Filed by AdTheorent, 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: AdTheorent, Inc.

(File No. 333-259027)

This filing relates to the proposed merger involving MCAP Acquisition Corporation and AdTheorent Holding Company, LLC (“AdTheorent”) pursuant to the terms of that certain Business Combination Agreement, dated as of July 27, 2021.

The following is a transcript of a fireside chat between Jim Lawson, AdTheorent’s CEO, and John Roy, Managing Director at Water Town Research LLC, held on December 8, 2021.

John Roy:

Welcome, everyone. I’m John Roy, Managing Director here at Water Tower Research. And I’m very pleased today to have Jim Lawson, the CEO of AdTheorent. Jim, welcome, and maybe if you could give us a little background on AdTheorent and yourself.

James Lawson:

Hey, John, it’s good to be here. Thanks for having me. AdTheorent is a programmatic demand side platform or DSP, which uses machine learning and data science to drive actual business outcomes for the world’s top digital advertisers, specializing in a privacy forward approach that does not require user profiles or individualized IDs for targeting. I’ve been with AdTheorent since 2012 and I am very proud of the team that we’ve built and excited to continue our success and growth.

John Roy:

Well, great. Yeah, that’s a great intro. So, now, on Monday, it was announced the MCAP registration statement that went effective with the SEC. So how is AdTheorent preparing to become a public company?

James Lawson:

Yeah, well, there’s never been greater demand for what we’re doing as a business. Customers are increasingly looking for the measurable performance that AdTheorent drives, and industry tailwinds and customers favor our privacy forward approach to ad targeting. Our 30% plus EBITDA margins and robust cash generation mean we can self-fund, as a business. We’ve already been investing for the future and executing on our 2022 growth plan and the financial projections we provided in our pipe deck do not assume incremental investment. We are, however, very excited by the incremental options that public company currency will afford us. And of course, there are valuable marketing benefits to being a public company. We are already benefiting from that in a number of ways. Our affiliations with new strategic partners we have met as part of this process, our access to industry leaders as we fill out our new board of directors. And, when we’re talking to big world class advertisers, being public will bring a level of transparency about the success, scale and vision of our business.

Some of the top priorities in our 2022 growth plan include actively expanding our sales and marketing footprint. We see more opportunity now than we are currently capable of capturing. We are also continuing to invest in our platform differentiation and expanding our capabilities to do that, so we can roll out more products and faster. We are investing in the growing CTV or Connected TV opportunity. Our video revenue grew 67% year-over-year in the third quarter with minimal investment and the competitive advantages we have elsewhere translate well to CTV and we’re very excited to talk about our opportunities in the CTV.

We’re also expanding our vertical capabilities focusing on making our offerings unique and value-adding and exploring other ways to grow revenue faster. There are a number of other exciting options available to us, for example, international expansion and M&A. But at present, we’re really focusing on accelerating our current strategy in order to capture a greater piece of this growing market.

John Roy:

Wow. So AdTheorent is a DSP or demand side platform, would you mind giving us not as technical or in depth ad tech experts as you, a little bit of your knowledge about what does that really mean?

James Lawson:

Of course, it’s a good question. At a very high level, a demand side platform or DSP connects sellers of digital media (or publishers) with buyers of that media (advertisers). Digital publishers desire to monetize their digital real estate by selling ads. Advertisers compete to buy this specific digital ad opportunity or digital impressions that are most likely to yield engagement with or business conversions by their customers. For example, a retailer seeking to drive online sales, or a financial institution seeking credit card applications or insurance quotes. Those would be two examples of business outcomes the advertisers try to drive with digital ads. A real time auction system allows the parties, the publishers and the advertisers to buy and sell ad opportunities in microseconds and that is the environment in which we work.

John Roy:

So, okay, that’s a DSP. So how is AdTheorent than other DSPs, since you’re not the only one out there?

James Lawson:

No, absolutely not. We are not the only DSP but we have really transformed what it means to be a DSP and how a DSP acts in the programmatic ecosystem. The most important difference is that machine learning powered predictive advertising, which is what we practice, performs better than traditional methods, as measured by the actual business outcomes that AdTheorent-powered ad campaigns deliver.

And as I mentioned, we do this in a privacy forward way. I also think we are quite different in terms of our market focus, and the specific opportunity that we are pursuing. We are focused more on the performance subset of the digital advertising market, the so called “lower funnel” of advertising, which deals with driving business conversions for customers. US programmatic digital media spending will likely exceed $90 billion in 2021, growing at about 18 or more percent. Approximately $13 billion of that is hyper-focused on performance driving executions, which we are uniquely able to drive and this is how we engage new customers.

Our clients increasingly find that it makes sense to use AdTheorent as a full funnel solution as well, meaning AdTheorent can drive the best performance in the lower funnel and we can also leverage campaign data from upper funnel (branding and awareness campaigns) that we execute to provide more data to inform our lower funnel campaign executions. We do see a lot of our clients expressing this interest in this full funnel solution. But as a performance first DSP, we typically get our seat at the table by showcasing to customers that we can drive real world business outcomes that are measurable and provable you using our ML systems in the DSP programmatic environment.

John Roy:

So the machine learning powered predictive advertising that you do, so what exactly is the primary advantage of that to AdTheorent?

James Lawson:

So there are really two primary advantages to it. The first is that it allows AdTheorent to drive superior performance for our customers. Meaning we can drive the customer KPIs, the online sales, the reservation bookings, the insurance registrations, the credit card signups. Whatever those business outcomes are, we can do that more effectively. Secondly, we can do this in the most privacy forward manner available in digital. And this is all powered by and made possible by data science and machine learning, without relying on sensitive or individualized personal data for targeting. And this comes together to drive these tangible business outcomes for our customers, as defined by them. And regulatory and industry changes currently disfavor individualized and user profile-based advertising, and this is accelerating demand for our privacy forward solutions and creating sustainable strategic advantages for AdTheorent.

Our business, I should note, was premised on ML based targeting dating back to 2012. This is not a pivot for AdTheorent. In 2012, we were a mobile focused startup trying to solve the problem of how you target mobile ads without cookies. We realized the power of data science and ML in this context. We proved that it worked and then we quickly expanded to become truly omni-channel, all screens, all ad types, and digital. In addition, on top of this platform, we deploy and used customized vertical solutions to address the needs of advertisers in specialized industries. These specialized solutions feature vertical specific capabilities related to things like targeting, measurement and audience validation. I’ll give you a couple examples.

Our pharmaceutical and health care offering harnesses the power of machine learning to drive superior performance on campaigns targeting both healthcare providers and patients, leveraging HIPAA compliant methods and targeting practices that comply with the NAI Code and other self regulatory standards.

Our banking, financial services and insurance solutions drive real world performance KPIs that these banking and financial services and insurance customers care about, within the context of regulatory requirements and data use best practices intended to prevent things like discrimination in the promotion of federally regulated credit extension products.

These are sensitivities and privacy safeguards that we’ve built into our machine learning models into our targeting practices, because we’ve leaned into the different verticals, we’ve learned their businesses, we understand what they’re trying to do, we understand what value they’re trying to create with their advertising dollars and we invest in creating solutions to do that and then prove that we did it.

And there are many others that we are fast at work improving and creating. We have created additional industry-tailored offerings to address the unique challenges and opportunities in a growing range of verticals, including retail, auto, dining, entertainment, many others. And we look forward to continuing to do that, because we think that on top of that ML platform, really investing in vertical specific solutions is why AdTheorent is very special.

John Roy:

Now, if you’re not using cookie IDs and you’re not using user IDs to target your ads, how do you effectively target ads?

James Lawson:

It’s a great question. We don’t need a user ID to target a digital ad. Instead of targeting user IDs, we target -- essentially, we target predictive scores. Our machine learning platform scores every impression that is processed by our platform. As a DSP we receive, from supply side platforms and publisher partners, a notification that there’s an opportunity to serve an ad to a user at a moment in time, in a given publication. We also receive a string of code that has a number of data attributes tied to that digital ad impression.

Our system scores every single digital ad impression request that comes into our system. And based on the likelihood that the ad is served to that user will convert on a business KPI that our customers are trying to drive. And I’ll get into that a little more in a minute but I’ll also quickly would like to contrast what we do with what are – with what the kind of the prevalent method of ad targeting has been prior to AdTheorent. The two -- and I’ll be very quick on this.

The two most common ad targeting methods used by competing DSPs are cookie-based retargeting. We’ve all heard about cookie-based retargeting and segment-based audience targeting. At AdTheorent, we believe that these methods alone are limited and deficient. And this represents a strategic opportunity for our advanced and holistic data driven methods, so quickly on each. Cookie-based retargeting obviously relies heavily on user’s web browsing histories. This method is very personalized, it can be quite invasive. It recycles prospective customer pools, essentially people who have already indicated interest in a given product or service or capability, rather than expanding the audience. And we expect that the usefulness of this technique will decline over time also, as a result of certain changes happening in the industry. Apple and Google, their respective initiatives to make it more difficult for advertisers to leverage cookie IDs and device IDs for ad targeting, as well as just generally speaking, the tide of privacy advocacy and awareness that I think is making consumers ask questions and making brands ask questions about whether they want to be associated with some of the more privacy backwards techniques.

The second most prevalent method is segment-based audiences. Essentially what these are, they are third-party licensed pools of IDs, of IDs of users who are perceived to be interested in a given product or service. These licensed audience segments often rely on data whose source age, reliability what have you is not known or provable, and in our view, it is often not accurate, and it can yield significantly lower conversions or advertiser ROI than AdTheorent’s ML-powered predictive advertising.

John Roy:

Now, you mentioned predictive targeting that AdTheorent does. That seems to be pretty different than then other ways that are done out there. Can you give me a little more color behind what is that and how do you target ads with predictive targeting?

James Lawson:

Yeah, absolutely. I think that sometimes that the words predictive advertising, AI, and ML can be buzzwords and they sometimes are empty buzzwords. We’re aware of no other programmatic media buying platform that actually uses machine learning and data science as we do, as the core method of ad targeting and campaign optimization at the impression level. So simply put, our platform uses ML and data science to identify the ad impressions with the highest likelihood of converting on a client’s desired action, maybe it’s an airline ticket sale online.

AdTheorent predictive advertising is not relying upon third party data licenses, or cookies or other device IDs. We don’t need to access ID lists in order to target ads. Again, we are targeting a predictive score for the opportunity to serve an ad to a given ad impression at a given time. Our platform ingests statistical and non-individualized data attributes in each bid request and then machine learning models inform our real time media buying decisions. So if our customers’ KPI is an online purchase, our platform will identify data correlations, which exists in the historic online purchase conversion activity. In other words, our platform identifies the data attributes or combinations thereof, which are present most often when there is a conversion. This can be things like the device type, the operating system, one or more keywords in the URL or keywords in the page content, geographic data, geo contextual data, are you in a Starbucks, are you in a supermarket. Time is one of close to 200 other data attributes that are available to inform our models and our models inform our bidders. Using historic conversion data, we can then determine the likelihood that each specific bid request will drive in the online or real world actions our customers’ desire.

So our platform assigns higher predictive scores to impressions with data attributes that correlate with historic conversions. And we look to buy those higher scored impressions for our customers because they perform better. And it’s important to note -- and this might be too technical, but I’ll mention it anyway. In the beginning of a campaign, there is a learning period for our ML models. Before there are enough conversions, one might say there’s nothing to optimize based on, you don’t have any conversion, so how does the platform optimize? So in the beginning, we have what we call a learning period for each one of our campaigns, and each one of our models, we optimize towards things like clicks or site engagement or ad engagement that typically precede online conversions. And then once we have enough conversion events, actual conversion events, airline bookings, or credit card registrations, etc, we optimize towards those impressions and the data points associated with those types of conversions where the actual KPI occurred. So the more conversion events occur, the more that the models can connect the dots and say, when these data attributes are more likely present, you are much, much more likely to have a conversion event, so optimize towards those statistics and those attributes in the future. And again, that’s contrasted with go find ID 123652 because that ID is in a segment profile that we license from someone and they tell us that this person is going to be interested something. It’s just a different way to target ads in 2021 and we believe it’s a better way to do it.

One last thing on scale. Our scale is virtually limitless. Our platform evaluates and assigns predictive scores to over a million impressions per second or 87 billion impressions per day. So driving digital conversions based on client specified KPIs is like searching for a needle in a haystack. It’s actually like searching for many, many needles in many, many haystacks. And machine learning and data science make this possible. Only computers powered by machine learning can do this. We bid on less than one-tenth of 1% of the impressions that we score. And as our machine learning platform ingests more conversion data, it learns and optimizes campaign delivery, driving both conversion performance and cost efficiencies as well.

John Roy:

Now maybe to take a slightly different look. Looks like you have a fairly solid financial record over a number of years actually. This is somewhat unique first back IPO. Can you give us a little bit of info on that, maybe some color?

James Lawson:

Yeah, thank you for that question. We have a long established history of operating efficiently, delivering top line and margin growth and strong cash flows. We have organically built a strong balance sheet over a period of 10 years, which serves as a foundation to accelerate our investment and growth opportunities. Our projections are conservative and reflect organic growth only. Potential benefits from international expansion or M&A are excluded from the figures that we’ve shared publicly. I’ll talk about the third quarter and then how we feel about the year as well and we’ve published this information recently, and we’re really, really proud of the results.

Our revenue increased year-over-year by 36% in the third quarter from $29 million in the third quarter of 2020 to 39.5 million in the third quarter of ‘21. Our adjusted gross profit increased 36% as well, year-over-year to 25 million up from Q3 2020 by 6.7 million. Our net income for the quarter increased 87% to 3 million, up from 1.6 million in the third quarter of 2020. Our adjusted EBITDA increased 49% year-over-year to 8.9 million. And our adjusted EBITDA margins are now 35% for the third quarter, which is an increase from 32% in the third quarter of 2020.

We’re very happy with how 2021 is coming together. As per our guidance, we expect to generate north of $161 million in revenue that’s over 30% year-over-year; over $106.2 million in adjusted gross profit, which is also more than 30% year-over-year growth, putting us in north of rule 50 category, if you will. And we look -- as per our most recent guidance, we’re looking to produce north of $35 million in EBITDA for the year as well. Our revenue and our EBITDA beat our expectations in the first three quarters and we feel really good about where we are and we see a lot of meaningful opportunity ahead.

John Roy:

So just to kind of follow up on that. Obviously a very strong third quarter, I mean, what in your business allowed you to navigate the headwinds that seemingly caught a few in the ad tech space?

James Lawson:

We have a number of really exciting advantages. I spoke about a number of those. I’d also mentioned, we have a very broad base of customers across many verticals. We have proven ourselves with our customers and we retain our customers at a very, very high level, and we grow our revenue with our customers. I think when you have contraction in the advertising market or in the economy, generally, advertisers tend to focus and consolidate their spent with partners that are proven in the area of performance and that would be AdTheorent. So customers who care about performance tend to consolidate their investments with us and we look forward to really working towards that and spending more time with these big customers and trying to grow those relationships.

Also, customers, which prioritize privacy, are increasingly interested in what we’re doing. I mentioned earlier that both in regulated areas, pharma, healthcare, financial services, but just in general, brands that don’t want to be associated with yesterday’s methods and some of the privacy -- some of the creepy privacy aspects of retargeting. They’re interested in what we’re doing, and they want to learn more, and we’re having a very good process working with them and educating them about predictive advertising, and how machine learning and data science can be used to replace some of those other methods.

And we have a very flexible model, and we work with customers based on what they need. Some customers want strategy and creative services and all types of campaign optimization support to achieve their goals. Others have whole teams, they might even had trading teams and they might need less of that and might have their own creative capability and their own strategy and their own views on best practices. So they might need a different level of support. Most customers we’re finding realized very quickly, that while it is easy to spend money through DSP, it is much less easy to drive measurable performance. And that’s, again, where we’ve put our flag in the ground. We believe that with AdTheorent, our platform, gives us unique advantages, and that’s really where we believe the future -- where the future is bright for AdTheorent.

John Roy:

Great. Well, Jim, thank you so much for this. This has been extremely useful. Investors, you can read all about this on our website, and you’ll be hearing more from AdTheorent in the near future. Thank you.

James Lawson:

Hey, thanks a lot. Appreciate it.

Cautionary Language Regarding Forward-Looking Statements

This communication contains “forward-looking statements” within the meaning of the Private Securities Litigation Reform Act of 1995. In general, forward-looking statements usually may be identified by through the use of words such as “will likely result,” “are expected to,” “will continue,” “is anticipated,” “estimated,” “may,” “believe,” “intend,” “plan,” “projection,” “outlook” or the negative of these terms or other comparable terminology and in this communication include, but are not limited to, future opportunities for AdTheorent and MCAP, AdTheorent’s financial guidance for the full year 2021 and, the proposed business combination between MCAP and AdTheorent, including the expected listing on Nasdaq. Such forward-looking statements are based upon the current beliefs and expectations of our management and are inherently subject to significant business, economic and competitive uncertainties and contingencies, many of which are difficult to predict and generally beyond our control. Actual results and the timing of events may differ materially from the results anticipated in these forward-looking statements.

The following factors, among others, could cause actual results and the timing of events to differ materially from the anticipated results or other expectations expressed in the forward-looking statements: inability to meet the closing conditions to the business combination, including the occurrence of any event, change or other circumstances that could give rise to the termination of the definitive agreement; the inability to complete the transactions contemplated by the definitive agreement due to the failure to obtain approval of MCAP’s stockholders; the failure to achieve the minimum amount of cash available following any redemptions by MCAP stockholders; redemptions exceeding a maximum threshold or the failure to meet The Nasdaq Stock Market’s initial listing standards in connection with the consummation of the contemplated transactions; costs related to the transactions contemplated by the definitive agreement; a delay or failure to realize the expected benefits from the proposed transaction; risks related to disruption of management’s time from ongoing business operations due to the proposed transaction; changes in the digital advertising markets in which AdTheorent competes, including with respect to its competitive landscape, technology evolution or regulatory changes; changes in domestic and global general economic conditions; risk that AdTheorent may not be able to execute its growth strategies, including identifying and executing acquisitions; risks related to the ongoing COVID-19 pandemic and response; and risk that AdTheorent may not be able to develop and maintain effective internal controls.

Actual results, performance or achievements may differ materially, and potentially adversely, from any projections and forward-looking statements and the assumptions on which those forward-looking statements are based. There can be no assurance that the data contained herein is reflective of future performance to any degree. You are cautioned not to place undue reliance on forward-looking statements as a predictor of future performance as projected financial information and other information are based on estimates and assumptions that are inherently subject to various significant risks, uncertainties and other factors, many of which are beyond our control. All information set forth herein speaks only as of the date hereof in the case of information about MCAP and AdTheorent or the date of such information in the case of information from persons other than MCAP or AdTheorent, and we disclaim any intention or obligation to update any forward-looking statements as a result of developments occurring after the date of this communication. Forecasts and estimates regarding AdTheorent’s industry and markets are based on sources we believe to be reliable, however there can be no assurance these forecasts and estimates will prove accurate in whole or in part. Annualized, pro forma, projected and estimated numbers are used for illustrative purpose only, are not forecasts and may not reflect actual results.

No Offer or Solicitation

This communication shall not constitute a solicitation of a proxy, consent, or authorization with respect to any securities or in respect of the proposed business combination. This communication shall also not constitute an offer to sell or the solicitation of an offer to buy any securities, nor shall there be any sale of securities in any states or jurisdictions in which such offer, solicitation, or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction.

Additional Information About the Proposed Business Combination and Where to Find It

In connection with the proposed transaction, MCAP filed with the U.S. Securities and Exchange Commission (the “SEC”) a registration statement on Form S-4, which includes a proxy statement/prospectus, and will file other documents regarding the proposed transaction with the SEC. MCAP’s stockholders and other interested persons are advised to read the definitive proxy statement and documents incorporated by reference therein filed in connection with the proposed business combination, as these materials will contain important information about AdTheorent, MCAP and the proposed business combination. MCAP is mailing the definitive proxy statement/prospectus and a proxy card to each stockholder entitled to vote at the meeting relating to the approval of the business combination and other proposals set forth in the proxy statement/prospectus. Before making any voting or investment decision, investors and stockholders of MCAP 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 will contain important information about the proposed transaction. The documents filed by MCAP with the SEC may be obtained free of charge at the SEC’s website at www.sec.gov, or by directing a request to MCAP Acquisition Corporation, 311 South Wacker Drive, Suite 6400, Chicago, Illinois 60606.

Participants in the Solicitation

MCAP, AdTheorent and certain of their respective directors and executive officers may be deemed participants in the solicitation of proxies from MCAP’s stockholders with respect to the business combination. A list of the names of those directors and executive officers and a description of their interests in MCAP will be included in the proxy statement/prospectus for the proposed business combination when available at www.sec.gov. Information about MCAP’s directors and executive officers and their ownership of MCAP common stock is set forth in MCAP’s prospectus, dated February 25, 2021, as modified or supplemented by any Form 3 or Form 4 filed with the SEC since the date of such filing. Other information regarding the interests of the participants in the proxy solicitation (including AdTheorent and its members and executive officers) will be included in the proxy statement/prospectus pertaining to the proposed business combination when it becomes available. These documents can be obtained free of charge as indicated above.