Why Networking Alone Won’t Build a Successful Career (And What You Need Instead)

Networking only works when the product being sold via it is top-class. It’s important to get this order right in any career strategy.

In my profession as a VC, I tend to cross paths with many people whose main professional superpower is networking. They tend to be visible at most events, are very active on social media, have at least a surface-level connection with most people who matter in their specific areas, and are likely to say, “You are pursuing this? Oh, I know XYZ who is also in this space really well”.

In most cases, the gigs these folks like to pursue include running communities, creating podcasts, running venture syndicates/ SPVs, GTM consulting/advisory, holding ecosystem events, and engaging with govt. bodies, think tanks & non-profits, etc.

In private, they often confide in me about their desire to take their careers to the next level, both monetarily as well as from an influence perspective. They feel like mere small cogs in the wheel, and despite doing a lot of grunt work, get only a small piece of the pie, with founders, domain operators, and investors grabbing a majority of the value created.

I have thought hard about this predicament, and one conclusion I have come to is that networking skills by themselves aren’t enough. They need to be combined as an amplifier alongside a core set of one or more of the following:

(1) Technical skills

(2) Education & work pedigree

(3) Proven track records in a domain

Without this core, a pure networker is categorized at the lower ends of the business hierarchy by various stakeholders in the ecosystem.

A few examples to illustrate this:

  • Shreyas Doshi being a great content creator, amplifies his top-tier product management career. Somebody just churning out product content without a proven product track record to back it will be considered a mere content marketer as opposed to a credible expert.
  • Fred Wilson (of Union Square Ventures) being an excellent writer, gives an extra edge to his proven skills as a VC. Somebody trying to “act” like a VC on LinkedIn & at events, trying to hustle into deals via SPVs/ syndicates without the core skills or pedigree of what it takes to become a solid venture investor, will be viewed as a venture grifter in a few years’ time by the ecosystem.
  • Ryan Hoover combined his main spike of community-building with his technical chops, both as a founder & product builder, to first create Product Hunt and then parlay it into venture investing via Weekend Fund. Somebody who is just a community creator/ curator, but without any edge-chops at a sector or operating level, will end up only as an amplifier for other companies, founders, and investors, and capture only a minute piece of the value.

I believe this is an important insight that is even more relevant in this age of social media, influencers, and communities. Especially in Tech, both companies & careers seem to be over-indexed on building “distribution” for themselves, without realizing that distribution will work only when the core “product” is top-class.

For any youngsters out there reading this, I urge you to first focus on transforming yourself into a compelling & differentiated “product”, which would typically require studying at the best quality university you can crack, working at the topmost market-leading company in your space, using both these platforms to build core technical skills of some kind, and then continuously executing & refining those skills to slowly & steadily build a track record in your field. This will realistically take at least a decade in the real world.

Only when you have made significant progress toward this goal of becoming a compelling & differentiated “product”, should you then start to focus on building various “distribution” channels for it, with networking & social media being important pillars.

If you get this order backward, there is a significant risk of ending up as a lower-end “ecosystem hustler” who ends up amplifying other companies & individuals that are more compelling products, and the latter end up capturing a lion’s share of the economic pie over you.

Why Cutting Losses Early Is the Hardest—and Most Crucial—Skill in Startups and Venture Capital

Cutting losses is one of the hardest decisions in startups, investing, and leadership—but it’s also what separates winners from those stuck in the sunk cost trap. Here’s why mastering this mindset is essential.

Recently read this Forbes article on Igor Tulchinsky, a Billionaire quant trader who runs the hedge fund WorldQuant. In particular, this section on cutting losses caught my eye:

Source: This Billionaire Quant Is Turbocharging His Trading Models With ChatGPT-Style AI

While I don’t come from the public markets world, I have taken a series of major risks as a founder, operator, and investor. Of course, now that I am a full-time venture investor, I live in a world where I take and manage risk every day, including macro, business, tech, portfolio construction, and people, among others.

Based on my journey so far, I can’t emphasize enough the importance of developing the ability to quickly cut losses. Interestingly, before making a major decision, most people are fairly good at identifying & mitigating key underlying risks. However, I have learnt with experience that even after executing the best risk management process, things will still go wrong. And once things go wrong, even the most intelligent organizations & individuals easily fall prey to the sunk cost fallacy (“throwing good money after bad money”).

Let’s take the classic example of finding your next job. As part of a thoughtful risk management process, an intelligent candidate consciously tries and figures out mutual fit during interviews, gathers feedback on the company’s culture, perhaps speaks to customers & competitors to evaluate the product, or, in the case of startups, even does a 1-2 week part-time project before commiting full-time.

A similar scenario is also playing out on the employer’s side. Most hiring managers give high weightage to candidates who come recommended from trusted connections or with whom they share a past history. The interview process consists of multiple rounds to stress-test skills & personality. The company does rigorous reference checks, often also focusing on off-sheet checks to eliminate bias.

So both employers and candidates follow a fairly rigorous risk management process. Yet, as most of us have seen in the real world, leadership hiring has a 50 %+ failure rate in Corporate America. Here are some summarized stats from ChatGPT on this:

In this case, even the most rigorous upfront risk management process can’t account for a variety of post-decision risks, including process weaknesses (a great hiring process can be undone by a weak onboarding & training process), uncontrollable externalities, and random one-off events.

In these scenarios, a willingness to quickly cut losses & limit further damage of time & money on both sides is the best way forward. And make no mistake, it requires a lot of courage. That’s why I found Starbucks firing their last CEO in less than 18 months of tenure to be a very bold move, especially for a company of that scale & history (you would expect them to be sluggish).

While exec hiring missteps can be major setbacks even for large companies, they can often become matters of life and death for an early-stage startup. A wrong hire for a critical role can do strategic & cultural damage that might be irreversible with the existing runway. That’s why the best founders believe in the “fire-fast” philosophy.

Zooming out from hiring, startups succeed by taking calibrated risks on top of a technology change that an incumbent would just find extremely hard to do. This requires running a bunch of iterative experiments with very limited upfront data, but balanced by an asymmetric risk-reward profile (if this works, it will massively move the needle).

By the very nature of these experiments, a majority of them will fail. Combine this with a very limited cash runway that even the best startups get at each stage to get to the next set of milestones, and founders need to combine controlling the cost of each such experiment with an active intent to cut losses once it’s clear that the experiment is not working.

Essentially, a mindset to cut losses early till you get to something that is clearly working is a key requirement for startups to successfully emerge from this maze of early experiments with real product-market-fit. Windsurf CEO & Co-Founder Varun Mohan framed this idea brilliantly in his recent interview with 20VC:

Never fall in love with your idea…

One of the weird thing about startups is that you don’t win an award for doing the same wrong thing for longer.

Coming to my world of venture capital, I have seen many instances where the aversion to cut losses has come back to bite the investor. The context I have seen this the most over the years is in ill-conceived bridge rounds.

Classic scenario – the company has exhausted most of its last round of capital, has created just enough progress to keep existing investors somewhat interested, but if looked at with rigor and intellectual honesty, is nowhere near product-market-fit. Combine this with a founder who is good at storytelling and can pitch “if we get just this much more money, we will break through”, and existing investors are highly likely to cave in & bridge the company.

Unfortunately, in my experience, a majority of these types of bridge rounds don’t end up working. Peter Thiel said this uncomfortable truth a few years back about what he has observed in the Founders Fund portfolio over the decades (paraphrasing):

Once something starts working, people often underestimate it. And when things aren’t working, people often underestimate how much trouble they are in

Everytime a company raised an up round done by a smart investor, it was almost always a good idea to participate…

Steeper the upround, the cheaper it was…

In flatrounds and downrounds, it was almost always a bad idea to participate…

This behavioral weakness is perhaps why Michael Kim of Cendana, a major LP in emerging managers, recently said in an interview that the biggest mistake he has seen GPs make is deploying reserves poorly. My logic is that reserves deployment, especially in rounds without quality external signaling or real business progress, is particularly prone to multiple human biases kicking in, including loss-aversion, likability bias, optimism bias, and overconfidence bias.

Funds with relatively large reserve ratios should think deeply about potential solutions to this problem. One thing I have seen a few funds do is have a dedicated GP whose sole job is to evaluate each reserves-deployment situation like a fresh late-stage deal from the ground up. This can help counter the personal biases of the lead GP on the original deal.

To summarize, the ability to avoid the sunk cost fallacy & cut losses early is critical not just for entrepreneurs & professional investors, but also for each of you as every contact with the real world exposes you to risks big and small, whether you realize it or not. Getting out of sticky situations early enough ensures that you stay in the game and keep compounding your advantages.

AI Musings #8 – Thoughts from South Park Commons Demo Day

Quick observations on the latest AI startup products.

Attended an amazing South Park Commons Summer Demo Faire yesterday!

Reporting back a few thoughts running through my head in real-time:

1. Essentially, the capabilities & design of every core SaaS use case are being reimagined by AI founders as we speak. In a future steady state, I see many of them living inside larger product suites as “features”, either via the incumbent fast-following and shipping them, or via small acquisitions/acqui-hires.

2. Consumer AI products remind me a lot of the 1st gen iPhone apps. Founders (developers) rapidly shipping entertaining, almost “toy-like” use cases. Like in mobile, will something massive eventually come out of these? So hard to tell…

3. An underlying capability of AI that a majority of products seem to be leveraging is “contextual artifact creation”. Eg. creating videos & decks in real time, replacing specific elements instantly in pre-existing media etc.

4. While the underlying “intelligence” capabilities of the products seem to be next-level, the UI/ UX as of now seems quite incremental relative to the mobile/cloud era. Lots more discovery & risk-taking needs to happen here.

5. Across enterprise & consumer/prosumer, it’s clear that these products can only manifest their power when they have access to extremely differentiated & diverse sources of data. In some contexts, it was unclear how a startup would get access to many such datasets in a fresh & relevant manner.

6. In legacy industries like govt/ public sector, AI-native products, even with game-changing capabilities, will still need to deal with age-old GTM challenges (long sales cycle, who will buy, what are the incentives for users to adopt etc).

7. Finally, it’s still pretty effin’ hard to pull off a glitch-free, low-latency AI demo.

Congrats to all the presenting SPC founders. Can’t wait for how these products shape up going forward!

Can Indian Vertical AI Startups Be the Contrarian Venture Bet of the Decade?

Driven by an ambitious talent pool, geopolitical tailwinds, operating model innovation & domestic risk capital, Indian vertical AI startups could be the breakout tech story of the decade.

A potential scenario running in my head on how 🇮🇳 startups get a significant share in global AI over the next decade:

AI “infra” winners get built in 🇺🇸 (OpenAI, Alphabet etc.) ➡️

AI “platform” winners too, emerge in 🇺🇸 (Salesforce & HubSpot equivalents; a bunch get built/ led by the Indian diaspora) ➡️

As 1st-gen “Application” winners emerge in 🇺🇸, 🇮🇳 startups fast-follow in specific enterprise verticals & grab market share.

The time lag to fast-follow is significantly lower than, say, what Zoho did to Salesforce, or Freshdesk did to Zendesk.

This time, they play an asymmetric game. Instead of only competing with US startups on their home turf, Indian enterprise AI startups also look to dominate the Global South (SEA, MENA, LatAm, etc.).

Moving beyond binary operating models of India or US-based, Indian enterprise AI startups innovate & develop new, globally fungible, cross-geo operating models, similar to Infosys in the 90s, BPOs/ KPOs in the 2000s, and Chennai SaaS in the 2010s.

Compared to the SaaS wave, Indian enterprise AI startups get 10-100x more market share in each vertical, driven by a more ambitious & courageous founder pool, a talent base with skillsets & knowledge from previous tech waves, democratized knowledge & tools access courtesy of AI, as well as more availability of domestic risk capital at each stage.

Rather than IPO or M&A in the US, verticalized Indian enterprise AI startups either go public domestically, or get domestic Private Equity & conglomerates on the cap table who help them scale way beyond the last gen of software companies.

All these games play out on top of a favorable geo-political alignment between India & rest of the democratic world, driven by a China counter-balance narrative.

Verticalized Indian enterprise AI startups could be the contrarian venture bet of this decade!

That Series A Billboard On The 101

A reminder not to fall into the trap of first-order thinking.

My LinkedIn feed is full of posts making fun of that startup that has its Series A announcement up on a billboard on the 101.

This incident reminds me of a mental model I have learned & developed with experience over my career:

“When you come across something that looks stupidly irrational on the surface, instead of falling prey to first-order thinking, pause, take a step back, try putting yourself in that situation and think through some reasons why someone could indulge in that seemingly foolish or irrational behavior?”

In the case of this billboard, clearly the founders are smart enough & shrewd enough that institutional investors are handing them $25Mn. So it’s highly likely that they are trying to achieve some goal by putting up this cringeworthy sign.

Most likely, the goal was to drive awareness & word-of-mouth by making this meme-worthy. Similar to how celebrities say & do crazy, PR-worthy things strategically close to a big movie release.

While this billboard case is a bit frivolous, it highlights an important idea that we all should have in our mesh of mental models – when something doesn’t add up in plain sight, or when the herd has 100% consensus on an idea, it shouldn’t be believed prima facie. Rather, it deserves an even deeper investigation.

The crowd is largely a blob of first-order thinkers. Value almost always resides in second-order thinking & beyond. Train your cognitive radar to spot these signals & act accordingly!

Co-founder Breakups

Sharing some insights/patterns from various co-founder breakups I have witnessed over the years.

Recently, I received the sad news of a potentially powerful co-founding team breaking up rather acrimoniously. I had been tracking this team closely for several months now as a potential deal, and this happened right as the company received a seed term sheet from a Tier 1 VC.

Over a 15-year career in venture, I have expectedly seen several co-founder breakups, both in my own portfolio as well as those I have known well/ observed from the sidelines. This recent breakup got me thinking about any patterns/ insights I have noticed over several such instances over the years. Here are a few:

1/ Undergrad batchmates seem to have higher endurance

For some reason, I have repeatedly noticed that teams where the co-founders have been undergrad batchmates tend to survive much longer. Perhaps relationships born in those fledgling, relatively innocent years tend to have higher levels of subconscious trust and, more importantly, a sense of love and tolerance.

While it’s easier to find people with complementary skills and similar pedigrees (both of which look great on paper on the team slide), what keeps co-founders together is also what keeps people together in long-term marriages – having an underlying mutual respect & fondness, which leads to daily hours of fun as well as the willingness to both extend higher levels of tolerance to each other, as well as introspect and evolve to meet the other person midway.

Especially at the seed stage, company missions can evolve with pivots, but this mutual vibe is what keeps co-founders together across multiple iterations and often, multiple companies.

2/ Ex-colleagues and work friends seem to have a higher risk

My hypothesis here is that most people tend to put on a work personality at the job that suits their manager’s preferences as well as the company’s culture. Therefore, even after working with someone as a colleague, it’s very hard to know their real, full personality and values. In many cases, people end up misjudging mutual fit, especially when it comes under the immense pressure of doing a 0-to-1 startup.

Interestingly, this applies to colleagues at both large companies as well as startups. As an investor, I often hear pitches where founders say, “We worked together in the trenches of this early-stage startup and discovered this idea”. While this gives the impression of a strong set of founders germinating inside the cauldron of another startup, I have frequently seen such teams breaking up soon. While they do have the claimed early product and GTM skills they together learned at the startup, the mutual co-founder vibe & grit end up breaking under pressure.

3/ Co-founders coming together via common friends/ relatives, without a strong shared history, is a miss

I see this scenario a lot – one person decides to start up, spreads the word around for a co-founder, connects with someone via a really strong common friend/ relative, and both decide to partner.

In the majority of these cases, there is no shared history, and the team also hasn’t had the opportunity to spend enough time in the trenches going through the ups and downs together. When pitching to seed investors, they usually tell the story of “our skills are perfectly complementary, and both of us have met each other multiple times at this X/Y/Z person’s parties over several years, and developed a shared passion for this idea”.

In most cases, this ends up being a window-dressed story of the co-founding team and lacks the underlying bond & trust needed to grind out the tough times.

4/ “Earned co-founders” are solid

In many cases, folks start as single founders, surround themselves with early founding team members, validate, iterate, and get to early PMF with them, and during this journey, 1-3 people naturally come up and start playing a critical role in the management team. In a sense, they start playing the co-founder role without the title (or the equity).

I call these earned co-founders, and these are solid personas. In many of these cases, I have pushed the solo founder to look at these 1-3 people as core parts of the leadership team, if not as full co-founders, and have it also reflect in their equity at the appropriate time.

AI Ecosystems – Silicon Valley vs India

During my recent India trip, a question I got asked repeatedly by both founders & investors was, “What are you seeing as the main differences between the AI ecosystem in the Valley vs India?”.

I currently see 2 main differences:

1/ Exposure (& therefore, Ambition)

AI founders in the Valley seem to have significantly more direct exposure to the work happening at the frontier. And not just in terms of the foundational technology, but also what battles the incumbents are taking on, how workflows are being iterated on, what lean, full-stack startup teams are doing to be able to generate significant product velocity & revenue, and how customers are thinking & allocating resources.

Essentially, they have the advantage of directly drinking from the Bay Area fountain of knowledge & information, spread primarily via networks.

A direct consequence of more exposure is that it uplevels the ambition of Valley AI founders and organically pushes them to raise the bar for execution within the company. Thus leading to sharper thinking, more courageous bets, and faster execution that all put together, improves the odds of a large outcome.

2/ Story-telling

I see that while AI founders in both the Valley and India are picking very similar problem statements to work on, the storytelling around the same use cases in the Valley is significantly superior.

I guess one reason is that operating directly in the target market (vs being a few degrees of freedom away from it) makes it much easier to get higher-quality early validation signals, making the story much more believable.

Also, AI founders in the Valley tend to emerge from the leading-edge companies of the last mobile/ cloud/ SaaS cycles. So they have a much better intuitive understanding of how to position & message the company in the early days to customers, investors & key hires.

Story-telling becomes even more important as how the AI landscape will evolve in specific market segments & verticals remains highly fuzzy.

So, what can India-based AI founders do to bridge these 2 gaps? Here are a few actionable things:

1/ Do extended sprints in the Bay Area regularly to drink from the same fountain.

2/ Surround yourself with Bay Area-based operators, angels & advisors (even remote is ok to begin with) who can regularly feed this knowledge & intel and, more importantly, help uplevel your thinking & ambition.

3/ Follow a conscious 0-to-1 strategy of only building for US design partners, so your product is held to the same bar as those from Valley startups.

4/ Specific suggestion for VCs – mine your network of LPs, Advisors & Portcos to hold regular AI knowledge sharing sessions with leaders of marquee AI-native companies that are building on the frontier in the Bay Area.

Quick US GTM Tip For India-Based Founders

From recording many episodes of An Operator’s Blog on US-India GTM, one clear pattern is emerging from the experiences of many founders:

“If you are new to the US, don’t have a strong brand and/or connections to existing cliques (eg. haven’t done your Masters here, haven’t worked a Big Tech job, haven’t done YC etc.), cold outbound is likely to have a low success rate, especially in the initial phases of US GTM.”

Cold outbound tends to work better when done on the back of adequate customer validation, social proofing & ecosystem reputation, all of which take time to build.

Rather than depending too much on cold outbound, a better use of time when on the ground in the US is to:

1/ Build 1:1 ecosystem-level relationships with influential/ connected founders, operators, and investors.

2/ As you meet each person, try and get some warm intros. That’s your best shot at getting a relevant 30-minute meeting where the other side is leaning in.

Meet → Ask for one intro → Meet this new person → Again ask for another intro → Rinse & repeat…

3/ In parallel, execute an ongoing track of building your early reputation in the US (Bay Area?) ecosystem via social media content, engaging in relevant communities, regularly showing up in VC mixers & meetups, and generating value for the people you are meeting.

The main objective of the first 6 months of US GTM is to put the foundational elements of a future GTM engine in place. At the heart of it is:

(1) unique value + (2) reputation + (3) relationships = (4) brand.

AI Is Creating A Reckoning For Big Tech Middle Managers

With AI disrupting middle-management roles, many professionals in their late 30s to 50s will need to reinvent themselves.

Anecdotally, in the Bay Area, I’m seeing middle managers—particularly at the Director level—disproportionately affected by recent layoffs at large tech companies.

The precedent was set by Meta in 2022/23 when Zuckerberg openly questioned the need for multiple organizational layers, arguing they slowed execution. Many of Meta’s layoffs were aimed at flattening teams.

Likewise, Elon Musk and Jensen Huang are known for engaging directly with frontline employees, even interns, to unblock key challenges. Brian Chesky’s “Founder Mode” philosophy echoes this approach, encouraging leaders to dive into details and manage execution at the ground level rather than delegating critical projects to layers of managers.

Now, AI is accelerating this shift. By supercharging individual contributors—turning them into self-sufficient, full-stack execution engines across coding, marketing, and sales—AI is reshaping how Big Tech structures its workforce. As companies prioritize efficiency, the middle management layer may be on the verge of disappearing.

In the last mobile/cloud/SaaS cycle, middle managers served as the bridge between executive leadership’s vision and frontline execution. However, as tech companies swelled due to ZIRP-driven capital excess, Directors and Senior Directors—whether intentionally or not—became bureaucratic bottlenecks.

With AI disrupting these roles, or at the very least redefining their purpose and required skillsets, many professionals in their late 30s to 50s will need to reinvent themselves. This could mean re-skilling or up-skilling to become AI-native knowledge workers, transitioning to different industries, or even leaving core tech altogether to apply their experience elsewhere.

This may sound extreme, but it’s exactly what I’m observing in my circles.

Mastering the Art of Pitching on Zoom

Zoom pitches demand quick engagement—capture attention in 60 seconds, use visuals wisely, and keep slides concise. Bring personality and storytelling to stand out.

More than half of the pitches I take as an investor happen on Zoom. I also frequently pitch to LPs on Zoom, so I’ve gathered plenty of experience here.

Over the years, I’ve realized that pitching effectively on Zoom is a completely different skill from pitching in person. In fact, I almost always nail in-person meetings, but Zoom can be hit or miss.

In-person meetings have a consistent energy and setting—standard surroundings, small talk, and even table arrangements. Zoom, however, introduces external factors that can impact the experience: audio quality, lighting, background noise, AI note-takers, joining delays, screen interruptions, and even the lingering mood from a previous (probably also Zoom) meeting.

TL;DR:

Attention spans and patience are significantly lower on Zoom than in person. Participants lose interest and get irritated much faster.

While first impressions, body language, and icebreakers set the tone in an in-person meeting, on Zoom, you have 60 seconds to capture attention and pull your audience into your pitch.

If that’s true, Zoom pitch meetings should be structured very differently. Here’s what I recommend:

1. Open with Your Strongest Points

With only 60 seconds to grab attention, avoid meandering intros or generic company overviews. People tune out fast on Zoom. Instead, start with the three strongest parts of your pitch in the first 30 seconds.

❌ BORING:
“I’m the founder of… We’re based in SF and started three years ago after identifying this opportunity while working at…”

✅ INTERESTING:
“[COMPANY NAME] is [1-line description]. We’re at $X ARR/N users, growing Y% week-over-week. Our team comes from [COMPANIES], and here’s our unique insight: [1-line unique value prop].”

Think of your Zoom opening like a 30-second elevator pitch. PS: I have covered detailed aspects of the 30-second pitch in the post ‘How to cold-pitch your startup in 30 seconds to VCs at events‘.

2. Use Slide Sharing Sparingly

Walking through slides one by one makes Zoom meetings boring. It also reduces face-to-face engagement, shrinking participants to tiny squares above a massive slide. This makes it harder for investors to read facial expressions, passion, and conviction.

Instead, use slide sharing only when diving into specifics—metrics, product visuals, or key data points. If a conversation naturally leads to deeper discussions, screen-sharing makes sense. And if someone asks for more details, that’s a great sign—they’re engaged.

3. Prioritize Stories Over Generic Narratives

Broad business narratives and jargon make Zoom meetings dull. They’re harder to internalize and, in the worst cases, cause brains to switch off entirely.

Instead, use specific, personalized stories to make your points.

  • Rather than saying “Our product saves companies X dollars”, share a real customer success story.
  • Show how one specific customer (name, picture, and all) used your product and what impact it had.

Investors remember compelling stories more than numbers and percentages.

4. Leverage Visuals – Videos & Images Work Best

Spoken words are harder to absorb on Zoom, but visuals—especially videos—have a much stronger impact.

After your opening, incorporate relevant images or a short video to drive home key points. A well-placed visual can communicate in seconds what might take minutes to explain verbally.

5. Design Zoom Slides Like a TED Talk Deck

Verbose slides won’t be read. While your full pitch deck can be detailed, the version you present on Zoom should be simple, bold, and visual—like a TED Talk deck.

  • Each slide should focus on one core idea
  • Use minimal text and large fonts
  • Whenever possible, let charts, images, or visuals tell the story

For inspiration, check out the following slides used by top TED speakers—they prove that less is more.

Inside the Mind of a Master Procrastinator | Tim Urban | TED
The science behind dramatically better conversations | Charles Duhigg | TEDxManchester
Why good leaders make you feel safe | Simon Sinek | TED

TLDR: for Zoom meetings, like for TED Talks, less is more…

6. Let Your Personality Shine

Too many Zoom pitches feel robotic—monotone delivery, deadpan expressions, and no effort to break the ice. That’s a missed opportunity.

The easiest way to be interesting? Be yourself. Show quirks, humor, and enthusiasm. Your journey, energy, and passion make the conversation engaging.

Your job isn’t to blend in—it’s to stand out. If you can’t get people to actively listen and engage, a future investment isn’t happening anyway.

Closing Thoughts…

Mastering Zoom pitches is an evolving skill, but structuring them thoughtfully can make all the difference. Adapt your approach, test what works, and refine as you go.

Have you found any techniques that work particularly well in Zoom pitches? Would love to hear your thoughts!

PS: as a general resource for getting better at sharing ideas, check out this video ‘TED’s secret to great public speaking‘.