AI & The Job Search: Anna McDougall's Revolutionary Interview Method

In this episode of Guidance Counselor 2.0, host Taylor Desseyn spoke with Anna McDougall, Engineering Manager at Hello Better, about her innovative approach to incorporating AI into the technical interview process. Anna's "McDougall Method" represents a paradigm shift from fighting against AI tools to embracing them as part of realistic job assessment, offering valuable insights for both hiring managers and job seekers navigating the AI-enhanced landscape.

The Current AI Adoption Landscape

Anna opens with a compelling analogy that frames our current moment in AI adoption: "I feel like right now where we're at with AI is where we were with JavaScript when React kind of came on the scene. There were some people resistant to it... and those people kind of fell behind and they had problems finding jobs."

This comparison highlights a critical truth about technology adoption in the development world. Just as frameworks like React, Vue, and Angular created a divide between early adopters and those who clung to jQuery or vanilla JavaScript, AI tools are creating a similar inflection point for developers today.

However, Anna emphasizes that we're still in what she calls the "learning phase" where catching up remains feasible. "People don't need to freak out right now. We're still in the stage where it's easy enough to catch up," she reassures. But she also acknowledges the reality that this window won't remain open indefinitely.

Understanding AI as Developer Tooling

One of Anna's most insightful observations concerns how AI fits into the broader context of developer tooling evolution. "I think that's one of the things that developers who have been in the industry for decades understand better than younger or less experienced developers. The tooling also changes. IDEs were not always a thing. Dev tools were not always a thing."

This perspective reframes AI adoption not as learning an entirely new programming paradigm, but as adapting to evolved tooling - something experienced developers have done repeatedly throughout their careers. "Getting comfortable with AI being part of your normal workflow is the most important thing," Anna emphasizes.

For developers feeling overwhelmed by the proliferation of AI tools, Anna recommends a focused approach: "Choose one and get good at using one rather than trying every tool available." She specifically mentions GitHub Copilot as a solid starting point, though she emphasizes that consistency matters more than the specific tool chosen.

The McDougall Method: A Revolutionary Interview Approach

Anna's most significant contribution to the conversation is her "McDougall Method" - an interview process that embraces rather than restricts AI tool usage. This approach represents a fundamental shift in how technical interviews can be conducted in the AI era.

The method stems from a recognition that traditional interview approaches have become obsolete in our current remote-first, AI-enabled environment. "We can't control the candidate's environment. They're not in a controlled environment. You don't know what screens they've got. They could have five laptops around them," Anna points out.

Rather than fighting this reality, the McDougall Method works with it. "We say to the candidates, we want you to use AI. We want to see how you do this. Get the help," Anna explains. This approach acknowledges that policing tool usage in remote interviews is both impossible and counterproductive.

Simulating Real Work Conditions

The core philosophy behind the McDougall Method is creating an interview experience that mirrors actual work conditions. "We give them a task as if it's their first week on the job," Anna describes. "What is your first week on the job? You're going to be using your normal tooling, right? You're going to be coding in the way you code."

This simulation approach includes several key elements:

Realistic Codebase: Candidates receive a code repository one hour before the interview, based on the company's actual codebase but anonymized and sanitized. This allows them to familiarize themselves with the structure and context.

Graduated Difficulty: The team creates five different difficulty levels for tasks, allowing them to start candidates at appropriate levels based on the role (junior, mid-level, or senior) and give them early wins to build confidence.

Normal Tool Access: Candidates are explicitly encouraged to use whatever tools they would normally use, including AI assistants, Google, documentation, and any other resources.

Collaborative Environment: The interview is structured as pair programming with two engineers (notably, not including the hiring manager to reduce stress), mimicking how they would actually receive help and guidance as a new team member.

Creating Psychological Safety

One of the most thoughtful aspects of Anna's approach is her emphasis on psychological safety during interviews. "If you go into an interview, your body doesn't know if you're in a technical interview or if you're being chased by a bear. It's reacting the same way," she observes.

To address this physiological reality, the McDougall Method incorporates several stress-reduction strategies:

Clear Expectations: Candidates are told upfront that the technical round isn't pass/fail but one factor among many in the overall assessment.

Trained Interviewers: Engineers conducting interviews receive training on being approachable and creating a welcoming environment.

Relationship Building: The first fifteen minutes focus on getting to know the candidate and helping them settle in before any coding begins.

No Hiring Manager Present: The person who makes the final hiring decision isn't in the technical interview, reducing the pressure candidates feel.

Flow State Protection: During the coding portion, interviewers avoid interrupting or asking questions that might break the candidate's concentration.

Assessing AI-Assisted Development

The most sophisticated aspect of the McDougall Method lies in how it evaluates candidates who use AI tools during the interview. Rather than viewing AI usage as cheating, Anna's team uses it as a window into the candidate's thinking process.

"If they're using AI, what prompts are they using? What are they accepting? What are they not accepting? What bits of the code are they taking? What are they not taking?" Anna explains. This approach recognizes that effective AI usage requires significant skill and judgment.

The real assessment happens in the post-coding conversation: "I saw the AI suggested you store things as objects and you went with an array instead. Why is that? Can you talk me through why you decided not to take that suggestion?"

This methodology evaluates several crucial competencies:

• Critical Thinking: Can the candidate evaluate AI suggestions and make informed decisions about what to accept or reject?

• Technical Understanding: Do they understand the implications and trade-offs of different implementation approaches?

• Communication Skills: Can they explain their reasoning in terms other engineers can understand?

• Problem-Solving Process: How do they approach unfamiliar challenges and integrate different sources of help?

The Reality Check on Current AI Impact

While enthusiastic about AI's potential, Anna provides a balanced perspective on its current impact. When asked about measurable productivity improvements, she acknowledges: "I would say that right now I have individual examples, but I wouldn't say my data set is particularly huge."

This honesty is refreshing in a landscape often filled with hyperbolic claims about AI transformation. Anna emphasizes being "quite wary of trying to measure this stuff too early" and recognizes that true productivity gains may follow an exponential curve - slow initial learning followed by significant acceleration.

She does see potential in specific areas: "I think there's a lot of potential for it to speed things up, especially in getting ideas. If you first encounter a problem, getting a handle on that, getting some ideas and using it for ideation or creating quick prototypes."

The Enterprise AI Future

Looking ahead, Anna identifies enterprise-level AI integration as the next major development: "I do think that the connection of agents and MCPs [Model Context Protocol] will be the thing that really brings it to the next level."

She envisions a future where AI agents can effectively traverse and understand large codebases, providing developers with multiple implementation options and enabling more sophisticated assistance. "What I would expect developers to be able to do in that scenario is to assess different options that can be provided by the AI and to know and understand the trade-offs."

This vision emphasizes AI as a tool for expanding possibilities rather than providing single solutions, requiring developers to develop stronger evaluation and decision-making skills.

Addressing the Knowledge Gap

One of Anna's most important observations concerns the gap between AI awareness in tech communities and the broader developer population. "We live in this LinkedIn bubble where we're hearing about new stuff happening all the time... But the reality is there are so many developers who are not even on LinkedIn... A lot of them are not subscribed to newsletters or listening to podcasts."

This insight highlights a critical challenge for engineering managers: ensuring that team members who aren't naturally inclined toward staying current with trends don't get left behind. Anna's team addresses this through proactive knowledge sharing: "People actually showing how did I use an AI agent to solve this problem, people doing demos on how they've used it."

The approach recognizes that cultural change requires intentional effort and that the most technically proficient team members aren't always the most effective teachers or evangelists.

Practical Implications for Job Seekers

For developers preparing for AI-era interviews, Anna's insights offer several practical guidelines:

Start with One Tool: Rather than trying to master multiple AI assistants, choose one (like GitHub Copilot) and develop fluency with it as part of your normal workflow.

Practice Critical Evaluation: Don't just use AI suggestions blindly. Develop the habit of evaluating what the AI proposes and understanding why you accept or reject different suggestions.

Prepare to Explain Your Process: Be ready to discuss not just what you implemented, but why you made specific choices and how you evaluated different options.

Embrace the Learning Curve: Recognize that initial productivity may actually decrease as you learn new tools, but long-term benefits justify the investment.

Stay Current Gradually: You don't need to be on the bleeding edge, but maintaining awareness of major developments in AI tooling is becoming increasingly important.

Implications for Hiring Managers

Anna's approach also offers valuable lessons for other engineering leaders designing interview processes:

Acknowledge Current Reality: Fighting against AI usage in interviews is futile and counterproductive. Design processes that work with current technological realities rather than against them.

Focus on Understanding Over Implementation: The ability to write code from memory matters less than the ability to understand, evaluate, and communicate about code regardless of how it was created.

Invest in Psychological Safety: Technical interviews are inherently stressful. Small investments in creating welcoming environments can dramatically improve the quality of interactions and assessments.

Train Your Interviewers: Conducting effective technical interviews is a skill that benefits from explicit training and practice.

Simulate Real Work: The closer your interview process resembles actual work conditions, the more predictive it will be of job performance.

The Broader Cultural Shift

Perhaps most importantly, Anna's approach represents a broader cultural shift in how we think about human-AI collaboration in professional settings. Rather than viewing AI as a threat to human capability or a crutch that weakens skills, she frames it as one tool among many that capable professionals learn to use effectively.

"I'm trusting them to use whatever tools they use to deliver good stuff," she notes about managing her current team. "I'm not sitting there watching them. I'm not policing the tools that they use."

This trust-based approach, extended to the interview process, creates opportunities to assess the qualities that matter most: judgment, communication, problem-solving, and the ability to work collaboratively with both human and artificial intelligence.

Looking Forward

As AI capabilities continue advancing, Anna's McDougall Method provides a framework for evolving technical assessment practices. The focus on understanding, communication, and critical evaluation of AI suggestions creates a foundation that should remain relevant regardless of how specific AI tools develop.

The method also acknowledges something that traditional interview approaches often miss: modern software development is inherently collaborative, both with other humans and increasingly with AI systems. Assessing someone's ability to work effectively within this ecosystem provides much more relevant information than testing their ability to solve problems in artificial isolation.

Conclusion

Anna McDougall's insights offer a pragmatic and forward-thinking approach to navigating AI's impact on software development careers. Her McDougall Method represents more than just an interview technique; it's a philosophy for embracing technological change while maintaining focus on the fundamentally human skills that drive effective software development.

For job seekers, the message is clear: AI fluency is becoming increasingly important, but it's just one skill among many. The developers who thrive will be those who can effectively combine AI capabilities with strong communication, critical thinking, and collaborative problem-solving skills.

For hiring managers, Anna's approach demonstrates that adapting to technological change doesn't require lowering standards or accepting diminished capability. Instead, it requires evolving assessment methods to evaluate the skills that matter most in current and future work environments.

Most importantly, Anna's work illustrates that successful AI adoption in software development isn't about replacing human judgment with artificial intelligence, but about augmenting human capabilities and focusing on the uniquely human aspects of software development: creativity, communication, ethical reasoning, and the ability to understand and solve complex business problems.

As we continue navigating this transition, approaches like the McDougall Method provide valuable models for embracing change while preserving the elements of software development that make it both challenging and rewarding as a career.

This blog post summarizes insights from Guidance Counselor 2.0, a live streaming show hosted by Taylor Desseyn that explores career development in the tech industry. Find the full video of the episode and more here: AI & The Job Search w/Engineering Leader Anna McDougall