How Top Universities Fuel Startups with Venture Capital

By Ramkumar Sundarakalatharan | September 10, 2024 | Comments 0 Comment

Top Universities Driving Global Startups Through Venture Capital: A Data-Backed Overview

Universities play a pivotal role in nurturing talent and fostering innovation, and the success of alumni-founded startups is a testament to the entrepreneurial culture present in these institutions. A recent analysis of venture capital funding across top universities reveals the strong influence of academic ecosystems on startup success. This article dives into the top 50 universities based on the venture capital raised by their alumni, explores key geographical trends, highlights key sectors, and references publicly available data to give a comprehensive view.

The Global Leaders: U.S. Universities Dominate the Startup Landscape

Key Statistics (U.S.):

Total Dollars Raised: $194 billion
Number of Companies Founded: 4,000+
Key Sectors: Technology, Healthcare, FinTech, SaaS, AI

According to Crunchbase and PitchBook data, U.S. universities such as Stanford University, Harvard University, and the University of California, Berkeley lead the pack in terms of venture capital raised and the number of companies founded. These institutions have produced successful ventures in technology, artificial intelligence, and SaaS (Software as a Service). Stanford’s proximity to Silicon Valley has helped drive the innovation boom, particularly in tech startups.

Some of the most notable startups originating from these institutions include:

Stanford University: Renowned for its close ties to Silicon Valley, Stanford is the birthplace of giants like Google (founded by Larry Page and Sergey Brin), Yahoo (founded by Jerry Yang and David Filo), and WhatsApp (co-founded by Brian Acton).
Harvard University: With alumni like Mark Zuckerberg (co-founder of Facebook) and Bill Gates (co-founder of Microsoft), Harvard is a key player in tech, biotech, and healthcare sectors. Startups like Cloudflare (founded by Matthew Prince) also emerged from Harvard.

Europe: A Growing Hub for Innovation

Key Statistics (Europe):

Total Dollars Raised: $23 billion
Number of Companies Founded: 500+
Key Sectors: FinTech, Healthcare, DeepTech, Renewable Energy

Europe has seen rapid growth in FinTech, deep tech, and renewable energy sectors. INSEAD and Cambridge University stand out as key contributors to the startup ecosystem. According to Dealroom.co, FinTech is particularly dominant, with startups like Revolut and TransferWise leading the way.

INSEAD alumni have raised over $23 billion, with many startups thriving in FinTech and consulting sectors. A standout example is BlaBlaCar, a ridesharing platform co-founded by Frédéric Mazzella that has transformed travel across Europe by offering affordable long-distance ride-sharing options.

University of Cambridge has contributed significantly to deep tech and healthcare innovations, producing companies like Arm Holdings, the semiconductor giant. Mike Lynch, founder of Autonomy, is another Cambridge alumnus who has disrupted the tech industry.

Asia: A Rising Force in the Startup World

Key Statistics (Asia):

Total Dollars Raised: $15 billion
Number of Companies Founded: 1,200+
Key Sectors: Technology, Biotech, E-commerce, Mobility

Asia, led by universities like the National University of Singapore (NUS) and Tsinghua University, is rapidly becoming a hotbed for biotech, e-commerce, and mobility startups. NUS has seen its alumni raise billions in venture capital, particularly in the tech sector. According to TechInAsia, NUS-produced startups like Grab, co-founded by Anthony Tan and Tan Hooi Ling, have dominated the Southeast Asian ride-hailing market.

In China, Tsinghua University has been integral in fostering technological advancements, with alumni like Charles Zhang, founder of Sohu, shaping the Chinese tech landscape. The university has become synonymous with engineering and tech entrepreneurship.

Startups in India: The IIT Ecosystem

Key Statistics (India):

Total Dollars Raised: $10 billion
Number of Companies Founded: 800+
Key Sectors: E-commerce, FinTech, SaaS, Mobility

The Indian Institutes of Technology (IITs), particularly IIT Bombay and IIT Delhi, are pivotal in India’s e-commerce, FinTech, and mobility sectors. According to Inc42, startups like Flipkart (co-founded by Sachin Bansal and Binny Bansal, both IIT Delhi graduates) and Zomato (Founded by Deepinder Goyal, IIT Delhi) are reshaping the Indian market and attracting substantial venture capital.

Israel: A Thriving Startup Nation

Key Statistics (Israel):

Total Dollars Raised: $8 billion
Number of Companies Founded: 600+
Key Sectors: Cybersecurity, AI, FinTech, Defense Tech

Israel, often referred to as the Startup Nation, has made a name for itself with innovation in cybersecurity and AI. Universities like the Hebrew University of Jerusalem and the Technion – Israel Institute of Technology have been critical in producing world-class startups. For instance, Waze, the navigation app acquired by Google, was co-founded by Ehud Shabtai, an alumnus of Tel Aviv University. The country’s deep focus on cybersecurity is also reflected in companies like Check Point Software Technologies, founded by Gil Shwed, a Technion graduate.

South Africa: Emerging in FinTech and E-commerce

Key Statistics (South Africa):

Total Dollars Raised: $3 billion
Number of Companies Founded: 150+
Key Sectors: FinTech, E-commerce, Agriculture

While South Africa may not boast the same number of startups as Silicon Valley, it has a growing presence in FinTech and e-commerce. Universities like the University of Cape Town have played a significant role in this growth. One notable company is Yoco, a FinTech startup co-founded by Katlego Maphai, which provides payment solutions for small businesses across Africa. South Africa is also a key player in agri-tech, with startups focusing on modernizing the agricultural supply chain.

South America: A Rising Contender in E-commerce and FinTech

Key Statistics (South America):

Total Dollars Raised: $5 billion
Number of Companies Founded: 500+
Key Sectors: E-commerce, FinTech, PropTech

South America, particularly Brazil and Argentina, has seen a significant rise in e-commerce and FinTech startups. Universities like Universidade de São Paulo and Universidad de Buenos Aires have contributed to this burgeoning ecosystem. Companies like MercadoLibre, co-founded by Marcos Galperin (Universidad de Buenos Aires alumnus), are leading the e-commerce revolution in the region, while Nubank, a FinTech unicorn co-founded by David Vélez, is transforming banking in Latin America.

Why Are These Regions Underrepresented in the Data?

While regions like Israel, South Africa, and South America are seeing growth in venture capital-backed startups, the numbers are still significantly smaller compared to the U.S. and Europe. This can be attributed to a smaller pool of venture capital available, fewer universities with established entrepreneurial ecosystems, and the nascent state of the venture capital markets in these regions. However, they are catching up quickly, and with increasing global attention, these regions are likely to play a larger role in the global startup ecosystem in the coming years.

Conclusion

The data paints a clear picture of the crucial role universities play in fostering entrepreneurship and innovation globally. While U.S. institutions like Stanford and Harvard continue to dominate the startup landscape, the rise of universities in Europe, Asia, and emerging regions such as Israel and South America signals a significant shift toward a more diversified and competitive global startup ecosystem. This is no longer just a Silicon Valley story.

European universities are making strides in deep tech and FinTech, while Asian institutions are positioning themselves at the forefront of sectors like e-commerce, mobility, and biotech. These regions, once considered underrepresented in venture capital, are rapidly scaling their entrepreneurial impact, thanks to increasingly robust academic ecosystems, governmental support, and access to global venture networks.

However, as these newer hubs mature, it becomes clear that the presence of an established entrepreneurial culture, combined with strong alumni networks and well-supported innovation hubs, is key to sustaining long-term growth. For universities aspiring to drive the next generation of unicorns, investing in interdisciplinary research, fostering global collaborations, and creating pipelines between academia and industry will be critical in the years ahead.

The entrepreneurial landscape is rapidly evolving, and universities that align themselves with this shift will not only fuel economic growth but will also shape the future of technology, healthcare, and innovation on a global scale. As venture capital continues to flow into emerging markets, the next wave of disruptive startups may very well come from unexpected regions, further diversifying the global innovation economy.

References:

Crunchbase – Crunchbase Venture Capital Database
Crunchbase is a comprehensive database of startup companies, venture capital firms, and funding rounds, offering insights into global startup ecosystems and venture trends.
PitchBook – PitchBook Data
PitchBook provides detailed reports on venture capital, private equity, and mergers & acquisitions, offering in-depth insights into sector-specific funding and university-driven startups.
Dealroom.co – Dealroom European Startup Data
Dealroom is a leading platform for discovering startups, scale-ups, and investment trends, particularly in the European startup ecosystem.
TechInAsia – Tech in Asia Startup Data
A platform dedicated to startup news and insights from Asia, providing information about venture capital, company profiles, and technology trends across the region.
Inc42 – Inc42 Indian Startup Ecosystem
Inc42 is a leading source for insights on the Indian startup ecosystem, offering reports on funding, growth trends, and key sectors like FinTech, SaaS, and E-commerce.
CB Insights – CB Insights Global Venture Capital
CB Insights is a market intelligence platform that tracks venture capital investments, industry insights, and emerging trends, providing data-driven analysis on startups and sectors.
NASSCOM – Indian Tech Startup Ecosystem Report
NASSCOM publishes reports on India’s growing startup ecosystem, covering key sectors, venture capital inflows, and the impact of technology-driven ventures.
TechCrunch – TechCrunch Global Startup News
A leading news outlet for global startup and venture capital news, TechCrunch reports on funding rounds, sector trends, and university-linked startup initiatives.

Key Reasons Founding CTOs Move Sideways in Tech Startups

By Ramkumar Sundarakalatharan | August 30, 2024 | Comments 0 Comment

In the world of startups, it’s not uncommon to hear about founding CTOs being ousted or sidelined within a few years of the company’s inception. For many, this seems paradoxical—after all, these are often individuals who are not only experts in their fields but also the technical visionaries who brought the company to life. Yet, within 3–5 years, many of them find themselves either pushed out of their executive roles or relegated to a more visionary or peripheral position in the organization.

But why does this happen?

The Curious Case of the Founding CTO

About 6-7 years back, while assisting a couple of VC firms in performing technical due diligence with their investments, I noticed a pattern: founding CTOs who had built groundbreaking technology and secured millions in funding were being removed from their positions. These were not just “any” technologists—they were often world-class experts, with pedigrees from prestigious institutions like Cambridge, Stanford, Oxford, MIT, IIT(Israel) and IIT (India). Their technical competence was beyond question, so what was causing this rapid turnover?

The Business Acumen Gap

After numerous conversations with both the displaced CTOs and the investors who backed their companies, a common theme emerged: there was a significant gap in business acumen between the CTOs and the boards of directors. As the companies grew, this gap widened, eventually becoming a chasm too large to bridge.

The Perception of Arrogance

One of the most frequently cited issues was the perception of arrogance. Many founding CTOs, steeped in deep technical knowledge, would often express disdain or impatience towards board members and executive leadership team (ELT) members who lacked a technical background. This disdain often manifested in meetings, where CTOs would engage in “geek speak,” using highly technical language that alienated non-technical stakeholders. This attitude can make the board feel undervalued and disconnected from the technology’s impact on the business, leading to friction between the CTO and other executives.

Failure to Translate Technology into Business Outcomes

Another critical issue was the inability—or unwillingness—to translate technical initiatives into tangible business outcomes. CTOs would present technology roadmaps without tying them to the company’s broader business objectives; and in extreme cases, even product roadmaps! This disconnect led to frustration among board members who wanted to understand how technology investments would drive revenue, reduce costs, or create competitive advantages. According to an article in Harvard Business Review, this lack of alignment between technical leadership and business strategy often results in a loss of confidence from investors & executive leadership who see the CTO as out of sync with the company’s growth trajectory.

Lack of Proactive Communication and Risk Management

Founding CTOs were also often criticized for failing to communicate proactively. When projects fell behind schedule or technical challenges arose, many CTOs would either remain silent or offer vague assurances such as, “You have to trust me.” Sometimes, they fail to communicate the underlying problems causing this. This lack of transparency and the absence of a clear, proactive plan to mitigate risks eroded the board’s confidence in their leadership. As noted by TechCrunch, this lack of foresight and communication can lead to the CTO being perceived as “dead weight” on the cap table, ultimately leading to their removal or sidelining.

The Statistics Behind the Trend

Research supports the observation that founding CTOs often struggle to maintain their roles as companies scale. According to a study by Harvard Business Review, more than 50% of founding CTOs in high-growth startups are replaced within the first 5 years. The reasons cited align with the issues mentioned above—poor communication, lack of business alignment, and a failure to scale leadership skills as the company grows.

Additionally, a survey by the Startup Leadership Journal revealed that 70% of venture capitalists have replaced a founding CTO at least once in their careers. This statistic underscores the importance of not only possessing technical expertise but also developing the necessary business acumen to maintain a leadership role in a rapidly growing company.

Real-World Examples: CTOs Who Fell from Grace

Several high-profile cases illustrate this trend. For instance, at Uber, founding CTO Oscar Salazar eventually took a step back from his leadership role as the company’s growth demanded a different set of skills. Similarly, at Twitter, co-founder and CTO Noah Glass was famously sidelined during the company’s early years, despite his pivotal role in its creation.

In another notable case, at Zenefits, founding CTO Laks Srini was moved to a less central role as the company faced regulatory challenges and rapid growth. The decision to shift his role was driven by the need for a leadership team that could navigate the complexities of a scaling business.

And, the list is too long, so I am adding about 8 names which is bound to elicit a reaction.

Name	Company	Fired/Left on Year	Most Likely Reason
Scott Forstall	Apple	2012	Abrasive management style and failure of Apple Maps
Kevin Lynch	Adobe	2013	Contention over Flash technology, departure to join Apple
Tony Fadell	Apple	2008	Internal conflicts over strategic directions
Amit Singhal	Google/Uber	2017	Dismissed from Uber due to harassment allegations
Balaji Srinivasan	Coinbase	2019	Strategic shifts away from decentralization
Alex Stamos	Facebook	2018	Disagreements over handling misinformation and security issues
Michael Abbott	Twitter	2011	Executive reshuffle during strategic redirection
Shiva Rajaraman	WeWork	2018	Departure during company instability and failed IPO

The Path Forward for Aspiring CTOs

For current and aspiring CTOs, the lessons are clear: technical expertise is essential, but it must be complemented by strong business acumen, communication skills, and a proactive approach to leadership. As a company scales, so too must the CTO’s ability to align technology with business objectives, communicate effectively with non-technical stakeholders, and manage both risks and expectations.

CTOs who can bridge the gap between technology and business are far more likely to maintain their executive roles and continue to drive their companies forward. For those who fail to adapt, the fate of being sidelined or replaced is an all-too-common outcome.

Conclusion

The role of the CTO is critical, especially in the early stages of a startup. However, as the company grows, the demands on the CTO evolve. Those who can develop the necessary business acumen, communicate effectively with a diverse range of stakeholders, and maintain a strategic focus will thrive. For others, the writing may be on the wall well before the 3–5 year mark.

What other reasons have you found that got the founding CTO fired? Share your thoughts in the comments.

References: & Further Reading

Harvard Business Review: Unpacking VCs’ Strange Instinct .
Adeline Chalmers on Medium: Without Business Acumen, Most CTOs in Scaleups Will Be Replaced in Time .
TechCrunch: Bad CTOs Mean Startups Have Millions Worth of Cap Table Dead Weight .

Tech Founder to CTO: The Hidden Challenges of Managing Growth in Startups

By Ramkumar Sundarakalatharan | July 29, 2024 | Comments 0 Comment

The role of the Chief Technology Officer (CTO) in a startup is dynamic and challenging, particularly for first-time technical cofounders. While the early stages of a startup demand intense technical involvement and innovation, the role evolves significantly as the company grows. This evolution often highlights stark differences in the required skill sets at different stages, posing challenges for first-time technical cofounders but offering opportunities for serial entrepreneurs.

The Initial Phase: Technical Mastery and Hands-On Development

In a startup’s early days, the technical cofounder, often assuming the CTO role, is deeply immersed in product development’s intricacies. This period is characterized by rapid prototyping, extensive coding, and constant iteration based on user feedback. The technical cofounder’s primary focus is to bring the product vision to life, often working with limited resources and under significant time pressure. This phase requires not just technical expertise but also a high degree of creativity and problem-solving prowess.

The Transition: From Builder to Leader

As the startup scales, the CTO’s demands change dramatically. The focus shifts from hands-on development to strategic leadership. This transition involves managing larger teams, setting long-term technical directions, and ensuring that the technology strategy aligns with the overall business goals. First-time technical cofounders often find this shift challenging because it demands skills they may not have developed. The ability to code and build is no longer enough; the role now requires people management, strategic planning, and the capacity to handle complex organizational dynamics.

The Skill Set Gap

For first-time technical cofounders, this transition can be particularly daunting. Their expertise lies in building and innovating, but scaling a technology team and managing a growing organization are entirely different challenges. These new responsibilities require experience in leadership, communication, and strategic thinking—areas where first-time founders might lack experience. The result is a skill set gap that can lead to frustration and inefficiency, both for the individual and the organization.

Serial Entrepreneurs: Experience Matters

In contrast, serial entrepreneurs often handle this transition more effectively. Having navigated the startup journey multiple times, they possess a broader range of skills and experiences. They are familiar with the different phases of growth and the changing demands of the CTO role. Serial entrepreneurs are better equipped to balance hands-on technical work with strategic leadership. They have likely experienced the pitfalls and challenges of scaling a company before and have developed the necessary skills to manage them.

Learning from Experience

Serial entrepreneurs and or seasoned engineering leaders bring a wealth of knowledge from their previous ventures, allowing them to anticipate challenges and implement solutions proactively. Their past experiences help them build robust management structures, delegate effectively, and maintain strategic focus. This adaptability and foresight are crucial for a scaling startup, where the ability to pivot and adjust is often the difference between success and failure.

The Burnout Factor

Another critical difference is how first-time technical cofounders and serial entrepreneurs handle burnout. The relentless pace and high stakes of a startup can lead to significant stress and fatigue. First-time founders, driven by their passion and vision, might find it hard to step back and delegate, leading to burnout. On the other hand, serial entrepreneurs, having experienced this before, are often more adept at recognizing the signs of burnout and taking steps to mitigate it. They understand the importance of work-life balance and are better at creating a sustainable work environment for themselves and their teams.

Strategic Decisions and Stakeholder Management

As startups grow, they attract more investors and stakeholders whose interests need to be managed. Serial entrepreneurs typically have more experience dealing with investors and understanding their expectations. They are skilled at navigating the complex landscape of stakeholder management, making strategic decisions that align with the broader goals of the company while maintaining the confidence of their investors.

Conclusion: The Path Forward

For startups, recognizing the strengths and limitations of their technical cofounders is crucial. While first-time technical cofounders bring passion and technical prowess, they may struggle with the strategic and managerial aspects as the company scales. In contrast, serial entrepreneurs, with their diverse experiences and refined skills, are often better suited to handle the evolving demands of the CTO role.

Startups should consider these dynamics when planning their leadership strategies. Providing support, mentorship, and training to first-time technical cofounders can help bridge the skill set gap. Alternatively, involving experienced leaders who can complement the technical cofounder’s strengths can create a balanced leadership team capable of steering the company through its growth phases.

Ultimately, the journey from a technical cofounder to a successful CTO is complex and challenging. Recognizing the unique contributions and potential limitations of first-time technical cofounders, while leveraging the experience of serial entrepreneurs, can significantly enhance a startup’s chances of success.

What Happens When Huge Capital Meets No Real Product? Welcome to AI Speculation!

By Ramkumar Sundarakalatharan | July 1, 2024 | Comments 0 Comment

Despite its hefty $1.3 billion investment, the recent collapse of Inflection serves as a stark reminder of the volatile AI startup landscape. Inflection’s flagship product, Pi, a ChatGPT rival, failed to gain traction, leading to the company’s dismantling by Microsoft. This case exemplifies the broader trend of massive capital influx into AI ventures lacking substantial products.

The Rise and Fall of Inflection

Inflection was founded by notable entrepreneurs such as Mustafa Suleyman of DeepMind, Karén Simonyan, and Reid Hoffman. Suleyman, a co-founder of DeepMind, had previously contributed to its advancements in AI, which eventually led to its acquisition by Google. Simonyan brought extensive experience from his work on AI research, while Hoffman, co-founder of LinkedIn, provided substantial entrepreneurial and investment acumen.

With backing from influential investors including Bill Gates and Eric Schmidt, Inflection aimed to create a more empathetic AI companion. The company took around two years to develop Pi, its primary product, hoping to leverage its founders’ reputations and the significant capital raised to break into the AI market.

Why Pi Failed

Pi’s failure is attributed to several factors:

Lack of Unique Value: Pi’s context window was significantly shorter than competitors, hindering its ability to provide sustained conversational quality.
Market Oversaturation: The AI companion market is fiercely competitive, with established players like ChatGPT and Character.ai leading the pack.
Financial Mismanagement: Heavy investment without a corresponding viable product highlighted the risks of capital-heavy ventures in AI.

AI Funding and Startup Failures

The AI sector saw an estimated $50 billion in investments in 2023 alone. However, many startups have failed to deliver on their promises. Some notable closures in the last 18 months include:

Inflection: Absorbed by Microsoft, ceasing independent operations.
Vicarious: Acquired by Alphabet, failing to achieve its goal of human-like AI.
Element AI: Acquired by ServiceNow after struggling to commercialize its research.

Startup	Total Investment ($M)	Years to Product Launch	Peak Annual Revenue ($M)	Outcome
Inflection	1300	2	5	Acquired by Microsoft
Vicarious	150	4	2	Acquired by Alphabet
Element AI	257	3	10	Acquired by ServiceNow
MetaMind	45	2	1	Acquired by Salesforce
Geometric Intelligence	60	1	0.5	Acquired by Uber

The Future of AI Investment

This trend of high investment but low product viability raises concerns about the future of AI innovation. Consolidation around major players like Microsoft, Google, and OpenAI could stifle competition and limit diversity in AI development.

Conclusion

The downfall of Inflection underscores the precarious nature of AI investments. As the industry continues to grow, investors must prioritize viable, innovative products over mere potential. This shift could foster a more sustainable and dynamic AI ecosystem.

Inside the Palantir Mafia: Secrets to Succeeding in the Tech Industry

By Ramkumar Sundarakalatharan | June 7, 2024 | Comments 0 Comment

In the world of technology, engineers are not just cogs in a machine; they are the builders, the dreamers, and the ones who solve the problems they see in the world. And sometimes, those solutions turn into billion-dollar businesses. This is the story of the “Palantir Mafia,” a group of former Palantir employees who have left the data analytics giant to found their own startups, just like the famed “PayPal Mafia” that produced companies like SpaceX, YouTube, LinkedIn, Palantir Technologies, Affirm, Slide, Kiva, and Yelp.

1. Introducing the Amazing People from Palantir

The “Palantir Mafia,” akin to the renowned “PayPal Mafia,” comprises former Palantir engineers and executives who left to tackle meaningful problems with technological innovation, creating substantial impact and wealth. Unlike ex-consultants from firms like McKinsey, BCG, or Bain, these tech leaders leverage their deep technical expertise to solve complex issues directly, resulting in profound advancements and successful ventures.

Key Figures and Their Ventures

Alex Karp – Palantir Technologies
- Former Role: Co-Founder and CEO
- Company: Palantir Technologies
- Focus: Data analytics
- Market Penetration: Widely used across government and commercial sectors
- Revenue: $1.5 billion annually
- Capital Raised: $3 billion (Wikipedia) (Business Insider)
Max Levchin – Affirm
- Former Role: Co-Founder (PayPal, associated with Palantir founders)
- Company: Affirm
- Focus: Buy now, pay later financial services
- Market Penetration: Significant presence in the consumer finance market
- Revenue: $870 million in fiscal 2021
- Capital Raised: $1.5 billion
Joe Lonsdale – 8VC
- Former Role: Co-Founder
- Company: 8VC
- Focus: Venture capital firm
- Market Penetration: Diverse portfolio, influential in tech sectors
- Assets Under Management: $3.6 billion
Palmer Luckey – Anduril Industries ( could be the blue blooded Musk of 2020-2030s)
- Former Role: Founder of Oculus VR, associated with Palantir through ventures
- Company: Anduril Industries
- Focus: Defense technology
- Innovation: Developed the Lattice AI platform for autonomous border surveillance and defense applications
- Market Penetration: Contracts with U.S. Department of Defense and border security agencies
- Revenue: $200 million annually
- Capital Raised: $700 million
Garrett Smallwood – Wag!
- Former Role: Executive roles at other startups before Wag!
- Company: Wag!
- Focus: On-demand pet care services
- Market Penetration: Operates in over 100 cities
- Revenue: $100 million annually
- Capital Raised: $361.5 million
Nima Ghamsari – Blend
- Former Role: Product Manager at Palantir
- Company: Blend
- Focus: Mortgage and lending software
- Market Penetration: Partners with major financial institutions
- Revenue: Estimated $100 million+ annually
- Capital Raised: $665 million
Stephen Cohen – Quantifind
- Former Role: Co-Founder of Palantir
- Company: Quantifind
- Focus: Risk and fraud detection using data science
- Market Penetration: Used by financial services and government sectors
- Capital Raised: $8.7 million
Vibhu Norby – B8ta
- Former Role: Engineer at Palantir
- Company: B8ta
- Focus: Retail-as-a-service platform
- Market Penetration: Transforming in-store retail experiences
- Capital Raised: $113 million
Joe Lonsdale – Addepar
- Former Role: Co-Founder of Palantir
- Company: Addepar
- Focus: Wealth management technology
- Market Penetration: Manages over $2 trillion in assets
- Capital Raised: $325 million
Raman Narayanan – SigOpt
- Former Role: Data Scientist at Palantir
- Company: SigOpt (acquired by Intel)
- Focus: Machine learning optimization
- Market Penetration: Utilized by top tech companies
- Capital Raised: $8.7 million (before acquisition)

2. Engineers Make Better Founders in the Tech Industry

Unlike ex-consultants from big 3 who may excel in strategy and communication but often lack the technical depth to truly understand the intricacies of building a tech product, these ex-Palantir engineers come armed with both the vision and the technical chops to bring their ideas to life. They’ve spent years wrestling with complex data problems at Palantir, and they’re now taking those hard-won lessons to solve new challenges across a wide range of industries.

Engineers bring a problem-solving mindset that focuses on creating practical, scalable solutions. This technical acumen has allowed former Palantir employees to launch transformative companies that push the boundaries of what’s possible in various industries.

3. Market Penetration and Success of Palantir Alumni

The success of these Palantir alumni is evident through their market penetration and revenue. For instance, Palantir Technologies itself is a major player in the data analytics field, with a revenue of $1.5 billion annually. Affirm, led by Max Levchin, has made significant inroads in the consumer finance market, generating $870 million in revenue in fiscal 2021. Anduril Industries, founded by Palmer Luckey, has secured substantial contracts with the U.S. Department of Defense, contributing to its $200 million annual revenue.

Other successful ventures include Blend, with its deep partnerships with major financial institutions, and Addepar, managing over $2 trillion in assets. These companies not only showcase the technical expertise of their founders but also highlight their ability to penetrate markets and achieve substantial financial success.

4. Engineers vs. Consultants: A Compelling Argument

The technical depth and problem-solving mindset of engineers make them particularly suited for founding and leading tech startups. Their ability to directly tackle complex problems contrasts with the approach of ex-consultants from firms like McKinsey, BCG, or Bain, who often focus more on financial and operational efficiencies.

While consultants excel in operations-heavy startups, where strategic planning, financial management, and operational efficiency are paramount, engineers thrive in tech startups that require innovative solutions and deep technical expertise. The success stories of the Palantir alumni underscore this distinction, demonstrating how their engineering backgrounds have enabled them to drive significant technological advancements and build successful companies.

Conclusion

The Palantir Mafia’s engineers have leveraged their technical expertise to create innovative solutions and successful ventures, driving significant impact across various industries. Their ability to tackle complex problems directly contrasts with the approach of ex-consultants from firms like McKinsey, BCG, or Bain, who often focus more on financial and operational efficiencies. This technical depth has enabled these former Palantir employees to become influential leaders, pushing the boundaries of technology and innovation.

References & Further Reading:

Achieve Peak Performance: AI Tools for Developers to Unlock Their Potential

By Ramkumar Sundarakalatharan | November 21, 2023 | Comments 0 Comment

You were scrolling through Twitter or your favourite SubReditt on the latest tech trend and a sudden feeling of FOMO creeps in. You’re not alone.

While the notion of a “10x developer” has traditionally been considered aspirational, the emergence of AI-powered tools is levelling the playing field, empowering developers to achieve remarkable productivity gains. While there might be 1000s of possible “AI tools”, I’ll restrict to tools which could yield a direct productivity boost to a developer’s day-to-day work as well as the outcome.

1. AI Pair-Developer / Code Assistants

Sourcegraph Cody & Github Copilot — Read, write and understand code

If you have used GitHub Copilot. Think of a Cody as a Turbocharger for Copilot. If you have not used Copilot, you should first try it. Either of these can understand your entire codebase, code graphs, and documentation and help you write efficient code, write unit tests, and document the codebase for you.

While the claim of a 10x speed increase is not substantiated, it shows clear intent to improve productivity drastically. However, it’s in beta, and the tool acknowledges that it’s not always correct, though they’re making rapid improvements. Yes, GitHub Copilot X is there — but then, your organisation needs to be on the Enterprise plan or you might have to add an additional $10-20 per user per month, and Cody is already here.

2. AI Code reviews – Offload the often mundane task of code reviews

While CodeRabbit and DeepCode (now acquired by Snyk) are some of the trailblazers in this space, I have not had the opportunity to work with either of them for any stretch of time. If you know about their relative strengths or benefits, please add a comment, and I will incorporate it.
The tool I use most regularly is called Robin-AI-Reviewer, from the good folks at Integral Healthcare (funded by Haystack). My reasoning is two-fold, It is open-source and if it is good enough for HIPPA-compliant app development and certification assessment, it’s a good starting point.

3. AI Test writing – Delegate the task of writing tests to AI- CodiumAI

CodiumAI serves as an AI test-writing assistant. It analyses your code, docstrings, and comments to suggest tests intelligently. CodiumAI addresses a critical aspect of software development that often consumes valuable time: testing. While numerous tools prioritize code writing and optimization, ensuring code functionality is equally vital. CodiumAI seamlessly fills this gap, and its intelligent test generation capability can substantially enhance development efficiency and maintain superior code quality.

4. AI Documentation Assistant — Get AI to write docs for you

This is a no-brainer, who loves writing code walkthroughs and docs? No? Didn’t think so! Mintlify serves as your team’s technical writer. It reads and interprets your code, turning it into a clear, readable document. By all accounts, it is a definite must.

Disclaimer: I have not personally used this and have been mostly able to get this done with Cody, itself. And then, I am no longer doing the primary documentation as my main responsibility.

5. AI Comment Assistant – Readable AI — Never write comments again

Readable AI automates the process of generating comments for your source code. It’s compatible with several popular IDEs, like VSCode, Visual Studio, IntelliJ, and PyCharm, and it can read most languages.

6. AI Tech Debt Assistant – Grit.io

Grit.io is an automated technical debt management tool. Its prime function is auto-generating pull requests that manage code migrations and dependency upgrades. Grit is in beta and available for free till beta moves to RC1. But it actually has about 50+ pattern libraries and it is growing.

I absolutely love it and Grit alleviates a significant portion of the manual work involved in managing migrations and dependency upgrades. They say it 10x’s the refactoring and migration process. I’d say at 33% of what they say, It will still be 300% of what productivity increases. And it is a considerable gain. If you’re an Engineering Leader and you have a “Budget” for 1 tool only, It should be this!

7. AI Pull Request Assistant – An “AI” powered DIff tool

What The Diff AI is an AI-powered code review tool. It writes pull request descriptions, scrutinises pull requests, identifies potential risks, and more. What The Diff claims to be able to significantly speed up development timelines and improve code quality in the long run. It could take a great deal of pain out of the process.

Disclaimer: I have not personally used this

8. AI-driven residential Wizard – Adrenaline AI — Explain it to me

Adrenaline AI helps you understand your codebase. The tool leverages static analysis, vector search, and advanced language models to clarify how features function and explain anything about it to you. The thing I like about this tool very much is, it can be leveraged to automate the “How tos” for your software engineering teams!

9. AI collaboration companion for software projects

Stepsize AI by Stepsize is an AI companion for software projects. It seamlessly integrates with tools like Slack, Jira, and GitHub, providing insightful overviews of your activities and offering strategic suggestions.

The tool uses a complex AI agent architecture, providing long-term “memory” and a deep understanding of the context of your projects.

10. AI-Driven Dev Metrics Collection – Hivel.ai

While strictly speaking, not an AI-driven “assistant” to an average developer, I feel it is nevertheless a good tool for the Engineering org and Engineering leaders to keep track and make course corrections. It provides a Cockpit/Dashboard of all the metrics that matter.

Hivel is built by an awesome team of devs and led by Sudheer

If You Can Do Your Job From Home, Be Scared. Be Very Scared!

By Ramkumar Sundarakalatharan | July 2, 2023 | Comments 0 Comment

Sometimes, It seems like most people have added “Remote Work” to a long list of taboo topics that no one should discuss at work. Topics like Religion, Politics, Sexual Orientation, Medical Issues, etc.

Most people know that remote work is a horrible idea for organisations of any size, but they are afraid to call it out because they don’t want to appear out of touch with their employees/colleagues. Others are afraid to be viewed as hostile figures who wish to create tension with employees or be perceived as a manager who doesn’t trust their employees.

So, employers stay silent, and now everyone thinks remote work is the best business idea of the last 200 years.

Most of these shenanigans use personal anecdotes to defend the benefit of remote work. They say they are more productive at home, with fewer distractions and more time for daily (personal) chores. I do not agree or disagree with that statement completely, as it could be dependent on specific roles.

When Remote Work Could be Beneficial:

If you are an individual contributor and mostly work without the need to collaborate in real time, you can be productive in remote work. For example, If you are a finance analyst who dives through ledgers, borough thru tonnes of CSVs and crunch numbers all day, you may be more productive in Home.

When Remote work could be less than Optimal

If you are a creative or knowledge worker (Designer/Developer), chances are you’d NEED to collaborate with, break down/delegate, get feedback etc. In these roles, it will be almost impossible to get a calendar from 6 different people to drive consensus. Whereas if all of your stakeholders are in the office, it is a mere “Shout” or “Wave” and 3 mins conversation following it.

Google has officially changed its mind about remote work

Google leadership publicly admitted that remote work no longer works for them, and that’s the reason they want all of their employees back behind their desks.

Last week, Fiona Cicconi, Google’s chief people officer, wrote an email to the entire company stating, “Going forward, we’ll consider new remote work requests by exception only.” This is horrible news for employees and some companies who believe that remote work is the greatest idea since the invention of the internet.

Let me declutter the above statement for you.

Google is the biggest tech company in the world that created 100s of resources and tools to enable employees to work remotely, admitting defeat.

Despite the release of many communications tools that enabled workers across the globe and all industries to work remotely, it is finally saying that remote work doesn’t work. Let that sink in. Google’s remote employees are unhappy, but Google’s leadership rarely pay attention to the feeling of their employees (after Larry left Alphabet inc). They only pay attention to the stock price and what is recommended at the shareholder’s meeting.

Google is not alone. Apple, Microsoft, Facebook, and Amazon also laid off most remote employees.

Remote work destroyed the most profitable industry

As you have seen above, Google leadership is no longer willing to offer their employees 100% remote jobs. They want their employees in the office and productive.

You might ask, why is this happening?

It is happening because you witnessed the destruction of the Tech industry in the last two years. The tech industry crumbled because most Leaders were afraid to tell their remote employees to come to the office, so they did the next best thing: they lost money and laid remote employees off (mostly).

According to the Layoffs tracker, more than 200,000 people were laid off in 2023, and over 164,000 employees were laid off in 2022. This is a clear message to anyone who works in the tech industry, stop working remotely or start interviewing soon.

This is exactly what the most innovative CEO in the world did when he bought Twitter.

When Elon Musk bought Twitter, he found the company in tatters. Musk gave them the same two options he gave his Tesla employees, “If you do not return to the office, you cannot remain at the company. End of story.”

That was the end of the story for many of Twitter’s employees, Musk fired anyone who refused to show up at the office, and his company is more productive and innovative than ever.

Musk ended remote work at Twitter, and most people hated him.

Not every executive had the guts to do what Elon Musk did, but now most wish they did because every company needs to lay off their unproductive employees.

Remote employees are less visible. When it comes to your value to your company, you have to be visible. If you think visibility is not important, ask Kayne West.
Remote employees don’t have a strong connection to other employees or their companies. Relationships are extremely important. I established my professional credibility and earned my colleagues’ respect by connecting with them face-to-face, not through a computer screen.
Remote employees are less invested in their work or career. Since most employer link visibility and ability together, I understand why more than 3000 HR managers believe that.

These reasons led Google, Microsoft, Amazon, Facebook, and Zoom to lay off their remote employees first. This is a horrible trend for people who want to work remotely, especially since most remote workers say it could take more than six months to find a new job

In conclusion, I’d like to recommend Richard Baldwin’s video

Is NoOps the End of DevOps?

By user | June 4, 2023 | Comments 0 Comment

Some say that NoOps is the end of DevOps. Is that really true? If you need to answer this question, you must first understand NoOps better.

Things are moving at warp speed in the field of software development. You can subscribe to almost anything “as a service” be it storage, network, computing, or security. Cloud providers are also increasingly investing in their automation ecosystem. This leads us to NoOps, where you wouldn’t require an operations team to manage the lifecycle of your apps, because everything would be automated.

You can use automation templates to provision your app components and automate component management, including provisioning, orchestration, deployments, maintenance, upgradation, patching and anything in between meaning significantly less overhead for you and minimal to no human interference. Does this sound wonderful?

But is this a wise choice, and what are some advantages and challenges to implementing it?

Find out the answers to these questions, including whether NoOps is DevOps’s end in this article.

NoOps — Is It a Wise Choice?

You already know that DevOps aims to make app deployments faster and smoother, focusing on continuous improvement. NoOps — no operations — a term coined by Mike Gualtieri at Forrester, has the same goal at its core but without operations professionals!

In an ideal NoOps scenario, a developer never has to collaborate with a member of the operations team. Instead, NoOps uses serverless and PaaS to get the resources they need when they need them. This means that you can use a set of services and tools to securely deploy the required cloud components (including the infrastructure and code). Additionally, NoOps leverages a CI/CD pipeline for deployment. What is more, Ops teams are incredibly effective with data-related tasks, seeing data collection, analysis, and storage as a crucial part of their functions. However, keep in mind that you can automate most of your data collection tasks, but you can’t always get the same level of insights from automating this analysis.

Essentially, NoOps can act as a self-service model where a cloud provider becomes your ops department, automating the underlying infrastructure layer and removing the need for a team to manage it.

Many argue that a completely automated IT environment requiring zero human involvement — true NoOps — is unwise, or even impossible.

Maybe people are afraid of Skynet becoming self-aware!

NoOps vs. DevOps — Pros and Cons

DevOps emphasizes the collaboration between developers and the operations team, while NoOps emphasizes complete automation. Yet, they both try to achieve the same thing — accelerated GTM and a better software deployment process. However, there are both advantages and challenges when considering a DevOps vs. a true NoOps approach.

Pros

More automation, less maintenance

By automating everything using code, NoOps aims to eliminate the additional effort required to support your code’s ecosystem. This means that there will be no need for manual intervention, and every component will be more maintainable in the long run because it’ll be deployed as part of the code. But does this affect DevOps jobs?

Uses the full power of the cloud

There are a lot of new technologies that support extreme automation, including Container as a Service (CaaS) or Function as a Service (FaaS) as opposed to just Serverless, so most big cloud service providers can help you kickstart NoOps adoption. This is excellent news because Ops can ramp up cloud resources as much as necessary, leading to higher capacity, performance & availability planning compared to DevOps (where Dev and Ops work together to decide where the app can run).

Rapid Deployment Cycles

NoOps focuses on business outcomes by shifting focus to priority tasks that deliver value to customers and eliminating the dependency on the operations team, further reducing time-to-market.

Cons

You still need Ops!

In theory, not relying on an operations team to take care of your underlying infrastructure can sound like a dream. Practically, you may need them to monitor outcomes or take care of exceptions. Expecting developers to handle these responsibilities exclusively would take their focus away from delivering business outcomes and wouldn’t be advantageous considering NoOps benefits.

It also wouldn’t be in your best interest to rely solely on developers, as their skill sets don’t necessarily include addressing operational issues. Plus, you don’t want to further overwhelm devs with even more tasks.

Security, Compliance, Privacy

You could abide by security best practices and align them with automatic deployments all you want, but that won’t completely eliminate the need for you to take delicate care of security. Attack methods evolve and change each day, therefore, so should your cloud security controls.

For example, you could introduce the wrong rules for your AI or automate flawed processes, inviting errors in your automation or creating flawed scripts for hundreds or thousands of infrastructure components or servers. If you completely remove your Ops team, you may want to consider investing additional funds into a security team to ensure you’re instilling the best security and compliance methods for your environments.

Consider your environment

Considering NoOps uses serverless and PaaS to get resources, this could become a limiting factor for you, especially during a refactor or transformation. Automation is still possible with legacy infrastructures and hybrid deployments, but you can’t entirely eliminate human intervention in these cases. So remember that not all environments can transition to NoOps, therefore, you must carefully evaluate the pros and cons of switching.

So Is NoOps Really the End of DevOps?

TL:DR: NO!

Detail: NoOps is not a Panacea. It is limited to apps that fit into existing #serverless and #PaaS solutions. As someone who builds B2B SaaS applications for a living, I know that most enterprises still run on monolithic legacy apps and even some of the new-gen Unicorns are in the middle of Refactoring/Migration which will require total rewrites or massive updates to work in a PaaS environment, you’d still need someone to take care of operations even if there’s a single legacy system left behind.

In this sense, NoOps is still a way away from handling long-running apps that run specialized processes or production environments with demanding applications. Conversely, operations occur before production, so, with DevOps, operations work happens before code goes to production. Releases include monitoring, testing, bug fixes, security and policy checks on every commit, etc.

You must have everyone on the team (including key stakeholders) involved from the beginning to enable fast feedback and ensure automated controls and tasks are effective and correct. Continuous learning and improvement (a pillar of DevOps teams) shouldn’t only happen when things go wrong; instead, members must work together and collaboratively to problem-solve and improve systems and processes.

The Upside

Thankfully, NoOps fits within some DevOps ways. It’s focused on learning and improvement, uses new tools, ideas, and techniques developed through continuous and open collaboration, and NoOps solutions remove friction to increase the flow of valuable features through the pipeline. This means that NoOps is a successful extension of DevOps.

In other words, DevOps is forever, and NoOps is just the beginning of the innovations that can take place together with DevOps, so to say that NoOps is the end of DevOps would mean that there isn’t anything new to learn or improve.

Destination: NoOps

There’s quite a lot of groundwork involved for true NoOps — you need to choose between serverless or PaaS, and take configuration, component management, and security controls into consideration to get started. Even then, you may still have some loose ends — like legacy systems — that would take more time to transition (or that you can’t transition at all).

One thing is certain, though, DevOps isn’t going anywhere and automation won’t make Ops obsolete. However, as serverless automation evolves, you may have to consider a new approach for development and operations at some point. Thankfully, you have a lot of help, like automation tools and EaaS, to make your transition easier should you choose to switch.

How to measure Engineering Productivity?

By Ramkumar Sundarakalatharan | November 27, 2022 | Comments 0 Comment

The fact that you clicked on this article tells me that you are leading/heading a Team, group or an entire Engineering function and most likely a fast-paced startup. Assume the following,

It was a regular weekday, and your CEO/CTO asked the most intriguing question.

Do we measure Engineering Productivity? How do we fare? What can we do to improve it?

Well, if your boss’s name is not Elon Musk or if you do not work for Twitter, you can still be saved. Go on and read through. I know it is a long read.

What is Engineering Productivity?

As with anything you’re trying to improve, it starts with measuring the right data. So, you can actually track the right metrics. This data will form the basis of your analysis and baseline. I strongly recommend you don’t change anything about your current engineering process before you can collect sex weeks’ worth of data about your processes. If you start working on processes, you could end up with a Survivorship Basis.

You should have sufficient historical data to make comparisons. On top of that, most teams work in sprints of two weeks, so six weeks of data allows you to collect data for at least three different sprints. This will give you the allowances for any spikes and eliminate any unusual stress or slack on the execution.

Next, you should make gradual changes to the engineering process to see what improves or impedes the value delivery. It’s ideal to only implement one change at a time, so you can see the effect of each change, with all other things being equal. (it never is :D)

For example, if your engineering squads suffer from significant technical debt, you may want to build an additional stub related to feature completion. Every time an engineer completes a new feature, they must document the new feature. This could mean describing the feature, how is it built, what are the outcomes, how it interacts with other functions and the reasoning behind the design decisions.

By continuously measuring engineering productivity metrics, you can determine if this change has positively impacted the developers’ productivity.

How Is Engineering Productivity Measured?

There are potentially 100s of metrics you can measure for an Engineering Org. Here are four key metrics that will help you to get started with measuring engineering productivity. And I have consciously excluded the Sprint Velocity.

4 Prime Directives of Engineering Metrics

1. The One Metrics to rule them all metrics – Cycle Time

Software development cycle time measures the amount of time from work started to work delivered. It is a metric “borrowed” from lean manufacturing, and it is one of the most important metrics for software development teams. In plain speak, cycle time measures the amount of time from the first commit to production release.

2. The Oracle of an Engineering Leader – Release Frequency

You should measure how often you deploy new changes to your customers (production). In addition, you can track deployments to various branches/instances, such as feature branches, hotfix branches, or QA branches. This data would show you how long it takes for a feature/fix to move through the different development stages. In addition, the Release Frequency reflects the throughput of your team. It’s a good stand-in replacement for Agile Velocity, so you don’t spook your Engineers and you are not blind as well.

3. The Guardrail – Number of Bugs

You should definitely track the number of bugs that your team has to resolve within 2 sprints of releasing a feature. This metric helps you to understand the quality of your code better. Higher-quality code should display fewer bugs after feature deployment.

While there are derivative and more evolved metrics like Defect Density, Mean Time to Detect (MTtD), Mean Time to Resolve (MTrR) and Code coverage, those onces makes sense after you’ve taken stock of and address the prime metric “ No: of Bugs” first.

If you want a more detailed list, methodology of QA metrics, refer the links given below.

4. What is your “Blocker” – Review to Merge Time (RTMT)

This may look like a zoom-in on “Cycle time” metric we discussed earlier. But, in fact it is very different. In fact, it is an interesting metric suggested by GitLab’s development handbook.

You should measure the time between asking for a pull request (PR) review and merging the PR. Ideally, you want to reduce the time a feature spends in the review state (or pending review state). A high RTMT prevents developers from progressing while they wait for feedback and encourages context-switching between different issues/features.

Arguably, Context-Switching is the highest productivity killer and should be avoided as much as possible.

So, why would you measure all these engineering productivity metrics?

Why Is Measuring Engineering Productivity Important?

When you’re a “fast-growing startup”, it’s important to keep an eye on engineering productivity. It happens that these startups favour growth through feature delivery at the cost of effectively scaling the engineering team and ensuring the team’s efficiency.

I hear your question.
But, why does my CEO/VP/MD not understand?
Answer is simple
Assume you have to manage multiple VP’s expectations and outcomes (Sales, Marketing, Support etc), Company’s OKRs, and investors (or) board, will you have more time to dedicate to Engineering Productivity?

In these cases, technical debt can quickly grow, which will slowly kill your team’s productivity. Technical debt can have many negative consequences:

More bugs for your team to fix
Lower code quality—not only bugs but also worse code design
Harder to debug code
Scalability issues
A decline in overall happiness and job satisfaction

To avoid all of these scenarios, you should measure the engineering team’s efficiency and avoid technical debt buildup. Avoiding these problems before they occur is an excellent Occam’s razor. But addressing them head-on will have a significant impact on your organisation, both materially and culturally.

In addition to preventing your team’s productivity from going down, the engineering productivity approach allows you to experiment with various approaches to try and improve throughput & efficiency.

So, the goal is to improve the engineering process itself. For example, introducing new tools or applying new techniques. Next, you can measure the impact of these changes on your team’s productivity.

In the next part, I will write down on how can measurement improve engineering productivity, Stay Tuned!

References:

Survivorship Bias.
1. https://www.masterclass.com/articles/survivorship-bias
2. https://en.wikipedia.org/wiki/Survivorship_bias
Cycle Time
1. https://tulip.co/blog/cycle-vs-lead-vs-takt
Release Frequency
1. https://community.atlassian.com/t5/DevOps-articles/Why-should-we-start-measuring-the-Release-Frequency/ba-p/1786430
Detailed QA Metrics to ponder (in addition to No: of bugs)
1. https://reqtest.com/agile-blog/agile-testing-metrics/
Review to Merge Time
1. https://about.gitlab.com/handbook/engineering/development/performance-indicators/#review-to-merge-time-rtmt
Context Switching
1. https://pacohq.com/blog/guide/the-high-price-of-context-switching-for-developers/

What Does It Take To Become a “Senior” Software Engineer.

By Ramkumar Sundarakalatharan | November 7, 2021 | Comments 0 Comment

This article is a result of a discussion with one of our ” Ninja-neer”. He was interested in “Delivering Business Value” but not interested to take up People Management or other responsibilities. Do I have any pointers for people like him? Of course. So, we started discussing on ways he can contribute at a different level. In the end we talked for about 90+ minutes. This is an extract & summary of that discussion.

In the late 2000s, it was a trend for companies to hire developers based on the programming language they had experience with, frameworks, tech stack, and such. (I still remember the disappointing gaze I got when I told the interviewer that I have only worked with CVS and Mercurial and not in SVN, which the team I was interviewing for was using)

It is preferable to hire engineers skilled with a particular stack, it is not crucial. After all, great software developers should be able to learn and ramp up quickly with the massive knowledge available on the internet.

With that being the absolute baseline, companies started to value developers with great complimentary soft skills, as their technical expertise is now baseline to work in the industry, setting the bar even higher for people starting a career right out of college.

The Three Fundamental Traits

After almost 12 years of managing/leading Software Engineering teams as a Technical Lead, PM, Engineering Manager, Director etc., I have observed the skills that tech organisations generally value the most. I believe I have identified a pattern that generally falls into three different categories:

1. Technical expertise and craftsmanship

Understanding the fundamental concepts of computing is the baseline to becoming a software engineer. Even though this looks like common sense, this science is vast and is continuously evolving. Gone are the days when knowing some data structures, array transformations and basic algorithms will get you over the ledge. Also, the organisational/product context is very important as well. For example, my peers at Paypal prided in getting sub 500ms latency for all the “processes” they wrote, while my colleagues in Hinduja Tech focussed on ensuring “zero-packet loss” from the telematics devices.

It really boils down to what is your company’s key priorities are. It can be quicker release cycles/velocity, resilience/ fault-tolerance or efficient memory management. Whatever it is, you need to first understand the “value” and then follow it in your implementations.

2. Scope and autonomy

We do not live in a world where working alone and implementing specifications from LLD/UML diagrams is sufficient anymore. For that matter, in the last 18+ years, I have met exactly two people who were able to pull it off and one was a 62-year-old Ingres developer, who was single-handedly managing the 40year old databases of PA. Those who know how to navigate complexity requiring minimum supervision are now extremely valuable professionals. Actively communicating and ensuring alignment is more important in these times of high-velocity organisations.

3. Communication and influence

Even though nobody expects you to be a skilled public speaker, we are long past the era where programmers were introverts that spoke an unintelligible alien language. Knowing how to work with people and interact with non-technical partners is a valued skill in the market.

I had a very first-hand experience of why clarity in communication is so important as you grow up the ladder in your tech org.

I was (hastily) called to a meeting, where my boss (VP) was explaining to our CEO, of why we should not be building the next generation of BRTS for Congo, Senegal and Ghanna on Desfire EV1. The primary concern was around security and privacy. There were major concerns around its security and was exposed just before the London Olympics. (It had taken me 3 meetings over 2 weeks to convince my Boss to go with EV2) I still do not know why he thought I might be able to do it in 10 minutes and that too in front of the CEO!! But the important thing was, My boss was willing to give me a chance to try it and in the process, he was giving me visibility to the inner workings on the 11th floor (C-Suite).

How do I convince my CEO to opt for a solution with almost 20% additional initial cost?

Is it with NXPs’ security from relay attack or with 16KB vs 4KB of usable memory or something else? Then it struck me if the topline is something my CEO was interested in he could definitely understand the bottom line! I fumbled something around potential “Revenue loss” and did a whiteboard tabulation of some numbers. (Desfire

Surprisingly, my CEO got it despite my ramblings and corrected my statement, It is a potential Revenue Leakage, not a loss!

That one meeting changed almost every communication I did after it.

Becoming a Senior IC – Sr.Engineer, Staff Engineer, Principal Engineer, Architect (or any of the other dozen designations)

Again, this article is aimed to give some clarity on what are the options for rising up the ranks as an Indivudual Contributor. The Tech Ladder in most Startups are similar with slight deviations. After you become a Sr. Engineer you have 2 tracks – Technical Excellence leading to Staff Engineer and Principal Engineer. The other track involves People and Budget management leading to Tech Lead, Engineering Manager and Director, VP etc.

I’d like to help you understand important aspects that can place you at higher levels based upon the expectations set on each level of proficiency grouped into the following tiers: Beginner, Intermediate and Advanced.

	Expertise	Scope & Autonomy	Influence
Beginner	Learning	Feature & Guided	Collobrators
Intermediate	Proficency	Product, Performance & Tactical	Team(s) Wide
Advanced	Expertise	Domain, Industry & Strategic	Teams & Function wide

This grid above depicts the career trajectory of software engineers in a super-simplified way. It is generally more complex than that, but it still serves as a good guideline to identify career points of inflection. Note that I did not use the so popular “associate,” “mid-level,” and “senior” on the different levels. This is more of a grouping of related circles of roles.

Starting a Career as a Software Engineer

As a beginner in this new and adventurous area, there are lots of low-hanging fruits you can learn from. In fact, learning should be your focus. You should acquire as much knowledge as you can from being exposed to a variety of problems.

Up until a point, you will work on “very specific” problems or small features or bug fixes until you ramp up and have a good understanding of the lay of the land (product or system) you are helping to develop.

You will pair with more experienced engineers and learn from code reviews and feedback from your partners. Engineers at this level spend a reasonable amount of time learning until they get proficient with tools and acquire more domain knowledge. You’ll learn a lot of Tricks and Tools from your senior peers and you will also find the kind of problems you’re proficient in solving. Which will result in similar problems, fixes or features you’re assigned with.

The trick is to emrace the “Streotype” and make it your “Niche”, while also diversifing enough to get a hang of other things and continue to learn.

Working as a Proficient Developer

At this point, you would have gotten your hands dirty for a few years and developed mastery of computer science including algorithm design, data structures, design patterns, and the tools and frameworks you work with. You have very deep experience with at least one or two part of the technology stack you work with.

It is now taken for granted you will be able to deliver complex pieces of software with very little supervision. In fact, there is also the expectation you can help less experienced engineers to grow and guide them to execute the tactical plan you created. You help people to review their code as well as solve problems and develop new features.

One thing to remember is, “Code Review is a Bidirectional Learning exercise” – The proficient ones understand/learn new approaches from the beginner, the beginner

It is the time in your career that you start getting the opportunity to lead small projects and time-bound initiatives and likely start to get more exposure to cross-functional partners and some non-technical stakeholders. Most software engineers stay at this level for many years.

The Non-Comissioned Officers are called as the backbone of an Army. Similarly, Sr.Developer is the backbone of any product/Engineering Team. As this is the most visible and “on-the-ground” leadership.

The Making of a Senior Software Engineer

At this level, Coding in general starts to become less important as you are now a visible voice for your team and across the organization. You now understand how to make difficult trade-offs in the architectural level of your application across the entire domain.

As a domain expert, you own a substantial part of your company’s codebase, supervise its evolution and work from other engineers, as well as advise other teams on how to better approach or integrate with your services and applications.

As an advisor, your contribution is clear and visible across multiple teams. You are highly influential and your advice is constantly sought from other engineers and cross-functional partners.

This is the inflection point where you start considering a transition to leadership roles. It usually takes some years to land at this level. The next step for you is growing the impact of your work across teams, organizations, companies, and industry-wide.

Even though colleges prepare you to develop software, as you grow in your career that skill starts to become less important and other soft skills turn out to be more relevant. I hope i was able to nake justification to the topic of growing as an indivudual contributor and make higher impact and inspire you to reflect on your own trajectory and how to proceed with the next steps.