Tech predictions often fail not because of wrong models or a lack of imagination, but because of a deeper human flaw: the absence of humility in admitting uncertainty about the future. Technology evolves within complex social, economic, and political systems, none of which behave in neat, predictable ways. Yet forecasters often speak with confidence, presenting linear projections in a world driven by nonlinear change.
History repeatedly illustrates this problem. Early experts predicted flying cars by the year 2000 but underestimated the impact of software, data, and the internet. Conversely, many dismissed the potential of personal computers, smartphones, and social media because they could not imagine how quickly adoption would scale once costs dropped and networks formed. These failures were not due to poor intelligence, but to overconfidence in limited assumptions.
A lack of humility also leads to ignoring unintended consequences. Social media was once celebrated as a tool for global connection and democracy; few foresaw its role in misinformation, polarization, and mental health concerns. When predictions assume control over outcomes, they overlook how users adapt, misuse, or redefine technology in unexpected ways.
Humility does not mean abandoning prediction altogether. Instead, it requires treating forecasts as provisional, embracing uncertainty, and planning for multiple futures rather than a single “inevitable” one. The most reliable thinkers are those who acknowledge what they do not know and remain open to surprise. In a rapidly changing technological landscape, humility is not a weakness, it is a necessary discipline.
Technology predictions often stumble because they are founded on the false notion that the future can be perfectly charted with existing models. This failure stems from an overconfidence in tools and methods, rather than a conscious acknowledgment of the limits of human foresight.
Humility plays a crucial role in approaching the unknown future. Experts often fail to admit gaps in their understanding, leading to predictions that ignore the inherent unpredictability of technological evolution. For example, early forecasts about the internet did not account for social media's profound impact or the rapid advancement of artificial intelligence. Similarly, the inability to foresee climate change's acceleration showcases the limitations of our models.
Admitting uncertainty doesn't undermine our efforts; instead, it allows for flexibility and adaptability. By acknowledging gaps in knowledge, experts can prepare for unexpected developments, fostering innovation and resilience.
In essence, the failure of tech predictions lies not in the complexity of the future but in our unwillingness to embrace its inherent uncertainties. Only by cultivating humility can we make meaningful strides toward a balanced and adaptive approach to predicting the unknown.
Among P, Q, R, S, T, and U, who is the heaviest?
Statement I - P is heavier than T and U and he is the second heaviest in the group.
Statement II - S is heavier than Q but not the heaviest.
Which direction is Raju facing at the moment?
Statement I - Raju took 2 consecutive right turns after covering a distance of 6m to reach the point X.
Statement II - After walking 4m early morning from point X, Raju is facing opposite direction of the sun.
Light Chemicals is an industrial paint supplier with presence in three locations: Mumbai, Hyderabad and Bengaluru. The sunburst chart below shows the distribution of the number of employees of different departments of Light Chemicals. There are four departments: Finance, IT, HR and Sales. The employees are deployed in four ranks: junior, mid, senior and executive. The chart shows four levels: location, department, rank and gender (M: male, F: female). At every level, the number of employees at a location/department/rank/gender are proportional to the corresponding area of the region represented in the chart.
Due to some issues with the software, the data on junior female employees have gone missing. Notice that there are junior female employees in Mumbai HR, Sales and IT departments, Hyderabad HR department, and Bengaluru IT and Finance departments. The corresponding missing numbers are marked u, v, w, x, y and z in the diagram, respectively.
It is also known that:
a) Light Chemicals has a total of 210 junior employees.
b) Light Chemicals has a total of 146 employees in the IT department.
c) Light Chemicals has a total of 777 employees in the Hyderabad office.
d) In the Mumbai office, the number of female employees is 55.

An investment company, Win Lose, recruit's employees to trade in the share market. For newcomers, they have a one-year probation period. During this period, the employees are given Rs. 1 lakh per month to invest the way they see fit. They are evaluated at the end of every month, using the following criteria:
1. If the total loss in any span of three consecutive months exceeds Rs. 20,000, their services are terminated at the end of that 3-month period,
2. If the total loss in any span of six consecutive months exceeds Rs. 10,000, their services are terminated at the end of that 6-month period.
Further, at the end of the 12-month probation period, if there are losses on their overall investment, their services are terminated.
Ratan, Shri, Tamal and Upanshu started working for Win Lose in January. Ratan was terminated after 4 months, Shri was terminated after 7 months, Tamal was terminated after 10 months, while Upanshu was not terminated even after 12 months. The table below, partially, lists their monthly profits (in Rs. ‘000’) over the 12-month period, where x, y and z are masked information.
Note:
• A negative profit value indicates a loss.
• The value in any cell is an integer.
Illustration: As Upanshu is continuing after March, that means his total profit during January-March (2z +2z +0) ≥
Rs.20,000. Similarly, as he is continuing after June, his total profit during January − June ≥
Rs.10,000, as well as his total profit during April-June ≥ Rs.10,000.