How Google’s AI Co-Scientist Hints at the Future of Marketing

DALLAS, TX (June 21, 2023)

Could AI co-marketers change how marketers plan and execute campaigns?

Last week, Google launched an AI system that can think like a scientist. The system uses a series of agents that work together to systematically form hypotheses, assess them for quality, and learn from the process to create even better hypotheses. Such an "AI co-scientist" could offer an interesting indication about where the world of marketing might be heading in the future....

In a number of areas, good life science marketing, like good science, relies on ideas and experimentation. We form theories about what will resonate with our audience, test these ideas carefully, and refine our approach based on results. Yet how many marketing teams can truly apply this scientific rigor to their campaigns at scale? And which companies have access to the necessary data and technologies to ensure their approach is informed, streamlined, and effective?

Google's new AI system brings a fresh perspective to this challenge, demonstrating how breaking complex problems into testable components and then systematically validating each element at scale using AI can lead to new insights.

In this blog post, we'll look at what this news could mean for marketers. 

To do this, we'll need to take a few leaps of faith (please stick with me, I promise it will be worth it!). First, I recognise that the usefulness of Google's new AI co-scientist for supporting research is still unclear, and I’m sure that people still working in the lab will spot numerous limitations and holes in the system that are beyond my initial assessment. 

Second, making the leap from scientific research to marketing may seem precarious, but I still feel the parallels are compelling enough to examine how the agents underpinning Google's AI co-scientist could be used to transform marketing and knowledge work in general. With that caveat covered, let’s dive in.

What is Google’s AI co-scientist?

At its core, Google's new AI co-scientist operates as an intelligent research partner. Initially, the researcher describes their research goals, preferences, and constraints using natural language via a chat interface. They can then continuously interact with the system using the same chat interface, adding ideas, reviewing proposals, and guiding the direction of research.

At the heart of the system lies a sophisticated multi-agent architecture. 

The Generation Agent explores the literature, web, and other data sources, gathering info to support simulated debates, while the Reflection Agent performs comprehensive reviews and verification of the insights and ideas generated. 

The Evolution Agent helps develop and simplify ideas, working alongside a Ranking Agent that evaluates and compares research hypotheses through “tournaments” to find the best ideas. These agents work in concert, creating a self-improving loop where research proposals are continuously refined and enhanced. 

Supporting these core functions are the Proximity Check Agent and Meta-review Agent, ensuring relevance and providing clear research overviews. The system culminates in delivering top-ranked research hypotheses and proposals back to the scientist, supported by comprehensive overviews and explanations. 

Throughout this process, the AI co-scientist leverages both a memory system for retaining crucial information and additional tools for search and analysis, creating a dynamic partnership between human scientist and artificial intelligence that enhances the research process.

What makes this approach particularly powerful is its iterative nature. Rather than settling for initial results, the system engages in what scientists call "self-play"—repeatedly testing and refining its hypotheses until they meet rigorous standards.

This process is already yielding early practical results in fields like drug discovery and antimicrobial resistance research. For example, after spending a decade investigating how superbugs become resistant to antibiotics, Professor José R Penadés and his team at Imperial College London decided to test the AI system with a simple prompt about their core research question. In just 48 hours, the AI not only reached the same conclusion about how superbugs form virus-like tails to spread between species, but it also proposed four additional hypotheses, one of which opened up an entirely new research direction his team hadn't considered. As Penadés told the BBC: “For one of them, we never thought about it, and we're now working on that."

For me, this begs the question: How could such an AI agent be adapted to help marketers create more effective marketing campaigns at scale?

From lab bench to marketing applications

Although it’s a bit of a stretch, it’s not inaccurate to say that marketing teams face similar challenges to scientific researchers in some areas. For example, we often need to generate fresh campaign ideas, evaluate them against existing knowledge, test their effectiveness, and continuously refine our approach to get better results. Given this, I would suggest that the success of Google's AI co-scientist could provide a glimpse into future AI marketing agents (something I refer to here as AI co-marketers).

Let’s break down how AI co-marketers could start to influence marketing processes. Using the AI co-scientists as a basis to work from, let's imagine how we might use such a tool to transform a marketing objective like "increase brand awareness and lead generation for product X when targeting audience Y" into a series of testable hypotheses. Just as scientists break down complex phenomena into measurable components, marketers can dissect their goals into specific, verifiable elements.

A marketing-focused, multi-agent system could mirror this approach through specialized roles. In this case, a Creative Generation Agent could first develop campaign concepts by synthesizing brand values, market trends, competitive activity, and the company’s proprietary internal historical campaign data. This agent wouldn't simply brainstorm randomly—it would generate ideas grounded in specific, testable assumptions about audience behavior to predict the messaging and creative that would be most likely to resonate and drive the most engagement. As generative AI-based text and image systems continue to improve, the Creative Generation Agent might even be able to produce the text and creative assets to underpin the campaigns (or at the very least, the initiative concepts for human creatives and copywriters to iterate and improve upon).

The parallel extends further through the evaluation process. Just as Google's system uses an Elo-based rating system to rank scientific hypotheses, a Concept Ranking Agent could evaluate marketing ideas based on multiple factors:

Perhaps most importantly, an Evolution Agent would continuously refine these concepts through iterative testing. Rather than launching a campaign and hoping for the best, this system would enable real-time optimization based on performance metrics and audience response.

What makes this framework particularly powerful is its ability to learn and adapt. Each campaign becomes not just a single effort but part of a continuous learning process, with insights feeding back into future strategy development. 

It's also feasible to imagine how such a system could learn how to develop, test, and launch campaigns in smarter and more efficient ways. For example, the system may learn that the best way to devise a new campaign is to develop 10 concepts, test them on the channel that offers the best blend of speed, cost, and audience reach (even if that is not a channel those marketers would usually turn to e.g.  something like TikTok), gather just enough data to select a winner, and then roll out the campaign more widely across another channels and tactics.

Fundamentally, this idea of AI supporting marketers is not novel. However, having a series of interconnected agents handling numerous aspects of the process independently could change the way we work by providing a number of benefits, which we’ll explore in more detail in the next section.

The likely benefits of AI agents in marketing

Let's examine the possible benefits this type of systematic, agent-driven approach could deliver for marketers.

1. Increased speed without sacrificing quality

Marketing teams often face an impossible choice to balance effort, creativity, resources, and timelines. In my experience, most marketers are under pressure to take the fastest and easiest route (even if it is not the most likely to succeed). A multi-agent system changes this equation, by taking on aspects of the work that would usually have been delivered by humans, reducing costs and speeding up the work. While this has pros and cons for human marketers, I envision this freeing us up to focus on planning and delivering a greater number of high-impact campaigns across multiple products, market segments, and target audiences, at a level that would have been far too time consuming and costly to even consider without access to the agent team. In other words, having access to these agents triggers a net increase in the amount of work for humans.

2. Data-driven decision making

Most marketers aspire to engage in data-driven marketing, but current approaches often feel like personal experience plays a bigger role than insights captured from more reliable, systematic data sources. A true multi-agent system transforms this dynamic by actively integrating multiple data streams into campaign planning, execution, measurement, and refinement. Market trends, consumer behavior patterns, and campaign performance metrics don't just inform decisions—they actively shape the evolution of marketing strategies in real time.

3. Truly novel ideas via cross-pollination

One of the most intriguing aspects of Google's scientific AI is its ability to draw unexpected connections across different fields of research. Applied to marketing, this same capability could help identify unexpected opportunities. For instance, an AI system might recognize patterns in user website behavior that mirror trends in completely different areas of the marketing program or even other areas of the business, such as logistics or customer support, leading to innovative campaign ideas and communication approaches.

4. Personalization at scale

Traditional segmentation looks crude compared to what's possible with this multi-agent approach. Instead of broad demographic categories, campaigns could adapt dynamically to individual consumer journeys while maintaining brand and campaign consistency. Imagine thousands of subtle variations of your campaign, each optimized for specific audience micro-segments, all running and learning simultaneously. This would be very powerful, but also very complex, so it would only be possible to run and maintain such a system by using AI agents to support. 

5. Mitigation of risks through campaign simulation

Perhaps the most practical benefit of using this type of agentic system would be the ability to fail safely. If the agents had access to enough accurate data on the target audience, marketers could run sophisticated simulations before launching campaigns. In this way, marketing teams can identify potential pitfalls and refine their approach without risking their brand’s reputation, marketing budget, or public relations budget.