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
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
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.
The recent FTC ruling banning most non-compete agreements nationwide has ignited a firestorm in the business world. While some cheer the increased freedom for workers, others fear a potential talent exodus and a decline in innovation. Let’s delve deeper into this debate, exploring the arguments for and against non-compete clauses, along with the potential consequences of the ruling.
Champions of the Free Agent: A Rising Tide Lifts All Boats
Proponents of the FTC’s decision paint a rosy picture. They argue that:
Increased Worker Mobility: With non-compete shackles removed, workers can freely pursue more lucrative opportunities. This competition between companies drives salaries upwards, forcing employers to offer competitive benefits packages to retain talent.
Innovation on Steroids: A more mobile workforce fosters a cross-pollination of ideas. Employees bring fresh perspectives and experiences from previous roles, leading to a more dynamic and innovative environment across industries.
Empowering the Underdog: Critics of non-competes argue that these clauses disproportionately affect low-wage workers. They often lack the resources to challenge them in court, effectively becoming trapped in jobs with limited upward mobility.
The Employer’s Lament: Protecting the Crown Jewels
Companies are understandably nervous about the FTC’s ruling. Here’s why:
Trade Secrets at Risk: Businesses worry that departing employees, especially those privy to sensitive information, might jump ship to a competitor, potentially taking valuable trade secrets with them. This could give a rival an unfair advantage and stifle innovation.
Customer Loyalty on the Move: Companies also fear losing established customer relationships when key salespeople or account managers move on to a competitor. This could lead to a decline in customer retention and revenue.
Poaching Wars: A Race to the Bottom: Without non-compete clauses, some companies worry about fierce “poaching wars” erupting, where competitors aggressively recruit talent and drive up salaries for specific roles. While this might benefit a select few employees, it could negatively impact smaller companies with limited resources.
The Nuance: Not All Non-Compete Clauses Are Created Equal
It’s important to acknowledge that the FTC ruling has some limitations. Here are some potential grey areas:
Executive Contracts: The ruling may not apply to high-level executives whose contracts often contain stricter non-disclosure and non-compete clauses. These agreements might still be enforceable depending on specific terms.
State Variations: While the FTC ruling aims to be a blanket policy, some states might have stricter or more lenient regulations regarding non-compete clauses. Employers and employees should be aware of their state’s specific laws.
Industry Specificity: The FTC ruling might have a more significant impact on specific industries like tech, where knowledge transfer and trade secrets are particularly valuable. Other sectors may be less affected.
The Future of Work: A Brave New World?
The FTC’s ruling is a major turning point that could significantly reshape the American workforce. It’s too early to predict the full impact, but some potential scenarios include:
Rise of the Free Agent Economy: Highly skilled workers with in-demand expertise may become more like free agents, negotiating short-term contracts or project-based work with various companies.
Focus on Retention Strategies: Companies may shift their focus towards creating a more positive work environment that fosters loyalty and discourages employees from leaving. This could include better benefits, training opportunities, and a strong company culture.
Increased Use of Confidentiality Agreements: Non-compete clauses may be replaced by stricter confidentiality agreements to protect sensitive information, although their enforceability might vary.
The Fork in the Road: The Curveball that Redis Pitched
In a move announced on March 20th, 2024, Redis, the ubiquitous in-memory data store, sent shockwaves through the tech world with a significant shift in its licensing model. Previously boasting a permissive BSD license, Redis transitioned to a dual-license approach, combining the Redis Source Available License (RSAL) and the Server Side Public License (SSPL). This move, while strategic for Redis Labs, has created ripples of concern in the SAAS ecosystem and the open-source community at large.
The Split: From Open to Source-Available
At its core, the change restricts how users, particularly cloud providers offering managed Redis services, can leverage the software commercially. The SSPL, outlined in the March 24th press release, stipulates that any derivative work offering the “same functionality as Redis” as a service must also be open-sourced. This directly impacts companies like Amazon (ElastiCache) and DigitalOcean, forcing them to potentially alter their service models or acquire commercial licenses from Redis Labs.
A History of Licensing Shifts
This isn’t the first time Redis Labs has ruffled feathers with licensing changes. As a 2019 TechCrunch article [1] highlights, Redis Labs has a history of tweaking its open-source license, sparking similar controversies. Back then, the company argued that cloud providers were profiting from Redis without giving back to the open-source community. The new SSPL appears to be an extension of this philosophy, aiming to compel greater contribution from commercial users.
SAAS Providers in a Squeeze
For SAAS providers, the new licensing throws a wrench into established business models. Modifying core functionality to comply with the SSPL might not be feasible, and open-sourcing their entire platform could expose proprietary code. This could lead to increased costs for SAAS companies, potentially impacting end-user pricing.
Open Source Community Divided
The open-source world is also grappling with the implications. While the core Redis functionality remains open-source under RSAL, the philosophical shift towards a more restrictive model has some worried. The Linux Foundation even announced a fork, Valkey, as an alternative, backed by tech giants like Google and Oracle. This fragmentation could create confusion and slow down innovation within the open-source Redis ecosystem.
The Road Ahead: Uncertainty and Innovation
The long-term effects of Redis’s licensing change remain to be seen. It might pave the way for a new model for open-source software sustainability, where companies can balance community development with commercial viability. However, it also raises concerns about control and potential fragmentation within open-source projects.
In conclusion, Redis’s licensing shift presents a complex scenario. While it aims to secure Redis Labs’ financial future, it disrupts the SAAS landscape and creates uncertainty in the open-source world. Only time will tell if this is a necessary evolution or a roadblock to future innovation.
Nobody plans to leave AWS in the startup world, but as they say, “sh** happens.”
As engineers, when we write software, we’re taught to keep it elegant by never depending directly on external systems. We write wrappers for external resources, we encapsulate data and behaviour and standardise functions with libraries.
But, When it comes to the cloud… “eerie silence”
Companies have died because they needed to move off AWS or GCP but couldn’t do it in a reasonable and cost-effective timeline.
We (at Itilite) had a close call with GCP, which served as our brush with the fire. Google had arguably one of the best Distance Matrix capabilities out there. It was used in one of our core logic and ML models. And on one fine Monday afternoon, I have to set up a meeting with my CEO to communicate that we will have to spend ~250% more on our cloud service bill in about 60 days.
Actually, google increased the pricing by 1400% and gave 60 days to rewrite, migrate, move out or perish!
The closest competitor in terms of capability was DistanceMatrix and a reliable “Large” player was Bing. But, both left a lot for in the “Accuracy”. So, for us, the business decision was simple: make the entire product work in “Reduced Functionality” mode for all or start differential pricing for better accuracy! In either case, those APIs must be rewritten with a new adaptor.
It is not an enigma why we do this. It’s simple: there are no alternatives, there is no time to GTM, But maybe there is. I’ll explain why you should take cloud-agnostic architecture seriously and then show you what I do to keep my projects cloud-agnostic.
Cloud Service Rationalisation
The prime reason you should consider the ability to switch clouds and cloud services is so you can choose to use the cloud service that is price and performance-optimized for your use case.
When I first got into serverless, we wrote a transformative API on Oracle Cloud (Bcoz we were part of their Accelerator Program and had a huge credit.) but it fed part of the data that the customer-facing API relied on.
No prize for guessing what happened?
It was a horrible mistake. Our API had an insane latency problem. Cold start requests added additional latency of at least 2 seconds per request. The AWS team has worked hard to build a service that can do things that GCP’s Cloud Functions simply can’t, specifically around cold starts and latency.
I had to move my infrastructure to a different service and a revised network topology.
Guess we would have learned the problem by now, but as we will find out, we did not.
This time it was a combination of Kafka and the AWS Lambda that created an issue. We had relied on Confluent’s connectors for much of the workload interfaces and had to shell out almost $1000 per month per connector!
Avoiding the Cloud Provider Killswitch
Protect Your Business from Unexpected Termination
As a CXO, you may not be aware that cloud providers like AWS, GCP, and Azure reserve the right to terminate your account and destroy your infrastructure at any time, effectively shutting down your business operations. While this may seem like an extreme measure, it’s important to understand that cloud providers have strict terms of service that can lead to account termination for a variety of reasons, even if you’re not engaged in illegal or harmful activities.
A Chilling Example
I recently spoke with a friend who is the founder of a fintech platform. He shared a chilling incident that highlights the risks of relying on cloud providers. His team was using GCP’s Cloud Run, a container service, to host their API. They had a unique use case that required them to call back to their own API to trigger additional work and keep the service active. Unfortunately, GCP monitors this type of behaviour and flags it as potential crypto-mining activity.
On an ordinary Sunday, their infrastructure vanished, and their account was locked. It took them six days of nonstop effort to migrate to AWS.
Protect Your Business
This incident serves as a stark reminder that any business operating on cloud infrastructure is vulnerable to unexpected termination. While you may not be intentionally engaging in activities that violate cloud provider terms of service, it’s crucial to build your infrastructure with the possibility of termination in mind.
Here are some key steps you can take to protect your business from the cloud provider killswitch:
Read and understand the terms of service for each cloud provider you use.
Choose a cloud provider that aligns with your industry and business model.
Avoid relying on a single cloud provider.
Have a backup plan in place.
Regularly review your cloud usage and ensure compliance with cloud provider terms of service.
By taking these proactive measures, you can significantly reduce the risk of your business being disrupted by cloud provider termination and ensure the continuity of your operations.
Unleash the Power of Free Cloud Credits
For early-stage startups operating on a shoestring budget, free cloud credits can be a lifeline, shielding your runway from the scorching heat of cloud infrastructure costs. Acquiring these credits is a breeze, but the way most startups build their infrastructure – akin to an unbreakable blood oath with their cloud provider – restricts them to the credits granted by that single provider.
Why limit yourself to the generosity of one cloud provider when you could seamlessly switch between them to optimize your resource allocation? Imagine the possibilities:
AWS to GCP: Upon depleting your AWS credits, you could effortlessly migrate your infrastructure to GCP, taking advantage of their generous $200,000 credit offer.
Y Combinator: As a Y Combinator startup, you’re entitled to a staggering $150,000 in AWS credits and a mind-boggling $200,000 on GCP.
AI-Powered Startups: If you’re developing AI solutions, Azure welcomes you with open arms, offering $300,000 in free credits to fuel your AI models on their cloud.
By embracing cloud-agnostic architecture, you unlock the freedom to switch between cloud providers, potentially saving you a significant $200,000 upfront. Why constrain yourself to a single cloud provider when cloud-agnosticism empowers you to navigate the cloud landscape with flexibility and cost-efficiency?
Building Resilience: The Importance of Cloud Redundancy
In the ever-evolving world of technology, no system is immune to failure. Even industry giants like Silicon Valley Bank can outright disappear over a weekend or AWS’ main Datacenter can go offline due to a power fluctuation, highlighting the importance of proactively safeguarding your business operations.
Imagine the potential financial impact of a 12-hour outage on AWS for your company. The costs could be staggering, not only in lost revenue but also in reputational damage and customer dissatisfaction or even potential churn.
This is where cloud redundancy comes into play. By running parallel segments of your platform on multiple cloud providers, such as AWS and GCP, you’re essentially creating a fail-safe mechanism.
In the event of an outage on one cloud platform, the other can seamlessly pick up the slack, ensuring uninterrupted service for your customers and minimizing the impact on your business. Cloud redundancy is not just about disaster preparedness; it’s also about optimizing performance and scalability. By distributing your workload across multiple cloud providers, you can tap into the unique strengths and resources of each platform, maximizing efficiency and responsiveness.
In our case, we run the OCR packages, SAML, and Accounts service on Azure, our core “Recommendation engine” and “Booking Engine” on AWS. Yes, having a multi-cloud will involve initial costs that might be prohibitive, but in the long run, the benefits will far outweigh the costs.
Cloud Cost Negotiation: A Matter of Leverage
In the realm of business negotiations, the ultimate power lies in the ability to walk away. If the other party senses your lack of alternatives, they gain a significant advantage, effectively holding you hostage. Cloud cost negotiations are no exception.
Imagine you’ve built a substantial $10 million infrastructure on AWS, heavily reliant on their proprietary APIs like S3, Cognito, and SQS. In such a scenario, walking away from AWS becomes an unrealistic option. You’re essentially at their mercy, accepting whatever cloud costs they dictate.
While negotiating cloud costs may seem insignificant to a small company, for an organization with $10 million of AWS infrastructure, even a 3% discount translates into substantial savings.
To gain leverage in cloud cost negotiations, you need to establish a credible threat of walking away. This requires careful planning and strategic implementation of cloud-agnostic architecture, enabling you to seamlessly switch between cloud providers without disrupting your operations.
Cloud Agnosticism: Your Negotiating Edge
Cloud-agnostic architecture empowers you to:
Diversify your infrastructure: Run your applications on multiple cloud platforms, reducing reliance on a single provider.
Reduce switching costs: Design your infrastructure to minimize the effort and cost of migrating to a new cloud provider.
Strengthen your negotiating position: Demonstrate to cloud providers that you have alternative options, giving you more bargaining power.
By embracing cloud-agnosticism, you transform from a captive customer to a savvy negotiator, capable of securing favorable cloud cost terms.
Unforeseen Challenges: The Importance of Cloud Agnosticism
In the dynamic world of business, unforeseen challenges (and opportunities) can arise at any moment. We often operate with limited visibility, unable to predict every possible scenario that could impact our success. Here’s an actual scenario that highlights the importance of cloud-agnostic architecture:
Acquisition Deal Goes Through
This happened with One of my previous organisations, we tirelessly built this company from the ground up. Our hard work and dedication paid off when a large SaaS Unicorn approached us with an acquisition proposal.
However, during the due diligence, a critical issue emerged: Our company’s infrastructure was entirely reliant on AWS. The Acquiring company had a multi-year multi-million dollar deal with Azure and the M&A team made it clear that unless our platform can operate on Azure, the deal is off the table!
Our team faced the daunting task of migrating the entire infrastructure to Azure within a limited timeframe and budget. Unfortunately, the complexities of the migration proved time-consuming and the merger took 5 months to complete and the offer was reduced by $2 million!
The Power of Cloud Agnosticism
This story serves as a stark reminder of the risks associated with a single-cloud strategy. Had our company embraced cloud-agnostic architecture, we would have possessed the flexibility to seamlessly switch between cloud providers, potentially leading to a bigger exit for all of us!
Cloud-agnostic architecture offers several benefits:
Reduced Vendor Lock-in: Avoids dependence on a single cloud provider, empowering you to switch to more favourable options based on your needs.
Improved Negotiation Power: Gains leverage in cloud cost negotiations by demonstrating the ability to switch providers.
Increased Resilience: Protects your business from disruptions caused by cloud provider outages or policy changes.
Enhanced Scalability: Enables seamless expansion of your infrastructure across multiple cloud platforms as your business grows.
Embrace Cloud Agnosticism for Business Continuity
In today’s ever-changing technological landscape, cloud-agnostic architecture is not just a benefit; it’s a necessity for businesses seeking long-term success and resilience. By adopting a cloud-agnostic approach, you empower your company to navigate the complexities of the cloud landscape with agility, adaptability, and cost-efficiency, ensuring that unforeseen challenges don’t derail your journey.
My Solution
Here’s what I do about it, now after the lessons learnt. I use Multy. Multy is an open-source tool that simplifies cloud infrastructure management by providing a cloud-agnostic API. This means that developers can define their infrastructure configurations once and deploy them to any cloud provider without having to worry about the specific syntax or nuances of each cloud platform. While Multy provides an abstraction layer for deploying cross-cloud environments, you will also need to incorporate cloud-environment agnostic libraries to really make a difference.
In this article, I will summarise effective strategies and best practices to tackle tech debt head-on.
Technical debt is an inevitable reality in software development. But, it can be leveraged just like a financial loan/debt can help you achieve your goals, if managed properly. It can be used to drive competitive advantage by allowing companies to launch new products and features faster, experiment with new technologies, and improve the scalability and performance of their systems. However, like all loans, it need to be “Repaid” properly and at the right time, failing on it will create a downward spiral.
If you’re not careful, technical debt can quickly become a major burden that slows down development and makes it difficult to add new features or even fix bugs in a timely manner.
We will discuss how to identify technical debt and the signs of poorly managed debt, and then provide a strategy for reducing it. We will also discuss what a healthy level of technical debt looks like and how leaders can use it to their advantage.
Bad debt takes money out of your pocket, while good debt puts money in your pocket.
– Robert Kiyosaki
The same is true of tech debt.
Technical debt is the cost of not doing things the right way the first time. Good technical debt is accrued when you make trade-offs to meet deadlines or deliver new features quickly.Bad technical debt is accrued when you make poor decisions or cut corners.
Bad tech debt will probably make your PMs, Sales and CEO happy for a quarter or two. But after that, they will be asking why everything is behind schedule and dealing with customer complaints because things aren’t working properly.
Now that I have presented the obvious in a familiar “Quadrant”, you can actually skip the terminologies and definitions part of this article! 😀
For my verbal brethren, Which is the Tech Debt you’d need to ruthlessly hunt down to extinction? Obviously, it is the untracked, undocumented ones. And the ones which are dragging your team on a downward spiral (immaterial of whether it is tracked or not)
Why does your Tech Debt keep accumulating?
Before we can think about building a strategy to solve tech debt, we need to understand how it gets out of control in the first place.
It’s called “impact visibility”.
Fixing code debt issues is impossible if:
1, You’ve no record of what technical debt issues you have
2, You’ve got a backlog, but you can’t see which issues are related to what code
In both cases, you can’t prioritise tech debt over shipping new features.
We need to get more granular about what impacts these two tech debt cases above.
Issue invisibility — There’s no source of shared knowledge. Codebase health info is locked in (few) engineers’ heads.
No code quality culture — Shipping fast, whatever the cost, like it’s going out of fashion.
Poor process — Tech debt work sucks. Nobody likes creating Jira tickets. “Jira” has become a dirty word.
Low-time investment — Justifying the time to fix tech debt or to refactor is a constant uphill battle. After a point, engineers become silent!
Lack of context — Issues in Jira are a world away from the hard reality of the codebase. They’re not related in any way.
So what’s the source of this? Let’s talk strategy.
Spoiler… It’s about changing organisational culture and developer behaviour to track issues properly.
Creating a strategy to reduce technical debt
Track. Issues. Properly.
Good tech debt management starts with team-wide excellence at tracking issues.
You can’t have a tech debt strategy without tracking.
The engineering leader’s job is to make that “issue tracking” easy for your team. There is supposed to be a software for that – Jira, Asana, Rally or something of that sort.
The problem is, I’ve never believed they really get to the bottom of the problem, and after speaking with scores of engineers and leaders about it, they usually don’t either. My personal belief is most companies suffer on the velocity after their Jira rollout! It is a bit like,
No two countries that both have a McDonald’s have ever fought a war against each other.
Show engineers when they’re working on code with tech debt, without them having to jump thru 3 hoops.
Make it really easy for team members to report tech debt.
Create a natural way to discuss codebase issues.
Integrate tech debt work into your workflows and involve PMs if required.
There are multiple ways to achieve this, the easiest is to not address it. Ie: not address it intentionally, just tweak your existing pipeline. This can be done by,
A very robust linting & integration to the IDE
Tighter Git rules for commits
SAST which runs on the pipeline
and can feed into the IDE
Prioritising impactful tech debt
At this point, it should be obvious, but prioritising the right issues is only possible if you’re tracking the impact of these “issues” and it could be direct or indirect (Dependency, Sequencing, Rework avoidance etc) .
Once you’ve got them, you should regularly and consistently use them to decide what to address. This usually happens during the backlog grooming or sprint planning sessions. But, this decision-making process needs to be strategic. Not at all tactical, ie: DO NOT delegate it to the whims and whimsicals of your TL/PM or even EM.
You or someone with a context of the organisation and position on sales, clients, revenue etc., should be doing this.
A good way to start is by choosing a theme each time you prioritise issues. For example, you could prioritise issues that…
Are impacting a specific feature you need to work on in the next quarter
Are impacting the customer’s UX
Are affecting efficiency/morale on the team
Are impacting the security posture
This is often straightforward if you’ve got high-quality issues that traceable to code and tagged as such.
Most people wonder how to get the time for these “Tasks”. I have two recommendations.
Take an entire sprint every quarter to repay the tech debt (Will need high-level buy-in, It is slightly harder to align your CXOs)
Allocate 15-20% of bandwidth in every sprint. (Easier to achieve buy-in from CXOs, harder to drive with engineers)
Engineers generally won’t prioritise tech debt work by themselves because of the conflict of interest/pressure of shipping fast. This was evident from multiple high velocity/impact software engineering teams including ones at AirBnb, Netflix and Spotify. A commitment to code refactoring and maintenance work should be endorsed and supported from the top and reinforced regularly.
How much Tech Debt can you take on?
Managing technical debt is like managing financial debt. You can use it to your advantage, but you need to be careful not to let it get out of control.
Your technical debt budget is the amount of technical debt that you are willing to take on in order to achieve your business goals. You should not try to solve all of your technical debt at once, but instead focus on the most important items.
Prudent technical debt is debt that you take on deliberately and knowingly, in order to achieve a specific goal. For example, you might take on technical debt to launch a new product quickly, or to add a new feature that is in high demand by your customers.
If you manage your technical debt properly, it can be a powerful tool for gaining a competitive advantage. However, if you let your technical debt get out of control, it can lead to serious problems, such as increased costs, delays, and security vulnerabilities.
Concluding remarks:
Technical debt is one of the most neglected areas of software development. It is often only given priority when it is too late and has already caused serious problems.
However, when leaders work together and develop a consistent and process-driven strategy, technical debt can be effectively managed.
The best engineering teams are constantly thinking about how to use their technical debt budget to their advantage.
Disclaimer: I have been an enormous proponent of Developer Productivity and have tried to implement automated metrics collection in 3 orgs with varied success. In my Mentoring sessions with early-stage startup leaders as well, I (re)enforce the importance of being aware of Dev Productivity. So much so, that I have written a 2-part article on the same here, here and here. I have also been a huge fan of McKinsey and how they seem to get answers which eluded the attention and resources of mega-corporations or governments alike. However, this article is written to communicate an entirely different perspective. In my opinion, McKinsey has got this entire “framework” thing about “dev productivity” wrong.
Introduction:
About a month back, McKinsey published an article claiming that they have developed a framework to measure productivity. They also acknowledged the fact that they were simply rehashing some of the existing metrics (like DORA and SPACE), which were used by Engineering Leaders and have simplified it (without the context) and are pitching it to their traditional buyers, the C-Suite executives in Mega corporations. Actually, some of these metrics can be useful tools if used correctly -One example is Hand-offs. But, the main reason I have chosen to write this article is their central focus seems to be “Coders should code”. It also appears to have A) missed the context of every metric, OR B) Omitted the context so as not to burden their target audience.
Finally, there is a mix-max of things to track, metrics to monitor and Opportunities to Focus, which looks like
Captain Ramius Pointing to a young Jack Ryan that Admiral Halsey was Stupid!
The Legendary Kent Beck has written a deep 2-part piece on countering the conjectures presented by McKinsey and elaborating on the gaps that engineering orgs are traditionally bound to manifest. It is very well written and covers almost everything. There are also a bunch of other eminent Software Engineers who have written on this and I have tried to give a quick lot at the bottom of this article.
What Was I concerned about?
Focus On Activities
I was primarily concerned about the lack of focus on Outcomes and Impact and a focus on the “Activities” in the proposed framework!
Any engineering leader or manager will tell you that Code Review Velocity and Deployment Frequency have nothing to do with measuring outcomes. While I will not discount Cycle Time or MTTR (I take pride in building multiple teams with one of the lowest MTTR and Cycle times in the ecosystem). They are indicators of some process elements/activities that could lead to outcomes. If we want to measure something, it should be Outcomes, not activities!
Focus on Optimisation of Irrelevant Metrics
Code Review Velocity:
If you want to time-motion the code review process in the entire stream map, you’ll find that async code review is killing your productivity. Pairing improves that dramatically. Instead of trying to sub-optimize for code review, measure the thing we actually want to improve. Which will be “Cycel Time”.
Story Points Completed:
Let’s agree on a basic fact. A “story point” is a made-up number. It was conceived as yet another way to obfuscate estimates for thought work that is difficult to estimate. As originally conceived, it represented the number of mythical “ideal days” of effort. There’s so much time wasted on getting better at “story pointing,” arguing about the Fibonacci sequence, “planning poker,” and other story point nonsense. Frankly, it is one of the “Bad” elements of Scrum! As a leader, you should find and remove handoffs and wait times. Story points are useless for anything and even more useless for this goal. Track throughput instead.
Handoffs:
This is a good one. Good job, McKinsey. You got something right. Stop using testing teams, use pairing instead of code review, operate what you build, and don’t have any people doing anything manual to the right of development.
Contribution Analysis and Opportunities focus
In the other focus areas, they have listed metrics at the individual level that can be useful unless you measure “developer satisfaction,” “retention,” and “interruptions” at the individual level. These should only be measured in aggregates to prevent any cognitive bias. IMO, Things start getting really toxic in the “Opportunities focus” section, though.
I have been part of organisations and processes where there was a focus on tracking and measuring the outcomes of individuals. It did not play out well, ever. My Conclusion after reading the article for the second time is that McKinsey thinks their intended audience (CEOs and CFOs) cannot understand “systems thinking.” Now, If you roll out this or a similar framework and announce this and what do you think will happen?
You have a group of people all working on the same backlog but not acting as a team. Code review suffers, mentoring sufferers, pairing is hard, work breakdown suffers, etc. Anything that requires more than one person to conduct/conclude, including helping someone get unstuck, will get deprioritised!
Overall, The inferences seem to be based on hard facts, but the conjectures are all flawed.
Why This Now?
At this point, I want to highlight what “Triggered” me to write this, read the following.,
For example, one company found that its most talented developers were spending excessive time on noncoding activities such as design sessions or managing interdependencies across teams. In response, the company changed its operating model and clarified roles and responsibilities to enable those highest-value developers to do what they do best: code.
McKinsey’s Article on the purported Framework
Wow. I pray for that company.
So, I believe after McKinsey pointed to the fact, that developers are involved in irrelevant things like design, architecture etc. They created separate towers of responsibility for design. In that case, I am puzzled about who will be responsible for the minor things like dependency management, prerequisites, versioning, capacity planning, concurrency, scalability etc.
Did they get anything Right?
Yes. There are tonnes, but they are buried at the bottom. Their focus on Hand-offs and cycle times are really worth tracking in any engineering org. To the authors’ credit, they have also identified some of the core issues with measuring Developer Productivity. But, someone higher in the firm seem to have suggested to soften the blow. So, they have diluted and buried those sections. I will share 2 gems here.
To truly benefit from measuring productivity, leaders and developers alike need to move past the outdated notion that leaders “cannot” understand the intricacies of software engineering, or that engineering is too complex to measure.
The real problem is that in many large organisations, “The Management” doesn’t understand the work they manage. Management can understand the intricacies of software engineering if they become leaders and study the work they manage. In a large behemoth, not all managers are leaders. They want a framework and will enforce it with an iron fist. Now, McKinsey has delivered them a framework!
Learn the basics. All C-suite leaders who are not engineers or who have been in management for a long time will need a primer on the software development process and how it is evolving.
This one Nailed it! The primary reason “Management” finds it difficult to measure the right thing is because they sometimes do not understand the work they want to measure. Leaders who understand do measure the right things. My primary concern with this framework is, in trying to solve this, McKinsey has made the problem worse!
Just google “McKinsey developer productivity” and you’ll find more articles on how this framework is flawed than the original article link!
Anto’s Response to the Article and the purported Framework.
Background: I work with multiple CTOs and Heads of Engineering of early-stage startups to help them set up their engineering orgs, review their product architecture, help them prioritise their hiring etc. I also help multiple engineering leaders via Plato. Recently, there have been too many questions on whether can I use ChatGPT for this or that, but the most interesting one among these is “Can ChatGPT accelerate No-Ops to Replace DevOps?”. I have answered that multiple times in verbatim. But thought that writing it down will help me with two things, I can point them to the URL and I can also clearly structure my thoughts on this. So this is an attempt at that.
Glossary First:
DevOps:
For the uninitiated, DevOps is a software development methodology that emphasizes collaboration and communication between developers and operations teams. The goal of DevOps is to increase the speed and quality of software delivery while reducing errors and downtime. DevOps engineers are responsible for the design, development, and delivery of software applications, as well as the management and maintenance of the underlying infrastructure including the pipelines, quality assurance automation etc.
No-ops:
No-ops, on the other hand, is a philosophy that aims to automate and simplify the operations side of software development. The goal of no-ops is to eliminate the need for manual intervention in the deployment and maintenance of applications, freeing up time for developers to focus on creating new features and fixing bugs.
ChatGPT:
ChatGPT is a language model developed by OpenAI that has the potential to revolutionize the way we interact with technology. It can perform a wide range of tasks, from answering questions to generating text and even a rudimentary bit of coding. In the context of DevOps, ChatGPT could be used to automate many of the manual tasks that DevOps engineers currently perform, such as infrastructure management, deployment, and monitoring.
Now, The question: Can ChatGPT accelerate No-Ops to Replace DevOps?
ChatGPT (or any of the other generative AIs) has the potential to automate many of the manual tasks performed by DevOps teams, it could potentially replace the most mundane tasks that DevOps perform pretty soon. So, before writing this piece, I wanted to actually put my understanding to test.
I had to use Bard for this experiment, but I do not think there is going to be much of a difference in the outcomes.
Experiment 1: Beginner-Level Task
Writing a simple Autoscaling script to scale as per CPU and memory utilisation.
Simple AutoScaling Script, written by Bard
Experiment 2: Intermediate-Level Task
Creation of a new VPC with 3 autoscaling groups of EC2 and 1 NAT gateway and VPC peering.
Results were mixed.
Though Bard did give information that this can be tweaked, there was one major gap between the ask and the outcome. The NAT gateway was supposed to be the single point of ingress/egress, whereas the script is entirely different.
Assume an early-stage startup that gets used to the early success of Generative AI to preempt the DevOps culture, at some point the AI won’t have the context of what all is in your infrastructure and you could end up misfiring things.
My submission is, ChatGPT or Bard or any Generative can be super helpful for a good DevOps engineer and cannot replace her/him anytime soon. In military parlance, certain materiels are termed Force Multipliers. But, they themselves cannot be the force! (Aircraft carriers or Tanker aircraft are the prime example)
Why do I believe so?
There are several reasons for this:
Human creativity: Despite its advanced capabilities, ChatGPT is just another AI model and lacks the creativity and innovation that a human DevOps engineer brings to the table. DevOps engineers can think outside the box and find new and innovative solutions to “Business” problems, whereas ChatGPT operates within the constraints of its programming and is simply solving the “Constraints” for that technical problem.
Human oversight: While ChatGPT can automate many tasks, it still requires human oversight to ensure that everything is running smoothly. DevOps play a crucial role in monitoring and troubleshooting any issues that may arise during the deployment and maintenance of applications.
Complexity: Many DevOps tasks are complex (or at the very least, the DevOps teams would want us to believe that) and require a deep understanding of the underlying infrastructure and applications. ChatGPT does not yet have the capability to perform these tasks at the same level of expertise as a human.
Customization: Every organization has unique requirements for its development and deployment process. ChatGPT may not be able to accommodate these specific needs, whereas DevOps engineers can tailor the process to meet the non-stated requirements and organisational and platform context
Responsibility: DevOps engineers are ultimately responsible for ensuring the success of the development and deployment process. While ChatGPT can assist in automating tasks, it is not capable of assuming full responsibility for the outcome.
In conclusion, while ChatGPT has the potential to automate many manual tasks performed by DevOps engineers, it is unlikely to replace DevOps entirely in the near future. The role of DevOps will continue to be important in ensuring the smooth deployment and maintenance of software applications, while ChatGPT can assist in automating certain tasks and increasing efficiency. Ultimately, the goal of both DevOps and no-ops is to increase the speed and quality of software delivery, and the use of ChatGPT in DevOps can play a significant role in achieving this goal.
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.
Picture Courtesy: GitHub Blog
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.
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!
