Author: Kristin Gleitsman Page 5 of 7

On June 21, 2025

In AI ∩ Bio

Tl;dr Statistical generalization isn’t scientific understanding—don’t confuse prediction with insight. Foundation models like GET may predict gene expression patterns from clean data and learn patterns that appear biologically meaningful, but that’s not the same as understanding transcriptional regulation. Its outputs should be treated as hypotheses to interrogate—not definitive answers.

This week’s AI ∩ Bio: Reading the Revolution series features a recent Nature paper on “General Expression Transformer” (GET), a deep learning model trained to predict gene expression across 213 human cell types using DNA sequence and chromatin accessibility data. It’s ambitious: a transformer architecture that claims to learn the “grammar” of transcription, generalize across diverse cell types, and model long-range interactions between enhancers and promoters, as well as between transcription factors (TFs). In one case study, the authors link a leukemia-associated genetic variant (a SNP) to a disrupted protein–protein interaction between TFs, using AlphaFold structural modeling.

The aim is compelling—bringing together machine learning, epigenomics, and protein structure prediction. GET outperforms previous models like Enformer on several tasks, including prediction of reporter assay results (like MPRA) and identifying enhancer–promoter relationships. It also offers interpretability features, such as motif-level and region-level importance scores, which are an improvement over traditional “black box” models. And yes, it suggests potential utility: in theory, GET could aid in prioritizing noncoding variants or mapping regulatory networks.

But here’s the catch: GET requires both DNA sequence and chromatin accessibility data from the specific cell type of interest. You can’t simply input a variant file (like a VCF from whole-genome sequencing) and get back useful predictions. At best, you can explore hypotheses using accessibility data from public reference tissues—helpful for interpreting genome-wide association study (GWAS) results, but not yet practical for clinical use.

The generalization here is statistical—not biological, and certainly not clinical. GET was trained on harmonized, high-quality single-cell datasets from healthy human tissues. It performs well on similar data it hasn’t seen before—but that’s a narrow slice of human biology. Clinical samples, especially those from inflamed, cancerous, or drug-altered environments, often have chromatin landscapes and transcriptional programs that fall outside the model’s training distribution. High correlation on held-out healthy data doesn’t guarantee reliability under pathological conditions. The model is rigorous in its computation—but its clinical readiness remains speculative.

AI ∩ Bio

To early-career scientists: Always know the limits of your tools. Foundation models can learn statistical patterns that look biological, without understanding the underlying mechanisms. A model trained on chromatin accessibility may capture useful correlations, but not the dynamic, causal logic of transcription. And interpretability tools like motif importance scores are only meaningful if they lead to testable, falsifiable predictions that hold up under rigorous testing. Treat every output as a hypothesis to challenge.

Illustration of a desk with a figure from a paper on ChatNT

ChatNT: The future of biological assistants—or a mirage in a lab coat?

On June 17, 2025

In AI ∩ Bio

The team behind ChatNT introduces a conversational AI agent trained to perform 27 genomics, transcriptomics, and proteomics tasks—by prompting it in plain English. Built on a DNA encoder (Nucleotide Transformer v2) and a frozen English decoder (Vicuna-7B), ChatNT achieves state-of-the-art or near-parity performance with many specialized models, solving tasks like splice site detection, RNA degradation prediction, and protein melting point estimation—all through natural language queries.

But before we celebrate too loudly…

What does it mean when we start predicting complex molecular properties by chatting with a model—and trusting the answer without understanding the underlying biology? ChatNT lowers the barrier to entry, making powerful models accessible to those without deep bioinformatics expertise. That’s a design strength—but also a risk. Scientific depth, if not deliberately preserved, can quietly erode. We could end up with users who can write reasonable prompts but lack the scientific grounding to recognize when the answers are wrong or incomplete, and don’t have the foundational knowledge needed for scientific creativity.

To their credit, the authors do include a post hoc, perplexity-based calibration method to understand the model’s confidence in its answer (in other words, they check how confidently the model would have chosen its answer by measuring how surprised it is by different options after the fact). But there’s no real-time uncertainty alert, no embedded safeguard for when the model is operating outside its training distribution—just statistical proxies layered onto a system that still speaks with unwarranted certainty. In regulated or high-stakes domains like diagnostics, that’s absolutely not enough. Hallucinations don’t come with warning labels. And a well-attributed motif—say, a TATA box or splice site—is no guarantee of biological correctness.

From a diagnostics strategy perspective, ChatNT is a credible preview of what’s coming: a unified interface for interpreting multi-omics data and compressing complex workflows into a single prompt. But we are not there yet. Trust, fidelity, and epistemic transparency remain unsolved. For now, these models should be treated as useful but fallible junior collaborators—not autonomous copilot researchers in their own right.

To early-career scientists: this is your edge. Tools like ChatNT are remarkable—but only in the hands of those who still understand the biology. The future still belongs to those who can spot an implausible claim, who know how to interrogate things from first principles, and who can still deploy their own knowledge to connect disparate dots and generate novel scientific hypotheses. Your role isn’t to step aside. It’s to double down on understanding, so that you can interrogate, shape, and lead the evolution of these tools.

AI ∩ Bio

How I’d want my team to use this tool: Use ChatNT to validate hypotheses you’ve already reasoned through—not to generate them in isolation. Let it help challenge assumptions, spot inconsistencies, or simulate mechanistic alternatives based on sequence features. Think of it as a fast, articulate assistant: useful for in-silico hypothesis exploration, not for making experimental decisions without expert oversight. And never input PHI or proprietary data into public-facing AI tools. Period.

Illustration of a human foreman in a construction helpmet co-editing a blog article from this website with a stylized, green robot

Scaling Thought, Not Just Content: Field Notes from the Messy Middle of Co-Writing with ChatGPT

On April 13, 2025

In Uncategorized

When I started the revised 2025 version of the Scaling Well series, I didn’t want to simply write ABOUT AI and its impact to scaling companies in 2025. I wanted to experience actively collaborating with it on a project that is deeply meaningful to me.

This piece is my reflection of that experience. While co-writing the previous two articles, I made notes about the process and want to share a behind-the-scenes look at how I think this experiment went. This isn’t meant to be a hype story, nor is it a cautionary tale. It’s field notes from the messy middle of using a new set of tools in hopes of discovering its strengths and weaknesses.

If you’re a curious leader, a stretched-thin builder, or someone who keeps hearing “AI can help” and thinking, “Help with what, exactly?”—you’re in the right place.

In the sections that follow, I’ll walk you through the prompts I used, the questions I asked, the traps I hit, and the patterns that emerged.

The intention is not to provide a plug-and-play workflow* though. In sharing my experiences with structure, friction, and the occasional detour into AI-generated nonsense, I hope to help others scale insight, not just output.

What This Is Really About

Despite all the buzz about AI multiplying people’s output, that wasn’t my primary goal as a writer. Or maybe that was the dream initially, but what became abundantly clear through the process is that one key benefit of working with AI was that it forced me to make my thinking clearer in order to co-produce something useful, without losing my voice.

On its own, ChatGPT didn’t do the work for me. The work became meaningful when I treated ChatGPT like a fast-learning collaborator— prompting it to ask me questions for reflection, or challenging it to critique my logic or clarity.

That’s not to say that there wasn’t a boost to my speed— working with AI definitely helped get me unstuck from staring at a blank page faster than ever before. But the bigger payoffs were the added clarity, repeatability, and a surprising amount of fun.

These payoffs really became apparent once I stopped expecting AI to generate ready-made content and started using it to help me distill what I actually wanted to say.

The breakthrough wasn’t what AI wrote—it was getting past the tyranny of the blank page and getting clear about what I wanted to say.

The Process (Messy, Real, Repeatable)

Approaching this collaboration with ChatGPT as an exercise in “getting AI to write an article for me” did not work.

Here’s what did.

1. Align with yourself and your AI collaborator first

One thing that surprised me in learning to use ChatGPT more effectively is how much of a Rorshark test it can be. If you don’t know what you want to say, you’re very likely to end up with a lot of ‘well written’ fluff. In the ‘olden days’ of writing without AI, clarity creation was part of the outlining and drafting process. Having ChatGPT as a super fast writing partner can sometimes shortcut the crucial step of figuring out what point you’re even trying to make. So, what seems to work better is to start with getting to clarity… with your genAI collaborator.

“Pasted below are my initial thoughts on scaling companies, please ask me 3 multiple choice questions one at a time to make sure we’re aligned.”

That clarity up front mattered more than I expected.

Further along in the writing process, ChatGPT can continue to add clarity:

“Please first ingest the below pasted text, then ask me 5 multiple choice questions one at a time to help us align on how to get to 10-fold improvement across the above metrics (and please suggest one additional metric to improve).”

The responses could be quite revealing. When answering these questions, I realized that half the time I didn’t know what I had really intended in my initial text until had to formulate these answers. It was like mirror to my own ideas, showing me angles I’d otherwise glossed over.

2. Don’t neglect structure and cohesion

“Find 3 places where transitions could be tighter or a seed could be planted for a later section. Give me 3 options for each.”

The results didn’t always land, but they exposed choices I hadn’t seen yet—and that very valuable.

Add something about chunking work – to keep the ai focused and to keep you focused too.

3. Calibrate tone on the fly

Without additional work, AI tends to gravitate towards the middle in its output. If you don’t mind generic mediocre writing, maybe that’s okay. But I wanted writing that sounded like me. Luckily, tone can be moderated fairly easily. In doing so, it provides another opportunity to review what the goals of your writing are, and if the text is achieving those goals.

“Let’s align the tone with the overall tone we are going for in the article.” (Spoiler alert: you’re going to need to define what that tone is)

“Section 1 feels a little fluffy and too long. Ask me 3 multiple choice questions to help us align on what we’re trying to say in section 1.”

Here it can also help if you’ve done your own writing in the past— you can input examples of past writing and ask ChatGPT to create a one-page style guide defining your writing style, and then use that to continually refocus the output. You can also have verbal interviews with ChatGPT and then ask it to ‘write like I talk,’ which is sometimes helpful in breaking free from the default indiscriminate bot language.

While shifting tone might seem like a photo filter at first, these adjustments to tone proved to be more than just editing tricks. They were valuable thinking tools as well.

4. Check your sources

At this point, you’ve probably heard that AI is prone to hallucinate details in order to satisfy your request. As it turns out, that’s not something you can simply ask it to improve upon, although there are ways to tune the model for accuracy versus creativity, and some LLMs are better at one thing than the other. At the end of the day, it’s your name going on the article, so you should probably just buckle up and do the homework of making sure the references are correct and on point.

“Flag anything not backed up by a real source. Let’s verify links and decide together how to fix anything questionable.”
Spoiler: most links were still broken. I fixed them (okay, actually my husband did that part).

5. Save what’s working

In working through this process for multiple pieces, one of the products I aimed to create was the repeatable process itself. This was something the AI proved helpful with, consolidating longer conversation threads into something closer to a reusable recipe.

“Review this whole conversation. Create a repeatable process others could follow.”

That became the scaffold I’m still using now.

And when a section still felt fuzzy? I read it aloud to my husband. The AI helped sharpen the thinking and do the initial drafting; he helped catch what still didn’t land.

What Surprised Me (and Might Surprise You)

My biggest surprise: I didn’t expect to enjoy the process quite so much. I thought I’d be sifting through AI garbage and editing it into something usable.

Instead, I found myself getting sharper—because I had to. The tool was fast, literal, and indifferent to context; if I wasn’t clear, it absolutely wasn’t. And that kind of brainstorming was really fun, not too different from a good brainstorm with a fellow human.

That was the first surprise.

The second was how much more I wanted to engage with the work once I had structure for working with ChatGPT in place. I wasn’t stuck at the blank page. I had a system of prompts and checkpoints that gave me momentum. Not faster, exactly—but steadier. And significantly less painful to start.

The last surprise: it really felt like collaboration.

When I gave it real direction—alignment prompts, tone corrections, specific feedback—it became a decent thought partner. Not insightful on its own, but responsive in a way that made my thinking more visible. It didn’t generate depth; it paved the way for me to get there faster.

Final Thoughts

From my experience co-writing with ChatGPT, generative AI isn’t necessarily a shortcut. You certainly can’t skip the thinking part of writing. What it provides instead is scaffolding—something to push against, iterate with, and structure the parts of thinking that usually get stuck in your head.

While it offered language, I honed in on shaping meaning. And the act of shaping—deciding what to keep, what to cut, what to say—was where the clarity came in.

The output was better (*more on that from my husband). The thinking was clearer. And the work of writing felt more engaging and joyful than it has in a while.

Not because the tool was smart—because the process was.

Appendix: High-Impact Prompt Set for Thoughtful Co-Writing with LLMs

Clarify the Thinking First

Use these to align on purpose, sharpen ideas, and build momentum.

“Ingest the following text. Ask 3 multiple choice questions—one at a time—to clarify intent, audience, and core argument.”
Purpose: Forces you to name your aim before drafting begins.
“Based on the ideas below, ask 5 multiple choice questions—one at a time—to explore how we might 10x the impact. Then suggest one metric I may be overlooking.”
Purpose: Pushes past surface iteration into leverage-based thinking.
“Review this full exchange. Summarize the workflow we followed. Write it as a 5-step process someone else could reuse.”
Purpose: Codifies emergent process into shareable frameworks.

Lock in Voice and Tone

Use these to calibrate writing style across tools or collaborators.

“Ingest this sample of my writing. Create a 1-page voice guide: tone, structure, sentence length, common patterns, and language quirks.”
Purpose: Teaches the model your style explicitly—reusable across tools.
“Match the tone of this section to my writing style guide. Highlight any areas where the tone drifts or feels generic.”
Purpose: Enforces consistency without losing precision.
“Here’s how I explain this verbally. Mirror the rhythm and phrasing—make it sound like a well-edited transcript of me.”
Purpose: Removes formality and aligns with natural delivery.

Deepen Structure, Reduce Bloat

Use these to improve flow, argument strength, and readability.

“Identify 3 transition points where flow weakens or ideas shift too abruptly. Suggest 2 alternate transitions or reframing options for each.”
Purpose: Strengthens internal scaffolding, especially across sections.
“Analyze this section. What’s the underlying assumption? Is it clearly supported? Suggest one way to reinforce or challenge it.”
Purpose: Prevents unexamined logic from slipping through.
“Review this section. What’s actually doing the work? What can be cut without losing meaning?”
Purpose: Helps trim filler while preserving depth.

Expand Insight, Avoid Shallow Thinking

Use these when your piece feels too safe, too obvious, or not sharp enough.

“Take the role of a skeptical executive. What would they question here? Suggest 2 ways to preempt or clarify the concern.”
Purpose: Builds in resistance testing before real-world exposure.
“List 3 ways this idea could backfire in real use. What failure modes or misinterpretations should we proactively address?”
Purpose: Adds robustness and resilience to big ideas.
“What part of this actually feels new or non-obvious? Highlight it. What parts sound like filler or expected takes?”
Purpose: Prioritizes originality and edge—especially in thought leadership.

Fact-Check and Refine Responsibly

Use these when citations, claims, or integrity are at stake.

“Review the draft below. Flag any factual claims or data points that lack a source. Suggest how to verify or reframe.”
Purpose: Reduces hallucination traps, keeps credibility intact.
“List every link, citation, or claim in this piece. Verify each one. Highlight any that are outdated or broken.”
Purpose: Turns verification into an explicit task—not an afterthought. You should still verify the results

Meta-Prompt for Debugging & Reflection

“Reflect this draft back to me in plain English. What’s clear, what’s muddled, and what’s trying too hard?”
Purpose: Works like a mirror. Best used before a big share-out.

Image of a construction manager looking at an oversized tablet computer to select from one of three robots standing in front of them. In the distance, a city is under construction

Scaling Well in 2025: AI Tools That Actually Help

On April 1, 2025

In Scaling Well

Scaling Smarter Starts with Designing for Resilience

The most successful use of AI in 2025 won’t necessarily come from technological breakthroughs. It will come from thoughtful rethinking how their organizations operate, reshaping how time, knowledge, and decisions flow across our organizations.

AI tools should support and augment the pillars of scaling that I’ve written about previously. They should free up capacity for deep work, enable culture that scales, and support infrastructure that can carry the weight of change.

This means the companies scaling well in 2025 aren’t asking, “How do we use AI?” They’re asking, “How do we use AI to make our company better, faster, and more resilient?”

In this piece, we explore how AI can support each of those pillars. Each section includes real-world tools, examples, and decision cues to help leaders think more clearly about what to pilot and how to scale well.

Let’s dive in.

Designing for Slack & Innovation

“Slack isn’t waste. It’s where creativity, resilience, and serendipity live.”

In the original article, we framed slack not as inefficiency, but as infrastructure for innovation—a concept grounded in queuing theory and visible in everything from engineering velocity to decision-making latency. In 2025, AI makes it possible to unlock “smart slack”: creating time, reducing bottlenecks, and diffusing knowledge—all without adding headcount.

Below are three AI tools that enable this.

Tool 1: Glean – Contextual Search to Reduce Bottlenecks

Problem: Teams lose time recreating knowledge that already exists—or waiting on the one person who has the answer.

Tool: Glean indexes all your internal systems (Docs, Slack, Jira, Drive, Confluence, etc.) and uses AI to deliver relevant, secure search results contextualized to the user.

Impact: Reduces time spent answering repeat questions, reduces key-person dependency, and reclaims hours per week for deep work.

Real Example: Confluent Case Study – Glean

Piloting Tip: Start with one department and track internal support volume, time-to-answer, and repeated questions. Pair with light documentation practices to amplify results.

How to Know if This Belongs on Your Roadmap

You’re hearing complaints about repetitive internal questions or knowledge buried in Slack
Onboarding timelines are dragging because institutional knowledge isn’t searchable
A few individuals are serving as knowledge routers across functions

Glean is worth piloting if you want to reduce dependency on key people and unlock capacity without increasing headcount. Ask your department leads how much time is spent answering questions that should already be documented.

Tool 2: Notion AI – Workflow Co-Pilot to Free Cognitive Slack

Problem: Knowledge workers are overloaded with routine documentation, synthesis, and follow-up work.

Tool: Notion AI automates meeting summaries, drafts project plans, and generates next steps across team workspaces.

Impact: Frees up “cognitive slack” for strategic thinking, synthesis, and creative problem-solving.

Real Example: Figma Case Study – Notion

Piloting Tip: Begin with retros, meeting notes, or OKR drafts. Then explore more structured workflows like customer issue triage or roadmap generation.

How to Know if This Belongs on Your Roadmap

Your OKR cycles feel slow or painful to prep
Meeting notes, follow-ups, and documentation tasks are draining your highest-leverage people
Strategic initiatives stall because everyone’s too busy with tactical maintenance

Notion AI is worth funding if your teams need more “thinking time” but can’t find it. Look for small pilots where cognitive load is high but output is low-value admin work.

Tool 3: Stack Overflow for Teams – Capture and Share Tribal Knowledge

Problem: Tribal knowledge lives in people’s heads or buried in Slack threads—causing bottlenecks and slow onboarding.

Tool: Stack Overflow for Teams creates a company-specific Q&A hub. AI enhances search, recommends related questions, and flags potential duplicates to reduce noise and improve speed-to-answer.

Impact: Accelerates onboarding, reduces repetitive internal questions, and builds resilience when key people leave.

Real Example: Box Case Study – Stack Overflow for Teams

Piloting Tip: Seed it with your most-asked questions. Create a recognition system for contributors. Link key answers directly from onboarding flows.

How to Know if This Belongs on Your Roadmap

You’re hearing “I didn’t know that existed” across teams
Ramping new hires feels fragile and overly dependent on 1:1 support
The same technical questions are being asked—and answered—on repeat

This tool is worth considering if your resilience is too dependent on institutional memory. Ask your leads how much time their teams spend re-answering solved problems.

Operationalizing Slack

Strategic slack isn’t found. It’s designed. The above tools create visibility into where time, attention, and knowledge are leaking—but the next step is turning that visibility into action.

Map your bottlenecks: Where does work pile up or depend on a few people? Use these tools to surface hotspots.
Reclaim 10–20% capacity: Track time saved, then explicitly reinvest it into innovation sprints, mentorship, or deep work time.
Start with one team: Pilot in a high-friction area with motivated adopters. Share wins before scaling org-wide.
Don’t over-automate: Tools should augment, not replace, human judgment. Start simple, layer complexity later.

Reference: For deeper guidance on build vs. buy vs. fine-tune, see Appendix A.

Once you’ve carved out slack, the next challenge is ensuring your teams use it well—without needing top-down oversight every time. That starts with culture.

Scaling Culture & Leadership

“Culture is how decisions get made when no one is watching.”

If slack gives teams processing power, then culture is the operating system that determines how that power gets used. It determines how teams behave under pressure, how decisions get made across time zones, and how mission translates into action when leaders aren’t in the room.

In 2025, generative AI unlocks new ways to scale cultural alignment, leadership development, and human connection—without requiring armies of coaches or L&D staff. Below are three tools that make culture more scalable, distributed, and actionable.

Tool 1: Humu – Culture-Driven Behavioral Nudges at Scale

Problem: Culture often degrades in the middle. Execs may model values, but middle managers struggle to reinforce them day-to-day.

Tool: Humu delivers science-backed, personalized nudges to encourage behaviors aligned with company goals.

Impact: Reinforces key behaviors across roles, time zones, and contexts—without requiring more oversight.

Real Example: Humu Case Studies

Piloting Tip: Start with one leadership behavior to reinforce—like recognition, inclusive meetings, or timely feedback. Layer nudges on top of any existing training or communications push.

How to Know if This Belongs on Your Roadmap

You’re struggling to turn stated values into visible day-to-day behaviors
Cultural consistency breaks down between levels or locations
Your engagement surveys surface issues like low feedback or recognition

Humu can bridge the gap between intention and action. Prioritize it if you’re investing in manager enablement or aiming to reinforce cultural behaviors without scaling your HR team.

Tool 2: Pinnacle AI – Custom Coaching & Leadership Simulations

Problem: Leadership coaching rarely scales beyond the executive level.

Tool: Pinnacle AI enables companies to create custom GPT-based avatars that simulate high-stakes conversations, coach leaders in real time, and reinforce values.

Impact: Expands coaching access, builds soft skills through practice, and embeds culture into day-to-day behavior.

Real Example: Pinnacle AI Overview

Piloting Tip: Start with a common friction point (e.g., giving tough feedback). Use a leadership avatar to simulate the conversation, then track usage and impact.

How to Know if This Belongs on Your Roadmap

You need to scale coaching or practice-based leadership development without adding headcount
Managers are hesitant or underprepared for hard conversations
Soft skill gaps are impacting team dynamics or delivery

Pinnacle is worth trying if you’re investing in middle manager capability-building or want to embed feedback culture through safe, scalable practice.

Tool 3: Together Platform – Peer Mentorship with AI-Enhanced Matching

Problem: Cultural transmission often relies on informal relationships that don’t scale.

Tool: Together uses AI to match employees into mentorship relationships based on goals, skills, and values.

Impact: Reinforces culture through peer connection. Supports belonging and cross-functional leadership.

Real Example: Together Case Studies

Piloting Tip: Start with onboarding cohorts or ERGs. Use AI-generated prompts to anchor discussions in real cultural moments and shared learning goals.

How to Know if This Belongs on Your Roadmap

You’re looking to scale belonging and connection in a hybrid or distributed org
Current mentorship programs are ad hoc or unsustainable
Peer learning feels like a missed opportunity across levels or silos

Together is a strong fit if you’re building internal talent pipelines or trying to support new hires and ERGs without overloading HR.

Cultural Playbook: Scaling Values, Not Just Policies

Pick your pressure points: Identify where cultural gaps show up—new manager transitions, post-reorg teams, or moments of change. Deploy nudges (Humu), simulations (Pinnacle), or mentorship (Together) at those friction points.
Time it right: Behavior change sticks when it’s timely. Align tools with onboarding, performance reviews, or team formation.
Reinforce with data: These tools generate signals. Use them to monitor alignment, not just activity.
Connect the dots: Use Together to reinforce Pinnacle simulations. Follow Humu nudges with live discussion prompts.

Of course, with every new AI tool comes another integration, another dashboard, another cognitive switch. It’s worth asking: do we really need three tools, or can one flexible platform get us 80% of the way there? This isn’t just a user experience issue—it’s an infrastructure one. And that’s where we go next.

The Case for Fewer, Flexible Tools

Leaders face a strategic question: should you prioritize tools that do one thing exceptionally well, or ones that solve multiple problems “well enough”?

Some considerations:

If your org is resource-constrained, favor tools that integrate natively with your existing stack (like Pinnacle AI into Slack) and solve for multiple moments (onboarding, feedback, DEI).
If your org is scaling fast with complex silos, best-in-class tools may still make sense—as long as IT can support them.

Several tools featured here offer multi-purpose value: Pinnacle AI can support onboarding, leadership development, and cultural alignment. Together can double as an onboarding accelerator and a peer learning platform. We’ll explore how to evaluate tools across breadth vs. depth in Appendix B.

The Hardest Part Isn’t the Tool. It’s the Terrain.

Cross-functional adoption is where AI efforts live or die.

Even the best tool won’t gain traction if:

IT says they’re already at capacity
Legal worries about risk
Enablement teams are juggling five other rollouts
Or end-users don’t trust the tool (or the motive)

If this sounds familiar, try this instead:

Start where there’s pull. Pilot with teams who are already stretched and asking for help.
Solve their problem, not yours. Frame the tool around a pain they already name.
Invite, don’t mandate. Use early adopters to tell the story, not top-down pressure.
Build in feedback. Create fast feedback loops between teams and tool sponsors.

Adoption isn’t just change management—it’s coalition-building. And how you roll out the first tool will shape whether people lean in or tune out when the next one comes.

Infrastructure & Enablement: Lightening the Load, Quietly Powering Scale

“You can’t scale what you can’t support.”

Slack and culture shape what’s visible—but infrastructure is the foundation. It decides how much complexity your organization can carry, and how well it holds up under change. And yet, infrastructure teams—especially IT, platform, and internal tooling—are often the most overburdened and underappreciated.

Generative AI won’t solve infrastructure debt overnight. But it can reduce manual load, increase visibility, and turn reactive work into proactive systems management. Below are a few tools helping infrastructure teams do more with less—and sometimes even breathe.

Tool 1: Moveworks – Conversational AI for IT Support

Problem: IT teams are flooded with repetitive tickets, onboarding requests, and access issues.

Tool: Moveworks resolves common IT support requests instantly through Slack, Teams, or email.

Impact: Reduces ticket volume, accelerates onboarding, and frees up IT capacity for more strategic work.

Real Example: Moveworks Case Studies

Piloting Tip: Start with high-volume, low-complexity tickets. Track resolution speed, team bandwidth, and internal satisfaction.

How to Know if This Belongs on Your Roadmap

Your IT support teams are drowning in high-volume, low-complexity tickets
Employees complain about slow or inconsistent onboarding and access
You want to improve internal experience without increasing IT headcount

Moveworks is worth piloting if you want to unlock IT capacity for infrastructure and security work. Ask: where could automation improve internal credibility?

Tool 2: Cortex – Internal Service Catalogs & Engineering Visibility

Problem: Engineering teams lose time navigating unclear ownership and shadow systems.

Tool: Cortex offers internal scorecards, visibility tools, and service catalogs that clarify what’s running—and who’s responsible.

Impact: Supports platform health, reduces hero culture, and increases resilience.

Real Example: Cortex Customers

Piloting Tip: Map key services with unclear ownership. Use Cortex to build scorecards and reinforce accountability.

How to Know if This Belongs on Your Roadmap

You’re seeing repeated outages or friction due to unclear service ownership
Engineering productivity depends on a few heroic individuals
Your team lacks visibility into internal reliability or SLA gaps

Cortex is a fit if you’re trying to mature your platform engineering or prepare for scale. Ask: who owns what—and do they know it?

Infrastructure Insight: Design for Longevity, Not Just Load

“If you’re thinking about infrastructure only when it fails, you’re already behind.”

Engage infrastructure early when evaluating AI tools—especially those touching data or permissions.
Give infrastructure leaders a seat at the table in AI strategy and tooling decisions.
Track infrastructure health visibly—and treat internal platforms as core products.
Recognize and reward the teams that make scalable systems possible.

Conclusion: Scaling Well Means Leading Differently

The leaders pulling ahead aren’t just adopting tools—they’re rethinking how their organizations operate. They’re treating slack as strategy, culture as infrastructure, and infrastructure as leverage.

This transformation isn’t really about the tech—it’s a business transformation that demands real leadership and coordination.As KPMG’s Vice Chair of Artificial Intelligence (AI) and Digital Innovation, Steve Chase, put it, we’ve entered a phase where companies need someone to lead the movement—someone who can cross silos, hold tension, and help the organization build real muscle memory around transformation.

Leading the movement to implement AI successfully means starting with intention—with intelligent pilots, cross-functional alignment, and a clear view of what you’re solving for.

The tools are here.

The stakes are real.

And how you lead this transition will define how well your company scales… or stalls.

Appendix A: Choosing the Right Tool – A Strategic Framework

Before choosing a tool—or even beginning a pilot—executives should step back and ask: What kind of leverage are we trying to create, and what constraints must it navigate?

This framework provides a higher-level lens to help you prioritize the right kind of AI investment:

1. Clarify the Friction

What are you actually trying to solve?

Time bottlenecks: Teams are stuck doing repetitive work or can’t move fast enough
Cultural inconsistency: Values and behaviors vary wildly across functions or levels
Infrastructure drag: Tooling, access, or service ownership are slowing everything down

2. Map Organizational Readiness

How prepared is your org to absorb a new AI tool?

Technical readiness: Do you have IT capacity and integration capability?
Behavioral readiness: Will people use it—or resist it?
Strategic alignment: Does this tool support a broader goal you already care about?

3. Decide the Mode of Leverage

What kind of support are you trying to amplify?

Automate: Remove low-leverage tasks (e.g. ticket triage, note-taking)
Augment: Enhance high-leverage talent (e.g. coaching, mentoring, cross-functional insight)
Diffuse: Spread knowledge and behavior (e.g. onboarding, Q&A, nudges)

4. Start Where You Have Urgency + Ownership

Don’t start where the problem is loudest—start where a function wants the help.

Look for “pull,” not “push” opportunities
Pilot in functions already experimenting or stretched thin but open-minded

This framework helps you move from “interesting tool” to “clear decision.”

Example: Applying the Framework by Company Size

Here’s how this framework might guide tool selection across company stages, using high-leverage, multi-purpose tools that can solve for multiple frictions:

Small Company (≤50 employees)

Friction: Time bottlenecks, no L&D team, documentation debt
Readiness: Agile culture, limited IT bandwidth
Mode of leverage: Automate and diffuse
Tool Match: Notion AI for streamlining workflow + basic coaching content; Together for onboarding + connection-building

Medium Company (50–300 employees)

Friction: Scaling teams, mixed leadership capability, mounting ticket load on IT
Readiness: Willing early adopters in some functions
Mode of leverage: Diffuse and augment
Tool Match: Pinnacle AI for scalable leadership coaching + feedback support; Moveworks to relieve IT and improve employee experience

Large Company (300+ employees or multiple sites)

Friction: Cultural inconsistency, distributed knowledge, infrastructure complexity
Readiness: Dedicated IT, HR, and Enablement functions—but political complexity
Mode of leverage: Systematize and augment
Tool Match: Pinnacle AI for global leadership training + cultural reinforcement; Notion AI to reduce admin burden; Moveworks for automated support across functions

→ Use this framework not to pick “the best tool,” but to clarify what kind of leverage your org actually needs.

Appendix B: Questions to Evaluate AI Tools Before You Commit

Before piloting or purchasing an AI tool, ask:

Strategic Fit

What exact friction is this solving?
Will it augment high-leverage work—or just automate noise?

Integration & Infrastructure

Does it work with our current stack (Slack, Teams, Drive, etc.)?
How much IT lift is required?
Who owns implementation?

Cultural & Change Readiness

Will people trust and use this tool?
Does this require behavior change—and do we have a plan for that?

Measurement & Momentum

What leading indicators will show this is working?
What story will we tell if we want to scale it org-wide?

Example: Evaluating Two Tools in Practice

Let’s say you’re weighing Notion AI vs. Pinnacle AI—both multi-use tools with clear executive appeal, but very different strengths.

Scenario 1: Notion AI

Strategic Fit: Your teams are overloaded with repeat documentation and task spillover.
Integration: Already using Notion lightly; integration is easy and user-friendly.
Cultural Fit: Teams are open to automation, especially if it feels like a personal productivity win.
Measurement: Track adoption in meetings and goal-setting workflows; estimate time saved weekly.

Great pilot if your goal is reclaiming capacity and improving org hygiene. Low-risk, broad benefit.

Scenario 2: Pinnacle AI

Strategic Fit: You’re struggling to scale coaching, leadership readiness, and feedback culture.
Integration: Can be deployed via Slack or Teams, with configuration support available.
Cultural Fit: Managers want help but don’t have time for traditional development.
Measurement: Track usage of leadership avatars, user satisfaction, and reported confidence in high-stakes conversations.

Worth piloting if you’re focused on middle-manager enablement or cultural consistency. Medium complexity, but potentially high cultural leverage.

These tools don’t compete—they complement. But knowing what type of leverage you need first is key. Use these questions to sharpen internal alignment, clarify trade-offs, and set realistic success metrics before making big bets.

Image of a construction manager looking at an oversized tablet computer with a bar graph indicating growth, standing in front of a city under construction. Depicted in a painted collage.

Scaling Smarter in 2025: Resilience, Adaptability, and Intelligent Growth

On March 22, 2025

In Scaling Well

Back in 2022, I wrote an article on how to scale companies and teams well.

At the time, I had just come out of a role at an ambitious, high growth company at the apex of the biotech market. This experience weighed heavily on my thoughts in that first article, which focused on how companies can leverage insights from cities to scale better.

Here’s the main gist from that article: the research would indicate that the vast majority of companies grow sublinearly—meaning that as headcount increases, per-employee economic output declines. Cities, on the other hand, grow superlinearly, becoming more innovative and efficient (in terms of economic output) as they expand. Some of the key features of growing cities is that they foster continuous idea flow, optimize judicious infrastructure, and embrace decentralized decision-making. So the question is: can organizations adopt these principles to unlock superlinear growth? (Geoffry West, Scale)

It’s now 2025, and things look very different. For one, ‘growth at all costs’ has been replaced by ‘get to profitability.’ Secondly, generative AI has provided a whole new set of tools for scaling businesses without necessarily adding new headcount or employing human contractors.

With these new forces shaping our collective reality, I want to revisit some of the main themes from the original article, and provide some new perspectives on what it means to scale well. And because genAI has incredible potential in scaling workforces and companies, I decided I would collaborate with ChatGPT in this article, so what you are reading is a combination of me and ChatGPT (I’ll come back to how this experience was in my final post on this topic— stay tuned). Let’s dive in.

1. Designing for Slack: Innovation and Bottleneck Prevention

One of the key frameworks discussed in the original article is that scaling well requires deliberate slack in resourcing. Some level of slack and redundancy should not be seen as inefficiency, but as a strategic lever for innovation and resilience.

The scientific backbone behind this idea comes from queuing theory. Queuing theory tells us that as a system nears 100% utilization, inefficiencies spike exponentially. In cities you see this phenomenon during rush hour traffic, when roads are at capacity. In companies, you see this phenomenon in supply chains, network traffic, decision-making, and engineering velocity (Kashef, 2019). Organizations that operate at full capacity leave no room for iteration, problem-solving, or market shifts.

Whereas in 2022, designing for slack mainly meant allowing for some strategic redundancy in human resources, AI now offers new ways to create smart slack, not just by freeing up human capacity, but also by helping teams preempt bottlenecks before they emerge.

For example, modern AI-driven knowledge systems (like Notion AI or Stack Overflow for Teams) reduce key-person dependencies, ensuring critical expertise is documented, searchable, and shareable.

The other possibility that AI opens up is simply reduced time spent on routine tasks, thereby opening up additional capacity in your existing workforce. In other words, training your workforce on how to effectively (and safely) use AI tools can help companies ‘find time.’ Instead of loading up the team with new work, allowing this ‘found’ time to be used for structured slack can pay dividends. Imagine if every knowledge worker in your company suddenly had 20% more free time. Could this slack be used like Google’s 20% time, which produced Gmail and Google Maps (Tran, 2017) to produce outsized returns on innovation?

2. Investing in Infrastructure and Other Shared Resources Without Slowing Down

One of the biggest failure points in scaling is making the wrong decisions on what functions to centralize vs distribute, particularly in moving from a one product company to having a portfolio of multiple products. Too much centralization slows decision-making and leads to conflict as multiple projects try to draw on the same resources. Too much decentralization leads to inefficiency, duplication, and chaos.

Cities scale by balancing shared infrastructure with distributed autonomy— local businesses tap into municipal sewer lines and electric grids, for instance, and leverage shared city parking lots. Companies should think hard about which functions they want to be their ‘shared infrastructure,’ and cultivate those resources thoughtfully. At the same time, most cities don’t strictly mandate the number of Burmese restaurants or whether yet another rug store can move into a vacant storefront, nor do cities micromanage local business beyond compliance to city ordinances. Analogies for these ideas exist in corporations as well, and providing local autonomy with reasonable guardrails can enable companies to move fast without being overly centralized.

Based on how cities approach infrastructure, in 2022 my guidance was the following: (1) Put highly interdependent teams together, since organizing closely linked functions into clusters reduces cross-team friction. (2) Invest early in infrastructure teams. Underfunding internal platforms, DevOps, and enablement functions will stall scaling efforts. Just as you can’t expect new housing developments to be successful in areas with no power or water (at least in the US), you can’t expect employees to be productive if they have to spend half their energy fighting ineffective infrastructure. (3) Adopt a ‘Team of Teams’ approach by organizing into dynamic, goal-oriented teams and decentralizing decision-making to shorten communication paths within the organization.

In 2025, all of these approaches remain highly relevant. If anything, investing in infrastructure teams that enable smart AI usage has become even more critical to scale well, as unlocking AI tools is a powerful lever for improving efficiency across the organization.

These generative AI tools can then significantly enhance a ‘Team of Teams’ approach. For instance, enterprise-level AI-driven platforms have begun to emerge that leverage conversational AI to streamline internal communication, automate employee support, and provide real-time analytics (example: Moveworks).

3. Scaling Culture Through Intentional Culture Creation

Effective use of slack and structure is foundational, but truly scalable growth requires individuals to independently advance the mission, openly share information, and foster collective intelligence without constant oversight, and this comes from the culture and norms you create. A cohesive culture—essentially “how things get done around here”—is shaped by shared knowledge, expectations, and behaviors, thereby reducing the need for top-down control (Joly, 2022; Clayton, 2019).

Unlike cities, where culture evolves organically, organizations must intentionally shape cultural norms. Leaders must articulate clear behavioral expectations, reward aligned behaviors, and visibly model cultural values.

Three key perspectives from 2022 for building the cultural foundations for scaling well included: (1) Focus on new leadership for maximal cultural impact, because leader behavior is a key determinant of overall organizational culture (ed. Constable, 2023). (2) Onboard to build belonging. Culture creates the foundation for belonging, which in turn leads to better employee engagement and lower attrition. (3) Scale belonging through peer-led groups. Allow peer networks to reinforce cultural norms as well as fostering connection and belonging.

In 2025, these perspectives continue to be crucial. By leveraging generative AI tools, companies now have powerful tools for providing real-time feedback and personalized coaching at scale without hiring an army of coaches or HR business partners. Companies can fairly easily create/train their own custom GPT tools to be specific for their culture and values, or partner with any number of emerging companies on the task of integrating AI tools across the enterprise communication suite (Teams, Slack, WorkDay, etc), so that coaching for culture can occur in the flow of work and has a larger specialized training dataset (example: Pinnacle AI).

Here are few additional specific use cases I have been noodling on over the past few months as I myself have started diving in to my own AI learning journey:

Create a customized leadership avatar that gives your team a safe, low-stakes environment to practice high-impact conversations. We’ve all experienced moments we’d like to replay or improve critical interactions; an AI-powered avatar offers your team real-time feedback and personalized coaching, building confidence before they step into crucial discussions.
Use generative AI to tailor your communication based on DISC or other behavioral profiles, ensuring your message resonates effectively with different audiences. For example, as someone who tends toward detailed, lengthy emails, I’ve found AI incredibly helpful for crafting concise messages better suited to busy executives, as well as thoughtfully adjusting tone in high-stakes or potentially sensitive situations.
Amplify the impact of peer-led coaching circles by leveraging AI-driven matching platforms (such as Together or Circles) to pair participants thoughtfully, based on shared goals, interests, and cultural alignment. With customized training on your organization’s values and norms, AI tools can also generate meaningful discussion prompts and real-time insights, strengthening cultural cohesion and engagement across distributed teams.

Conclusion

Scaling companies is still messy and hard in 2025, maybe even harder because of increased global uncertainty and current economic conditions. However, by drawing inspiration from cities and harnessing new generative AI tools, we can scale more intentionally and adaptively, even amid inevitable chaos. And maybe, just as with cities, embracing that chaos—with a touch of AI-powered magic—is precisely what can help organizations thrive and scale despite the challenges.

Photo of "The Art of Possibility" book on a white wooden table next to a cup of coffee and a pen

The Excitingly Practical Potential Found in The Art of Possibility

On August 31, 2024

In Book Reviews

One theme I am working with in my professional life this year is approaching work with a more expansive perspective, using a lens of abundance, possibility, and collaboration instead of scarcity, limitation, and competition. However, we’re seven months into the year, and if I were evaluating my success so far on this effort, I would have given myself a B-. That is until I read The Art of Possibility by Rosamund Stone Zander and Benjamin Zander.

The Art of Possibility: Transforming Professional and Personal Life is a beautiful guide to unlocking creativity and potential in all areas of life, drawing heavily on the worlds of classical music and personal development as it presents twelve practices designed to help readers shift their perspectives and embrace a mindset of possibility.

Let’s pause on this phrase ‘mindset of possibility,’ because many scientists, myself included, have an aversive reaction to this sort of phrase. It usually draws to mind ideas about ‘manifesting possibility in the world’ and feels a little too woo-woo for my analytical brain. Luckily, that is not what is meant in this case.

In the words of the authors, “This is a how-to book of an unusual kind. Unlike the genre of how-to books that offer strategies to surmount the hurdles of a competitive world and move out ahead, the objective of this book is to provide the reader the means to lift off from that world of struggle and sail into a vast universe of possibility. Our premise is that many of the circumstances that seem to block us in our daily lives may only appear to do so based on a framework of assumptions we carry with us. Draw a different frame around the same set of circumstances and new pathways come into view. Find the right framework and extraordinary accomplishment becomes an everyday experience. Each chapter of this book presents a different facet of this approach and describes a new practice for bringing possibility to life.”

In this book you find practices that resonate with modern management thinking around growth mindset and psychological safety before any of these concepts became buzzwords.

Mindset of Possibility

Most books on management and leadership fall into two categories. The first category is comprised of overly theoretical tomes written by professors who have limited experience actually managing in the real world. Interesting frameworks; hard to apply. The second category contains tactical advice offered towards specific scenarios— how to give better feedback, how to create more compelling presentations, how to collaborate more effectively. Highly useful when brought to bear in the right context, but not usually transformative.

The Art of Possibility falls into a unique third category, one that opens up new ways of framing the world that allow for more effective patterns of interaction and thinking. While expansive in their goals, these patterns are offered in set of 12 practices, making them approachable and practical to put into use in the context of your choosing. The end result is something both useful and expansive, and I am so incredibly grateful to have encountered this book.

Practical Implementation

I will let the reader explore the 12 practices in the book themselves, but I want to touch on how I applied the practice of ‘Giving an A’ with my team recently.

The “Giving an A” practice is about shifting from a mindset of judgment and comparison to one of possibility and empowerment. Instead of evaluating people based how well they can match their performance against a set standard, you start by assuming they are already capable and worthy of an “A.” Individuals are still encouraged to reach their full potential, but without the fear of failure or judgment holding them back.

This practice originated from an observation that Benjamin Zander made among his elite music students: the high-achieving students often felt so anxious and inhibited by the pressure to perform perfectly that they weren’t fully developing their musical talents because they weren’t taking risk. By giving them an automatic “A,” he created an environment where they felt free to take risks, express themselves, and grow. But he didn’t just give them an automatic ‘A.’ He required every student to write from their future self a letter dated at the end of the course about what they had done over the course of the year to deserve that ‘A.’ Through the process of writing the letter and connecting with the vision of their future selves, students showed up in class as ‘A’ students.

I’m not a teacher and don’t award grades, nor do I work on time scales of semesters or academic years. Nonetheless, the practice of imaging the person you want to be in a year’s time helps us all see more clearly the behaviors and activities needed to get from here to there, and help us show up differently in our day-to-day interactions in a way that supports reaching our goals.

With that in mind, I took my team through a version of it as part of our mid-year development practice. Technically, what I call ‘mid-year development practice’ is actually my company’s mid-year review cycle, but I have long felt that referring to these practices as a ‘review’ is counterproductive. Using the word ‘review’ sets a tone of evaluation instead of collaboration. If you are managing well, any performance issues should be part of ongoing conversations, not presented in twice yearly processes. As a result, in my organization, we use these ‘review’ cycles as an opportunity to make sure meaningful professional development conversations are happening. The practice of ‘Giving an A’ fits nicely with this perspective.

In a recent team meeting, we set aside our usual agenda of reviewing progress and looking at data, and spent 30 minutes ‘alone, together’ working independently on such letters from our future selves. I did not require anyone to share these letters, but am hoping that the development conversations that ensued were richer and more inspiring as a result.

Of course, during this exercise I was forced to reframe my B- into giving myself an A. And I’m looking forward to earning that A this year!

Happy reading!

The Art of Possibility: Transforming Professional and Personal Life by Rosamund Stone Zander and Benjamin Zander. Penguin Books; Reprint edition (September 24, 2002)

Illustration of a scientist pitching a new project to a finance-oriented executive

Net Present Value: Making the Financial Case for Advancing Scientific Projects You Care About

On March 4, 2024

Well folks, we really saved the best for last here. In our final post on Finance for Scientists, we’ll be talking about one of the single most valuable tool I have learned in my adventures in finance: Net Present Value.

Why is NPV such a helpful concept for scientists? Because this tool is used by your business and finance counterparts to decide whether a particular project is worth pursuing. It is used to answer the question: relative to the low risk option of holding onto the cash needed to fund a given new project, how much money could this project generate over a particular time horizon? Understanding how NPV is calculated can not only help you make better sense of your company’s decisions, it can also help you structure projects in such a way as to make them more likely to be palatable to upper management (play around with these tools a bit and you will have a whole new appreciation for why timelines are so important.)

So what is NPV?

Net Present Value (NPV) is a way of measuring how much money an investment will generate in the future, compared to how much it costs today. NPV takes into account the time value of money, which means that a dollar today is worth more than a dollar tomorrow, because you can invest it and earn interest.

NPV is a powerful tool and the finance professional’s first choice for analyzing capital expenditures. There are three key reasons for this:

It takes into account the time value of money. Future cash flow is discounted to understand their value in today’s dollars.
It considers a business’s cost of capital or other hurdle rate. Cost of capital is the return expected by those who provide capital for the business. Hurdle rate is the minimum acceptable rate of return investors use to analyze profitability when evaluating a potential investment. In other words, NPV takes into account the specific situation of the business (which you will see shortly includes current interest rates).
It provides an answer in today’s dollars, allowing you to compare the initial cash outlay with the present value of the return.

So, to calculate NPV, you need to estimate the future cash flows (inflows and outflows) of the investment, and discount them by a certain rate that reflects the risk and opportunity cost of the investment. The discount rate is usually based on the cost of capital or the expected return of similar investments. The NPV is the sum of all the discounted cash flows.

The discounting equation looks like this:

PV = FV1/(1+i) + FV2/(1+i)2 + … + FVn/(1+i)n

PV = Present Value

FV = projected cash flow for each time period

i = discount or hurdle rate

N = number of time periods (typically years in biotech/diagnostics) you are looking at

NPV = PV – initial cash outlay

For example, suppose you want to invest in a new diagnostic device for a rare disease. You estimate that the device will cost $10 million to develop and launch, and will generate $2 million per year for 10 years. You also estimate that the discount rate for this project is 10%, which means that you expect to earn 10% per year on average from similar investments.

To calculate the NPV, you need to discount each cash flow by 10% per year.

So, for the first year, the discounted cash flow is:

$2 million / (1 + 0.1) ^ 1 = $1.82 million

For the second year, it is:

$2 million / (1 + 0.1) ^ 2 = $1.65 million

And so on, until the tenth year:

$2 million / (1 + 0.1) ^ 10 = $0.77 million

The NPV is the sum of all these discounted cash flows, minus the initial cost of $10 million:

NPV = ($1.82 million + $1.65 million + … + $0.77 million) – $10 million

NPV = $3.17 million

This means that investing in this device will generate a net profit of $3.17 million in today’s dollars, after accounting for the time value of money and the risk of the project.

However, this calculation assumes that the cash flows are certain and constant, which is rarely the case in reality. In practice, there are many uncertainties and risks involved in developing and launching a new diagnostic device, such as regulatory approval, market demand, competition, pricing, reimbursement, etc. These factors can affect both the amount and timing of the cash flows.

To account for these uncertainties and risks, some analysts use a modified version of NPV called risk-adjusted NPV (r-NPV). r-NPV adjusts each cash flow by multiplying it by a probability factor that reflects the likelihood of achieving that stage of development or commercialization. The probability factor is usually based on historical data or expert opinion.

Interest and Discount Rates vs. Your Research Project

Let’s talk briefly about interest rates and their impact on discount rates. Having never taken an economics class before, my mind was blown the first time someone walked me through this information, and it certainly has helped me better understand our current macroeconomic headwinds and Wall Street’s collective obsession with interest rates.

Higher interest rates mean a higher opportunity cost for funds. If your CFO uses a hurdle rate of 20%, it means she is pretty darn confident she can get almost that much return elsewhere for a similar level of risk. A high hurdle rate then sets a very high bar for new investments. When interest rates are high, there is always the low risk option of just sitting on your cash and still getting a pretty attractive return. So high interest rates increase the bar for investment. Similarly, if you need to raise capital to invest in this new opportunity, the cost of that capital will be higher due to the higher interest rates.

Conversely, if interest rates are very low, almost everything is better than sitting on your cash, so there is pressure for growth and investment. This situation was responsible for the halcyon days of 2019-2021. But as we are seeing now, because time scales are long in the biotech and life sciences sector, companies can get caught out by assuming that low interest rate/cost of capital days will last forever. And we are seeing this situation play out now as companies jettison development programs in order to preserve cash in the current economy (i.e., the discount rate has changed, so NPV calculations done in 2021 likely do not hold in 2024, and some opportunities are no longer worth pursuing— another reason why everyone likes shorter project timelines).

In summary, interest rates and NPV have the following relationship:

As the interest rate increases, NPV decreases, and the bar for what a good investment is increases
As the interest rate decreases, NPV increases, and the bar for what a good investment is decreases

One other important factor for scientists to consider in NPV calculations (which WILL be used to evaluate whether your pet project is worthwhile), is how the cost of the project is being estimated and how the projected cash flow is being estimated.

To do that, let’s go all the way back to the beginning of this series and think about the income statement. To refresh your memory, below is our favorite BioTechne example:

Many of the line items here will be used to estimate projected returns, so you need to know how your particular project is being ‘burdened,’ for example, with SG&A.

Quick example: let’s say you are developing a new product that runs on top of an existing platform. It will require some R&D investment, but because you are leveraging an existing platform, those costs are smaller than a new product that requires a totally new platform to be built. So your initial cash outlay will be smaller. Similarly, operating costs associated with running something on an existing platform will be relatively low (and scale with product volume). As a result, as you are calculating your projected cash flow, you would want to include some incremental operating costs in the first years after launch that then scale with sample volume. Conversely, if this new product requires building out a new sales and marketing team, those expenses can dramatically decrease the return expected in the early years after launch. However, if you are creating a new offering for an existing sales channel, then your returns will be higher. G&A can usually be approximated as a percentage of overall volume.

All of these details will be important to understand as you are thinking about starting a new line of research, and then to consider more carefully (and this part is usually led by finance) when putting together the business case. Having your own understanding about these calculations can help you challenge assumptions that your finance team may be making that cause the business case to look significantly worse than it ought to. Similarly, as a savvy scientist, you can perhaps think about ways to decrease the upfront spend or brainstorm with your business development and marketing colleagues on alternative routes to revenue in the early days post product launch (can you reach some customer segments through existing channels? Are there channels that have lower regulatory or reimbursement requirements that can be accessed sooner? etc.). And by running a minimal NPV analysis for yourself in the initial concept phase of projects, you can get a feel for the likelihood of eventual success and prioritize your efforts accordingly.

I could probably write another 3 posts on NPV, if this topic is of sufficient interest to folks (reach out in the comments).

Otherwise, I will leave you with a few useful resources as I close out the final planned post of this series!

Thanks for following along! And a big thank you to Jeff Krimmel for inspiration and the authors of Financial Intelligence. A Manager’s Guide to Knowing What the Numbers Really Mean (Karen Berman and Joe Knight) for their easy-to-read book on finance!

An illustration of a scientist looking up at a sort of monument to the the Big Five financial indicators, each perched on top of an Greek ionic pillar

The “Big Five” Numbers — What Investors Care About and Why

On February 13, 2024

In this post, we cover the ratios and other financial indicators Wall Street or other outside investors care about. These ratios can have a large impact on managerial decisions, because companies generally need to keep shareholders and investors happy and how well leadership does in this endeavor will impact stock price, which in turn influences the success of a company.

Here are the ‘Big Five’:

Revenue Growth
Earnings per Share (EPS)
Earnings Before Interest, Taxes, Depreciation, and Amortization (EBIDTA)
Free Cash Flow (FCF)
Return on Total Capital (ROTC)

Let’s dive deeper.

Revenue Growth

This metric measures how fast the company is increasing its sales over a period of time. A high revenue growth rate indicates that the company has a strong demand for its products or services, and that it can scale up its operations. No investor is going to put money in a company where the value of their investment will stay flat or decrease over time (okay— well there are really sneaky things people do with the intention of decreasing their tax liability, but we’ll set that aside for now). If a company wants to be successful, they need to show the potential for growth.

Earnings per Share (EPS)

First up is EPS, which we encountered in the last post on Earnings Calls. This metric measures the profitability of a company by dividing its net income by the number of outstanding shares. EPS reflects the earnings potential of a company and its ability to generate returns for shareholders. All other things being equal, a growing EPS portends an increase in stock price. In an economic slowdown, companies will work hard to keep EPS up by reducing costs (not always great for the humans working at these companies). Shareholders can accept revenue decline, but are unhappy to see a decrease in EPS.

Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA) margin

This metric measures how much of the revenue is left after deducting the operating expenses, excluding interest, taxes, depreciation and amortization. A high EBITDA margin indicates that the company has a high profitability and cash flow generation; it’s a good indicator of future operating cash flow. EBIDTA is also often used in valuing a business, as a company’s sale price is often negotiated as a multiple of EBIDTA.

Free Cash Flow (FCF)

This metric measures the amount of cash that a company generates from its operations after deducting capital expenditures. FCF indicates the financial flexibility of a company and its capacity to invest in growth opportunities, pay dividends, reduce debt, or repurchase shares. In this economic climate, it’s especially important, as a company with a healthy free cash flow can continue to fund its own growth even when investment dollars are hard to come by.

Note that EBIDTA and FCF can be used together to understand how well a company is doing at converting profit to cash, by taking the ratio of FCF divided by EBIDTA. When this ratio is low, it can mean that a company is trying to make EBIDTA look strong through gimmickry even while its cash flow is weak.

Return on Total Capital (ROTC)

This metric measures a company’s net income relative to the sum of its debt and equity value, or the efficiency with which invested funds are used in a business. Essentially it helps investors understand whether a business is generating a return high enough to justify their investment.

More terms of interest

A few other metrics investors care about: Market Cap, Price-to-Earnings, and Shareholder Value

Market Cap

Market cap is simply the current stock price of a company multiplied by the number of shares outstanding, which appears on the balance sheet under Capital Stock. This metric shows a snapshot of what the company is worth to investors. Savvy investors like Warren Buffett will look at the ratio of the market cap to the ‘book value,’ or the value of the equity as shown on the balance sheet. It is said that Buffett tries to find and invest in companies that are trading at market cap close to or below their book value, as this situation may indicate that a company is undervalued (note: another thing that is said about Buffett is that he invests only in businesses he understands, so I would imagine there is an element of critical thinking in determining whether the business is actually undervalued… also note, this strategy is inherently banking on the market eventually recognizing the value of a company in the LONG TERM).

Price-to-Earnings

This metric is the current stock price divided by the prior year’s earning per share, and measures how much the market is willing to pay for each unit of earnings. Companies with higher ratios are considered to have high growth potential.

Shareholder Value

This term does not point to any single concrete metric— in fact, every ratio discussed could be said to indicate ‘shareholder value.’ But regardless of the specific definition, increasing shareholder value is important to everyone— not just shareholders. Lenders, investors, employees, customers all like to work with/invest in companies with high shareholder value, as these companies are better positioned to survive through hard times, keep employees employed, pay back their loans, and offer more pricing flexibility. Total shareholder return (TSR), the stock price appreciation plus reinvested dividends over a period, is one way of looking at shareholder value, and is sometimes considered the ultimate measure of a company’s achievement for shareholders over the long term. The formula for calculating TSR is { (current price – purchase price) + dividends } ÷ purchase price. Higher TSR results in greater capital gains for shareholders, stock price appreciation for employee-owners and potential for future success.

Long-term Investor Value Appropriation (LIVA)

A newer alternative metric is Long-term Investor Value Appropriation (LIVA). The idea behind LIVA is simple is to use historical data to estimate how much value a company either created or destroyed for its entire investor base. This measure is closely related to net present value (NPV), which estimates the value of a project based on expected future cash flow. NPV, which we will cover in the next post, is the gold standard for CFOs to decide which projects to invest in.

After the last several posts, hopefully you have a better understanding of how to understand financial ratios from management and investors perspectives (and how those perspectives come together in an earnings call).

In our next post we will talk about ways to understand ‘return on investment.’ In my mind, this is one of the most critical pieces of financial intelligence a scientific leader can acquire, as they will help you translate the value of the projects you want to advance into a framework your finance partners can more easily understand. So join the mailing list and stay tuned!

Illustration of a scientist listening to an office speaker phone. The phone has various speech balloons with icons to cover topics typically covered in a company earnings call

Your Company’s Quarterly Earnings Call: How to Make Sense of It All

On December 6, 2023

An earnings call is a quarterly touch-base for publicly-owned companies to provide an inside look at their performance and expectations for the future.

Earning calls are not legally mandated, so a company doesn’t have to have one. However, almost all publicly traded companies host quarterly earnings calls, because they provide an opportunity for the company’s management team to explain and contextualize their most recent financial results, and to offer a small glimpse into the future. It also often provides the opportunity for investment analysts to engage directly with the company’s executives.

These quarterly calls align with a company’s fiscal year, which may be different from the calendar year. For instance, our favorite exemplar company, Bio-Techne, closed its third quarter in the spring, for instance.

You can either listen to these calls live via webcast. Or you can listen to the recording on the company’s website a short while after the call occurs. Or you can read the transcript of the call, which is what I most often do.

Let’s walk through Bio-Techne’s third quarter earnings call to better understand the structure and content of a typical earnings call. A transcript of this call can be found here: https://seekingalpha.com/article/4599539-bio-techne-corporation-tech-q3-2023-earnings-call-transcript

Safe harbor statement

A call usually begins with a safe harbor statement, which lets everyone know that financial results may include predictions about the future that may not necessarily come true. This disclaimer limits the company’s liability if the predictions about the future differ wildly from what the future actually brings.

In this part of the BioTechne earnings call, they also state, “During the call, non-GAAP financial measures may be used to provide information pertinent to ongoing business performance.” Recall from our early posts (Part 2), that GAAP stands for Generally Accepted Accounting Practices and are standardized accounting practices utilized in ensuring that financials are accurately recorded and managed. The justification for reporting non-GAAP earnings is that large one-off costs, such as asset write-downs or organizational restructuring, should not be considered normal operational costs because they distort the true financial performance of a company. In other words, context.

Presentation and discussion of the financial results

Once the Safe Harbor statement is out of the way, the CEO (in this case Chuck Kummeth) kicks off with some opening messages that essentially boil down to a sales pitch for the company. Typically the CEO will present a narrative designed to coach listeners about the company’s position in the market and how they should think about the subsequent information. As a result, this section gives listeners a deeper understanding of how the company is positioning themselves in the market, and how optimistic they are about competitors and external market forces.

In the case of the Bio-Techne call, Chuck opens with all the things investors should be happy about (milestones, strong growth, etc), and then talks about “continued challenges of COVID in China, lower biotech funding and OEM destocking from supply chain disruption concerns last year,” but ends with an optimistic statement about these headwinds decreasing in the coming year. He also welcomes a new senior leader to the company, and then dives into performance by geography and end market, essentially fleshing out the themes he opened the call with.

Following the pattern of a typical earnings call, they then move into the detailed financial section, where the CFO takes the baton and dive into numbers that will give investors a sense of the relative health of the business and how that compares to past periods. In the Bio-Techne earnings call, the CFO (Jim Hippel) starts with the EPS (earnings per share). We’ll cover EPS in the next post on the investor Big Five financial ratios, but here’s a sneak preview:

EPS measures the profitability of a company by dividing its net income by the number of outstanding shares. EPS reflects the earnings potential of a company and its ability to generate returns for shareholders. All other things being equal, a growing EPS portends an increase in stock price. In an economic slowdown, companies will work hard to keep EPS up by reducing costs (not always great for the humans working at these companies). Shareholders can accept revenue decline, but are unhappy to see a decrease in EPS.

Similar to the opening business statements, Jim starts with an overall perspective, and then goes through the financials from various geographies and business lines. At several points, he refers to ‘unfavorable foreign exchange’ as a headwind (to refresh your memory on how exchange rates can impact a life sciences company, see Part 12).

Q&A

The final, and probably most interesting, section on an earnings call is the Q&A, and it is usually the longest part of the call. The host company can call upon analysts in their preferred order, prioritizing the most relevant individuals and deprioritizing the rest. Some investors will consider the tenor of how an earnings call unfolds, paying close attention to how leadership explains key pieces of information and how they navigate analyst questions at the backend of the call.

One interesting exchange in this section was from Dan Leonard from Credit Suisse who asked, “I want to make sure I understood your summary comments appropriately. Did you say that Bio-Techne would return to double-digit growth in fiscal 2024?”

For context, the summary comments from the CEO included: “Through it all, and as Q3 demonstrated, our growth platforms are still winning with double-digit growth. As we enter Q4, some of the headwinds should diminish, especially in China, but some are likely to remain, namely the OEM destocking and smaller biotech rationalize spending.

Looking further ahead into fiscal year 2024, these remaining headwinds should further diminish a double-digit revenue increases we see in our strategic growth platforms to once again be reflected in our headline numbers. In the meantime, we expect Q4 overall momentum to continue to improve from Q2 and Q3 with an overall growth rate likely similar to how we started the fiscal year in Q1.”

The issue around OEM destocking comes up several times in the Q&A and warrants some additional context. OEM will be a term for those more connected with the manufacturing world- it stands for original equipment manufacturer, or an organization that makes devices from component parts bought from other organizations. Destocking is just what it sounds like- to reduce the amount of stock/inventory held. So OEM destocking here means that BioTechne’s OEM customers are cutting back on the inventory they are buying, which is obviously not great for BioTechne.

Let’s dig a bit deeper into the macro environment underlying this destocking trend. Pre-pandemic many companies tilted towards ‘JIT’ (just in time) inventory management practices- there are many reasons this practice is attractive for a company’s financial position (in the extreme: imagine how your cash flow statement looks like if you can sell and receive payment on inventory before you have to pay the vendors from which you bought the parts, because you’ve negotiated amazing terms- Net 90, for instance). But there is no free lunch, and JIT comes with risks to a company’s resilience, and during the pandemic massive supply chain disruptions made that risk very apparent. The upshot is that many distributors moved away from JIT and stocked up on inventory. In addition, many businesses diversified, strengthening their supplier base and giving them increased optionality with qualified sources for materials. So now with the pandemic now officially over, distributors are feeling more confident in ‘destocking’ a bit, or drawing down on that inventory. But it’s not only that, there are market pressures driving this destocking behavior as well. As we all know from the last 14 parts of this series, destocking will lead to an increase in operating cash flow. With debt markets enduring higher interest rates for at least the foreseeable future, CFOs are turning to alternative sources of capital, i.e., no one wants to borrow money at high interest rates if they don’t have to, so turning existing stock into products without replenishing it as quickly provides a convenient route to generate cash. This strategy may be great for the companies doing the destocking, but it’s not so great for the company’s selling the inventory that is now being destocked. Thus the BioTechne situation.

Okay, so with that context, what was the BioTechne’s response?

First the CFO chimes in, “Well, we’re in the process of building our plan right now for next year, right? What I was trying to indicate in my closing remarks was that if you take out the very isolated events OEM destocking. China, as an example, the ExoTRU deal and the rest of our business collectively is at double digits already. And our key growth programs, which are going to carry us to $2 billion and beyond are also all growing well in the double digits. And so it suggests that we get past these headwinds in fiscal year 2023, during fiscal year 2024, this underlying double-digit growth was seen not only in our core but definitely in our growth programs, growth platforms we’ll start to once again resonate and you’ll see it in the overall company results. And that’s our goal.”

And then the CEO pipes up: “Let me put a little ribbon on that. So, I mentioned our run rate – we watch our run rate and how we’re doing digitally with our catalogs. We are funded first and foremost, the catalog business for life sciences across the board, biopharma down through academia. And that’s remaining in teens tells us that things are okay. Then you look for other holes and we bridge it for you. This OEM thing is going to come and go, you pull that back, we’re back to normality. And on top of that, you have these growth programs. Our three top growth areas all hedged spectacular quarters. Spatial had double-digit, 45% GMP protein, 20%-plus in cell and gene therapy overall and Exosome at 87%. They’re not material enough right now to carry the average. But by next year, there are going to be a lot more material and they’re going to carry the average. So all the stuff fundamental coming back on top of these growth programs, we don’t give guidance, but we won’t be very happy here if we’re not a double-digit growth and so.”

This exchange highlights some of the richness of an earnings call. First, on this question both the CEO and CFO jump in to answer, one of the few times this happens in the Q&A, which may tell us something about the importance of the topic. Second, you can see the ‘hedging’ in language like, ‘we don’t give guidance, but we won’t be very happy here if we’re not a double-digit growth.’ And finally, you can see the narrative that BioTechne is advancing very clearly in this exchange: we have very strong growth areas and are optimistic that the headwinds bringing us down now are temporary and likely to improve in future quarters. Basically, they are trying to reassure investors about some less than spectacular numbers.

A few more notes on how to listen to an earnings call

If you are following a company for multiple quarters, you can gain insight from listening closely for what the company disclosed relative to what prior research or earnings calls suggested they would disclose. Which topics did they emphasize, and which did they de-emphasize?

You might hear the word “guidance” on an earnings call, which is a term used to describe how the company orients analysts and investors around their projected future performance. Guidances are often given in ranges and with the caveat that they are directional only, so take them with a grain of salt. In the case of BioTechne, the CEO explicitly states ‘we don’t give guidance, but…’ When guidance is given, it’s generally a good idea to give it a close listen because sometimes the changes made to the guidance do provide important insight into the company’s future.

As stated above, some investors pay close attention to the tone used when delivering information. Is the information being delivered with energy and optimism? Or do the presenters seem sheepish and concerned? Of course, these assessments are highly subjective and can lead to false signal (and are much harder to get at from reading a transcript), but some investors will factor them into their overall read of an earnings call.

I strongly urge you to dig through past transcripts to get a sense of how both management and investors are thinking about the performance of the business.

Just for fun, here’s an article on GenAI prompts for analyzing earnings calls: https://www.mlq.ai/prompts/earnings-calls/

Next time we’ll talk about the ‘Big Five’ numbers that investors care about, including revisiting EPS. In the meantime, you can join the mailing list to be notified of when that comes out!

Illustration of a scientist working at a chalkboard covered in pictures representing financial ratios

Making Sense of the Numbers Through Financial Ratios

On November 9, 2023

In this post we will move beyond the financial statements and consider briefly a few key financial ratios and why they are important.

In previous posts, we’ve sometimes evoked ratios to try to contextualize and compare various numbers in the financial statements. And in general, this is exactly what ratios are good for. In this post, we’ll cover four different categories of financial ratios:

Profitability,
Leverage,
Liquidity, and
Efficiency.

Profitability Ratios

Simply put, these ratios evaluate a company’s ability to generate profits. There are dozens of profitability ratios, so below I highlight some of the most important ones.

Gross Profit Margin
- What it is: Gross Profit Margin (sometimes called simply gross margin) = gross profit/revenue
- Why it matters: Gross margin shows the basic profitability of the product and/or service itself, before expenses or overhead. In other words, how much of the sale price is going to the materials and direct labor to produce that product or service.
- What to pay attention to:
  - Higher is generally better- if you can’t cover your direct costs by selling your goods, you won’t be in business long.
  - Trend lines are important- negative trend can mean that the company may be discounting sales to increase volume or CoGs is increasing (or both). A negative trend can be an early warning indicator that a business may be in trouble.
Operating Profit Margin
- What it is: Operating Profit Margin (Operating Margin) = operating profit (EBIT)/revenue
- Why it matters: Operating margin is a more comprehensive measure of a company’s ability to generate a profit, as it uses operating profit, or EBIT (earnings before interest and taxes), as a numerator. Here you are looking at how well the company is running itself.
- What to pay attention to: a downward trend is a signal for management (and investors) to pay attention- costs are rising faster than sales.
Net Profit Margin
- What it is: Net Profit Margin (Net Margin) = net profit/revenue
- Why it matters: This ratio indicates how much of every sales dollar a company gets to keep after everything else has been paid for. Net profit is that proverbial bottom line, to this is the bottom line ratio. People sometimes refer to this as return on sales, or ROS.
- What to pay attention to: This ratio is highly variable from industry to industry, so it is usually most meaningful to compare a company’s net margin across time and/or compare with players in the same industry.
Return on Assets
- What it is: Return on Assets (ROA) = net profit/total assets
- Why it matters: ROA tells you what percentage of every dollar invested in the business was returned as profit. Stated otherwise, this ratio looks at how well a company is putting its assets (cash, facilities, inventory, etc) to work.
- What to pay attention to: Unlike the other Profitability Ratios we’ve discussed so far, ROA can be too high. An ROA above the industry norm can indicate that a company is not investing in its asset base for the future, which can compromise long term prospects. ROA varies from industry to industry, so like with the other metrics, it’s best to compare amongst peers within an industry. There are more nefarious reasons ROA can be inflated (remember Enron?), but we’ll set those aside for now.
Return of Equity
- What it is: Return on Equity (ROE) = net profit/shareholders’ equity
- Why it matters: While assets refer to what the company owns, equity refers to its net worth as determined by accounting rules. So ROE indicates the profit made for every dollar of equity invested in the company.
- What to pay attention to: From an investor’s perspective, s/he’ll generally be looking for a ROE above the going interest rates and asking the basic question: is this company even capable of generating a return that is worth the risk of investment? From a financial health perspective, this ratio is a bit tricky vis-a-vis comparison to other companies. A company’s ROE can be higher than a competitor because it has borrowed more money. Whether this position is good or bad will heavily depend on how smart the company has been in borrowing money.
Other/Variations on a theme
- Return on net assets (RONA), Return on total capital (ROTC), return on invested capital (ROIC), and return on capital employed (ROCE) are some other profitability ratios. They all answer the same basic question: did the company earn enough profit to justify the amount of ‘other people’s money’ it is using?
- Generic formula: (net income before interest on debt and after tax)/(total equity + total interest bearing debt)
- For the sake of brevity, we will not go into more depth here.

Leverage Ratios

These ratios evaluate how (and how extensively) a company uses debt. Fun fact: a financial analyst’s word for debt is leverage. So now you know. Leverage allows companies to make more money, but it also increases risk, because it introduces fixed costs that cannot easily be cut in the case of revenue reduction.

Debt-to-Equity
- What it is: Debt-to-Equity Ratio = Total Liabilities/Shareholders’ Equity
- Why it matters: This ratio is often used by bankers to determine whether or not to offer a company a loan.
- What to pay attention to: Managers should care about this ratio, as raising cash through borrowing may be more difficult if this ratio is high, and expansion could require more equity investment.
Interest Coverage
- What it is: Interest Coverage = operating profit/annual interest charges
- Why it matters: The ratio shows how easy it will be for a company to pay its interest. Bankers love it.
- What to pay attention to: High ratio indicates that a company can reasonably take on more debt. If this ratio is close to 1, it indicates that most of the operative profit is used to pay interest- not good. In that situation, senior management will need to focus on paying off debt.
- Side note: there’s a sneaky construct called an operating lease, where companies lease equipment from an investor instead of buying it- then these lease payments show up as an expense on the income statement, but there is no asset and so no asset-related debt.

Liquidity Ratios

Simply put, these ratios evaluate how well a company is able to pay its bills.

Current Ratio
- What it is: Current ratio = current assets/current liabilities
- Why it matters: It provides a measure of the company’s cash runway.
- What to pay attention to: Too high or too low is a problem. Too high suggests the company is sitting on cash instead of investing it or returning it to shareholders. Too low (close to 1) means that the company is just barely able to cover its liabilities with the cash coming in. If this ratio is less than 1, then the company is going to run out of cash in the coming year and needs to generate more cash or attract more from investors.
Quick Ratio
- What it is: Quick ratio = (current assets – inventory)/current liabilities
- Why it matters: Also known as the ‘acid test,’ this ratio is a variation on the Current Ratio, but subtracting inventory, which is harder to convert to cash quickly. In other words, this ratio indicates a company’s ability to pay off short-term debt without needing to wait for inventory to sell.
- What to pay attention to: If a particular business has a lot of their assets tied up in inventory (for instance, a reagent or consumable manufacturer), then lenders and vendors will be looking to see that this ratio is well about 1.