Filed by Andretti Acquisition Corp. and Zapata Computing, Inc.

Pursuant to Rule 425 under the Securities Act of 1933 and deemed filed

pursuant to Rule 14a-12 under the Securities Exchange Act of 1934

Subject Company: Andretti Acquisition Corp.

Commission File No. 001-41218

Date: February 8, 2024

 

Zapata AI Virtual Analyst & Investor Webinar

February 8, 2024 | 12:00pm ET

Mick Emmett, VP of Marketing and Communications, Zapata AI

Hello everybody, thank you for joining us today to discuss Zapata AI. My name is Mick Emmett, I’m the VP of Marketing at Zapata AI, and today I’m joined by Bill Sandbrook, Co-Chief Executive Officer and Chairman of the Board of Directors at Andretti Acquisition Corp., as well as Christopher Savoie, CEO and Co-Founder of Zapata AI. We’re looking forward to telling you more about the company, our offerings, and why we’re so excited to go public. At the end Christopher will try to answer as many questions as he can.

Before we begin, I remind you that certain comments made during this webinar may constitute forward-looking statements and these are subject to significant risks and uncertainties that could cause our actual results to differ materially from expectations or historical performance. Please review the disclosure on forward looking statements included in Andretti Acquisition Corp.’s filings with the SEC for a discussion on these risks and uncertainties.

A recorded replay of this call and related materials will be available on the Zapata AI investor page. Please be advised that statements are current only as of the date of this call and, while we, or Andretti, may choose to update these statements in the future, we are under no obligation to do so unless required by applicable law or regulation.

Now, I’ll hand it over to Bill to tell you more about the proposed business combination between Andretti and Zapata AI.

Bill Sandbrook, Co-Chief Executive Officer & Chairman of the Board of Directors, Andretti Acquisition Corp.

The team at Andretti Acquisition Corp. is incredibly excited about our proposed business combination announced with Zapata AI. Put simply, with Zapata, we are racing toward a winning future with generative AI. We are excited to bring the company and awareness of their technology to the public markets at what is an incredibly exciting time for generative AI.

I'd like to share why we see this as a compelling acquisition, at a foundational time. We see four main pillars for this transaction:

1.Generative AI is quickly expanding at a seemingly exponential rate across multiple verticals
2.Zapata is a first mover in Generative AI
   
  

 

 

3.Zapata delivers products and solutions that are tailor-made for the specific needs and costs of their business customers. And:
4.The transaction is aligned with Andretti Acquisition Corp’s goal of pushing emerging technologies into mobility, which stems from Michael Andretti’s visionary leadership of leveraging technology within motorsports

To briefly elaborate. You have likely heard the buzz around Generative AI – it seems to be “the” focus of Big Tech, with some eyepopping estimates around its future Total Addressable Market size. It has been a topic of significant discussion during the past few earnings seasons, with Big Tech noting the positive impact it is having on their business outlooks.

As a fun fact, Alphabet, the parent company of Google, mentioned AI an eye-catching 70 times on its July earnings call. Christopher will elaborate more on market size in a few minutes, but we think it is very important to note that generative AI’s applicability spans across many, many verticals and industries, including areas like automotive, pharmaceuticals, and finance, just to name a few.

Building on this, Zapata is a well-known and respected first-mover in the Generative AI space, with some of the brightest scientists, engineers, and developers in the business, and an existing revenue base from some impressive Fortune 1000 customers and the U.S. Government. They’ve been working on Generative AI since 2018, with a strong patent portfolio. You’ll hear more on this from Christopher too.

Further, the Zapata team is tackling the unique challenges posed by enterprises by deploying an Industrial AI tool. Businesses have very different needs from those of consumers. By combining, among other differentiating features, their background in quantum computing with their work in Generative AI, they can deliver products and solutions that are tailor-made for the specific needs and costs of their business customers. This very much fits in with our mission of identifying a company that displays technological leadership – a company with a business model that addresses or creates a market need that other companies have not.

Lastly, Zapata is putting their tech into practice right in the “wheelhouse” of the Andretti brand – mobility, specifically motorsports – through their partnership with Andretti Autosport in the NTT INDYCAR Series. Pushing the limits of technology is key to success in motorsports, which has long been a part of the Andretti vision. As one such example, look no further than the formation of Andretti Technologies in 2015 to build electric powertrains for Formula E while the all-electric series was still in its infancy. For this reason, one of our goals with Andretti Acquisition Corp from day one has been to identify a partner that is pushing emerging technologies into mobility and, to the extent possible, motorsports. Zapata could not check this box any more firmly.

The Zapata and Andretti Autosport teams entered into a commercial relationship in early 2022 to seek out ways to enhance results in their INDYCAR Program through Zapata’s advanced analytics. In motorsports, every hundredth of a second counts, and every strategic decision counts. Races can be won or lost due to pit strategy and the timing of yellow flags. By deploying Zapata’s Orquestra platform in our racing operations, we believe we will be able to realize a real-time performance edge on race day.

  1

 

 

I’ll now turn it over to Christopher Savoie, CEO and Co-founder of Zapata AI, to give more color on how Zapata is helping us with its technology and how this technology can be applied to other industries.

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Thank you, Bill.

At a high level, Zapata AI is an Industrial Generative AI software company that develops generative AI applications and provides accompanying services to solve complex industrial problems. Our computational approaches leverage the statistical advantages of math based on quantum physics.

Industrial generative AI is similar to consumer generative AI tools like ChatGPT that generate text and images. But it’s tailored to enterprise use cases, taking the generative models behind these popular tools and applying them to critical, industrial-scale applications involving both language and other forms of data, for example sensor data. Industrial Generative AI is for challenging problems specific to an enterprise or industry, where we believe a general-purpose tool like ChatGPT would not be useful or sufficient. I’ll get into some examples in just a minute.

Zapata has been working on generative AI for years before ChatGPT was publicly launched. Our first generative AI patent was filed within a year after we were founded in 2017.

Since then, we have now built two core offerings that include software and software tools supported by services:

·The first is Zapata AI Prose: a large language model – or “LLM” — generative AI solution with operations similar to ChatGPT but customized to an enterprise’s industry and its unique problems. We believe Prose can help companies speed up time-consuming language tasks like filing for regulatory approval or patents, filling in customs forms, or creating consumer documents or reports. We believe it can also be a user-friendly and accessible way to interface with a company’s corpus of information or data, imagine a chatbot that can help you analyze data and create charts or reports simply by asking.
·We've also built a complementary solution called Zapata AI Sense that can handle mathematical models – something that ChatGPT and similar LLM applications cannot do very well. These models leverage the statistical advantages that come from math used in quantum physics and quantum information science. Sense can be useful for lot of different applications, including creating so-called “virtual sensors” that infer data for variables that wouldn’t otherwise be measurable, speeding up Monte Carlo simulations used in finance, detecting anomalies, simulating hypothetical scenarios, and much more.

Our generative AI solution set is “industrial”, meaning it is meant for use in a business context to address specific challenges or improve efficiencies. Again, this is not really the case with a ChatGPT-like application today. The stakes are much higher when doing things for business – especially at the enterprise level -- which I will elaborate on in a bit as well.

  2

 

 

All of this is provided over our full-stack software platform — Orquestra — which allows us to train and deliver our complex models over various cloud solutions, including Azure Cloud, AWS Cloud and others.

One of Orquestra’s most impactful benefits is its flexibility, which allows our customers to experiment with different models and not be tied to any one cloud provider, which they tell us is very valuable to them.

To-date, we’ve gained traction with customers across a variety of industries, including automotive, oil and gas, chemicals and finance, to name a few. Our customers have included BASF, BP, the global bank BBVA, DARPA, which is the innovation arm of the Department of Defense, Sumitomo Mitsui Trust Bank, and, of course, Andretti Global.

While our current customers operate in only a few specific industries, we envision opportunities for Zapata AI to utilize our software tools in almost any industry. Some examples of problems for which we believe our Industrial Generative AI applications could be designed to address include helping a banking institution optimize its financial portfolio or helping a pharmaceutical company with drug discovery.

Before getting into Zapata’s enterprise offering, I want to describe the basics of generative AI for anybody who has yet to sample a product like ChatGPT.

Generative AI technology is a huge step forward compared to “traditional” AI. At a simple level, traditional AI answers a specific question or performs a relatively simple, or straightforward task.

Generative AI, however, uses machine learning to generate what is, in effect, a new, original output or product. It will synthesize various inputs to distill and produce an original output.

For example, in a matter of seconds, it can draft a multi-paragraph answer — with varying degrees of specificity per how the question was asked — to explain a concept. Compare this to a search engine like Google simply pointing you to a variety of different sources, from which you would then have to synthesize and distill the information by yourself.

This works well enough most of the time. But as we all know, what seems like a simple inquiry can sometimes consume more time than any of us would like.

While society has seen rapid growth in and awareness in generative AI among the general public, it is important to note that for Zapata, this is not an “overnight thing.”

Company-wide, as of January 2024, we have 47 patent families, including 86 total patents and patent applications covering a diverse range of algorithms, use cases, and supporting software and hardware. To the best of our knowledge, there isn't a startup or other company out there that's a pure-play in this category, and none are in the process of going public aside from Zapata AI.

One of the most important takeaways I want to leave you with is this: the applicability of industrial generative AI technology seems almost limitless.

  3

 

 

I say this because companies across the globe — and across sectors — recognize this trend and are now racing to find the “killer app” for industrial generative AI. Executive decision-makers are eager to know how they can leverage this technology to improve their business.

This is evidenced by a Gartner Poll from this past October that revealed that 45% of executives reported piloting generative AI, while another 10% have it in production, and more than half have increased generative AI investment in the previous 10 months.

We are experiencing this momentum everyday at Zapata AI, as awareness and the popularity of Generative AI have drastically increased in the past year, and there has been a significant increase in interest from and conversations with current and potential future customers.

However, there are still several significant challenges with scaling up and commercializing generative AI in a business setting.

·For one, the potential for errors and inaccuracies is too high, which is unacceptable in many business contexts. For example, recent research found that GPT-4, the more powerful model behind the paid version of ChatGPT, has seen its mathematical accuracy drop from 97.6% in March 2023 to 2.4% in June 2023.
·Secondly, the costs can be enormous when you factor in how much compute time and resources are required to run and train large generative models. For example, GPT-3 costs around $4.6 million to train, and the more powerful GPT-4 likely will cost more than $100 million to train.
·There are also legitimate issues around privacy and security, monitoring, ethics, and so on and so forth.

Said differently, we believe businesses do not need massive, costly, inefficient generative AI models that are trained on the entire internet to do very specific, customized applications. They do not want general purpose models with unreliable outputs for their domain-specific problems. They also do not — by and large — want to be locked into a single vendor’s compute and cloud choices.

Rather, we believe enterprises want to keep their own data and their own models… they want to run these models on their own cloud and with their own security measures. They do not want to worry that a Big Tech generative model trained on their private data and IP will expose this sensitive information or use it against them. They want to have complete control over their generative AI applications.

We’ve had one of our Global Fortune 100 customers express to us that these exact concerns exist at their board level. They’ve expressed to us that they know the power of generative AI, but they have serious concerns that must be alleviated before they can commit the resources – financial and otherwise – to a generative AI solution set.

In addition to the challenges and concerns of implementing generative AI specifically, we believe there are several common challenges that enterprises face more generally that make their industrial problems difficult to solve with traditional computing-based solutions. These challenges include the chaos that comes with managing incomplete, fragmented or out of sync data from dozens of sources across the

  4

 

 

organization. It includes the unpredictability and large numbers of possible solutions inherent to industrial-scale operations. Problems are constrained by the need to compute solutions within a certain time frame, with the compute resources available, and at a certain level of accuracy. And of course, there is the persistent challenge of keeping sensitive data secure. We believe Industrial Generative AI is well positioned to address each of these challenges for enterprises.

This is where Zapata’s value proposition really comes into play. Put simply, we have developed a suite of custom industrial generative AI solutions that can harness the power of language and numerical models for critical, sensitive industrial applications. Our solutions are fine-tuned for our customers’ domain-specific problems, optimized for cost, benchmarked to the highest level of accuracy possible, and run securely in our customers’ own environments.

So with that said, let’s dive in a little deeper into our technology to demonstrate its advantages for generative AI.

Our technology is derived from math inspired by quantum physics. And if you’re thinking, “You mean, like electrons and photons and things like that?“ — you would be correct. The hard part is turning that discipline of physics into useful technology. Fortunately, our work in this area has many transferable and positive implications for generative AI.

Being experts at quantum math — which is another one of Zapata’s differentiators given the robust staff of 21 Ph.Ds we employ — allows us to enhance what we believe are the key, desirable qualities of generative models. Namely, quantum statistics can enhance generative models’ ability to generalize — or extrapolate missing information and generate new, high-quality information — as well as their ability to generate a greater range of solutions. This is called “expressibility”.

We recently demonstrated the advantages for quantum math for generative AI in research published with Foxconn, Insilico Medicine and the University of Toronto – with major implications for drug discovery. Our research showed that generative models enhanced with quantum components generated more desirable drug-like molecules than those generated by a traditional generative model.

Our technology is also efficient. Large language models, as the name suggests, are extremely large. This means they can use a lot of computer processing resources — typically powered by Graphics Processing Unit chips, or GPUs as they are commonly called. These specialized chips are required to run models with the size and complexity of LLMs. And with GPUs comes the high costs and large carbon footprints that are required to run them.

We believe that we have demonstrated the largest language model compressed with quantum-inspired algorithms, as we have not seen any evidence in academic publications or elsewhere that demonstrates the level of compression we have achieved. We believe this ultimately means that Zapata can deliver a high-quality and significantly more cost effective and environmentally friendly product than products and solutions offered by our competitors that use traditional neural network techniques.

Another very important factor is speed. A good example is what’s called a Monte Carlo simulation. This is a model used to predict the probability of a variety of outcomes when the potential for random variables

  5

 

 

is present. It can be used across a variety of fields — examples include in finance to assess risks associated with an investment; and in project planning to arrive at informed views on the probability of completing a project within a certain timeframe.

At Zapata, we have demonstrated an example whereby we ran very complex scenarios using both our quantum techniques on Orquestra and via a traditional Monte Carlo simulation. We found that running the simulation using quantum techniques was 8400x faster than the traditional Monte Carlo simulation — with the Zapata method arriving at a solution in three seconds versus the seven hours it took using the Monte Carlo model.

We understand this can be a challenging topic for many to fully understand and process, so let’s turn to some real-world examples.

As touched on earlier in Bill’s opening remarks, the origin of our relationship with Andretti Acquisition Corp is the strong partnership Zapata has had with Andretti Autosport for the past two seasons of the NTT INDYCAR SERIES. There is no better, or more exciting, way to provide an example of real-world applicability than this unique and, frankly, fun case study, so here goes.

As any motorsports enthusiast knows, race cars are incredibly technologically advanced, and data is critical to performance. Races are often won or lost — sometimes by just milliseconds — based on strategy, and strategy relies on data.

An NTT INDYCAR is outfitted with many sensors that gather data in real-time, including on factors that are critical to performance. However, not everything that is important to performance can be measured in real-time with sensors while a car is moving around a racetrack at speeds that can exceed 240 miles per hour.

One such example of useful information that cannot be measured via sensors is the slip angle of a car, which correlates to the pace and severity of tire wear while the car is lapping the track.

When the vehicle turns left, it wants to go to the right because of the centrifugal force, and this force is very pronounced at high speeds. The exact degree of force and how it evolves over a tire cycle impacts tire degradation, or tire wear, and thus the impact on lap time over that period.

Knowing exactly how the tires are wearing with sensors in real-time is impossible, but being able to accurately predict tire wear with generative modeling can inform the team when the optimal time is to make a pit stop to change the tires — and if there are adjustments to the car’s set-up that can be made to potentially improve tire degradation rates.

With Zapata’s industrial generative AI offering, we can generate virtual sensors that gather this important data that is otherwise unattainable in real-time, race-day conditions to predict tire wear, or predict race behavior.

  6

 

 

Andretti Autosport’s team has collected terabytes of data over twenty years, and using generative models, the Zapata platform has demonstrated that it can accurately model the slip angle of the tires during live race conditions.

In fact, when reviewing the data, the Andretti and Zapata teams have found that there is hardly any difference between the Zapata-predicted data – also known as synthetic data — and the actual performance data from the cars – with a less than 1% difference between the synthetic data from the generative model and the real data that was gathered. As you might imagine, the potential competitive edge is most certainly real.

This ability to generate realistic data isn’t just useful for auto-racing, it can also be applied across industries. For example, in our work with Sumitomo Mitsui Trust Bank, we’re showing how Industrial AI can be applied in the financial sector. Much like how we’re generating virtual sensor data for Andretti, we’re generating financial time series data for Sumitomo Mitsui Trust Bank. This synthetic data can be used to simulate plausible scenarios about future market movements. It can help risk managers conduct more sophisticated stress tests and support derivative traders in better hedging their portfolios. It can also enable more efficient and trustworthy derivative pricing calculations and value adjustments.

In addition to Finance, we believe Industrial Generative AI will make an impact on other industrial companies too. Our technology can be used to suggest new solutions to industrial optimization problems, particularly when it comes to making complex industrial processes more efficient. Our proprietary technique, which we call Generator Enhanced Optimization, or GEO, uses generative models to learn the distributions of possible solutions and then propose better solutions.

A helpful example of this in practice can be found in manufacturing plant optimization and the work we’ve done with BMW to prove this out.

Automotive manufacturing is incredibly complex, with hundreds of parts from various suppliers all coming together, skilled labor trained to perform specific functions, and various union and labor laws that dictate when and how many of the employees can work. Given these various inputs that all need to be synced together, inefficiencies and lost productivity can quickly become a serious problem. Because of this reality, finding the most efficient worker schedules to achieve production targets while minimizing idle hours is imperative for plant managers.

By partnering with BMW and deploying our GEO framework on our Orquestra platform across multiple BMW plants, we found that in 71% of the cases, our generative algorithms were able to tie or outperform the existing state-of-the-art optimization algorithms, demonstrating that we can help them optimize their scheduling.

Turning now to our go-to-market strategy. As touched on earlier, we already work with — or have worked with — a number of very large and well-known companies across the automotive, chemicals and finance industries, just to name a few.

When evaluating the market, it is imperative, we believe, to understand the key vertical industries that stand to benefit from generative AI, and how they’ll benefit in estimated dollar terms.

  7

 

 

Of note, the global services integrator McKinsey & Company last year conducted a study to evaluate estimated Profit and Loss impact of deploying generative AI technology across a variety of industries and the results are, we believe, pretty staggering.

For example, in the pharmaceuticals and medical products industry, generative AI could result in a $60-$110 billion impact on industry revenue through things like generating new drug candidates, which was the subject of our research with Foxconn, Insilico Medicine and the University of Toronto. Across advanced manufacturing, as we discussed earlier with the BMW example, the upside could be 1.4% to 2.4%. And lastly, in banking, there is an estimated 2.8% to 4.7% upside in revenues. We are talking about massive companies and massive industries, so benefits in the aggregate are in the tens of billions per industry.

In fact, together, the study points to what could be a $4.4 trillion (with a ‘T’) generative AI market just from new generative AI use cases alone. Even if off by an order of magnitude, this is still representative of a huge Total Addressable Market.

So, within that context, how are we going to grow our business?

We have two primary sales channels — a direct channel, where we approach companies with C-level relationships – and through a partner ecosystem.

Today, we have a global salesforce in the U.S., Europe and Asia, but we cannot be in the market speaking to every company – that would be impossible. As such, we have partnered with companies such as Microsoft Azure, IBM and Nvidia, to name a few, as well as a top-5 global consultancy company, to amplify our reach.

The business model and how we generate revenues is, in our view, pretty straightforward. We sell our product as a bundled subscription of software and scientific expertise, also referred to as professional services.

Repeatable solutions built with services firms are a major part of our growth strategy. By repeatable solutions, we mean solutions for industry use cases that can be resold to other companies within the same industry. We expect that working with service firms will enable us to grow faster by delivering these repeatable solutions to our partners’ existing client ecosystems and leveraging our partners’ sales and post-sales resources. We believe partners will assume an active role in the sales and post-sales cycles, such as project management, strategy and transformation, change management, and data management.

Zapata AI is widely known for its expertise in the research community, as evidenced by more than 85,000 citations for research associated with Zapata AI as of October 2023. We will continue to leverage thought leadership, including through conferences and engagement with the media, to build awareness of our offerings among our target customers. Publishing innovative research and collaborating with academia, research and consortia organizations is also part of our plan to drive awareness and demand for our technology.

  8

 

 

Turning to the Zapata team: we are proud of the people who are driving Zapata forward. Even before our proposed business combination came into being, we had — and currently have — a public-ready board comprised of operators and people with rich histories in the enterprise software industry.

Some notable individuals include:

·Jeff Huber, founding CEO of GRAIL and former SVP of Google Ads, Apps, Maps and Google’s X
·Clark Golestani, the former Global CIO at Merck
·Dana Jones, CEO of RealPage and the former CEO at Sparta Systems, a life sciences-focused next gen Software as a Service company that sold to Honeywell a few years ago

To conclude, Zapata is a pure-play company within the burgeoning and transformational industrial generative AI technology space.

Through the proposed business combination with Andretti Acquisition Corp., we can become what we believe would be the first publicly traded, pure-play, industrial generative AI company. In a large and rapidly growing total addressable market, we have proprietary, industrial generative AI techniques and algorithms that are at the leading edge of these new frontiers. They are capable of demonstrating a 10X to 1000X improvement in modeling performance, and Zapata brings its proprietary, full stack software platform to deliver these solutions.

We have substantial near-term enterprise revenue opportunities with large language models and other models in AI simulation and optimization, and a pioneering, founder-led and visionary management team and a board with a track record of execution.

These are exciting times for our company and our industry, and we hope this session has made you appreciate where we are coming from. Let me now turn it back over to Mick, who will serve as MC for Q&A.

Mick Emmett, VP of Marketing and Communications, Zapata AI

All right, thank you, Bill and Christopher, for that presentation. With that, let's get right to the questions. A lot of good ones have come in. This first one I'll direct to you, Christopher. “The generative AI market is a crowded space. Who do you see as your main competitors?”

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Well, I think there are a lot of people who've put the generative AI tag on what they're doing, maybe by repurposing ChatGPT and creating a layer over ChatGPT. But as far as using generative AI in numerical analysis and predictive analytics, I think we've been kind of unique in this space in that manner. However, having said that, there are other people doing AI writ large, Palantir, C3.ai, Microsoft, Amazon. All the big players are kind of in this game. So obviously it really is those large players, the incumbents, that we are in competition with them. We met them in the lobby of some of our customers and we've been successful at winning accounts against some of those folks as well.

  9

 

 

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. Let me get to the next one. "I've been following Zapata long enough to remember your beginnings as a quantum computing software company. How does your quantum history fit into your current focus on generative AI?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

That's a very interesting question. In our disclosures and the presentations included there, you'll find that our history includes, and it was in one of the charts that I just presented, that we started thinking about generative AI as one of the places where you can apply quantum math back in 2017. It was hard to talk about it back then, to be honest, because in 2017 or 2018, before ChatGPT, if you said, "We're doing generative AI," they said, "Well, deep faking, is that what you mean? What do you want to do? Make fake pictures of cats? And what's the business model for that?" We had known that this generative AI, even at the time, was going to be useful in other areas. So it was part of our DNA from the very beginning. It was just hard to talk about with investors. They heard that we're applying quantum to generative AI and they caught the quantum part, not the generative AI part.

But you'll see, if you look back a couple of years ago in our history, we even had an experiment with IonQ that we did on actual quantum hardware using generative AI, and that was one of the first ones that we're aware of where we actually did perform generative AI on actual quantum hardware. So we've been doing this for a long time before there was even a ChatGPT, before this was a thing. It was just kind of hard to talk about before, honestly, OpenAI came out and ChatGPT came out there and consumers were actually using this stuff.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Right. And now that's all people want to talk about it seems. All right, so this next question... Actually, we got a few of these -- sort of on the same topic. This is a two-parter. "Are there a couple industries that make the most sense for you to chase today? And are there any industries that you think would enable you to scale more quickly?" So two part question there.

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Sure. Well, the great thing about this for us as an opportunity over time is that it's a very horizontal technology. The math is applicable in many industrial spaces. We've had some quick wins obviously through the Andretti connection and our work with them as a customer in Autosport, which is applicable to the OEM industry. But we've applied the same work to banking. It's the same time series kind of data. So we've applied the same math in the recent deal that we announced with SMTB Bank to do trading strategies. So this is very horizontally based, but we think it's really the large industrial verticals, finance, and when we say finance, it's not just banks, but it's also insurance and a number of areas there. That's a huge one in and of itself.

  10

 

 

Other places where use time series data for predictive maintenance and these kinds of things. So large manufacturing. You may think of obviously automotive OEMs, but other OEMs as well. So those are some key verticals that we're involved with. And defense is another big one. We're already a defense contractor through DARPA and we think that defense is one area where generative AI will be really important going forward.

Mick Emmett, VP of Marketing and Communications, Zapata AI

All right, great. This next question, another two-parter, but it's related to questions we get even before we announced some of the things we've announced recently, but let me get right to it. It is, "How can quantum algorithms run on classical hardware?" And then the second part, "Why build quantum computers at all, if you can run quantum algorithms on classical hardware?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Yeah, I think this takes a little bit of math, but let's talk about linear algebra because that's the easiest way to say this, right? Matrix multiplication, that is what quantum computers do, it's what we do. But we can do matrix multiplication on GPUs, right? The promise of quantum computers is we're going to be able to do linear algebra, matrix multiplication if you will, much faster and much more accurately on large dimensional spaces once we have quantum computers that are large enough and accurate enough to do that, faster than classical computers. And there's a lot of promise in that area and that really is a thing that will happen and will converge. So when we have those computers that can do linear algebra faster, we will use those. And we've already done some experiments with IonQ to show that we can use that directly in generative AI.

And we've done it with D-Wave and others. We had an announcement today on that. So we will be able to use actual quantum equipment to do this faster, exponentially faster is the hope eventually. But we can do linear algebra on GPUs today and we can use the kind of math that we use in quantum physics and quantum chemistry, linear algebra, matrix multiplication. So matrix product states, MPS, is what we would call, and it's kind of an estimation. Just like you simulate qubits using GPUs today, you simulate those results using GPUs for other problems. We can use matrix product states in the context of GANs and transformers and generative AI today. Eventually we will be able to use actual quantum computers. And we've demonstrated that with IonQ in the presentations we've done with them and recently with D-Wave and others. We have that drug paper that was done on GPUs -- that was done with simulated qubits.

So we can do all this linear algebra on GPUs. The hope is that someday we will be able to do it even faster. But already that quantum math, as we saw in that Foxconn-Insilico paper, causes us to have better answers than just using the classical non-linear algebra math. So using quantum math is better, it's slower on GPUs today, but it's still faster than the alternatives and it will be even better and faster. And I think if you looked at Microsoft's recent call with Satya where he was talking about... In the last half hour of that talk he gave, he was talking about the convergence of AI in quantum, and that's what we mean, is that, "Yes, we can do it on GPUs today, but we will be able to do even more and get even better answers, more accurate answers, faster answers when we have quantum computing actually working."

  11

 

 

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. This next question I know is on a lot of people's minds. "Earlier you mentioned that there are some securities concerns with generative AI. Can you elaborate on those and how you plan to address them?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Yeah, this is a big deal because a lot of people are thinking, "Okay, we can use the ChatGPT generically and whatever," but if you're an industry and you're a bank, you don't necessarily want to have a big model like that hoover up all your data and... Then we've seen it. People can find out email addresses and bank account numbers and things like that through a model, through what would be an attack by... They call it a prompt attack. So you ask the model a ton of questions, you eventually get stuff that wasn't meant to be released by that model. And this is an additional problem that people haven't thought a lot about until we had these massive models available. The other problem is that it's really hard to steal a bank's entire information, even if you're a great hacker or a hacker backed by a nation state.

To take all of the data is a really hard thing. But if you go and create a model that's condensed all of the important insights of your data into a model... The metaphor I'd like to give is, well, I could have all of my money in 500 bank accounts distributed around the world, or I can buy one set of gold bars that I leave on my kitchen table. Which one's easier to steal? So what we're doing effectively is taking all that information and putting into a much more stealable model because it's smaller data wise. And so that is a thing that hasn't been thought about, so model security. Also just the ability to steal the model. Also you need to be able to protect that model from being queried when you don't want it to, and being able to ask questions that you don't want the model to be asked necessarily. So these are all considerations that are new to this technology and its deployment and enterprise, and we intend to be one of the forefront people in dealing with this because it's our customers coming to us with these questions and these problems.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. Safe to say that's a universal concern. All right. You alluded to this earlier, Christopher, and this is new. This is just based on news that came out this morning, but the question is, "Why did you partner with D-Wave and how does this compare to other quantum computing partnerships you've had?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Well, as some people may know, D-Wave has the longest history of being a quantum computing company with actual hardware that you can play with. They've been around longer than anyone. They also have 5,000 qubits now. These qubits are different. They're annealing qubits, so they work differently from our other partners' machines like IonQ and whatnot. But we have found out how to actually use an annealing machine to give us the data streams that we need to integrate into generative AI. And this is new and incredibly important because we can now use 5,000 potentially annealing qubits to do our work in generative AI, which is kind of beyond the capacity of a lot of folks right now.

  12

 

 

So as far as practically implementing these things... Also their Leap platform is there on the cloud. It's being used for optimization of other problems. We can access that over the cloud and use that and actually offer it with this capability, with generative AI pretty immediately, as you've seen in the drug stuff with Foxconn and Insilico. We can do that work with more qubits and actual qubits, not simulated qubits, in production, but we're also partnering with other folks. We continue to partner with IonQ and others, and we'll also be able to offer the advantages of those platforms and ions and superconducting qubits and these things going forward.

Mick Emmett, VP of Marketing and Communications, Zapata AI

All right, great. Next question. Again, this is another one that came in a couple of different forms. "There's massive buzz for the AI sphere. How will Zapata determine where to focus resources to target specific markets?" That's basically the gist of the question.

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Yeah, I think we got a similar question previously, like, "What verticals do you focus on?" We have some areas where we have experience in where we're already doing work like banking and finance, in the mobility space, but also predictive maintenance. Some people have seen our optimization work we did with BMW, and that's about advanced manufacturing anywhere. So these are big enough chunks for a small company like us to grow a lot. The possibilities are endless, but I will say we are industrial focused. It's in our tagline. We are not necessarily a consumer based play right now, and we don't think that that's really where this is going to have its biggest impact overall economically. I think that when you look at the estimates about generative AI and where it's going to really have some impact, it's going to be on the top and bottom line of actual companies in the industrial space. And so we're excited about that, bringing this disruptive technology to some of the older incumbent industries out there, the boring industrial stuff. But that's where the rubber's really going to hit the road and we're really excited about that.

Mick Emmett, VP of Marketing and Communications, Zapata AI

All right. On the theme of rubber hitting the road, another question that's come in a few different forms, and you alluded to this a bit in your presentation, but that's, "Applying the work that Zapata is doing with the Andretti Global team into... It could be racing, but it could just be other industries. Could you provide a little bit more color on how that work translates?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Yeah, I mean obviously some of the work that we're doing applies to every racing sport. It also applies to OEMs and cars that we drive every day and optimization of electric motors and hybrid systems and everything that we're doing in IndyCar with the advent of hybrid engines this year is going to be applicable in OEM hybrid cars and electrified cars. Andretti Global won the Formula E competition last year. So this is applicable to a lot of areas. And when you talk about electrification and all of this, it's not just cars. So this expands well beyond that. And just in the sporting world, yes, this can work for other racing teams and whatnot, but it also applies to other sports. The shift was an important thing in baseball. The statistics

  13

 

 

worked so well that they made it illegal. Data science is really important in sports nowadays, and sports is a multi-billion dollar industry in and of itself.

So obviously other sports that are huge, soccer, football, basketball, hockey. These are areas that these statistics can be applied. And then when you take it from sports, that's something that people understand, it becomes really obvious to other people that, wow, those same things that you're doing in sport can be used in banking and insurance and other industrial areas. So we're excited about, yes, the sports industry, but more importantly how this time series data that we're doing can be used elsewhere. So it really was SMTB, Sumitomo Mitsui Trust Bank, looking at the work that we did with Andretti that gave them the idea that, "Well, hey, that's time series data and it's really a lot of data. Wow, we have that same problem." So I think that that is really the cool thing here is that yes, it's racing, yeah, it's out front, yeah, it's a fun thing but the work that we're doing there with this huge massive data and this time series data is applicable to all these other areas where you have time series data like in predictive maintenance and other areas.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. All right, the next question, this is actually for Bill Sandbrook, if we can get him back on the stage. Hi, Bill. Bill, a question came in. "Can you talk about how you came about doing this transaction with Zapata AI?"

Bill Sandbrook, Co-Chief Executive Officer & Chairman of the Board of Directors, Andretti Acquisition Corp.

Sure. Our team, when we started this a couple of years ago, we ended up vetting over 90 companies. And our thesis with the Andrettis always was to find somebody in the mobility technology space with some applicability hopefully in racing, but that wasn't necessarily a requirement. So obviously with our partnership with Michael and Mario, our SPAC was attending a lot of races and by that time, we had looked at a lot of companies that just didn't really fit the bill as Zapata did as a first mover, a large TAM, an experienced management team, an exciting space, and a crossover into racing, which Christopher has already described. So it was a natural for us then to run with Zapata and negotiate a transaction that us and the Andrettis and obviously Zapata is very thankful and very excited about.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. This is actually sort of an open comment to both of you, but Bill, in your piece earlier you mentioned how in Google's earning... I think it was Google's earning call or Alphabet's earning call, how many times they mentioned AI. And actually this is a statistic that just came up for January, more than 38% of the S&P 500 earnings calls in January mentioned AI up from just 10% at the end of 2022. I mean, I know it supports what we're saying, but I don't know if you guys have any comments on that one.

  14

 

 

Bill Sandbrook, Co-Chief Executive Officer & Chairman of the Board of Directors, Andretti Acquisition Corp.

Well, I will. First, I mean I'm on a couple other public company boards and in the boardrooms, the feeling is that, "What is this? How can it help us? How can it hurt us? How are we going to lose competitive advantage if we're not a first mover in this space?" And because it's so prolific in the press and the financial press, all public company boards are talking about it. That's not a surprising statistic.

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

And I would just add, in Palantir's quarterly calls, they mentioned how they're growing obviously as an AI company, but one of the comments that I caught there was that they said that the demand for large language model applications is unrelenting for them, and that's actually a big source of their growth. So we don't see that as any different in what we've been observing.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. And I think we might have one more that we have time for. A lot of these questions that came in are flavors of questions that you two have already answered... but, oh, actually we do have one last one to get in. This is for you Christopher. "What's the benefit of the IonQ quantum hardware partnership and can you discuss that a little bit more?"

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Yeah, absolutely. We're working together, actually. They're a subcontractor to us on the DARPA program as well. We're in deep partnership with them. They have a different platform which is ion based, which has some advantages in the quantum area. So we're excited about that hardware going forward and we continue to partner with all of the major players because as I said, we do linear algebra on GPUs today, but when we do get the ability to use this... And we did generative AI with them already on their platform and with others. When we get the access to this, it's going to be a huge accelerator. It's really exciting. It's a bit more feature oriented as to, "Okay, when are we going to get that?" I'm not here to predict that and nobody should try to predict on this call when we're going to have the hardware that will give us that exponential acceleration in commercial use. But when we get that, there's no question out there that it will be useful and it will accelerate all of this great work that we're doing in these very real applications.

Mick Emmett, VP of Marketing and Communications, Zapata AI

All right, great. We're just about at time here, so I want to thank you Bill and Christopher for being part of this and the presentation. Christopher, if you have any final words before we let everybody go?

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

No, just thank you everyone for spending time with us today. We're really excited... Bill and I are really excited to bring this to market. We're interested in putting this stuff to use and doing it very publicly.

  15

 

 

Mick Emmett, VP of Marketing and Communications, Zapata AI

Great. And any parting thoughts, Bill?

Bill Sandbrook, Co-Chief Executive Officer & Chairman of the Board of Directors, Andretti Acquisition Corp.

We're just excited about this partnership and we're looking forward to the future.

Mick Emmett, VP of Marketing and Communications, Zapata AI

Awesome. Thank you everyone for attending. We will have a recording of this available afterwards, and thank you for the great questions.

Christopher Savoie, Chief Executive Officer & Co-Founder, Zapata AI

Thank you.

Bill Sandbrook, Co-Chief Executive Officer & Chairman of the Board of Directors, Andretti Acquisition Corp.

Thank you.

 

 

FORWARD LOOKING STATEMENTS

Certain statements included in this communication, and certain oral statements made from time to time by representatives of Andretti or Zapata Holdings, Inc. (“Zapata”), that are not historical facts are forward-looking statements for purposes of the safe harbor provisions under the United States Private Securities Litigation Reform Act of 1995. Forward-looking statements generally are accompanied by words such as “believe,” “may,” “will,” “continue,” “intend,” “expect,” “should,” “would,” “plan,” “predict,” “potential,” “seem” “seek” “future” “outlook,” and similar expressions that predict or indicate future events or trends or that are not statements of historical matters. These forward-looking statements include, but are not limited to, statements regarding projections of market opportunity. These statements are based on various assumptions, whether or not identified in this Current Report, and on the current expectations of the management of Zapata and Andretti, as the case may be, and are not predictions of actual performance. These forward-looking statements are provided for illustrative purposes only and are not intended to serve as, and must not be relied on by an investor as, a guarantee, an assurance, a prediction or a definitive statement of fact or probability. Actual events and circumstances are beyond the control of Zapata and Andretti. These forward-looking statements are subject to a number of risks and uncertainties, including changes in domestic and foreign business, market, financial, political and legal conditions, the inability of Zapata or Andretti to successfully or timely consummate the proposed business combination of Zapata and a wholly owned subsidiary of Andretti (the “Business Combination”), the occurrence of any event, change or other circumstances that could give rise to the termination of

 

  16

 

 

negotiations and any subsequent definitive agreements with respect to the Business Combination; the outcome of any legal proceedings that may be instituted against Andretti, Zapata, the Surviving Company or others following the announcement of the Business Combination and any definitive agreements with respect thereto; the inability to complete the Business Combination due to the failure to obtain approval of the shareholders of Andretti, the ability to meet stock exchange listing standards following the consummation of the Business Combination; the risk that the Business Combination disrupts current plans and operations of Zapata as a result of the announcement and consummation of the Business Combination, failure to realize the anticipated benefits of the Business Combination, risks related to the performance of Zapata’s business and the timing of expected business or revenue milestones, and the effects of competition on Zapata’s business. If any of these risks materialize or our assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. In addition, forward-looking statements reflect Zapata’s expectations, plans or forecasts of future events and views as of the date of this Current Report. Zapata anticipates that subsequent events and developments will cause Zapata’s assessments to change. Neither Andretti nor Zapata undertakes or accepts any obligation to release publicly any updates or revisions to any forward-looking statements to reflect any change in its expectations or any change in events, conditions or circumstances on which any such statement is based. These forward-looking statements should not be relied upon as representing Andretti’s or Zapata’s assessments of any date subsequent to the date of this Current Report. Accordingly, undue reliance should not be placed upon the forward-looking statements.

 

 

IMPORTANT ADDITIONAL INFORMATION AND WHERE TO FIND IT

In connection with the contemplated transaction, Andretti filed a Registration Statement, which includes a proxy statement/prospectus, with the SEC. Additionally, Andretti will file other relevant materials with the SEC in connection with the transaction. A definitive proxy statement/final prospectus will also be sent to the shareholders of Andretti, seeking any required shareholder approval. This Current Report is not a substitute for the Registration Statement, the definitive proxy statement/final prospectus, or any other document that Andretti will send to its shareholders. Before making any voting or investment decision, investors and security holders of Andretti are urged to carefully read the entire Registration Statement and proxy statement/prospectus and any other relevant documents filed with the SEC as well as any amendments or supplements to these documents, because they contain important information about the transaction. Shareholders also can obtain copies of such documents, without charge, at the SEC’s website at www.sec.gov. In addition, the documents filed by Andretti may be obtained free of charge from Andretti at andrettiacquisition.com. Alternatively, these documents can be obtained free of charge from Andretti upon written request to Andretti Acquisition Corp., 7615 Zionsville Road, Indianapolis, Indiana 46268, or by calling (317) 872-2700. The information contained on, or that may be accessed through, the websites referenced in this Current Report is not incorporated by reference into, and is not a part of, this communication.

 

 

PARTICIPANTS IN THE SOLICITATION

Andretti, Andretti’s sponsors, Zapata and certain of their respective directors and executive officers may be deemed to be participants in the solicitation of proxies from the shareholders of Andretti, in connection

 

  

 

 

with the Business Combination. Information regarding Andretti’s directors and executive officers is contained in Andretti’s Annual Report on Form 10-K for the year ended December 31, 2022, which is filed with the SEC. Additional information regarding the interests of those participants, the directors and executive officers of Zapata and other persons who may be deemed participants in the transaction may be obtained by reading the Registration Statement and the proxy statement/prospectus and other relevant documents filed with the SEC. Free copies of these documents may be obtained as described above.

 

 

NO OFFER OR SOLICITATION

This Current Report is for informational purposes only and shall not constitute a proxy statement or solicitation of a proxy, consent, or authorization with respect to any securities or in respect of the Business Combination. This Current Report shall also not constitute an offer to sell or a solicitation of an offer to buy any securities, nor shall there be any sale, issuance, or transfer of securities in any state or jurisdiction in which such offer, solicitation, or sale would be unlawful prior to registration or qualification under the securities laws of any such state or jurisdiction. No offering of securities shall be made except by means of a prospectus meeting the requirements of Section 10 of the Securities Act or an exemption therefrom.

 

 

 

 

  

 


Andretti Acquisition (NYSE:WNNR)
Historical Stock Chart
From Jun 2024 to Jul 2024 Click Here for more Andretti Acquisition Charts.
Andretti Acquisition (NYSE:WNNR)
Historical Stock Chart
From Jul 2023 to Jul 2024 Click Here for more Andretti Acquisition Charts.