael: I really like Brooke’s explanation and agree with it. From the business side, I think about where value is being created. The patentability issue will impact certain industries more than others. Take pharmaceuticals, where companies invest hundreds of millions over years to develop a product. The R&D pipeline will move exponentially faster, and that speed will be unprecedented. What does that mean for their ability to get patents? These cases will have a huge impact on the value these companies can create through their pipelines.

For others, patentability is interesting, but the bigger question is: How should I use AI? Do I need to spend $10 or $20 million on an LLM for my company? For most companies, AI is just a way to do what they do better—it’s not the product, but the way they deliver or account for their product. Patentability matters in some industries, not as much in others. Speed and go-to-market are still most important.

Unlike copyright, I think we’ll see statutory changes. Recently, President Trump went before the Supreme Court, which ruled that federal judges will be more limited in issuing nationwide injunctions on certain laws. So, if there’s a copyright case in the Ninth Circuit and another in the Fourth, district judges will be restricted. We’ll see more conflicting jurisprudence. Congress, state legislatures, and international groups will need to harmonize statutes, or we’ll have a patchwork. It’s all up in the air.

Brooke Quist: There’s another factor with patent law: the patent office is not always in alignment with the courts on what’s patent eligible. You can have things granted as patent eligible by the patent office, then knocked down by the courts. The courts don’t look to the patent office for guidance; they have their own jurisprudence. So sometimes it’s a two-step process. We like to think everything is perfectly aligned, but that’s not reality.

Jeff Dance: Overall, it sounds like we’re entering a domain where new legal standards will be needed to protect both creators and innovators.

Michael: Absolutely. One fascinating thing is the concept of AI and regulation. Brooke, you and I have talked about this before: Do we give AI a legal persona? That’s not new in our legal system—we give corporations quasi-persona rights. A company can own property, assets, sue, or be sued. Those are individual rights. So, as you think about patentability and authorship, can AI be an inventor? Some say never, because they’re not people and ingenuity comes from people. But we’ve granted such rights before. Existing frameworks may support some of this, but some will have to be net new to keep up with how fast and deep AI is transforming society.

Jeff Dance: That’s interesting. It’s clear that generative AI can generate something new. Sometimes we hear a lot about hallucinations. Aren’t humans prone to hallucinations, too? It seems like that’s a very human thing—just open up social media and you can see lots of unique, different viewpoints.

Michael: Yeah.

Michael: And then the lawyers get involved.

Brooke Quist: The thing is, when we hallucinate, we usually reread what we wrote and realize, “Oh, that’s no good.” The problem is some people are using AI and they’re not reviewing it before sending it out. So their hallucinations get incorporated into the final product. If we have the appropriate checks and balances, those get caught. Interestingly, hallucinations come up the most when we’re trying to make the AI more human—when we crank up the creativity, that’s when hallucinations happen the most. When we dial back the creativity, they happen less.

Jeff Dance: A lot of people don’t understand that there’s actually a spectrum—anything from a RAG (retrieval-augmented generation) search, which uses only existing information, to the other side, which is generating new concepts from different inputs.

Brooke Quist: Mm-hmm.

“You can’t ignore technology that makes you more expensive than everyone else”

Jeff Dance: So if you’re building something of your own, you can dial that creativity in either direction. When we develop things, you can say, “I want this to be more like a search,” or, “I want full creativity and a lot of extrapolation.” I’m curious, Brooke, as companies think about developing their own AI—and it sounds like you all are in that mode—do you have any guidance or considerations for companies choosing between developing their own AI platforms and using third-party AI solutions?

Brooke Quist: I have a lot of thoughts. If you can develop your own internal program, that’s always best. For example, my firm writes patents in pharmacology. If we’ve written thousands of patents in that area, we can use our own patents—which we trust—to train our system to assist us in drafting or reviewing applications. We know it will match our standards. But if we use something external, we’re using other people’s information. We get a bigger dataset, but we may not like all the data we’re including, and it’s hard to determine that.

One thing happening more and more is the rise of “sandboxed” versions of AI. You get something built to a certain level, bring it in-house, put it inside your firewall, and modify it from there. You can add your own software functions or training data, creating something that is your own from a base built by someone else. You’re trying to get the best of both worlds: broad-based capabilities, but in-house control, avoiding confidentiality issues, and prioritizing your own material. That’s a more ideal scenario, depending on your company’s size.

When I talk to friends at big tech companies like Meta, Apple, or Microsoft, they’ll say, “We’re not allowed to use that, but we have our own system.” They can do that, and the cost is quite small compared to the value of the company. If you’re a startup, you can’t do that, so you’ll be more reliant on third-party platforms to get leverage. If you use them correctly, taking necessary precautions and addressing confidentiality, it can work. Confidentiality is a bigger concern in the US. Internationally, some people say, “What do you mean confidentiality? Our government sees everything.” So it depends where you are.

Jeff Dance: I was speaking to a business owner who got acquired by a foreign entity, and the different cultural backgrounds are striking. For generations, some people have seen intellectual property as commonly owned. It’s a completely different mindset. With AI, people will have different perspectives as well. It’s good to remind ourselves there’s a continuum—not just proprietary versus third-party. There’s a strong in-between: bringing your proprietary reviews or agentic agents in sequence that make it yours, or using your own knowledge even with a third-party system. There’s a lot of flexibility to fit what best suits a company, balancing efficiency and risk.

Brooke Quist: We think about it from our clients’ perspective too. We want them to know we’re bringing value that others can’t, that they’re getting something unique from us—not just what an AI would produce. But we also want to offer the most efficient, cost-effective product. You can’t ignore technology that makes you more expensive than everyone else. You have to balance leveraging technology cost-effectively without stripping out what gives your service its value.

Michael: Yeah.

Shadow AI & governance

Jeff Dance: Thank you. Michael, for you—38% of employees acknowledge sharing sensitive information with AI tools without permission. That includes intellectual property, source code, regulated data, passwords. People often don’t realize what they’ve shared, and sometimes their folders are indexed. This is very common, and we call  it “shadow AI,” the rise of unauthorized employee AI use. When we surveyed 500 lawyers, parent companies didn’t know, but everyone was using it. How can companies manage shadow AI and ensure their data doesn’t flow back into larger training sets?

Michael: The first answer, as Brooke said, is to build your own if you can. If you have the ability, you’re barricading those outer reaches—put whatever you want in, as long as it stays within the entity’s proprietary corral. For companies leveraging off-the-shelf AIs for internal processes, there are technology solutions. You can control how much information the engine learns from. You have to look at the licenses and the tools you’re bringing in—what rights are you giving up, and what rights do you retain to your IP? Start from a technical and legal perspective.

There’s also a lot of conversation around governance. That’s a squishy word, but having policies in place matters a lot. These should be in your employee handbooks, standard operating procedures, and have teeth—not necessarily firing someone for a first offense, but for repeat offenders, maybe. Policies and training are key, but the onus is on the employer. There’s a mixed message out there: “Use AI and be efficient, but be careful.” Employees get caught in the middle. Companies need to lean into this, whether they understand it or not, and empower employees to use the technology, with IT support and scaffolding. Start with legal, contracts, and technology solutions, then work back to the people and make sure they’re prepared.

Jeff Dance: Thanks for those holistic thoughts. Brooke, can you elaborate on the inevitability of this trend? When we talk about the future, what are we envisioning, especially regarding intellectual property?

Brooke Quist: Going back to that statistic about people sharing sensitive data, I’d be curious to know if it was unintentional, trying to do the best for the company, or more nefarious. You can block websites like ChatGPT if your company doesn’t want access, but you may want access, depending on your vertical. As AI improves, you’ll want to leverage it more, but you need rules about how it can and cannot be used. Sometimes it’s just a matter of training.

My firm, and many others, now go through security training to avoid compromising systems. We may need similar training for AI—what you can and can’t do. I heard an anecdote about a professional who used an AI program to tone down their written communications. That’s great, but if it’s a third-party program and you’re putting confidential information in, you could create security issues. That person wasn’t trying to create security issues, but it’s a risk.

As Michael said, this will need to be in employee manuals and training. My firm has already been involved in agreements for companies dealing with AI, specifying exact parameters for its use. They want to ensure they’re getting the benefits without the dangers.

Jeff Dance: I think companies need, at a minimum, a business account that commits not to training larger models. If you don’t provide a company account, people will use free tools anyway, often without the right mindset. So getting a business account is foundational. Everything else you said makes sense. What about the future, Brooke? Any overall thoughts as we look ahead?

“The skill will be knowing when to trust it”

Brooke Quist: As we discussed, it’s not a matter of if, but when and how. For most professions, AI will become part of your life. The question is how to make it more efficient and not see it as the enemy—how to understand it and know when to rely on it. That set point will change as AI improves. The skill will be knowing when to trust it.

One issue is that some tasks done by junior employees can now be done by AI. If you stop hiring young people for that work, they can’t become mid-tier or senior people. You need people in the pipeline for tasks AI can’t do yet. How do you train new people not to over-rely on AI? Younger people are more technical by nature, so it’s natural for them to ask their AI bot for answers. We need to train them on what’s safe and what isn’t, just like we do with network security.

AI is a tool we’ll use more and more, and we need to educate ourselves on its proper use. Some industries will find this tougher, as some areas may get pushed out. For example, the CEO of Nvidia said software coding might disappear in five years—probably an exaggeration, but AI is very good at coding. In other areas, like reading X-rays or MRIs, AI can be trained on huge datasets and do a lot of the work, with a skilled professional confirming at the end. In those areas, AI will be more of an adversary; in others, it’s just a helpful tool.

Jeff Dance: They were born into that environment.

Brooke Quist: Exactly. For them, it’s obvious to just ask their AI bot, like using Siri. So how do we keep them from breaking rules they don’t even see as rules? That will take training and education, just like with network security. For some industries, it will be harder, but we need to find the right way to use AI.

The CEO of Nvidia said if he had a kid, he wouldn’t have them learn coding because AI will do it. That’s probably an exaggeration, but AI bots are already very good at coding. In fields where you’re reading large amounts of data, like medical imaging, AI is a natural fit. In those areas, you’ll see AI as more of a competitor, while in others, it’s a tool that makes life easier.

Jeff Dance: Thank you. Michael, what about you? Any thoughts on the future, especially regarding intellectual property and AI?

The AI economy

Michael: Ultimately, I think AI will drive us back to data. Right now, the conversation is about AI as a competitive advantage—do you have it or not? But like all innovations, eventually it will become ubiquitous and level the playing field. Everyone will use AI in their space, so the competitive advantage will shift to the data-driving AI. That’s where you’ll still get an edge. The AI economy will be bigger because people will be more productive, able to produce, learn, and create more.

Eventually, we’ll get past the debate of whether AI is net positive or negative for jobs—the economy will just grow around the innovation, as it always has. From an IP perspective, my mind goes to the Internet of Things. Right now, AI is mostly about data creation and image generation, but I’m interested in the world where AI connects physical things—ordering pizza through a non-handheld device, with automated kitchens and delivery. As we plug more physical things into AI, lawyers will have a heyday figuring out the IP implications. It’s exciting.

I tend to think beyond whether this is good or bad—it’s just going to become the economy we’re in. So how do we adjust, and what skills do people need? IP will remain important, as it provides the incentive to create. Musk and Dorsey have said, “Let’s get rid of IP,” but IP has always been the bedrock of innovation. You need proprietary rights to reward hard work and investment.

Brooke Quist: On the Musk-Dorsey point, it’s not clear what type of intellectual property they meant—copyright, patents, trade secrets, or trademark law. The clearest line is to copyright, where many AI companies face lawsuits. They may not have to delete copyright; they can rely on fair use and transformative use arguments.

Regarding patents, Musk has said, “Patents are for the weak.” In a way, he’s not wrong—patents level the playing field so small companies aren’t run over by big ones. It’s a feature, not a bug. Some patents will benefit big companies, like pharmaceuticals. If AI can help them run down research paths faster, it may be easier to bring new products to market. For example, AI has made huge advances in protein folding. Some data-intensive tasks will benefit big companies, not just small ones.

Michael: Policies are meant to drive the flow of money. The solution has always been that startups get bought by larger companies for their innovation. AI may level that out, making big companies more innovative too. But policies will reinforce economic flow. Any policy that stymies that won’t be favored by courts, politicians, or the public. The incentive will be to create or redesign frameworks that keep money flowing through the innovation pipeline. Larger companies will keep buying smaller ones, and the whole ecosystem will grow.

Jeff Dance: More efficient, more output—if AI accelerates the entire GDP, that’s an interesting thought. It goes beyond individuals and could benefit the whole economy. Change is hard, but is this an accelerator for the economy at large?

Michael: Think about the timing. Population studies show we’re entering an era of net population decline. It’s already happening in Europe, and only places like Sub-Saharan Africa are growing. So how will the economy grow? Not by adding more people, but by increasing productivity. People worried about AI and autonomy should also consider the economy—otherwise, it will shrink, which no country wants.

Brooke Quist: I heard an economic speech recently about how to grow the economy and reduce national debt. One answer was increased productivity via AI. You’ve hit the pulse, Michael.

Michael: These are long-term things. Going back to IP, Jeff, the frameworks, regulations, and laws will have to evolve to make AI useful. The biggest risk is overcorrecting and breaking things, but the incentive is to support innovation.

Brooke Quist: One thing you brought up earlier, Jeff, was the concept of AI as a black box. For various reasons, we’ll see a move toward more supervised and structured learning, and less unsupervised learning. That will make IP easier to patent when we can point to the logic along the way. For ethical and socioeconomic reasons, people will want answers: Why was this decision made? Was it fair? Did you consider certain issues? With a black box, you can’t answer those questions.

Usually, that’s not detrimental to the technology. You can put in flags to track how decisions are made. When you get answers from AI, you’ll want to see footnotes and sources, just like you do with people. When someone says something smart, you check their background. Same with AI—you’ll want to know where the answer came from.

Michael: It’s going to have to be the first one.

Jeff Dance: That’s a good point. We’ve been very black box and neural net focused, but you can get similar results with math, which isn’t as opaque. Moving toward more supervised learning and transparency should help solve some IP issues.

Michael: Did you hear about the lawsuit against Workday? They ran every resume through their AI, and apparently not a single person over 40 got an interview. As a 52-year-old, that hurts. But with a black box, it’s not defensible. If you give rights to AI, you have to create obligations too. There will need to be transparency and defensibility.

Brooke Quist: That’s a good point, Michael, about resumes. The counterbalance is that AI makes it easy to send out resumes, so companies get flooded. There was a comic where one person says, “AI is great, I write one sentence and it turns into a 10-page email.” The other says, “AI is great, I get a 10-page email and it turns into a one-line sentence.”

Jeff Dance: Two last questions before we wrap up. First, ethics or principles as they relate to IP—what do we need to get right as we design for the future? Brooke?

Ethical inventorship & the future of AI

Brooke Quist: In patenting, some of this will come down to inventorship—how are you generating this technology? AI is a bit like how open source changed software. Before open source, people coded everything themselves. With open source, it became easier to use shared code. With AI, you can outsource even more of your product. There will be ethical reasons people use AI in ways their company doesn’t want.

In patents, if coders use AI to help code platforms and the company wants to patent them, you could later find out most of the code was written by third-party AI. That could create ethical and legal problems, even if the coder was just trying to keep up with pressure. Lack of ownership can create issues for the company.

Jeff Dance: Thanks for sharing those thoughts. Last question: What advancements in AI are you personally most excited for? Michael?

Michael: I’m interested to see AI start connecting things. For most people, AI just feels like an enhanced Google search. But when it starts connecting physical processes—sensing and responding, not just taking inputs from us—that will transform industries. Not talking about Terminators, but about machines and devices that are truly intelligent. That’s the most exciting part for me.

Jeff Dance: The robotic side, where machines are truly intelligent and you can put intelligence on any physical object—that’s an exciting future, and it’s moving fast. Brooke, what about you?

Brooke Quist: Right now, AI is trained to answer fairly narrow tasks. When it moves toward general AI, that will be really interesting. Practically, AI already provides useful tools to quickly determine the current level of technology, so we can see when a client has done something new. Sometimes there’s so much information, it’s hard to know if something is truly novel. Quickly establishing the status quo is very useful.

I also think we’ll see very personal versions of AI that act like executive assistants, knowing your activities and optimizing your life—reminding you of things, helping with work and health, and making life more efficient.

Jeff Dance: Thank you, Brooke. Thank you, Michael. It’s great having you here and hearing your insights and wisdom as leaders in this space. To our listeners, if you have a unique situation or want to learn more, feel free to reach out. Thanks again for being on the show.

Michael: Thanks for having me.

Brooke Quist: Thanks for having us, Jeff.