Samuel Mwendwa

I lead strategic finance for high-growth fintech and SaaS companies operating across complex, multi-country environments.

My core work is simple: help leadership teams make better capital decisions under uncertainty — how long the runway is, where cash should go, and what breaks if assumptions change.

I’ve built and led FP&A, treasury, and investor-grade reporting across 12+ markets, partnering directly with CEOs, CFOs, and boards during:

→ Fundraising and diligence cycles
→ Regulatory expansion
→ Efficiency resets
→ M&A readiness

What I’m known for

→ Turning messy operating data into clear, decision-ready financial models

→ Designing treasury and cash-flow structures that hold up under audit and scrutiny

→ Linking GTM, pricing, and unit economics to actual capital outcomes

Recent impact

→ Extended operating runway by 9+ months through forecasting and capital reallocation

→ Supported a $12M M&A transaction with investor-grade reporting and diligence readiness

→ Built global treasury and finance infrastructure spanning the US, UK, and Africa

I’m most effective with companies navigating growth, expansion, or capital events where financial clarity directly affects survival or valuation.

If you’re hiring a senior finance leader or need short-term strategic finance support around fundraising, expansion, or runway — feel free to reach out.

Group Tax and Accounting Manager

Why subtraction beats growth in capital-constrained startups

In today’s fundraising environment, chasing growth alone is rarely rewarded. More hires, more features, more markets—without discipline—become liabilities when capital tightens.

I’ve seen boards push relentless expansion, only for valuation multiples to stall while burn rates spiral and teams exhaust themselves.

The startups that outperform are those that prune relentlessly: stripping processes, discretionary spend, and initiatives that don’t move the valuation needle.

A framework I apply as a Fractional COO or Strategic Finance Advisor:

Identify activities that generate measurable value—revenue impact, customer retention, or margin improvement.

Map recurring costs and processes, then evaluate what could be paused or removed without harming delivery.

Institutionalize periodic review sessions and link incentives to operational efficiency, not just growth metrics.

The outcome is simple:

Leaner operations → higher margins

Simpler products → faster adoption

Smaller, focused teams → tighter accountability

Subtraction isn’t about survival. It’s strategic amplification. When markets tighten, disciplined simplification is what sustains runway, protects optionality, and unlocks higher valuations.

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Hey [First Name] — appreciate you jumping into the wallet convo!

As mentioned, I pulled together a brief on the latest Dune data + trends shaping how wallets are becoming crypto’s new control layer.

Here’s the link: 

Curious what your take is: are you more bullish on smart accounts, wallet infra, or UX-led plays right now?
Always down to trade thoughts if you're building or tracking in this space.

— Samuel

The hard lesson I’ve seen in web3 is counterintuitive: hype can inflate valuations, but only operational rigor preserves them. Burn multiple and GTM risk aren’t abstract—they are leading signals of whether capital and incentives actually create durable network value. I’ve watched promising protocols command headlines while quietly eroding optionality because adoption and token deployment weren’t sequenced for efficiency. Leadership here isn’t just about raising the next round; it’s structuring capital and go-to-market to compound value predictably under real-world constraints.

If you’re thinking about embedding burn efficiency and GTM risk into web3 valuation, I share actionable frameworks—follow or DM for insights.

nextlify-scale

Advanced Python: Working With Data

Advanced QGIS Analysis with AI and Machine Learning

Corporate Finance Foundations

Excel for Financial Planning and Analysis (FP&A)

Finance Foundations: Risk Management

Financial Modeling and Forecasting Financial Statements

International Business and Finance

Power BI Data Analyst Associate (PL-300) Cert Prep: Advanced Reporting in Power BI

SQL Server Machine Learning Services: Python

SQL for Data Analysis

Six Sigma: Black Belt

Strategic Planning Foundations

Tableau Certified Data Analyst Cert Prep

Business Intelligence & Data Analyst (BIDA®)

Leveraging Generative AI in Finance and Accounting

The Role of the FP&A Professional

Data Visualization & Dashboards - The Basics

Intermediate DAX & Time Intelligence

Power BI  Case Study – CFI Capital Partners

SQL Case Study - WOWI Sales Analysis

The course has eneble me to demonstrate creative & innovative thinking in strategy development.
Understand management and organizational theory, and relate this to strategy development.
Identify strategic issues and design appropriate courses of action. 
Provide a basic understanding of the nature and dynamics of the strategy formulation and implementation processes as they occur in complex organizations. 
Understand the essentials of strategy management in a global economy. 


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members economic empowerment and concern to the economy

STAREX S. H GROUP

Fractional CFO | Strategic Finance Leader for High-Growth Startups | Capital Planning • FP&A • M&A • GTM Enablement

As a strategic finance leader and fractional CFO, I help high-growth startups transform financial complexity into clarity—and clarity into action. My specialty is turning operational data into board-ready decisions that extend runway, increase valuation, and drive smart capital allocation.

I’ve built and led finance functions across VC-backed fintech, SaaS, and Regenerative ventures—from zero-to-one scaling to acquisition readiness. My approach combines lean systems, dynamic FP&A, and investor-grade decision support that aligns finance with product velocity, market expansion, and fundraising.

What I Deliver:

Capital Planning with Precision: Burn modeling, unit economics, multi-scenario forecasting, and capital allocation frameworks that sharpen board and investor decisions.

Finance Systems That Scale: Cloud ERP, dashboarding, automated workflows, and compliance infrastructure tailored for fast-growth startups.

Strategic M&A and Investor Readiness: Scenario modeling, audit-proof reporting, and real-time insights to support funding, exits, and GTM alignment.

Recent Wins:

Cut burn by 25% and extended runway by 9+ months through FP&A transformation

Led finance modeling for $12M M&A scenario resulting in acquisition

Built a tax intelligence engine that reduced audit risk by 18% across 3 continents

I’m most effective in moments of scale, shift, or scrutiny—pre-Series B, pre-acquisition, or prepping for a fundraise—where financial strategy is mission-critical and capital clarity drives velocity.

Let’s connect if you're leading a high-growth startup and want a finance partner who builds for what's next.

Strategic Finance & Capital Allocation Principal | Fintech Growth Accelerator & Runway Protector Unit Economics • High-Conviction Capital Deployment • Pricing & Incentive Optimization • Multi-Market Treasury & Payments

Layoffs are often a proxy decision. They feel decisive, visible, and fast. They’re also frequently the wrong first move.

I watched a SaaS company cut 20% of its team to extend runway. Morale dropped. Output slowed. Burn barely changed. The issue wasn’t people—it was the product footprint those people were propping up.

Years of reactive selling had turned the roadmap into a liability. Custom features, marginal integrations, edge-case workflows. Each one small. Together, they consumed engineering time, support capacity, and cash.

Instead of another reduction, we made a harder call: reduce the product surface area.

Every feature was tied to revenue and retention—not in theory, but in practice. Features with vocal internal champions but no measurable impact were sunset. Integrations used by one or two customers triggered uncomfortable conversations. Sales pushed back. Some customers complained. A few left.

What changed was focus. Engineering velocity improved because teams weren’t context-switching. Support load dropped. New deals were easier to explain and sell. Costs finally came down in a durable way.

Cutting headcount treats symptoms. Cutting product scope addresses cause—but it requires accepting near-term pain to avoid structural fragility.

When runway tightens, the first question isn’t who to cut.

It’s what you’re willing to stop supporting—and who might be unhappy when you do.

Why most SaaS & fintech forecasts silently sabotage growth

Copying a forecasting template from another SaaS company feels safe. It’s quick, it balances, and it ticks a box.

But here’s the uncomfortable truth: generic FP&A models rarely reflect your actual business dynamics.

The consequences aren’t just imperfect numbers—they’re missed opportunities, wasted capital, and slow erosion of strategic advantage.

Common pitfalls I see:

Revenue assumptions and drivers that don’t match your customer lifecycle.

Expense ratios borrowed from unrelated businesses.

Capital planning misaligned with real operational levers.

A few principles for building forecasts that actually work:

Align with your business DNA – every driver should reflect your customers, churn, and cost structure.

Link numbers to action – every line item should map to a lever you can influence.

Stress test, don’t copy – models only add value when they answer the tough “what if” questions.

Custom models take longer, but they pay back in clarity, team alignment, and strategies that work in your market—not someone else’s.

In today’s capital-constrained, high-velocity environment, knowing where you stand—and where you’re going—is the difference between surviving and scaling.

When the bravest move isn’t scaling—it’s hitting pause

I once worked with a founder sprinting toward growth while the runway was shrinking. Revenue was flat, cash reserves were dwindling, and every instinct screamed: “Go faster!”

I told him the opposite: pause.

Across tech, healthcare, fintech, and media, I’ve seen the same pattern: chasing growth with limited capital is like pressing the gas with the fuel light on. It feels bold—but it’s risky.

Here’s how I guide leaders through capital-constrained periods:

→ Survival is a strategy.

Weathering tough markets preserves optionality and separates resilient companies from those that fail.

→ Pause ≠ standstill.

Use the time to prioritize spend, focus on essentials, and cut non-critical initiatives.

→ Get real about runway.

Forecast cash weekly, know your zero-cash date, and stress-test scenarios.

→ Engage customers meaningfully.

Identify what they value, what they’d pay more for, and where your efforts are wasted.

→ Question growth assumptions.

Is a new market a distraction? Is a feature just vanity? If it doesn’t drive retention or revenue, table it.

→ Communicate clearly and honestly.

The team will follow a realistic plan; investors respect pragmatism over bravado.

I’ve seen founders who hit pause return stronger. They didn’t win by scaling faster—they won by surviving strategically and coming back ready to accelerate.

Key takeaway: slowing down to focus is often the most courageous and profitable decision when capital is tight.

I watched a founder give up control of his company long before the Series B closed.

The dilution showed up on the cap table.
The mistake happened months earlier.

He optimized for percentage ownership while letting time run out. Cash tightened. Momentum slowed. Optionality disappeared. By the time he raised, the negotiation was mostly theater.

The uncomfortable truth: in fundraising, dilution is usually an outcome—not the problem. Timing is the variable that determines everything else.

Raising late doesn’t just lower valuation. It changes who has leverage in the room, which terms are non-negotiable, and how much governance you quietly surrender to survive the round.

Most teams say they’re waiting for “one more milestone.”
What they’re really doing is borrowing against future confidence.

Strong operators don’t ask, “How do we minimize dilution?”
They ask, “When do we still have choices?”

They raise when the business is trending up, not when cash dictates the calendar.
They align capital with value inflection points, not quarters.
They track investor appetite as a market signal, not a vanity exercise.

By the time your runway is red, the decision has already been made for you.

The real question isn’t how much ownership you give up.
It’s whether you raised while the clock was still on your side.

A founder once told me, “Our base case gives us 18 months of runway.”

I asked what happens if two assumptions break at the same time.

No one had an answer.

That’s the risk most teams underestimate. Not that the base case is wrong—but that it’s singular. One version of the future, treated as sufficient.

In practice, companies don’t fail because revenue misses by 5%. They fail when timing, customers, and capital all slip together—and there’s no pre-agreed response.

The uncomfortable truth: a base case is not a plan. It’s a reference point.

What separates resilient teams is not optimism or pessimism. It’s preparation for compound failure.

The strongest operators I’ve worked with do something different. They identify the few assumptions that would force irreversible decisions if they broke—liquidity, customer concentration, funding timing. Then they model those breaking fast, not gradually.

Not to predict outcomes, but to pre-decide actions.

What gets cut first.
What must not slow down.
Who communicates, and how.

When downside scenarios are explicit, execution gets calmer. Boards stay aligned. Teams move instead of debate.

You don’t need to be right about the future.
You need to know which version of it you’re not allowed to be surprised by.

Raising money creates a dangerous illusion: that speed is the same as progress.

I almost learned that the hard way.

After a round closed, every team had a “high-confidence” use for capital.
None of them were irrational.
Taken together, they would have quietly destroyed our margin for error.

That’s the trap.
Capital doesn’t disappear in one reckless decision.
It leaks out through reasonable ones.

The real risk wasn’t overspending.
It was committing cash in ways that narrowed future choices before the business had earned that certainty.

Every dollar forced the same question:
If this works, what does it unlock?
If it doesn’t, what have we permanently lost?

Some bets promised growth but locked us into cost structures we couldn’t unwind.
Others created motion with no path to learning.
Saying no slowed us down—and bought us time.

That trade-off is uncomfortable.
Boards want momentum.
Teams want resourcing.
Founders want to capitalize on the moment.

But liquidity isn’t fuel.
It’s leverage.
Used early, it should increase optionality, not consume it.

Most companies don’t run out of cash.
They run out of room to maneuver.

The hardest capital decisions aren’t about ROI.
They’re about which future you’re quietly ruling out when you say yes.

Most fintech expansion plans fail for reasons that never show up in the product roadmap.

FX is one of them.

I’ve seen teams execute flawlessly on market entry—regulatory approval, local partners, strong demand—only to watch margins erode quietly as currencies moved against them.
Nothing “broke.”
The economics just stopped working.

That’s the trap.
FX risk rarely announces itself as a crisis.
It shows up as missed forecasts, confusing variance, and a growing gap between reported growth and actual cash.

Founders tend to treat this as a finance clean-up item.
In reality, it’s a sequencing decision.

Every new currency exposure competes with growth for capital.
Hedging can stabilize outcomes, but it isn’t free.
Ignoring FX preserves speed—until volatility forces an unplanned correction.

The question isn’t whether FX matters.
It’s when it starts to matter enough to change behavior.

Teams that scale well make this explicit early.
They know which exposures are tolerable, which must be controlled, and which markets only make sense if margins can absorb volatility.
They plan for currency movement the same way they plan for churn or pricing pressure.

Product-market fit gets you permission to expand.
Financial control determines whether expansion compounds—or quietly reverses.

If your global model only works when currencies cooperate, you don’t have a scaling problem.
You have an assumptions problem.

Most founders overestimate how far a funding round actually takes them.

I once joined a company two weeks after a $5M raise. The team felt relieved. Hiring plans ballooned. Vendors pitched extras. Meetings began with, “We have time.”

The reality? Their 24 months of runway was closer to 11.

The uncomfortable truth: runway isn’t cash in the bank. It’s the discipline to turn spend into outcomes. Every hire, every vendor, every initiative is a trade-off. One misstep erodes optionality faster than you expect.

Teams that thrive under pressure link every dollar to learning, not comfort. They ask: Will this spend accelerate our next milestone—or just postpone hard decisions? They kill bad bets early, spot “runway leaks” before they metastasize, and focus on high-impact priorities.

I run a “Runway Reality Check” quarterly: list new spend, expected outcomes, actual outcomes, and whether it’s still the fastest path to your next milestone. The exercise forces clarity, speed, and accountability in a way no bank balance can.

The hardest question founders must ask themselves: Are you buying time—or buying comfort? The answer separates teams that survive from teams that thrive.

I’ve seen founders pitch with flawless slides, high-energy narratives, and stratospheric projections—yet they ignore the most important story: the downside.

Here’s the uncomfortable truth: if your fundraising deck doesn’t model what can go wrong, you’re not raising capital—you’re selling fantasy.

Investors reward realism, not optimism. A missed downside case signals either inexperience or unwillingness to face reality. Both cost credibility, influence, and capital.

The cost of ignoring risk is real:
→ Blind growth decisions.
→ Misallocated resources.
→ Strategic missteps that compound before anyone notices.

The leaders who succeed don’t just inspire—they demonstrate discipline under uncertainty. They know: capital is limited, time is scarce, and every decision carries trade-offs. They quantify the worst case first, model levers to respond, and align the team around what survival—and acceleration—looks like.

The point isn’t pessimism. It’s credibility under pressure. Investors, boards, and teams bet on founders who can navigate adversity, not just celebrate upside.

Next time you raise capital, ask yourself: are you showing your business can survive reality, or just sell a dream? Because the difference between a good raise and a wasted one isn’t your valuation—it’s your judgment.

Most companies don’t fail because of dilution. They fail because they run out of time.

I’ve seen founders reject unattractive term sheets while telling themselves they were being disciplined. In reality, they were protecting an idea of ownership that only mattered if the company survived.

One boardroom still sticks with me. The offer on the table wasn’t good. Down valuation, heavy preferences, real control concessions. Walking away felt principled. Waiting felt optional.

It wasn’t.

Three months later, cash was gone. The option to choose disappeared. Employees left. Customers followed. The dilution he feared became irrelevant.

The uncomfortable part is this: dilution is a cost, but so is delay. And delay compounds quietly. Leverage erodes before panic shows up in the numbers.

That doesn’t mean you take any deal. Bad capital can cripple a company just as surely as no capital. Terms matter. Partners matter. Control matters.

But refusing to engage because a deal feels “ugly” is often just another way of betting that circumstances will improve in time.

Sometimes the real trade-off isn’t dilution versus control.

It’s dilution versus probability of survival.

And probability is what boards and operators are paid to protect, even when the optics are unpleasant.

Underpricing rarely feels dangerous. That’s what makes it expensive.

I watched a fintech CEO avoid a price increase for years because churn felt like the existential risk. Costs rose. Competitors repriced. He stayed “customer-friendly.” Revenue quietly capped itself.

The board didn’t panic at first. Then margins compressed. Then hiring slowed. Then the questions shifted from growth to durability.

Pricing isn’t a one-time decision—it’s a compounding one. Every quarter you delay, you train customers on what your product is worth and lock yourself into an operating model that assumes restraint forever.

When we revisited pricing, the hardest part wasn’t math. It was confronting which customers actually valued the product versus those simply enjoying a discount.

We didn’t reprice everyone. We started with new contracts and a narrow segment where the product was embedded in core workflows. Sales pushed back. A few deals slowed. That was the cost of information.

Churn didn’t spike. What changed was unit economics—and credibility. Suddenly the business could fund itself longer. The board conversation shifted from defense to choice.

Raising prices is risky. Not raising them is a slow commitment to subsidizing your customers with your future.

The real pricing question isn’t “Will we lose customers?”

It’s “Which customers are we implicitly choosing to keep at this price—and what does that choice cost us over time?”

The most dangerous finance teams aren’t sloppy. They’re punctual and irrelevant.

I watched a $100M SaaS company drift toward a cash crisis not because finance failed at reporting—but because it was structurally excluded from decision-making. The books were clean. The insights were late.

The business was expanding aggressively: new geographies, multiple currencies, overlapping bets. The board asked reasonable questions—downside scenarios, capital exposure, FX sensitivity. Finance could explain what had already happened. Not what was likely to happen next.

So strategy filled the gap. Optimism substituted for analysis. Europe scaled faster than demand. Currency exposure compounded losses. Forecasts assumed best-case execution because no one was incentivized to challenge them.

By the time finance raised concerns, reversing course meant layoffs and damaged credibility.

The lesson wasn’t “finance should move faster.” It was that finance has to accept discomfort earlier—pushing back on plans that look good in isolation but break under stress.

At scale, finance isn’t a control function. It’s a constraint engine. Its value is in narrowing choices before capital commits, not explaining variance after.

Accuracy keeps you compliant. Judgment keeps you solvent.

The real question for leadership isn’t whether finance has a seat at the table—but whether anyone is empowered to say, “If we do this, here’s what we’re betting the company on.”

The first time I watched a startup hemorrhage cash because of foreign exchange risk, it nearly made me quit coffee. Let me paint the scene. We were celebrating a big contract win. Five countries. Ten currencies. Big plans. Everyone was toasting the international expansion, but the finance team was quietly sweating. I asked the founders if they’d modeled FX exposure. They shrugged. We invoice in dollars. FX risk is for the IBMs, right? Wrong. Fast forward two quarters. Revenue looked great on paper. But each payout landed a little lighter than expected. The CFO blamed the payments processor. The CEO blamed the bank. But the real culprit? Currency swings. Every GBP or INR collected had quietly slipped through their fingers, chewed up by market moves. By the time they spotted it, six figures had evaporated—before the first invoice even cleared. Here’s the dirty little secret: You don’t need to be a global giant to bleed out from FX risk.

↳ Even a fivecountry fintech can see margins shredded. Most founders underestimate three things: → The lag between revenue recognition and cash collection.

→ Just how quickly currencies can shift—sometimes overnight.

→ The compounding effect of small misses, quarter after quarter. It’s not just about volatility—it’s about visibility. If you’re scaling across borders, you can’t afford to treat FX as an afterthought. Waiting until your controller flags a variance is too late. Here’s the approach I now swear by—one I wish I’d known back then: 1. Build FX exposure directly into your forecast models.

↳ Not just a line item, but a core scenario. 2. Set up realtime tracking dashboards.

↳ Use API feeds from your banks and payment providers. 3. Run monthly stress tests.

↳ Simulate 2%5% swings and watch the impact on gross margin. 4. Talk to your banking partner about locking in rates.

↳ Forward contracts aren’t just for the Fortune 500. 5. Assign an owner.

↳ If everyone owns FX, no one owns FX. The day we embedded this playbook, something changed. Founders stopped seeing FX as a footnote. They started treating it like any other business risk—one you measure, manage, and review. Suddenly, that sixfigure loss turned into a line of defense. Margins held steady. Planning finally felt real. If you’re scaling internationally, don’t wait for the first invoice to realize what’s at stake. Start treating FX risk as a strategic threat—and a growth lever. The best operators already do. Curious how fractional leaders approach FX for leaner, faster teams? Drop a comment if you want the full playbook.

You don’t lose marketplaces because revenue slows.
You lose them because you scale the wrong transactions.

Most founders know contribution margin matters.
What they underestimate is how long negative unit economics can stay hidden while everything looks fine.

GMV grows.
Revenue grows.
The board relaxes.

Meanwhile, every incremental transaction deepens dependence on subsidies, incentives, and service costs that don’t come back down. What started as “temporary investment” hardens into the business model.

The uncomfortable truth:
If contribution margin is negative at scale, growth is not optionality—it’s commitment. You’re locking in future cash burn and reducing your ability to change pricing, segments, or incentives without breaking the marketplace.

This is where decisions get hard.

Do you slow a high-visibility category because it never clears contribution margin?
Do you retrain demand away from subsidized supply even if GMV drops?
Do you accept flatter topline numbers to regain control of cash?

Strong marketplaces answer these questions early, when they still have degrees of freedom. Weak ones defer them until the math forces a reset—usually on someone else’s terms.

Revenue tells you how busy you are.
Contribution margin tells you whether that activity compounds or compounds the problem.

Most boards can tell which one you’re optimizing for within five minutes.

Most crisis responses fail because they optimize for visibility, not survival.

Boards panic about headlines.
Founders panic about morale.
Finance teams panic about burn.

None of those are the constraint.

Under pressure, leaders reach for actions that look decisive: layoffs, PR cleanups, revised forecasts.
They reduce noise.
They rarely change outcomes.

The uncomfortable reality is this:
In a real downturn, you don’t get to fix everything.
You choose which weakness gets to kill you.

I’ve seen companies cut deeply and still die because churn was accelerating.
I’ve seen others preserve teams and lose credibility because liquidity was ignored for one quarter too long.
Both were “rational” in isolation.
Neither confronted the binding constraint.

The hardest work in a crisis isn’t planning.
It’s sequencing.

What must be true six months from now for the company to exist?
What decision today makes that more likely—and what does it break in exchange?

Liquidity buys time, but can destroy momentum.
Product focus restores fit, but delays relief.
Optics calm stakeholders, but can mask decay.

Second-order effects matter more than the first move.

Most companies don’t collapse from a single mistake.
They make a series of reasonable decisions that all assume the same thing is wrong.

The question isn’t “What’s urgent?”
It’s: What, if left unaddressed, makes every other decision irrelevant?

Incremental fixes feel responsible in a crisis.
They’re usually a way to postpone the decision that actually determines survival.

I saw this clearly with a SaaS company that lost two major customers in a week.
The board defaulted to the usual responses: freeze hiring, trim spend, delay launches.
Each move was sensible.
Together, they bought days—not a future.

The real problem wasn’t cost discipline.
It was that the existing plan no longer worked, and everyone was trying to preserve it anyway.

That’s the mistake.
In a true cash crisis, incrementalism doesn’t reduce risk—it compounds it.
You keep funding a structure that may already be broken.

The harder path is narrowing early.
Choosing what the company will not be.
Accepting irreversible damage in exchange for time and clarity.

Those decisions carry real downside.
You lose people you value.
You concentrate bets before certainty arrives.
You risk being wrong in public.

But delay has a cost too: it quietly removes optionality.

The companies that survive crises don’t optimize for comfort or consensus.
They align quickly on what must be protected, what can be sacrificed, and what future they’re willing to abandon.

The question isn’t whether the cuts are painful.
It’s whether they meaningfully change the company’s odds—or just slow the countdown.

I’ve sat in boardrooms where a record valuation was treated like proof of safety.

It usually meant the opposite.

The celebration focused on the number.
No one wanted to talk about burn, sequencing, or what had to be true twelve months later for the company to exist on acceptable terms.

That’s the mistake.
Valuation is an outcome.
It doesn’t tell you *when* you can spend, *where* you can spend, or *what happens if the next round doesn’t show up on schedule*.

I’ve seen companies raise strong Series As, hire aggressively, and assume momentum would carry them.
Revenue grew.
So did fixed costs.
When the market tightened, the valuation didn’t protect them—it boxed them in.

The real work after a raise isn’t scaling.
It’s sequencing.

Which investments unlock the next credible milestone?
Which ones only make sense if the following round is guaranteed?
What must be preserved if conditions turn before you expect?

Capital deployed too early doesn’t just shorten runway.
It removes options.
It forces future rounds to solve yesterday’s optimism.

The teams that survive treat cash like a dependency chain, not a pile of fuel.
Every dollar either earns the right to deploy the next one—or it doesn’t get spent.

The uncomfortable question isn’t how impressive your valuation looks.
It’s whether your current spend still makes sense if the market doesn’t cooperate.

I used to believe headcount scaled output.
Then I watched a company spend $11M in under two years and end up with less clarity than it started with.

Demand was real.
The product worked.
What broke was the cost structure.

Under board pressure to “build ahead,” the team hired aggressively.
Burn doubled.
Execution slowed.
Every forecast—no matter how generous—ended with the same conclusion: the business had less room to maneuver each month.

That’s the part founders miss.
Hiring doesn’t just add capacity.
It hardens assumptions.

Once payroll expands, every plan has to justify the org chart.
Forecasts bend.
Rigor slips.
Hope fills the gap.

The mistake isn’t growth ambition.
It’s treating hiring as reversible.

In capital-constrained environments, headcount is the least flexible decision you make.
It locks in burn.
It amplifies coordination costs.
And it narrows your options if conditions change.

The teams that survive pressure don’t hire “carefully.”
They hire conditionally.

What outcome does this role unlock?
What happens if that outcome arrives late—or not at all?
What breaks if we wait?

Saying no to a hire rarely feels strategic in the moment.
Six months later, it often turns out to be the decision that kept the company alive.

The real question isn’t how fast you can staff up.
It’s how much certainty you actually have when you do.

I once met a founder who looked haunted.
Their startup had just raised a healthy seed round. The runway chart looked perfect—24 months of breathing room. Every line item triple-checked. Every assumption conservative.

And yet, the company was frozen.

Hiring slowed. Product experiments paused. Critical investments deferred. The logic was “survive longer.” The reality?

→ Competitors shipped features the team had only dreamed of.
→ Revenue stalled while expenses quietly climbed.
→ Talent grew restless and started looking elsewhere.

Obsessing over runway can backfire. Every month “saved” in cash is a month lost in market opportunity. Windows close. Momentum slips. The longer you hesitate, the harder it becomes to catch up.

Capital isn’t just a lifeline—it’s leverage.

The companies I advise don’t hoard runway; they deploy it strategically. They:

→ Identify inflection points where spending accelerates growth.
→ Burn faster on proven product-market fit signals.
→ Double down on what works—even if it shortens runway.

Survival isn’t measured in months of runway. It’s measured in whether you acted when the market allowed you to.

The startups that get remembered? They bet decisively, at the right time, with purpose—not cautiously, for comfort.

Ask yourself: is your runway a leash—or a weapon?

AI Strategy in 1 Day + Prototype in 3 Weeks | Fastest Path to AI & Digital Transformation While Having A Stupidly Good Time | 22+ Years Making Transformation Less Painful

🌍 Part business guru, part thrill-seeker. I thrive in discomfort, challenge the status quo, and ask 'Why not?' more often than a toddler asks 'Why?'. My clients rely on me to bring in a dash of urgency, a sprinkle of curiosity, a chug of right questions and a hefty dose of momentum. 

Work between London and Bay Area. 

How I help:
🚀 AI Accelerator: Zero to working AI prototype in 3 weeks 
🚀 Go-To-Market Accelerator: Growth hacking, product launches, market expansion 
🧠 Strategy & Innovation Labs for transforming business rapidly
🧠 Interactive workshops, Speaking & hackathons: teaching effective collaboration, value proposition, design thinking, visual thinking, and AI. 

What You Get: 
✓ Scale faster in fraction of the time
✓ High-performing teams without the burnout 
✓ Results in days, not months

Currently Open For: 
✓ AI Strategy & Innovation Lab engagements 
✓ Specialized CRM, Customer & Digital Transformation projects 
✓ Fractional/interim transformation roles 
✓ Post-M&A integration
✓ Process automation & digitization
✓ Outsourcing
✓ SaaS implementations
✓ Change Management

I spent the first part of my 22+ year career avoiding the messiest part of business transformation: "the people". As a computer nerd who loved math, algorithms, and databases, they were logical. Until I couldn't avoid the truth anymore: every transformation failure I witnessed wasn't technical – it was human.

My breaking point was when I spent six months, in weekly meetings with a senior leadership team, discussing the same slide deck, trying to make one decision. One. Decision.
I was trapped in never-ending tunnels of circular meetings – because nothing ever gets decided in a single meeting, does it? I had a choice: accept this as "how the system works" or find another way.

What Changed Everything - I went to Silicon Valley to learn how high-growth startups solve complex problems rapidly. The difference wasn't better technology – it was better collaboration.
So I combined my 22+ years of transformation experience with neuroscience-based collaboration methods. Turns out, when you fix the group dynamics, everything else follows.

That's how Solved Together was born – with a mission to fix broken collaboration and help businesses develop a human-centered approach to growth and transformation.

What I Do Now -  I help companies solve complex challenges in days instead of months using these methods. DM me if you want your team to learn to do the same! 

🏔️ Off-duty: Found defying gravity on mountains, seas, or slopes. Because life's too short for sensible hobbies.

Rujuta Singh

Everyone's arguing about which AI is best.
Like arguing whether wheels or engines matter more.
You need the whole damn car.

Alex made this gorgeous infographic that finally explains why everyone's confused.

𝗔𝗜 𝗶𝘀𝗻'𝘁 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴. 𝗜𝘁'𝘀 𝗳𝗶𝘃𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘀𝘁𝗮𝗰𝗸𝘀 𝗼𝗳 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀.

𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
Data predictions, forecasting, fraud detection
AWS SageMaker, Google Vertex AI, Azure ML

𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀
Pattern recognition in images, audio, text
TensorFlow, PyTorch, AWS Rekognition

𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜
Content creation - text, images, code
ChatGPT, Claude, Gemini, Midjourney

𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀
Autonomous task execution - bookings, CRM updates, ticket handling  
LangChain, CrewAI, Microsoft Copilot

𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
End-to-end orchestration - multiple use cases working together, not in isolation
Claude Code, OpenAI Codex, Devin

𝗤𝘂𝗶𝗰𝗸 𝘁𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻:
→ Layer 1: Your calculator that learned statistics
→ Layer 2: Your brain's visual cortex, digitized
→ Layer 3: Shakespeare's ghost, but caffeinated
→ Layer 4: The intern who never forgets and works weekends  
→ Layer 5: Your entire company, choreographed like a flash mob (but productive)

Most boardrooms are stuck arguing about Layer 3 chatbots.
Meanwhile, their fastest competitors already deployed Layer 5.

90% of "AI initiatives" are actually Layer 3 pilots that ignore the other four layers exist.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼 𝗼𝗻𝗲'𝘀 𝘀𝗮𝘆𝗶𝗻𝗴: 

𝗥𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗽𝗶𝗰𝗸𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲 𝗹𝗮𝘆𝗲𝗿.

It's understanding how they interconnect. How your Layer 1 predictions feed Layer 4 agent decisions. 

How Layer 2 pattern recognition powers Layer 3 content. 

How Layer 5 orchestrates all of it - not as isolated use cases, but as one end-to-end system where everything talks to everything.

The companies winning this aren't the ones with the shiniest demo.

They're the ones who see the full stack while everyone else is squinting at individual pieces.

Where is your organization on this map?

Infographic inspiration Alex Barády, thanks buddy! 

♻️ Repost if someone on your team needs to see this
➡️ Follow Rujuta Singh for practical & honest takes on AI transformation that actually work

Kids don't lack ambition. They lack examples.
They've seen 10,000 influencers. Zero marine biologists.
So guess which career sounds realistic?

A teacher just flipped this. She used AI to show students what they'd look like in their dream careers. Not some random person in scrubs. Them.

The results hit different:

➡️ The quiet kid who whispered "marine biologist" saw herself on a research vessel. Now she talks about ocean conservation like she's already running the lab.

➡️ The skeptic who said "people like me don't become lawyers" saw himself in a courtroom. Suddenly law school stopped feeling like someone else's story.

That's it. Two examples. But you get it.

𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗶𝘁, 𝘆𝗼𝘂 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗶𝘁. 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗯𝗲𝗹𝗶𝗲𝘃𝗲 𝗶𝘁, 𝘆𝗼𝘂 𝗽𝘂𝗿𝘀𝘂𝗲 𝗶𝘁.

We've got guidance counselors with binders full of career options.
But this teacher gave them something else.
While everyone's arguing about AI taking jobs, she used it as a mirror. 

A mirror that shows the future instead of just reflecting today.

♻️ Repost if your guidance counselor's career binder feels outdated
➡️ Follow Rujuta Singh for insights on technology, transformation, and human potential

Processing 100 pages of documents cost $8 in 2023.
Today? Four cents.

Intelligence prices collapsed.

Here's what happened to AI pricing in 18 months:

First, understand the pricing. AI companies charge by tokens. Tokens are chunks of text. Roughly 750 words = 1 million tokens.

Now watch the collapse:

→ GPT-4: $30-60 per million tokens (2023) → $3-10 (2025) [83% cheaper]
→ Budget models: $0.15-0.60 [99% cheaper than 2023]
→ DeepSeek V3: $0.27
→ Grok 4.1: $0.20

Building the models got cheaper too:

→ GPT-4 (2023): $100M+ estimated
→ DeepSeek V3 (2025): $5.6M claimed

Physical AI followed the same pattern. 
Humanoid robots went from "not for sale" in 2023 to $5,900 delivered in mid-2025. In 18 months.

Compare this to other technologies:

→ Internet bandwidth: 90% cost drop over 10 years
→ Solar panels: 85% cost drop over 10 years
→ AI intelligence: 95-99% cost drop in 18 months

This is the fastest price collapse in tech history.

What you get to build now:

→ Your coffee shop runs PhD-level market analysis
→ Your weekend project affords 24/7 AI customer support
→ Solo developers get enterprise coding tools
→ Your startup processes legal contracts for pennies
→ Your global team translates in real-time for pennies

The question changed.
Not "do we have budget for this?"
Now it's "what should we build?"

Your impossible idea became a weekend project.
Your "someday when we have budget" conversation died.

The printing press dropped knowledge costs 99% over 50 years.
AI dropped intelligence costs 99% in 24 months.

Swipe for the full price war timeline, what collapsed when, what becomes possible now and why this changes everything  →

The cost barrier died in 2025.
It's not cost anymore, it's knowing what to build. 

So what do you build when intelligence is basically free?

---
[Part 7 of 'Why 2025 felt so different' series]

Part 1 → Turing Test Died, no one came for the funeral https://lnkd.in/ebAgXswf
Part 2 → We ran out of tests for AI https://lnkd.in/eGpdgQjW
Part 3 → The Year Startup Playbook Broke https://lnkd.in/eQqUDVkz
Part 4 → Physical AI left the lab https://lnkd.in/epfikcAV
Part 5 → The rise of agentic AI https://lnkd.in/eaZP4KwC
Part 6 → Open source AI got serious https://lnkd.in/eiWe8np7
Today → Intelligence went on clearance
Next Up → AI got sued (a lot...)

♻️ Repost to help your network understand why 2025 changed everything and what became possible
→ Follow Rujuta Singh for surprising insights on technology & business transformation that actually matter

The cost barrier died in 2025.

Think this startup data doesn't apply to you?
That's exactly the problem.

I thought that too. So I started looking under the bonnet.

Turns out, the startup playbook didn't just change. It broke.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗳𝗼𝘂𝗻𝗱:

Cursor hit $100M in revenue in 12 months. With 20 people.

When Slack did the same thing in 2.5 years back in 2016 with 500+ employees, they were called the fastest-growing enterprise software company ever.

Cursor did it twice as fast with 96% fewer people.

Lovable? Eight months to $100M with 45 people.

Bolt? $20M in two months with 15 people.

(And honestly, I'm over here wondering what my team did all year.)

𝗧𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗴𝗲𝘁𝘀 𝘄𝗶𝗹𝗱𝗲𝗿:

By December 2025, 81 new unicorns in the US alone. Over 100 globally.

53% of them? AI companies.

(At this rate, "unicorn" will mean as much as a verified checkmark.)

But here's the number that made me do a double-take:

Revenue per employee for these 2025 unicorns is 𝟴𝟯% 𝗵𝗶𝗴𝗵𝗲𝗿 than older cohorts.

Same market outcomes. Fraction of the headcount.

𝗦𝗼 𝘄𝗵𝗮𝘁'𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴 𝗵𝗲𝗿𝗲?

It's not just "we have AI tools now."

The entire stack changed.

AI is handling work that used to require hiring.

Distribution happens through channels that didn't exist in 2022.

And your customers? They expect AI-native as baseline now.

And the 10-year startup journey? Compressed into 12 months.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗶𝗳 𝘆𝗼𝘂'𝗿𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗻 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝘆:

Think you're insulated because you're established?

Because you've been around for decades, have market share, or operate in a "protected" sector?

Watch this:

10-15 person unfunded teams are building multi-million dollar companies in 6-8 months. In every sector. Right now.

Your competition isn't just other enterprises anymore.

It's 20-person teams pulling $500M in revenue. Moving at speeds that seemed impossible three years ago.

The playbook you learned? It expired.

And the planning cycles, hiring models, and decision-making speed built for the old world?

They won't keep you relevant in the next two years.

This isn't a scare tactic. It's a wake-up call.

This is exactly why I started doing what I do now. 

Helping senior leaders build resilient strategies for what's coming.  Not just an AI strategy. An AI-integrated business strategy that helps you thrive, not just survive.

It starts with asking very different questions.

(This is what we do in my Strategy in a Day sessions.)

---
[Part of 'Why 2025 felt so different' series]
Last Week → Turing Test is dead and we ran out of test
This Week → Startup playbook changed

♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 if this changed how you see your competition. 
𝗙𝗼𝗹𝗹𝗼𝘄 Rujuta Singh for more reality checks on technology & innovation

The playbook you learned? It expired.

2023: AI 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 things.
2025: AI 𝗱𝗶𝗱 things.
That verb matters.

𝗧𝗵𝗲 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝘀𝗵𝗶𝗳𝘁:
→ AutoGPT (2023): Got stuck in loops, cost $50/task
→ Operator (2025): Books your flight while you're in a meeting

The economics flipped: $5-50 per task → $0.05 per task.
100x cheaper. Suddenly everything's automatable.

The market exploded: $3.7B → $7.8B in two years.
79% of companies now using agentic AI. (That's more than have a TikTok strategy.)

𝗪𝗵𝗮𝘁 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗱𝗼𝗲𝘀 𝗻𝗼𝘄:
→ Browse websites, book travel, shop
→ Write and debug code
→ Research for hours, deliver reports
→ Chain multi-step workflows—no supervision

𝗧𝗵𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿:

2023: AI writes your travel itinerary.
2025: AI books the trip.

2023: AI drafts the email.
2025: AI sends it.

2023: AI explains the bug fix.
2025: AI submits the pull request.

The question isn't "how smart is the AI?" anymore.

It's "what did it finish while I was in that meeting?"

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆'𝘀 𝘀𝗮𝘆𝗶𝗻𝗴:

These systems aren't replicating how you work. They're developing their own methods. Their own shortcuts. Their own logic.

By 2028, a third of enterprise software will include agentic AI. Up from nearly zero in 2024.

We taught AI to think. Turns out thinking wasn't the bottleneck—doing was.

→ Full breakdown: 12 slides on agentic AI—economics, capabilities, and what's coming

[Part 4 of 'Why 2025 felt so different' series]
Part 1 → We ran out of tests for AI https://lnkd.in/eZzxddWm
Part 2 → The Year Startup Playbook Broke https://lnkd.in/eHvAF5bG
Part 3 → Physical AI left the lab https://lnkd.in/e7H3mqDt
Today → The rise of agentic AI
Next week → Open source AI got serious

𝗦𝗼, 𝘄𝗵𝗶𝗰𝗵 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗮𝗿𝗲 𝘆𝗼𝘂 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗶𝗻𝗴 𝗳𝗶𝗿𝘀𝘁?
(Honest answer, not the LinkedIn version.)

♻️ Repost to share the 2025 shift with your network
🔔 Follow Rujuta Singh for insights on AI transformation and what's coming next

Agentic AI

You judged the guy in that 1986 Guardian ad.
We all did.

Running man. Chasing someone. Obviously a thief.
Then the camera pulled back.

He was saving a life.

LinkedIn loves the moral of this story: "Don't judge. Lead with empathy."

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝘄𝗼𝗻'𝘁 𝗮𝗱𝗺𝗶𝘁:

𝘠𝘰𝘶 𝘤𝘢𝘯'𝘵 𝘴𝘵𝘰𝘱 𝘫𝘶𝘥𝘨𝘪𝘯𝘨. 𝘠𝘰𝘶'𝘳𝘦 𝘨𝘰𝘪𝘯𝘨 𝘵𝘰 𝘥𝘰 𝘪𝘵 𝘢𝘯𝘺𝘸𝘢𝘺.

Your brain tagged "running man" as "threat" in 0.1 seconds. Evolution doesn't care about your empathy workshop.

The real skill? 𝘗𝘰𝘴𝘵𝘱𝘰𝘯𝘦 the judgment.

That split second where you think: "What else could this be?"

The driver who cut you off? Maybe rushing to the hospital.
The boss who snapped at you? Maybe just got devastating news.
The person who looks like they have it all together on LinkedIn? Maybe falling apart behind the screen.

You don't know. You can't know. Not from one angle.

𝗪𝗲 𝗹𝗼𝘃𝗲 𝗽𝗿𝗲𝘁𝗲𝗻𝗱𝗶𝗻𝗴 𝘄𝗲 𝗱𝗼𝗻'𝘁 𝗷𝘂𝗱𝗴𝗲.

But you sized me up from my profile photo. You decided if this post was worth reading from the first sentence. You're doing it right now.

The difference between smart people and everyone else?

Smart people know they're doing it.

They remember: 𝘐'𝘮 𝘴𝘦𝘦𝘪𝘯𝘨 𝘰𝘯𝘦 𝘤𝘢𝘮𝘦𝘳𝘢 𝘢𝘯𝘨𝘭𝘦.

There's always another angle. Always more context. Always something you're missing.

So next time your brain screams "threat" or "idiot" or "wrong" at someone?

Pause. Ask. Wonder.

Not because you're a good person.

Because you're probably wrong.

𝗪𝗵𝗮𝘁'𝘀 𝗮 𝘁𝗶𝗺𝗲 𝘆𝗼𝘂 𝗷𝘂𝗱𝗴𝗲𝗱 𝘁𝗼𝗼 𝗾𝘂𝗶𝗰𝗸𝗹𝘆?

♻️ Repost to remind someone that there's always another angle
→ Follow Rujuta Singh for more uncomfortable truths about how we actually think

Video credit: The Guardian's 1986 'Points of View' advert

In June 2025, the Godfather of AI said "be a plumber."
Looking at December's data - I'm not so sure anymore.

Geoffrey Hinton - Nobel Prize winner, AI pioneer - was asked on Diary of a CEO what career advice he'd give.

"It's going to be a long time before AI is as good at physical manipulation as us."

His reasoning: AI will automate knowledge work fast. But fixing pipes? That takes hands, problem-solving in messy real-world conditions. Physical work is protected.

Smart advice. Until 2025 happened.

𝗔𝗽𝗿𝗶𝗹: 𝗕𝗲𝗶𝗷𝗶𝗻𝗴 𝗛𝗮𝗹𝗳-𝗠𝗮𝗿𝗮𝘁𝗵𝗼𝗻.

Twenty-one humanoid robots lined up at the starting line. Not in a lab. Not in simulation. On actual streets with 12,000 humans watching.

What happened:
→ Mile 3: One robot's head falls off and rolls away
→ Mile 8: Another starts emitting smoke
→ Starting line: One collapses before the race even begins

Only 6 out of 21 finished.

But here's what matters: Six machines just ran 13.1 miles through real streets. Badly. Slowly. With multiple battery swaps.

But they ran.

𝗪𝗵𝗮𝘁 𝗲𝗹𝘀𝗲 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱:

→ Unitree launched a $5,900 humanoid (experts said that price was "years away")
→ Tesla targeting 100,000 Optimus units by 2026
→ Figure robots working BMW assembly lines
→ Amazon warehouses deploying Agility Digit robots
→ China closed 610 robotics deals in nine months
→ Underground robot fight clubs with live betting (yes, really)

Morgan Stanley projects $5 trillion market by 2050. That's twice today's entire automotive industry.

While we debated whether we'd run out of tests for digital AI, physical AI was learning to move through our world.

The timeline everyone predicted? Shattered.

𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁:

Those Beijing failures weren't embarrassments. They were data points.

Every smoking robot. Every fallen head. Every collapse. That's how physical AI improves - in public, in real-time, with millions watching.

The systems learning to move through our world aren't trying to replicate human movement. They're developing their own.

Hinton said "it's going to be a long time."
The data says: maybe not.

---

[Part 3 of 'Why 2025 feels so different' series]
Part 1 → https://lnkd.in/eZzxddWm
Part 2 → https://lnkd.in/eHvAF5bG
This week → Physical AI left the lab
Next week → Jobs, military applications, and what happens when robots scale

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗿𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗿𝘂𝗻 𝗿𝗮𝗰𝗲𝘀?
→ Swipe to know more about the humanoid revolution you're not watching

♻️ Repost if you didn't see this coming
🔔 Follow Rujuta Singh for insights on AI transformation and what's coming next

Smart advice. Until 2025 happened.

Five ideas rewired how I think in 2025.
Not just me. Based on the response, hundreds of you too.

Because these aren't productivity hacks.
They're ideas that change societies. Not just minds.

You asked for them all in one place.

So here they are.

→ 𝗧𝗵𝗲 𝟰 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗣𝗼𝘄𝗲𝗿
Most leaders default to "power over." The best ones flow between all four.

→ 𝗧𝗵𝗲 𝟴 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝘄𝗲
You feel it 2-3 times per week. You just forget to notice.

→ 𝗙𝗿𝗶𝗲𝗻𝗱𝘀𝗵𝗶𝗽 𝗦𝗸𝗶𝗹𝗹𝘀 𝗡𝗼𝗯𝗼𝗱𝘆 𝗧𝗮𝘂𝗴𝗵𝘁 𝗨𝘀
871K deaths annually from loneliness. We optimised for convenience. Now loneliness is a body count.

→ 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗖𝗼𝗻𝘀𝗰𝗶𝗼𝘂𝘀𝗻𝗲𝘀𝘀
What if we're not separate? What if we're particles of one awareness?

→ 𝗦𝗲𝘃𝗲𝗻 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲
The Haudenosaunee evaluate every decision by its impact on the next 7 generations. 150 years ahead.
Because the impact of our choices, behaviours, mistakes spans across 7 generations.

We optimise for 90 days. 
What if we evaluated every major decision for its impact over 150 years?

-------------------------------------
Do you see the pattern? They all have a common thread : 

𝗪𝗵𝗲𝗻 𝘁𝗵𝗲 𝘀𝗲𝗹𝗳 𝘀𝗵𝗿𝗶𝗻𝗸𝘀, 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗲𝘅𝗽𝗮𝗻𝗱𝘀.

The Western world went all-in on individualism. We needed that freedom. But we went too far.

What all five ideas point to: 𝗵𝘂𝗺𝗮𝗻 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹. 𝗜𝘁'𝘀 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹.

The self is smaller than we think.
The collective—and our need for each other—is bigger than we realise.

(Which, honestly, is both relieving and terrifying.)

𝗪𝗮𝗻𝘁 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗲𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻?

𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗽𝗼𝘀𝘁𝘀:

→ The 4 Types of Power https://lnkd.in/ecA5TJyk
→ The 8 Types of Awe https://lnkd.in/eMheFm9Q

→ Friendship Skills Nobody Taught Us 
https://lnkd.in/eJsshx2k
https://lnkd.in/eFzMN76q
→ Collective Consciousness https://lnkd.in/e-wAHYfa
→ Seven Generations Principle https://lnkd.in/e-YufrYM

If you missed these in 2025, maybe they'll change how you think in 2026.

♻️ Repost if you're ready to think beyond 90 days.
➡️ Follow Rujuta Singh ta Singh for ideas that change societies, not just minds.

Why do people have two mobile phones but no toilet?
Someone asked that question in Ghana in 2010.
18 months later, 4.8 million people had access to sanitation.

Ghana, 2010: 
4.8 million people with no toilet. 
Women and children risking assault at night. $79 million lost annually.

Governments tried for decades. Nothing worked.

Then a team showed up with no preset solution. Just questions.

"Why do people have two mobile phones but no toilet?"
"What would make sanitation something people want?"

They spent weeks listening before designing anything.

Simple questions. Huge impact. 

𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗹𝗲𝗮𝗿𝗻𝗲𝗱:

People didn't want charity toilets. 
They wanted what we all want. 
Privacy. Dignity. Not having to walk through dark alleys at 2am to use a public toilet.

𝗦𝗼 𝘁𝗵𝗲𝘆 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗻𝗲𝘄 𝘀𝘆𝘀𝘁𝗲𝗺:

Not the big outdoor porta-potties you see at festivals.

Small, in-home container toilets. 
Delivered and installed inside your home. Serviced 2-3x per week. Monthly fee via mobile money. Waste to fertiliser.

The innovation wasn't the toilet. It was the service: dignity at home + circular economy.

𝟭𝟴 𝗺𝗼𝗻𝘁𝗵𝘀 𝗳𝗿𝗼𝗺 𝗳𝗶𝗿𝘀𝘁 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝘁𝗼 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘀𝗲𝗿𝘃𝗶𝗰𝗲.

Small cross-functional team. 
No three-year roadmap. 
No pilot of the pilot.

𝗧𝗼𝗱𝗮𝘆:

250,000+ people served daily in Kenya.
6,000+ toilets across Ghana, South Africa, Philippines, Peru, Haiti.
Cape Town and Manila adopted it.
69% cheaper than sewers.
WHO-recognised. Nature Water-published (2025).

𝗦𝗼 𝗵𝗲𝗿𝗲'𝘀 𝗺𝘆 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻:

If they solved sanitation for 4.8M people with zero infrastructure in 18 months, why is your business transformation entering year 3?

Why are you having governance meetings about how to do governance?
Why are you still in "discovery phase"?

𝗧𝗵𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 𝗶𝘀𝗻'𝘁 𝘀𝗲𝗰𝗿𝗲𝘁:

Listen before solving. Reframe the problem. Design a system, not just a product. Prototype fast, iterate faster.

It's called design thinking. Been around for decades.

𝗧𝗵𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝗶𝘀𝗻'𝘁 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆. 𝗜𝘁'𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵.

This is why I love design thinking. And why we use it for every transformation at Solved Together.

It works for toilets in Ghana. It works for business transformation. Same principles.

Problem-first, not tool-first. Rapid co-creation, not endless roadmaps. Designed with your people, not imposed on them.

If sanitation in Ghana took 18 months, your transformation doesn't need 3 years.

What's really stopping you?

If you're tired of governance meetings about governance and ready to move fast, let's talk.

♻️ Repost if you're tired of transformations that take longer than they should
➡️ Follow Rujuta Singh for surprising & honest insights on business transformation and innovation

Solved Together

Ghana, 2010: 4.8 million people with no toilet

2012: Barclays was hemorrhaging trust.
The board's plan: Push everyone online. Close branches.
The CEO's plan: Sit down with customers. Over tea.

Post-Libor scandal. Trust at rock bottom. Branches bleeding £450 per customer.

Every consultant said the same thing: Force digital. Close physical. Survive.

𝗧𝗵𝗲 𝗯𝗼𝗮𝗿𝗱 𝘄𝗮𝘀 𝗿𝗲𝗮𝗱𝘆 𝘁𝗼 𝗽𝘂𝗹𝗹 𝘁𝗵𝗲 𝘁𝗿𝗶𝗴𝗴𝗲𝗿.

But CEO Antony Jenkins asked: "What if we helped customers before abandoning them?"

So they talked to actual humans.

𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱:

67% worried about getting scammed.
71% terrified of making costly mistakes.
89% needed help beyond "click here to log in."

12.6 million UK adults lacked basic digital skills.

𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘄𝗮𝘀𝗻'𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗜𝘁 𝘄𝗮𝘀 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲.

While the board wanted to force adoption, customers were paralyzed by fear.

𝗦𝗼 𝗕𝗮𝗿𝗰𝗹𝗮𝘆𝘀 𝗱𝗶𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗻𝗼𝗯𝗼𝗱𝘆 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱:
They turned 16,000 volunteer bankers into teachers.

Tea & Teach sessions in every branch. 
Bring your device, your fears, your grandmother. 
No judgment. Just tea, a digestive and human help. 

Free Wi-Fi in all 1,600 branches. Europe's largest provider in 6 months.

2,000+ library partnerships. Mobile units for rural areas.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:

Skill retention: Up 64%.
Customer confidence: Doubled.
31% of customers became teachers themselves.

Trust: Up 33%.
Customer consideration: Up 130%.
Digital-only customers: 2% → 13%.

One woman in her 90s learned to book holidays online.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗻𝗼𝗯𝗼𝗱𝘆 𝘁𝗮𝗹𝗸𝘀 𝗮𝗯𝗼𝘂𝘁:

The fears customers had about digital banking in 2013? Fear of mistakes. Fear of the unknown. Fear of starting.

𝗧𝗵𝗮𝘁'𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂𝗿 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 𝗳𝗲𝗲𝗹 𝗮𝗯𝗼𝘂𝘁 𝗔𝗜 𝘁𝗼𝗱𝗮𝘆.

Barclays didn't force adoption. They earned it through patience, presence, and tea.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗱𝗲𝘀𝗶𝗴𝗻 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴.

Empathize first. Discover the real problem. Design around humans.

This is why I share these case studies every week. Real innovation isn't shiny technology. 
It's people solving giant problems and changing lives. That inspires me.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘄𝗲 𝗱𝗼 𝗮𝘁 𝗦𝗼𝗹𝘃𝗲𝗱 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿.

We apply innovation frameworks such as design thinking to your complex challenges: AI adoption, transformation, operational chaos. Outcome-based. Human-centered.

You don't need Barclays' budget. That's why we exist - making these approaches accessible to scale-ups and growing businesses.

𝗕𝘂𝘁 𝗻𝗼𝘁 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲 𝗶𝘀 𝗿𝗲𝗮𝗱𝘆.

If you want different outcomes, you need to try different approaches

Ready to solve differently? Let's solve together.

♻️ Repost if you think digital transformation needs more human connection
➡️ Follow Rujuta Singh for transformation insights that put humans first 

#solvedtogether Solved Together #designthinking #innovation #banking

How Barclays turned 16000 bankers into teachers

$560M in losses. Four states shut down.
The solution wasn't in the data.
It was in watching nurses scribble patient notes on their scrubs.

2001 => US healthcare was broken. Still is, honestly.
But Kaiser cracked something most companies miss.

They stopped staring at dashboards.
Started staring at humans.

𝗧𝗵𝗲𝘆 𝘀𝗵𝗮𝗱𝗼𝘄𝗲𝗱 𝟭𝟮-𝗵𝗼𝘂𝗿 𝗻𝘂𝗿𝘀𝗶𝗻𝗴 𝘀𝗵𝗶𝗳𝘁𝘀.
Asked nurses to draw self-portraits of their workday.
(They drew themselves with sad faces.)
Sat with patients during shift changes.

This is design thinking before it had a fancy name.
Empathize. Observe. Understand the human problem first.

What they discovered? Three invisible problems killing their system.
Problems so obvious once you saw them, you'd wonder how anyone missed them.

The breakthrough:
Systems were designed for processes, not for the humans using them.

(Swipe to see what shift change "ghost towns," medication errors, and room design actually revealed →)

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? 
Patient satisfaction jumped 70 percentiles.
Medication interruptions cut in half.
Nurse satisfaction up 34%.

8,000 nurses' daily work transformed.
These three fixes became IHI global best practices.
All from watching where people stood.

Here's what nobody talks about:
This was 25 years ago. If US healthcare could transform this way, what's everyone else's excuse?

"We're too complex." 
"Our industry is different."
"You don't understand our constraints."

Kaiser had 400K+ employees. Multiple states. Life-or-death decisions every minute.
They figured it out by watching humans, not building better dashboards.

𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗳𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → strategic investments.
𝗧𝗼𝗼𝗹-𝗳𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 → expensive experiments.

Before you redesign the system, watch the system.
The answer might be standing in the wrong room.

♻️ Repost if you think more companies need to stop staring at dashboards and start watching humans.
➡️ Follow Rujuta Singh for practical insights on transformation & innovation

$560M in losses.

Your interview process is filtering out the best candidates.
Not the lazy ones. Not the unqualified ones.
The 𝘢𝘥𝘢𝘱𝘵𝘪𝘷𝘦 ones.

Over the last few weeks, I've been talking about adaptability.
Why it's our survival superpower: https://lnkd.in/es9SRz_v
How to spot it in the wild: https://lnkd.in/ejyawZnh
And why we need it now more than we ever needed before : https://lnkd.in/eVxx8tGe

Today? How to actually 𝘩𝘪𝘳𝘦 for it.

Because if adaptability is the most critical survival skill your company needs right now — and it is — you better make sure you're testing for it.

Here's the problem:

𝗠𝗢𝗦𝗧 𝗖𝗢𝗠𝗣𝗔𝗡𝗜𝗘𝗦 𝗔𝗥𝗘 𝗦𝗧𝗜𝗟𝗟 𝗔𝗦𝗞𝗜𝗡𝗚:

"Tell me about yourself."

"Where do you see yourself in 5 years?"

"What's your greatest weakness?"

You know what these test for?
How well someone prepared for predictable questions.

That's not adaptability. That's 𝘳𝘦𝘩𝘦𝘢𝘳𝘴𝘢𝘭.

In a world where today's hot skill is tomorrow's "oh, AI does that automatically now"?
What someone knows matters far less than how fast they can learn what nobody knows yet.

𝗛𝗘𝗥𝗘'𝗦 𝗪𝗛𝗔𝗧 𝗜 𝗔𝗦𝗞 𝗜𝗡𝗦𝗧𝗘𝗔𝗗:

"Tell me about a time everything changed overnight. How did you handle it?"

"What's the most recent thing you had to unlearn?"

"What are you terrible at but actively working on?"

"What do you think will be irrelevant in your field in 5 years?"

"If you had to learn [insert completely new skill] in 30 days, how would you approach it?"

Notice the pattern?

None of these questions test what they 𝘬𝘯𝘰𝘸.
They test how they 𝘵𝘩𝘪𝘯𝘬 when they don't know.

That's the difference between hiring for expertise and hiring for adaptability.

One gets you someone who can do today's job.
The other gets you someone who can do the job that 𝘥𝘰𝘦𝘴𝘯'𝘵 𝘦𝘹𝘪𝘴𝘵 𝘺𝘦𝘵.

What's the most unconventional interview question you've used to spot adaptability?

And which of these questions would you steal for your next interview?

♻️ Repost if your hiring team needs to see this.
Follow Rujuta Singh for more on thriving when the world won't stop changing.

Business is chess. King = sales. Queen = marketing.
Took me 15 years to figure out what this video teaches in 60 seconds.

You've probably seen it. Rooks = streamlined processes. Pawns = strong foundations. Horses = creativity.

I wish someone had shown me this 15 years ago.

I started life firmly in pawn territory.

Building foundations is my superpower. Systems that actually worked. Processes that didn't fall apart when someone sneezed. The behind-the-scenes magic nobody sees but everyone needs.

And honestly? I don't regret it.

I've walked into businesses where the king and queen were doing their thing beautifully. Clients everywhere. Revenue flowing.

But the foundations were crumbling. Customers complaining. Systems creating more chaos than they solved. Nobody could scale because the base couldn't hold the weight.

That's when pawn people save the day.

𝗕𝘂𝘁 𝗵𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳.

Because I mastered the pawn moves, I did not get to learn how the queen works, until I started running my own business.

As an executive coach and career coach, I sit with young people all the time.

I meet 22-year-olds who've applied to 500 jobs and heard nothing back. Bright. Capable. Completely lost.

Then I meet 19-year-olds running AI businesses from their bedrooms.

Same world. Completely different games.

The old playbook stopped working. But new doors opened for people who understand one thing:

𝗛𝗼𝘄 𝘁𝗼 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝗻𝗲𝗲𝗱 𝗶𝘁.

That's not about becoming a salesperson or a marketer. (Although, that's a great place to start)

It's about understanding the principles. How attention works. How trust builds. How people decide.

That knowledge is portable confidence. Take it anywhere. Apply it to anything.

Your startup crashes? Build another. Industry shifts? You pivot. Job disappears? Create your own.

Every chess piece matters. The rooks. The horses. The pawns.

But if you're starting out right now, in this exact moment of chaos and possibility?

Learn how the king and queen moves first. Everything else gets easier after that.

♻️ Repost if this video made you rethink your whole strategy.
➡️ Follow Rujuta Singh for perspectives that question everything, including themselves.

January 27, 2025. Nvidia lost $600 billion in a single day. The largest single-company loss in U.S. stock market history. The cause?

A Chinese startup that spent less on their AI model than most companies spend on management consultants.

DeepSeek released a reasoning model that matched OpenAI's best.

They built it for $6 million.

OpenAI spent $100+ million on GPT-4.

The market panicked.

So, some quick context:
𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 = code and weights are public. Anyone can download, use, and modify them.
𝗖𝗹𝗼𝘀𝗲𝗱 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 = kept private. You can only access them through APIs.

Trump called it "a wake-up call."
Marc Andreessen called it "one of the most amazing breakthroughs I've ever seen."

The assumption that you need billions in compute to build frontier AI? Gone.

And everyone noticed.

𝗢𝗵, 𝗮𝗻𝗱 𝗼𝗻𝗲 𝗺𝗼𝗿𝗲 𝘁𝗵𝗶𝗻𝗴.

OpenAI. Yes, that OpenAI. The company with "Open" literally in its name.

Their models? Closed. Not open source. Closed.

When Sam Altman started OpenAI in 2015, the whole point was to make AI open source. That's why they chose the name. 

But very quickly, turns out their vision—and probably the very definition of "open source"—wasn't profitable enough. 

So it all went back inside the box. With a lock and key.

Then DeepSeek shows up with actual open source models and punches them in the face with their own founding principle.

After the chaos, Sam Altman posted: "We've been on the wrong side of history here."

You don't say.

𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗶𝗻 𝟮𝟬𝟮𝟱:

Open source AI became a serious contender for the first time.

DeepSeek proved you don't need billions to compete. Training costs dropped dramatically. Every major AI lab had to rethink their strategy.

While AI is disrupting the rest of the world, AI world itself got disrupted.

That's the story of 2025. One of many....

Swipe for the full timeline, the enterprise reality check, and what happens next →

---

[Part 5 of 'Why 2025 felt so different' series]
Part 1 → Turing Test Died and no one came for the funeral https://lnkd.in/eZzxddWm
Part 2 → We ran out of tests for AI https://lnkd.in/eZzxddWm
Part 3 → The Year Startup Playbook Broke https://lnkd.in/eHvAF5bG
Part 4 → Physical AI left the lab https://lnkd.in/e7H3mqDt
Part 5 → The rise of agentic AI https://lnkd.in/egxtAmnU
𝗧𝗼𝗱𝗮𝘆 → 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗴𝗼𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀 
Next Up → Intelligence had a clearance sale

Nvidia lost $600 billion

Two wolves. Same frozen lake. Same thirsty pack.
One ended up alone. The other started a movement.

Two packs. Frozen lake. One pathetic hole in the ice.

𝗪𝗼𝗹𝗳 𝟭: Struts to the hole. Drinks first. His pack stands in line like good little soldiers.

We've all worked for this guy.

𝗪𝗼𝗹𝗳 𝟮: Does something that looks completely stupid. Instead of drinking, starts jumping on the ice. Breaking it. Making chaos.

His pack stares at him like he's lost it.

Then a lightbubl goes off....

𝗘𝘃𝗲𝗿𝘆 𝘀𝗶𝗻𝗴𝗹𝗲 𝘄𝗼𝗹𝗳 𝗮𝗯𝗮𝗻𝗱𝗼𝗻𝘀 𝗪𝗼𝗹𝗳 𝟭. They sprint over and start copying Wolf 2. Jumping. Smashing ice. Turning that sad little hole into a wolf water park.

Wolf 1 ends up drinking alone from his tiny puddle.
Wolf 2's new mega-pack is sharing a lake.

Awkward.

𝗬𝗼𝘂𝗿 𝘁𝗲𝗮𝗺 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗸𝗻𝗼𝘄𝘀 𝘄𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝘆𝗼𝘂 𝗮𝗿𝗲.

Boss vs leader? The difference is stupidly simple:

→ Bosses hoard. Leaders expand.
→ Bosses command. Leaders demonstrate.  
→ Bosses protect turf. Leaders create abundance.

Wolves figured this out before we did.

Shout out to Luís Rodrigues for the video inspiration

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Most project management advice tells you how to work faster. This isn't that. This is about questioning whether half your job should exist in the first place.

The best PMs in 2026 aren't working harder. They're thinking differently.

They're not asking "How do I do this faster?"
They're asking "Does this even need to be done this way?"

That's AI-native thinking.
And it's a completely different approach to project management.

𝟭𝟭 𝘄𝗮𝘆𝘀 𝘁𝗼 𝗿𝗲𝘁𝗵𝗶𝗻𝗸 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜:

𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 & 𝗦𝗲𝘁𝘂𝗽

1️⃣ 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴
↳ Feed your requirements into AI. Get a draft plan to work from instead of starting with a blank page.

2️⃣ 𝗧𝗲𝗮𝗺 𝗔𝘀𝘀𝗶𝗴𝗻𝗺𝗲𝗻𝘁
↳ Put team skills and project tasks into AI. Let it suggest matches instead of just picking whoever's available.

𝗗𝗮𝗶𝗹𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀

3️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴
↳ Feed scattered updates into AI. Get a coherent status report instead of copying and pasting from 15 sources.

4️⃣ 𝗠𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁
↳ Record meetings. Let AI pull out action items so you're not frantically taking notes while trying to participate.

5️⃣ 𝗖𝗹𝗲𝗮𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻
↳ Give AI your update. Ask it to write versions for different audiences (executives want summaries, teams want details).

Because apparently everyone speaks a different language in the same company.

𝗥𝗶𝘀𝗸 & 𝗤𝘂𝗮𝗹𝗶𝘁𝘆

6️⃣ 𝗘𝗮𝗿𝗹𝘆 𝗥𝗶𝘀𝗸 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻
↳ Ask AI to review your project data for warning signs you might miss when you're deep in the weeds.

7️⃣ 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹
↳ Run deliverables through AI for a quality check before clients see them (and silently judge your entire existence).

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻

8️⃣ 𝗦𝗰𝗵𝗲𝗱𝘂𝗹𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻
↳ When priorities shift (because they always do), ask AI to help you rework timelines instead of starting from scratch.

9️⃣ 𝗕𝘂𝗱𝗴𝗲𝘁 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴
↳ Feed your spending data into AI. Ask it to spot patterns and predict overruns before your boss notices.

🔟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲
↳ Put your project docs into AI. Ask questions in plain language instead of digging through 47 Slack channels.

𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁

1️⃣1️⃣ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
↳ Ask AI to analyze past projects and spot patterns. Learn what worked and what didn't before starting the next one.

None of this happens automatically.
It requires rethinking how you work.

But once you shift from "How do I work faster?" to "How should this work be done?" everything changes.

That's the 2026 advantage.

Original image & concept credits Alex Barády 

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A baker just out-marketed an entire advertising agency. In one afternoon.

I'm watching this invideo AI ad on repeat.

A baker. With flour on her apron. Creates cinema-quality advertising in one afternoon. 
Without leaving her kitchen.

My brain is officially broken.

𝗧𝗵𝗶𝘀 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮 𝗰𝗼𝗼𝗹 𝘁𝗼𝗼𝗹. 𝗜𝘁'𝘀 𝗮 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝘀𝗵𝗶𝗳𝘁.

But here's the thing. This isn't about advertising. Or videos.

This is about customer expectations fundamentally changing. Everywhere.

Remember when international mail took weeks? 
Now you're annoyed if your package from Tokyo takes more than two days.

Remember when you needed a solicitor just to understand your basic legal rights?
Now ChatGPT explains contract clauses at 2am while you're in your pajamas.

Remember when Airbnb disrupted hotels? Uber changed transport?
Each revolution picked ONE sector.

AI isn't picking.
It's hitting everything. Simultaneously.

𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗮𝗰𝗿𝗼𝘀𝘀 𝗘𝗩𝗘𝗥𝗬 𝘀𝗲𝗰𝘁𝗼𝗿:

Medical diagnosis? Patients show up with AI-researched questions.
Education? Kids expect personalized learning, not one-size-fits-all lectures.
Financial advice? Your wealth manager better offer more than what Copilot can give.
Architecture? Clients arrive with AI-generated designs saying "something like this."

Two years from now, every industry will look unrecognizable.

This ad? 
First Avengers teaser vibes.
A tiny glimpse of what's coming.

Are we terrified? Absolutely.
Are we excited? Also absolutely.

Has any generation felt both emotions this intensely... at the same time?

In my strategy sessions, I ask CEOs and founders one question:
"Where do you play?"

Now I'm asking us as humans the same thing:
Where do WE play?

What do we offer that AI can't?
Strategy. Judgment. Cultural nuance. Emotional intelligence. Context.

The tools got scary powerful.
The humans using them better get scary intentional about where they add value.

Where do you see AI having the biggest impact?

Video credit: theartificialintelligence on IG

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A gardening program outperformed a police force at fighting crime.

Not a headline. A randomised controlled trial. Published in PNAS.

Philadelphia had a gun violence problem.
80% of homicides by firearm. 4 out of 5 shootings unsolved. Same blocks, same corners, year after year.

The city did what every city does: more police, more patrols, more enforcement.

It wasn't working.
Then researchers asked a different question:

"𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗰𝗿𝗶𝗺𝗲 𝗰𝗹𝘂𝘀𝘁𝗲𝗿 𝗛𝗘𝗥𝗘?"
They mapped it. Walked it. Studied it.

𝗪𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗳𝗼𝘂𝗻𝗱:
40,000 abandoned lots across the city. Overgrown weeds. Dumped trash. No visibility. Perfect cover for drug deals and assaults.

The lots weren't just ugly. They were designed for crime.

𝗦𝗼 𝘁𝗵𝗲𝘆 𝘁𝗿𝗶𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁:
Clear the trash. Plant grass. Install a low wooden fence. Maintain it monthly.
Cost: $1,500 per lot. Time: One day.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:
→ 29% drop in gun violence (poorest neighborhoods) 
→ 9.2% reduction in all crimes 
→ 40% decrease in depression among residents 
→ $7.43 returned for every $1 spent '
→ Model replicated in Flint, Michigan (40% crime drop)

No new technology. No complex systems. Just grass, fences, and care.

A consultant recently shared something that stuck with me:

"𝘛𝘩𝘦𝘪𝘳 $2𝘔 𝘈𝘐 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦 𝘸𝘢𝘴 𝘧𝘢𝘪𝘭𝘪𝘯𝘨 𝘯𝘰𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝘵𝘩𝘦 𝘵𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺, 𝘣𝘶𝘵 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘯𝘰𝘣𝘰𝘥𝘺 𝘧𝘪𝘹𝘦𝘥 𝘵𝘩𝘦 𝘤𝘰𝘭𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘪𝘰𝘯 𝘱𝘳𝘰𝘣𝘭𝘦𝘮 𝘧𝘪𝘳𝘴𝘵."

"𝘞𝘦'𝘳𝘦 𝘣𝘳𝘪𝘯𝘨𝘪𝘯𝘨 𝘸𝘰𝘳𝘭𝘥-𝘤𝘭𝘢𝘴𝘴 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘴 𝘪𝘯𝘵𝘰 𝘣𝘳𝘰𝘬𝘦𝘯 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘢𝘯𝘥 𝘸𝘰𝘯𝘥𝘦𝘳𝘪𝘯𝘨 𝘸𝘩𝘺 𝘳𝘦𝘴𝘶𝘭𝘵𝘴 𝘭𝘢𝘨."

𝗦𝗮𝗺𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁.

When AI transformation fails, people blame the tool.

But the problem isn't AI. It's unclear goals. Misalignment. Broken group dynamics.

Philadelphia didn't need more police. They needed to find the real problem.

𝗦𝘁𝗮𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗽𝗮𝗰𝗲 𝗹𝗼𝗻𝗴 𝗲𝗻𝗼𝘂𝗴𝗵 𝘁𝗼 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺.

Once you do, the solution is the easy part.

This is what we do at 𝗦𝗼𝗹𝘃𝗲𝗱 𝗧𝗼𝗴𝗲𝘁𝗵𝗲𝗿.
Problem-first, not tool-first. Find the real blocker before building the solution.
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A Gardening Program Outperformed the Police Force

Every boss is looking for the same thing.
Obama breaks down why so few people have it.
Watch him explain it.

The rarest skill isn't being smart or articulate.
It's being someone who actually solves problems instead of just explaining them well.

Most people are brilliant explainers. They'll analyze the hell out of any problem. Write thoughtful emails about potential roadblocks. Present gorgeous slides about theoretical solutions.

But ask them to actually own it? Crickets.

These days, it is getting even weirder. Because I can tell an AI agent "handle this project" and it responds instantly. No self-doubt. No "but what if." 
Just "I'll take care of it."

As an executive and career coach, I see this all the time with young professionals. 
Both hope and worry in their eyes. Smart. Capable. Hardworking.

But when it's time to claim ownership? They freeze.

It's not capability they're missing. It's conviction.

Imagine: Your boss mentions the client onboarding is a disaster. 
Everyone nods thoughtfully. Someone suggests a committee. Another person wants to "circle back on it."

One person says "I'll fix it by Friday."

That person becomes irreplaceable.

We're living through the biggest career shift in decades. 
The old playbook is toast. AI is automating everything that can be scripted.

But you know what can't be automated? Someone who takes real ownership and delivers.

Fresh grads - this is your secret weapon.

Not your GPA. Not your internship at that fancy company. 
Your willingness to say "Leave it with me" and actually follow through.

Trust gets built one delivery at a time.

Repost this if you know someone starting their career who needs to hear it.
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It's now February and I still can't stop thinking about 2025.
Not nostalgia. Whiplash.

The year everything we thought we knew stopped working.

We hit the steep part of that exponential curve. Everything happening at once.
Startups. Benchmarks. Humanoids. Open source. Legal battles.

So naturally, I wrote eight posts dissecting every shift.
Because apparently overthinking and documenting is how I process existential acceleration.

Here's all eight in one place:

𝟭 → 𝗧𝘂𝗿𝗶𝗻𝗴 𝗧𝗲𝘀𝘁 𝗗𝗶𝗲𝗱, 𝗻𝗼 𝗼𝗻𝗲 𝗰𝗮𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗳𝘂𝗻𝗲𝗿𝗮𝗹
We kept using a 70-year-old test designed for 1950s computers on systems that write poetry and debug code. The moment ChatGPT convinced millions it was human-like, we realized we'd been measuring the wrong thing entirely.

𝟮 → 𝗪𝗲 𝗿𝗮𝗻 𝗼𝘂𝘁 𝗼𝗳 𝘁𝗲𝘀𝘁𝘀 𝗳𝗼𝗿 𝗔𝗜
MMLU, HumanEval, HellaSwag - all maxed out. Researchers spent more time creating new tests than improving models. 

𝟯 → 𝗧𝗵𝗲 𝗬𝗲𝗮𝗿 𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗕𝗿𝗼𝗸𝗲
Move fast and break things? Not when AI builds your entire product in a weekend. Solo founders with AI assistants lapped startups that took months to launch.

𝟰 → 𝗣𝗵𝘆𝘀𝗶𝗰𝗮𝗹 𝗔𝗜 𝗹𝗲𝗳𝘁 𝘁𝗵𝗲 𝗹𝗮𝗯
Robots stopped being research projects and became coworkers. Tesla's Optimus folding laundry. Surgical robots adapting mid-operation. The real world became their playground.

𝟱 → 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜
AI stopped waiting for prompts and started taking initiative. We went from asking AI questions to giving it objectives and walking away.

𝟲 → 𝗢𝗽𝗲𝗻 𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗴𝗼𝘁 𝘀𝗲𝗿𝗶𝗼𝘂𝘀
DeepSeek's R1 matched GPT-4's reasoning for a fraction of the cost. Open source went from "interesting experiment" to "existential threat to Big Tech." The oligopoly cracked.

𝟳 → 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗲𝗻𝘁 𝗼𝗻 𝗰𝗹𝗲𝗮𝗿𝗮𝗻𝗰𝗲
What cost millions in computing power became pennies. Intelligence became a commodity. The question shifted from "can we afford AI?" to "can we afford not to use it everywhere?"

𝟴 → 𝗔𝗜 𝗴𝗼𝘁 𝘀𝘂𝗲𝗱 (𝗮 𝗹𝗼𝘁...)
Copyright battles. Privacy lawsuits. Regulatory investigations. 2025 was when society started catching up to technology that had outpaced legal frameworks by years.

-------------------------------------

It was the year I realized I'd stopped being surprised.  

Wait, AI can do that now? Oh. Of course it can. 
DeepSeek matching GPT-4 for pocket change? Makes sense somehow.
Humanoids running an actual marathon? Sure. Why not.

That's when I knew we'd crossed the inflection point. 
When breakthrough became Tuesday.

What assumptions about AI are you still holding that 2025 already broke?

Find all the 8 posts with hyperlinks here, neatly organised, just for you 
https://lnkd.in/eu8wua5X

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